Comprehensive energy system low-carbon scheduling method, system, equipment and medium

Abstract

The invention discloses a low-carbon scheduling method, system, equipment and medium for an integrated energy system, and belongs to the technical field of low-carbon scheduling, and the method comprises the steps: obtaining power network data, thermal network data and natural gas network data of the integrated energy system, establishing a multi-energy-flow carbon flow dynamic model according to the power network data, the thermal network data and the natural gas network data; calculating the time-varying node carbon intensity of each node based on the multi-energy-flow carbon flow dynamic model; calculating carbon flow betweenness centrality according to the time-varying node carbon intensity and a network topology structure, and identifying a carbon flow propagation key node through the carbon flow betweenness centrality; and establishing a low-carbon optimal scheduling model based on the time-varying node carbon intensity, and determining a low-carbon scheduling scheme of the integrated energy system in combination with the low-carbon optimal scheduling model and the carbon flow propagation key node. The method has the beneficial effects that the carbon emission of the comprehensive energy system can be effectively reduced, and meanwhile, the increase amplitude of the operation cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of low-carbon scheduling, and in particular to a low-carbon scheduling method, system, device and medium for a comprehensive energy system. BACKGROUND

[0002] As a core component of the new power system, the comprehensive energy system plays a key role in realizing the clean transformation of energy. Through the coordination and complementation of various energy forms such as electricity, heat and gas, the comprehensive energy system can effectively improve energy utilization efficiency and reduce system carbon emissions. At present, in the field of comprehensive energy system optimization scheduling, various uncertainty processing methods such as robust optimization, distributed robust optimization and interval optimization have been proposed to cope with the randomness of renewable energy output. At the same time, multi-objective optimization algorithms have made progress in balancing system economy and environmental protection. By constructing an optimization model containing multiple objectives such as operating cost and carbon emission cost, important technical support is provided for low-carbon economic scheduling of the comprehensive energy system. In addition, the rapid development of multi-energy coupling technologies such as electric-gas conversion and combined heat and power generation has further enhanced the coordination and optimization capability between different energy networks, creating favorable conditions for overall carbon emission reduction of the system.

[0003] However, the existing low-carbon scheduling technology for comprehensive energy systems still has deficiencies in accurate allocation and tracking of carbon emission responsibilities. First, traditional methods mostly use fixed carbon emission factors for carbon accounting, which cannot accurately reflect the real-time impact of new energy output fluctuations, energy storage charging and discharging state changes on node carbon intensity, resulting in insufficient carbon emission assessment accuracy and difficulty in providing accurate carbon signals for fine low-carbon scheduling. Second, existing technologies lack effective identification methods for carbon flow propagation paths and key nodes in the multi-energy flow system of electricity, heat and gas, which cannot explore carbon emission reduction potential from the perspective of network topology structure, affecting the scientificity of new energy site selection and system planning. Third, the current carbon responsibility allocation theory under multi-energy flow coupling is not perfect, and the modeling of carbon flow interaction mechanism across energy networks is not deep enough, which cannot accurately quantify the impact of coupling devices such as combined heat and power generation and electric-gas conversion on overall system carbon emissions, restricting the development of multi-energy collaborative carbon reduction strategies. In addition, existing scheduling models mostly focus on source- load optimization, ignoring the impact of network structure on carbon flow propagation, and lack optimization mechanisms based on carbon flow propagation characteristics, which have problems such as inaccurate carbon emission responsibility allocation, lack of carbon flow propagation key node identification capability, and imperfect carbon flow modeling under multi-energy flow coupling. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is: how to solve the problem that the current multi-energy system collaborative optimization scheduling model lacks effective research in dynamic carbon responsibility allocation under multi-energy flow coupling, dynamic carbon flow tracking theory is lacking, and carbon reduction potential cannot be tapped from the mechanism of carbon flow, by proposing the concept of time-varying node carbon intensity, and taking it as the goal of low-carbon optimization scheduling of integrated energy system, while reducing the economic cost of system carbon emission, while proposing the carbon flow betweenness centrality, the influence of the network topology on the carbon flow is clear, and the key hub of carbon flow propagation is identified, which can provide reference for new energy site selection, and the carbon flow propagation of the electric, heat and gas networks is modeled by coupling matrix, and the cross allocation problem of carbon emission responsibility in the multi-energy flow system is solved.

[0006] To solve the above technical problems, the present application provides the following technical solutions: an integrated energy system low-carbon scheduling method, comprising,

[0007] Obtaining power network data, heat network data and natural gas network data of an integrated energy system, and establishing a multi-energy flow carbon flow dynamic model according to the power network data, the heat network data and the natural gas network data; calculating time-varying node carbon intensity of each node based on the multi-energy flow carbon flow dynamic model; calculating carbon flow betweenness centrality according to the time-varying node carbon intensity and network topology, and identifying carbon flow propagation key nodes through the carbon flow betweenness centrality; establishing a low-carbon optimization scheduling model based on the time-varying node carbon intensity, and determining a low-carbon scheduling scheme of the integrated energy system in combination with the low-carbon optimization scheduling model and the carbon flow propagation key nodes.

[0008] As a preferred scheme of the integrated energy system low-carbon scheduling method, wherein: the multi-energy flow carbon flow dynamic model is established according to the power network data, the heat network data and the natural gas network data, comprising: establishing an electric carbon flow model, a heat carbon flow model and a natural gas carbon flow model according to the power network data, the heat network data and the natural gas network data; connecting the electric carbon flow model, the heat carbon flow model and the natural gas carbon flow model through a cross-energy coupling matrix to obtain a multi-energy flow carbon flow dynamic model. The beneficial effects of the preferred technical scheme are that by establishing an electric carbon flow model, a heat carbon flow model and a natural gas carbon flow model respectively, and connecting them by a cross-energy coupling matrix, the carbon flow propagation characteristics of multiple energy networks can be accurately described, and unified modeling of heterogeneous energy systems is realized, providing a mathematical basis for carbon flow analysis of integrated energy systems.

[0009] As a preferred scheme of the low-carbon scheduling method of the integrated energy system, wherein: the time-varying node carbon intensity of each node is calculated based on the multi-energy flow carbon flow dynamic model, comprising: a rolling update mechanism of the time-varying node carbon intensity is established based on a model predictive control framework; real-time data of the integrated energy system is obtained, and the dynamic carbon flow matrix and the carbon source vector in the multi-energy flow carbon flow dynamic model are updated based on the real-time data and the rolling update mechanism; and the time-varying node carbon intensity of each node is obtained by solving the updated multi-energy flow carbon flow dynamic model. The beneficial effect of the preferred technical scheme is that the rolling update mechanism is established based on the model predictive control framework, and the carbon flow matrix and the carbon source vector are dynamically updated by using the real-time data, so that the accurate calculation and real-time tracking of the time-varying node carbon intensity can be realized, the influence of new energy output fluctuation on the carbon intensity is effectively reflected, and the precision of carbon emission evaluation is improved.

[0010] As a preferred scheme of the low-carbon scheduling method of the integrated energy system, wherein: the carbon flow betweenness centrality is calculated according to the time-varying node carbon intensity and the network topology structure, comprising: the network topology structure of the integrated energy system is obtained; the carbon flow propagation weight of each line in the network topology structure is determined based on the time-varying node carbon intensity; the shortest carbon flow path between each node pair is determined by using a shortest path algorithm; and the carbon flow betweenness centrality of each node is calculated according to the number of the shortest carbon flow paths passing through each node and the carbon flow. The beneficial effect of the preferred technical scheme is that the carbon flow propagation weight is determined based on the time-varying node carbon intensity, and the carbon flow betweenness centrality is calculated by using the shortest path algorithm, so that the key path and the important node of the carbon flow propagation in the network can be accurately identified, and a scientific and quantitative basis is provided for low-carbon reconstruction and key equipment site selection.

[0011] As a preferred scheme of the low-carbon scheduling method of the integrated energy system, wherein: the carbon flow propagation key node is identified by using the carbon flow betweenness centrality, comprising: each node is sorted according to the carbon flow betweenness centrality, and a sorting result of each node is obtained; and the carbon flow propagation key node is obtained by selecting according to the sorting result of each node.

[0012] As a preferred scheme of the low-carbon scheduling method of the integrated energy system, wherein: the low-carbon optimal scheduling model is established based on the time-varying node carbon intensity, comprising: a target function is established based on the time-varying node carbon intensity, the target function comprising total carbon emission cost and total operation cost; power system constraints, heat system constraints, gas network system constraints and cross-energy coupling constraints are established; and a carbon intensity constraint is established based on the time-varying node carbon intensity.

[0013] As a preferred embodiment of the low-carbon dispatching method for an integrated energy system described in this invention, the establishment of the cross-energy coupling constraints includes: determining the relationship between power output and heat output based on electrothermal efficiency to obtain the electrothermal coupling constraints of the cogeneration equipment; and determining the relationship between power input and natural gas output based on conversion efficiency to obtain the constraints of the power-to-gas conversion equipment.

[0014] This invention provides a low-carbon dispatching system for integrated energy systems.

[0015] To address the aforementioned technical problems, this invention provides the following technical solution: a low-carbon dispatching system for an integrated energy system, comprising: a multi-energy flow carbon flow module, used to acquire power network data, heating network data, and natural gas network data of the integrated energy system, and to establish a multi-energy flow carbon flow dynamic model based on the power network data, heating network data, and natural gas network data; a carbon intensity module, used to calculate the time-varying node carbon intensity of each node based on the multi-energy flow carbon flow dynamic model; a carbon flow betweenness centrality module, used to calculate the carbon flow betweenness centrality based on the time-varying node carbon intensity and network topology, and to identify key nodes for carbon flow propagation through the carbon flow betweenness centrality; and a low-carbon dispatching module, used to establish a low-carbon optimized dispatching model based on the time-varying node carbon intensity, and to determine a low-carbon dispatching scheme for the integrated energy system by combining the low-carbon optimized dispatching model and the key nodes for carbon flow propagation.

[0016] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the integrated energy system low-carbon scheduling method.

[0017] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the integrated energy system low-carbon scheduling method.

[0018] The beneficial effects of this invention are as follows: By proposing a time-varying node carbon intensity, which can be updated on a minute-level time scale, this invention demonstrates the impact of new energy units on carbon flow changes, providing a more accurate evaluation index for low-carbon dispatch and planning of power systems. By utilizing the carbon flow betweenness centrality to calculate the shortest flow path of carbon flow and the nodes and lines it passes through, this invention demonstrates the impact of grid structure on carbon flow propagation paths, which can be applied to identify high-carbon nodes and lines in power systems, providing new consideration indicators for low-carbon grid planning. By constructing a model with time-varying node carbon intensity and economic indicators as objective functions, this invention refines the granularity of node carbon intensity time changes, strengthens the impact of carbon flow changes on the low-carbon dispatch of integrated energy systems, effectively reduces carbon emissions of integrated energy systems while reducing the increase in operating costs, and improves the effectiveness of low-carbon economic dispatch of integrated energy systems. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0020] Figure 1 The above is an overall flowchart of a low-carbon scheduling method for an integrated energy system provided in one embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of a multi-energy flow system provided in one embodiment of the present invention.

[0022] Figure 3 This is a flowchart of a rolling update algorithm for time-varying node carbon intensity provided in one embodiment of the present invention.

[0023] Figure 4 A flowchart illustrating the calculation of carbon flow betweenness centrality is provided for one embodiment of the present invention.

[0024] Figure 5 This is a schematic diagram of the time-varying carbon intensity variation curve of node 5 provided in an embodiment of the present invention.

[0025] Figure 6 A key node carbon flux betweenness centrality distribution diagram provided for one embodiment of the present invention.

[0026] Figure 7 A key circuit carbon flux betweenness centrality distribution diagram provided for one embodiment of the present invention.

[0027] Figure 8 This is a schematic diagram comparing the scheduling results of three methods provided in one embodiment of the present invention.

[0028] Figure 9 This is a schematic diagram of three methods for obtaining Pareto frontier data, provided as an embodiment of the present invention. Detailed Implementation

[0029] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0030] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0031] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0032] Example 1, referring to Figure 1 As one embodiment of the present invention, this embodiment provides a low-carbon dispatching method for an integrated energy system, comprising:

[0033] S100: Acquire power network data, heating network data, and natural gas network data of the integrated energy system, and establish a multi-energy flow carbon flow dynamic model based on the power network data, heating network data, and natural gas network data.

[0034] S200: Calculates the time-varying nodal carbon intensity of each node based on the multi-energy flow carbon flow dynamic model.

[0035] S300: Calculates carbon flow betweenness centrality based on time-varying node carbon intensity and network topology, and identifies key nodes for carbon flow propagation through carbon flow betweenness centrality.

[0036] S400: Establish a low-carbon optimization scheduling model based on time-varying node carbon intensity, and determine the low-carbon scheduling scheme of the integrated energy system by combining the low-carbon optimization scheduling model and key nodes of carbon flow propagation.

[0037] It should be noted that the static nature and low resolution of traditional carbon intensity indicators cannot adapt to the increasing penetration rate of new energy sources and the demands of load changes. Fluctuations in wind and solar power output, energy storage charging and discharging, and demand response cause carbon emission intensity to change on a minute to hourly scale, which traditional indicators cannot capture. Fixed carbon emission factors also cannot distinguish the carbon emission differences between electricity consumption during periods of high photovoltaic power generation and electricity consumption during periods dominated by thermal power, thus affecting user-side carbon reduction incentives. At the same time, current multi-energy system collaborative optimization scheduling models lack effective research on dynamic carbon responsibility allocation under multi-energy flow coupling, and their dynamic carbon flow tracking theory is missing. They cannot explore carbon reduction potential from the carbon flow mechanism. Traditional methods sever the intrinsic relationship between deviation and prediction data, making it difficult to guarantee the accuracy of scheduling results. Moreover, in integrated energy systems, the interaction effects of carbon flows of electricity, heat, and gas need to be tracked. Current methods are limited to single-energy considerations and cannot meet the actual application needs of integrated energy systems.

[0038] Therefore, to address the aforementioned issues of dynamic carbon flow tracking and carbon responsibility allocation under multi-energy flow coupling, this paper establishes a multi-energy flow carbon flow dynamic model covering power, heat, and natural gas networks through steps S100-S400. It also establishes a time-varying node carbon intensity calculation method, identifies key nodes in carbon flow propagation, and establishes a low-carbon optimization scheduling model. This enables precise tracking of carbon emissions and clear allocation of responsibility within the integrated energy system. Furthermore, it quantifies the immediate effects of renewable energy volatility, energy storage system status, and multi-energy conversion processes on node carbon intensity, updating the carbon emission intensity of each node in real time. Simultaneously, based on carbon flow betweenness centrality, it identifies key carbon flow propagation hubs in the network, providing a reference for renewable energy site selection and system transformation, and enabling the power system to shift from an energy optimization model to a carbon flow optimization model.

[0039] Example 2, refer to Figures 1-4 As one embodiment of the present invention, based on the previous embodiment, a low-carbon scheduling method for an integrated energy system is provided, comprising:

[0040] It should be noted that this invention establishes a comprehensive energy system model including power-to-gas (P2G) equipment and combined heat and power (CHP) equipment, and performs theoretical verification on the model, such as... Figure 2 The diagram shows the structure of a multi-energy flow system. The power grid system connects to wind power, photovoltaic units, and diesel generators, as well as energy storage devices. The power grid system is connected to the natural gas network via gas turbines and power-to-gas (H2G) equipment. Excess electricity from the power grid system is supplied to the H2G equipment, which converts electrical energy into natural gas or hydrogen for energy storage. The natural gas network can also supply electricity to the power grid via gas turbines. The heating network is connected to the natural gas network via a gas-fired boiler, which obtains natural gas from the natural gas network and supplies heat to the heating network. The cogeneration equipment connects the heating network and the power grid system. The cogeneration equipment uses natural gas or hydrogen to drive power generation equipment, supplying electricity to the power grid while transferring the waste heat of the equipment to the heating network.

[0041] In this embodiment of the invention, step S100, which establishes a multi-energy flow carbon flow dynamic model based on power network data, heating network data, and natural gas network data, includes the following steps A1-A2:

[0042] A1: Establish electricity carbon flow models, thermal carbon flow models, and natural gas carbon flow models based on power network data, heat network data, and natural gas network data.

[0043] A2: By connecting the power carbon flow model, thermal carbon flow model and natural gas carbon flow model through a cross-energy coupling matrix, a multi-energy flow carbon flow dynamic model is obtained.

[0044] It should be noted that this invention establishes an electricity carbon flow model based on carbon emission flow theory and proposes the concept of electricity carbon flow conservation equation, embedding carbon emission factors into traditional electricity flow.

[0045] Specifically, establishing the electricity carbon flow model in step A1 includes the following steps A11-A13:

[0046] A11: Define carbon flow rate R, which characterizes the amount of carbon flowing through a node or branch per unit time, with units of tCO2 / h. This can be expressed by the following formula:

[0047]

[0048] Where R is the carbon flow rate; F CEF t represents the carbon flow rate through a node or branch; t represents time.

[0049] A12: Define carbon emission intensity (CI) to represent the equivalent carbon emissions generated on the power generation side per unit of energy, with units of tCO2 / (MW·h). Node carbon emission intensity (NCI) represents the ratio of total carbon flow into the node to total electricity generation, i.e., the equivalent carbon emissions per unit of electricity generated at the node. This can be specifically represented by the following formula:

[0050]

[0051] Among them, NCI i P represents the carbon emission intensity at node i; l ρ represents the amount of electricity injected into node i from branch l. l R is the carbon flux intensity of branch l; l N represents the carbon flow rate of line l; + This is the set of lines that receive electricity into a node.

[0052] A13: Based on the law of conservation of mass and network flow theory, carbon emissions are considered as a virtual quantity flowing in the network, with its propagation path consistent with the power flow. A user's carbon footprint at a node depends not only on the local generator but also on the carbon emissions implied by electricity transmitted from other nodes, ensuring that the input and output of carbon flow in the network are balanced with local sources and sinks. Based on the power flow calculation results and local carbon sources, a power carbon flow conservation equation can be derived, characterizing the conservation relationship between carbon flow and power flow at the node. The power carbon flow conservation equation can be specifically expressed as:

[0053] A e ·NCI e =B e ;

[0054] Among them, A e ∈R N×N For the electricity carbon flow correlation matrix, element Ae (i,j)=-|P ij |(Absolute value of power flow), diagonal element A e (i,i)=Σ k |P ik |+P L,i (Line outflow + local load); B e ∈R N For node carbon source terms, (Engine carbon emissions); NCI e Let i be the nodal carbon intensity matrix, and let n be the nodal carbon intensity NCI of node i. i (i∈1,2,...,N); N is the total number of nodes; R N×N Let A be the set of all NxN real square matrices. e ∈R N×N A represents e P is an N-order square matrix; ij P represents the power flow value from line i to line j. ik ,P Li R represents the line outflow and local load flow values. N A set of N-dimensional vectors used to illustrate B e It is an N-dimensional vector; G i P represents the set of engines connected at node i; g ,EF g These represent the engine's power (g) and carbon emission factor, respectively.

[0055] It should be noted that NCI in the electricity carbon flow conservation equation e For the unknown quantity, the time-varying carbon intensity (TV-NCI) ​​at each node can be accurately calculated using the power carbon flow conservation equation. The equation is usually a sparse linear system of equations, which is solved using LU decomposition or iterative methods. During the solution process, matrix A is... e Adding a small number of diagonal elements and performing regularization to avoid singularities; based on the power carbon flow conservation equation, the carbon emission responsibility of each node becomes clear, providing a dynamic carbon signal for the system, and the power carbon flow conservation equation is the core module of the electric-thermal-gas multi-energy flow coupling model, providing a mathematical framework for cross-energy carbon responsibility allocation.

[0056] Specifically, establishing the thermal carbon flow model in step A1 includes the following steps A14-A15:

[0057] A14: Referring to the carbon emission flow theory, the carbon emissions of the thermal system are virtualized as carbon flows along the thermal pipes. The responsibility for carbon emissions is allocated to the load end. Considering the pipe thermal delay effect (time lag τ), a thermal carbon flow transfer equation is established, which can be specifically expressed as:

[0058]

[0059] Among them, CP h (t) represents the carbon emissions per unit of heat at time t; Q ji B represents the heat flow (including pipe losses) from node j to i, in kW. h (i,t) represents the carbon emissions from the heat source at node i.

[0060] A15: Combined heat and power (CHP) equipment is a core component of integrated energy systems, a highly efficient energy utilization system capable of simultaneously generating electricity and heat. As a major source of carbon emissions in thermal systems, CHP equipment, which generates both electricity and heat, needs to allocate carbon emissions. This can be specifically represented by the following formula:

[0061] B e (i)=η e ·P CHP ·EF gas ;

[0062] B h (i)=η h ·Q CHP ·EF gas ;

[0063] Among them, B e (i) and B h (i) represents the electrical and thermal carbon sources at node i; η e and η h For electrical efficiency and thermal efficiency; EF gas P is the carbon emission factor for natural gas. CHP and Q CHP These are the electrical output power and thermal output power of the combined heat and power (CHP) equipment, respectively.

[0064] Specifically, the steps for establishing the natural gas carbon flow model in step A1 can be as follows:

[0065] Carbon flow in the natural gas network follows the gas flow in the natural gas pipeline. Referring to carbon emission flow theory, its carbon flow relationship can be established, which can be specifically expressed as:

[0066] B gas (i)=B gas (i)-η P2G ·P P2G ·EF CO2_capture ;

[0067] Among them, B gas (i) represents the carbon flow at node i in the natural gas network; η P2G ,P P2G These represent the efficiency and power of the electro-pneumatic converter, respectively; EF CO2_capture This represents the carbon capture rate.

[0068] Furthermore, in step A2, the electricity carbon flow model, thermal carbon flow model, and natural gas carbon flow model are connected through a cross-energy coupling matrix to obtain a multi-energy flow carbon flow dynamic model. The specific operation can be as follows:

[0069] In the context of multi-energy flow coupling in integrated energy systems (electricity-heat-gas), different energy networks convert and transfer energy through equipment (such as cogeneration equipment, electric boilers, gas turbines, etc.). A cross-energy coupling matrix is ​​established to quantify the carbon emission transfer relationships during this energy conversion process. This model connects the carbon flow equations of the electricity, heat, and natural gas energy networks into a unified model. The matrix structure is a block matrix, specifically represented as follows:

[0070]

[0071] Among them, CP e CP h and CP g Carbon potential (gCO2 / kWh) of nodal nodes in an electrical, thermal, and gas network; A e A h and A g Carbon flow correlation matrix for power, heat, and gas networks; C eh C he C hg And C gh For cross-energy coupling matrix; B e B h and B g These are the carbon source items for each energy network.

[0072] In one optional implementation, in step S100, a multi-energy flow carbon flow dynamic model is established based on power network data, heat network data, and natural gas network data. Alternatively, a topological representation learning model of heterogeneous energy networks can be constructed using graph neural network technology. The coupling weights between different energy networks are adaptively learned using an attention mechanism. The potential patterns of carbon flow propagation are extracted directly from multi-dimensional time-series data through an end-to-end deep learning framework, avoiding the mathematical modeling process in traditional methods. At the same time, nonlinear coupling relationships can be automatically identified.

[0073] In another optional implementation, in step S100, a multi-energy flow carbon flow dynamic model is established based on power network data, heat network data, and natural gas network data. A virtual image of the physical integrated energy system can also be constructed using digital twin technology. The model is calibrated by combining real-time operational data collected by IoT sensors with the simulation model. The model is updated and predicted in real time using a cloud-edge collaborative computing architecture. The carbon flow propagation mechanism under multi-energy flow coupling is accurately characterized through virtual-real fusion, thereby improving the model's adaptability to various operating conditions and its prediction accuracy.

[0074] It should be noted that this invention acquires power network data, thermal network data, and natural gas network data from an integrated energy system, establishes carbon flow models for each individual energy source, and unifies and connects the carbon flow models of multiple energy sources through a cross-energy coupling matrix. Compared with the existing technology that analyzes each energy network independently, this invention solves the problem that traditional methods cannot accurately track the propagation of cross-energy carbon flows by constructing a multi-energy flow carbon flow dynamic model. In particular, by establishing the conservation equation for power carbon flow, the transfer equation for thermal carbon flow, and the relationship between natural gas carbon flow, it achieves the accurate allocation of carbon emission responsibility among different energy networks. This not only provides carbon signal tracking capabilities but also provides a scientific mathematical framework for the low-carbon optimization scheduling of integrated energy systems, transforming the originally difficult-to-quantify multi-energy flow coupled carbon emission problem into a mathematical model that can be accurately calculated and optimized.

[0075] In this embodiment of the invention, step S200, which calculates the time-varying nodal carbon intensity of each node based on the multi-energy flow carbon flow dynamic model, includes the following steps B1-B3:

[0076] B1: Establish a rolling update mechanism for time-varying node carbon intensity based on a model predictive control framework.

[0077] B2: Acquire real-time data of the integrated energy system and update the dynamic carbon flow matrix and carbon source vector in the multi-energy flow carbon flow dynamic model based on the real-time data and rolling update mechanism.

[0078] B3: Solve the updated multi-energy flow carbon flow dynamic model to obtain the time-varying nodal carbon intensity at each node.

[0079] It should be noted that in integrated energy systems (IES), the interaction effects of electricity, heat, and gas carbon flows need to be tracked. Current methods are limited to single energy considerations. This invention draws on the concept of nodal carbon intensity and introduces the concept of time-varying nodal carbon intensity (TV-NCI). It constructs an update mechanism for TV-NCI and develops multi-energy flow coupling modeling and network analysis technology, thereby achieving accurate tracking of carbon emissions and clear division of responsibility. It further quantifies the real-time effects of new energy volatility, energy storage system status, and multi-energy conversion processes on nodal carbon intensity.

[0080] Specifically, step B1 establishes a rolling update mechanism for time-varying nodal carbon intensity based on a model predictive control framework, including the following steps B11-B12:

[0081] B11: First, establish a discrete-time model of time-varying nodal carbon intensity (TV-NCI) ​​with a sampling period of Δt, which can be represented by the following formula:

[0082]

[0083] Among them, P ji (t k ) for t k Power from node j to i at any given time; B i (t k ) for t k Local carbon source for time node i; TV_NCI i (t k ) represents node i in t k Nodal carbon intensity at time; N i P is the set of all nodes connected to node i; load,i (t k ) for t k Load at time node i; P im (t k ) for t k Power from node i to m at any given time.

[0084] B12: Based on the Model Predictive Control (MPC) framework, it updates with a rolling time window, dynamically updating with a 5-minute rolling time window. Combining real-time data and short-term forecasts, it ensures the real-time performance and robustness of carbon intensity calculation. The update process of time-varying node carbon intensity explicitly describes the carbon flow interaction of the electric, heat, and gas systems through a dynamic carbon flow matrix, thus overcoming the limitations of traditional single-energy flow models.

[0085] Specifically, such as Figure 3 The flowchart shown is a rolling update algorithm for time-varying node carbon intensity. Step B2 obtains real-time data of the integrated energy system and updates the dynamic carbon flow matrix and carbon source vector in the multi-energy flow carbon flow dynamic model based on the real-time data and the rolling update mechanism, including the following steps B21-B23:

[0086] B21: Acquire real-time data on node load, renewable energy output, energy storage status, multi-energy flow coupling device output power, and carbon transfer coefficient.

[0087] B22: Predicts the renewable energy output (photovoltaic) curve, load demand curve, and behavior of multi-energy flow coupled equipment for the next hour.

[0088] B23: Update the multi-energy flow coupling matrix A and the carbon source vector B based on real-time and predicted data.

[0089] Step B3 involves solving the updated multi-energy flow carbon dynamic model to obtain the time-varying nodal carbon intensity at each node, including the following steps B31-B33:

[0090] B31: Based on the updated dynamic carbon flow matrix A and carbon source vector B, solve the multi-energy flow carbon flow conservation equation to obtain the time-varying nodal carbon intensity (components of the nodal carbon intensity vector CP to be determined) for each node in the future time window.

[0091] B32: Output the updated time-varying nodal carbon intensity.

[0092] B33: Perform time window sliding, and repeat steps B21-B23 and B31-B32.

[0093] For example, the conservation equation for multi-energy flow carbon flow in step B31 can be specifically expressed as:

[0094] A·CP=B;

[0095] Where A∈R N×N This is a dynamic carbon flow matrix, where the diagonal elements represent nodal loads (per unit value), and the off-diagonal elements represent line power flow (including direction) and carbon transfer coefficients across energy devices, reflecting the carbon flow propagation path; B∈R N This is a carbon source vector, including direct carbon emissions from equipment such as generator sets; CP∈R N Let be the nodal carbon intensity vector to be determined.

[0096] In one optional implementation, step S200 calculates the time-varying node carbon intensity of each node based on the multi-energy flow carbon flow dynamic model. The nonlinear evolution process of the system state can also be handled by the extended Kalman filter algorithm, and the uncertainty propagation in the high-dimensional state space can be handled by combining the unscented transformation technique. The process noise and observation noise parameters can be adjusted by using noise estimation methods to achieve the optimal estimation of time-varying carbon intensity. This method is suitable for energy systems containing random disturbances and measurement errors.

[0097] In another optional implementation, in step S200, the time-varying node carbon intensity of each node is calculated based on the multi-energy flow carbon flow dynamic model. Alternatively, a deep time-series prediction model can be constructed by combining a long short-term memory network with an attention mechanism. The multi-head attention mechanism is used to capture the carbon intensity change patterns at different time scales. The model training stability is improved by residual connection and layer normalization techniques. The sliding window mechanism is used to achieve incremental learning, which can learn the time-varying pattern of carbon intensity from historical data.

[0098] It should be noted that this invention establishes a rolling update mechanism for time-varying node carbon intensity based on a model predictive control framework, acquires multi-dimensional real-time data and updates model parameters, and obtains the real-time carbon intensity of each node by solving the multi-energy flow carbon flow conservation equation. Compared with the existing technology that uses static carbon emission factors or low-resolution carbon intensity indicators, this invention solves the problem that traditional methods cannot adapt to the increasing penetration rate of new energy and the demand for load changes through a time-varying node carbon intensity calculation method. In particular, the rolling update mechanism of the model predictive control framework can capture the changes in carbon emission intensity on the minute to hour scale caused by factors such as wind and solar power output fluctuations, energy storage charging and discharging, and demand response. At the same time, the multi-energy flow coupling modeling technology quantifies the immediate effects of new energy volatility, energy storage system status, and multi-energy conversion processes on node carbon intensity. This not only achieves accurate tracking of carbon emissions and clear division of responsibility, but also provides a solid theoretical foundation for the transformation of the power system from an energy optimization mode to a carbon flow optimization mode. This enables users to distinguish the carbon emission differences between electricity consumption during periods of high photovoltaic power generation and electricity consumption during periods dominated by thermal power, thus providing a scientific basis for user-side carbon reduction incentives.

[0099] It should be noted that betweenness centrality is an important concept in network analysis, used to measure the degree of mediating role of each node in the flow of information or resources in a network. It refers to the proportion of the number of shortest paths passing through a node in the total number of shortest paths in the network. Specifically, the higher the betweenness centrality of a node, the more shortest paths pass through that node, meaning that the node has more control or influence in the network. Betweenness centrality is generally applied in various network analyses, such as identifying traffic bottlenecks in transportation networks, identifying key proteins or genes in biological networks, and studying the structure and function of biological networks. This invention introduces the concept of betweenness centrality into the analysis of power systems to identify key nodes in the carbon flow of power networks.

[0100] Secondly, traditional carbon emission reduction research focuses on the source-load ends, neglecting the impact of power grid topology and energy transmission paths on carbon propagation. Therefore, this invention introduces betweenness centrality in the network into carbon flow analysis, proposing the Carbon Flow Betweenness Centrality (CFBC) index. After obtaining the topology of the power grid system, all node pairs in the network can be calculated through graph traversal algorithms or matrix operations to obtain the number of shortest paths through each node, and then the carbon flow betweenness centrality of each node can be obtained. By quantifying the mediating role of nodes in the carbon flow path, the carbon propagation bottlenecks that need to be prioritized for improvement can be identified.

[0101] In embodiments of the present invention, such as Figure 4The diagram shows the calculation flowchart for carbon flow betweenness centrality. Step S300 calculates carbon flow betweenness centrality based on time-varying node carbon intensity and network topology, including the following steps C1-C4:

[0102] C1: Obtain the network topology of the integrated energy system.

[0103] C2: Determine the carbon flow propagation weights of each path in the network topology based on the time-varying node carbon intensity.

[0104] C3: The shortest path algorithm is used to determine the shortest carbon flow path between each pair of nodes.

[0105] C4: Calculate the carbon flow betweenness centrality of each node based on the number of shortest carbon flow paths passing through each node and the carbon flow rate.

[0106] Specifically, in step C1, obtaining the network topology of the integrated energy system can be achieved through the following steps:

[0107] Construct a carbon flow network graph to obtain power grid topology data, including input data such as nodes, line impedance, capacity, generator carbon emission factor, load distribution, and renewable energy output. After obtaining the power grid topology data, all node pairs in the network can be calculated using graph traversal algorithms or matrix operations to obtain the number of shortest paths through each node.

[0108] Specifically, in step C2, the carbon flow propagation weights of each path in the network topology are determined based on the time-varying node carbon intensity. The specific operation can be as follows:

[0109] The NCI of each node is solved using an improved carbon emission flow algorithm.

[0110] Calculate the equivalent carbon emission factor EF of the line. i The equivalent carbon emission factor of line i (from node j to i) is the time-varying node carbon intensity of source node j.

[0111] Specifically, in step C3, the shortest path algorithm is used to determine the shortest carbon flow path between each pair of nodes. The specific operation can be as follows:

[0112] Iterate through all nodes i and j.

[0113] Dijkstra's algorithm is used to find the path from line j to line i that minimizes the total weight of the path (total carbon emissions), thus obtaining the shortest carbon flow path from line j to line i. Dijkstra's algorithm is a classic algorithm for solving single-source shortest paths in directed or undirected graphs with non-negative weights.

[0114] Specifically, in step C4, the carbon flux betweenness centrality of each node is calculated based on the number of shortest carbon flux paths passing through each node and the carbon flux. The specific operation can be as follows:

[0115] Calculate the shortest carbon flow path from line j to line i, and determine the number of paths passing through node k based on the shortest carbon flow path from line j to line i.

[0116] Calculate the carbon flow rate through the shortest carbon flow path passing through node k.

[0117] The carbon flow betweenness centrality (CFBC) of node k is calculated based on the number of paths passing through node k and the carbon flow of the shortest carbon flow path passing through node k, which is the sum of the contributions of all paths passing through node k.

[0118] For example, the carbon flow betweenness centrality of node k in step C4 is used to measure its degree as a key hub for carbon flow propagation, and can be specifically expressed as:

[0119]

[0120] Where, σ ij σ is the number of all shortest carbon flow paths from node i to j; ij (k) represents the number of shortest paths passing through node k; C ij C represents the carbon flux from node i to node j (in gCO2 / h); total Let be the total carbon flux of the system; CFBC(k) is the carbon flux betweenness centrality of node k.

[0121] In an optional implementation, in step S300, the carbon flow betweenness centrality is calculated based on the time-varying node carbon intensity and network topology. Alternatively, the embedding representation of nodes in the carbon flow propagation network can be learned through a graph convolutional neural network. The feature information of neighboring nodes is aggregated using multi-layer graph convolution operations, and the hierarchical structural features of the network are extracted by combining a pooling mechanism. The importance ranking of nodes is directly predicted through an end-to-end learning method, avoiding the computational complexity of traditional shortest path algorithms, while capturing high-order adjacency relationships in the network.

[0122] In another optional implementation, in step S300, the carbon flow betweenness centrality is calculated based on the time-varying node carbon intensity and network topology. The network nodes can also be divided into different carbon flow communities using a spectral clustering algorithm, key connection components in the network can be identified using a modularity optimization method, and the effective distance between nodes can be calculated using a random walk algorithm. By combining community detection and centrality analysis, key nodes and bottleneck paths that control carbon flow propagation can be identified from both the network structure and function perspectives.

[0123] In this embodiment of the invention, step S300, which identifies key nodes in carbon flow propagation through carbon flow betweenness centrality, includes the following steps D1-D2:

[0124] D1: Sort each node according to the carbon flow betweenness centrality to obtain the sorting result of each node.

[0125] D2: Select the key nodes for carbon flow propagation based on the sorting results of each node.

[0126] Specifically, in step D1, the nodes are sorted according to the carbon flow betweenness centrality to obtain the sorting result of each node. The specific operation can be as follows:

[0127] Traverse all node pairs in the network topology and calculate the carbon flow betweenness centrality of all nodes. If not all node pairs in the network topology have been traversed, repeat step C3.

[0128] If all pairs of nodes in the network topology have been traversed, then sort all nodes according to carbon flow betweenness centrality.

[0129] In step D2, the selection of key nodes for carbon flow propagation based on the ranking results of each node refers to selecting nodes as key nodes for carbon flow propagation based on the carbon flow betweenness centrality, and identifying the carbon propagation bottlenecks that need to be prioritized for modification.

[0130] In one optional implementation, step S300 identifies key nodes in carbon flow propagation by carbon flow betweenness centrality. Alternatively, a multi-attribute decision analysis method combined with the hierarchical analysis process can be used to comprehensively consider the node's carbon intensity, connectivity, clustering coefficient, and load capacity indicators to construct a hierarchical evaluation system. The importance of each indicator is determined using the entropy weight method, and the comprehensive importance score of the node is calculated through a comprehensive evaluation model. This avoids the limitations of a single indicator and provides a more comprehensive and objective assessment of node importance, making it suitable for decision-making scenarios that require balancing multiple objectives.

[0131] In another optional implementation, in step S300, key nodes for carbon flow propagation are identified by carbon flow betweenness centrality. Alternatively, a vulnerability analysis method based on network attack simulation can be used, employing node failure experiments and edge removal strategies to simulate the impact of key node failures on the network's carbon flow propagation capability. The degree of network performance degradation can be quantified using network efficiency, connectivity, and aggregation. By analyzing responses under different failure scenarios through Monte Carlo simulation, the key nodes with the greatest impact on network stability can be identified from a robustness perspective, providing a scientific basis for emergency plan formulation and protection strategy design.

[0132] It should be noted that this invention introduces the concept of betweenness centrality from network theory into the field of carbon flow analysis, solving the problems of neglecting the impact of network topology on carbon propagation and failing to effectively identify key nodes in carbon flow propagation in existing technologies. Traditional carbon reduction strategies mainly focus on optimizing the source and load ends on the generation and consumption sides, lacking in-depth analysis of carbon propagation paths in the intermediate links of the power grid, and failing to tap carbon reduction potential from the network level. This invention, by calculating the betweenness centrality of carbon flow, can quantify the degree of mediating role of each node in the carbon flow propagation path, identify key nodes and bottleneck lines that have a critical control effect on carbon flow propagation, and provide a new scientific basis for planning decisions such as new energy site selection, power grid transformation and upgrading, and energy storage configuration. At the same time, this invention can also provide power grid operators with carbon propagation situation awareness capabilities, help identify carbon propagation bottlenecks that need to be prioritized for transformation, and provide important analytical tools and decision support for building a clean and low-carbon new power system.

[0133] It should be noted that this invention constructs a mixed-integer linear programming (MILP) model with the objective of minimizing the total operating cost and carbon emission cost of an integrated energy system. To meet the mixed-integer requirements in integrated energy system scheduling, the model is implemented using a GLPK solver, employing the branch-and-bound method to handle integer variables, and combining it with the dual simplex method to accelerate the solution of LP subproblems. The low-carbon optimization scheduling model aims to minimize the total carbon emission cost and system operating cost of the power system while satisfying constraints on the power system, heating system, and natural gas network. Simultaneously considering carbon emission minimization and operational economics, a weighted summation method is used to transform the multi-objective optimization problem into a single-objective optimization problem.

[0134] In this embodiment of the invention, step S400, which establishes a low-carbon optimal scheduling model based on time-varying node carbon intensity, includes the following steps E1-E3:

[0135] E1: Establish an objective function based on time-varying node carbon intensity. The objective function includes total carbon emission cost and total operating cost.

[0136] E2: Establish constraints for power systems, thermal systems, gas grid systems, and cross-energy coupling.

[0137] E3: Establish carbon intensity constraints based on time-varying node carbon intensity.

[0138] Specifically, step E1 establishes an objective function based on time-varying nodal carbon intensity, including the following steps E11-E12:

[0139] E11: Weighted load calculation is performed using time-varying node carbon intensity (TV-NCI), and the carbon emissions from electricity consumption at all nodes over all time periods are summed to obtain the total carbon emission cost (TCE). The total carbon emission cost can be specifically expressed as:

[0140]

[0141] Among them, TV_NCI i (t) represents the carbon intensity (gCO2 / kWh) at node i at time t; P load (t) represents the load power (kW) of node i at time t; Δt represents the time interval (hours); T represents the sum of all time intervals; and N represents the set of all nodes.

[0142] E12: Total Operation Cost (TOC) includes the fuel cost of the natural gas network, the maintenance cost of energy storage equipment, and the penalty for curtailment of renewable energy, which can be specifically expressed as:

[0143]

[0144] in, Let a be the fuel cost function of generator g ($ / h). g ,b g ,c g C is the cost coefficient. soc (SOC s (t))=k SOC ·(SOC s (t)-SOC ref ) 2 To reduce energy storage charging and discharging maintenance costs and prevent drastic fluctuations in SOC; P curt (t) represents the amount of renewable energy wasted in time period t (kW); λ represents the wasted energy penalty coefficient ($ / kW); P g (t) represents the power of engine unit g at time t; k SOC SOC s (t) and SOC ref These are the energy storage maintenance cost factor and the state of charge (SOC) before and after discharge, respectively.

[0145] For example, the objective function in step E1 can be specifically represented by the following formula:

[0146] F = min{α×TCE + β×TOC};

[0147] Where F is the objective function; α and β are weighting coefficients.

[0148] Specifically, establishing power system constraints in step E2 refers to using power flow balance constraints to ensure that the power generation output of each node (including traditional units, new energy sources, and energy storage discharge) matches the load demand and line transmission power. This is the foundation for maintaining stable system operation and includes the following steps E21-E23:

[0149] E21: The nodal power balance constraint ensures that the power flowing into a node and the carbon flow attached to it equal the outflow value, thereby maintaining the power supply and demand balance of the system. This is a fundamental condition for the stable operation of power systems and integrated energy systems. The nodal power balance constraint can be specifically expressed as:

[0150]

[0151] Among them, G i S is the set of generator sets that supply power to node i; i Let N be the set of energy storage devices connected to node i; i The set of nodes into which power is injected or emanated from node i; η dis ,η ch Energy storage discharge / charge efficiency; P dis,s (t),P ch,s (t) represents the discharge / charge power (kW) of the energy storage s; P g (t), P load,i (t) and P ij (t) represents the power of engine g at time t, the load of node i at time t, and the power flowing from i to j at time t, respectively.

[0152] E22: To ensure stable system operation, the transmission capacity of lines and the output of generators must be limited, including the transmission lines between nodes in the power grid system and the generator sets connected to the nodes. Specifically, this can be expressed as:

[0153]

[0154] Among them, P ij (t) and Let be the transmission power and maximum transmission capacity from node i to j at time t, respectively; ζ is the set of normally connected states for any time i and j. P g (t) and Let G be the minimum output power of generator g, the output power at any time t, and the maximum output power of generator g, respectively; G is the set of normal operating states of generator g at any time.

[0155] E23: Utilizing the state of charge (POC) to establish operational constraints for energy storage devices is used to optimize energy distribution and utilization, thereby improving the overall system efficiency. The POC represents the percentage of a battery's remaining charge relative to its rated capacity. The POC dynamic equation can be specifically expressed as:

[0156]

[0157] The upper and lower limits of the state of charge can be specifically expressed as:

[0158]

[0159] The mutual exclusion of charging and discharging can be specifically expressed as:

[0160]

[0161] Among them, SOC s (t+1) and SOC s (t) represent the state of charge at times t+1 and t, respectively; η ch and P ch,s (t) represents the charging efficiency and power, respectively; η dis and P dis,s (t) represent discharge efficiency and power, respectively; Self-discharge rate; SOC s (t) and Here, u represents the upper limit of the state of charge, the state of charge at time t, and the upper limit of the state of charge. s (t)∈{0,1} is the charging state flag; M is a large constant; P ch,s (t) represents the charging power; P dis,s (t) represents the discharge power.

[0162] It should be noted that energy storage devices can play a role in peak shaving and valley filling in the power system. They are generally used to store excess green electricity from new energy units or electricity generated by cogeneration equipment and gas turbines, and release the electricity during peak load periods to reduce the workload of diesel generators. Adjusting the operating status of energy storage devices can improve the absorption of green electricity and also affect the operating status of cogeneration equipment and power-to-gas equipment, thereby reducing overall carbon emissions. Therefore, energy storage devices are the most important adjustment target of integrated energy systems.

[0163] Specifically, establishing thermal system constraints in step E2 refers to the constraints of the thermal system in the integrated energy system, which mainly originate from heat sources, including cogeneration equipment, gas boilers, and waste heat from gas turbine units. Other constraints include user load and pipeline transportation constraints. The thermal network balance constraints consider pipeline transmission delays and heat losses to ensure dynamic matching between heat source output, pipeline transmission, and user load, while limiting node temperatures within a safe range, including the following steps E24-E25:

[0164] E24: Energy balance constraints at heating network nodes ensure the balance between the heat flowing into and out of the nodes, thereby maintaining the supply and demand balance between the system's heat source and load. Specifically, this can be expressed as:

[0165]

[0166] Among them, Q gen,i (t) represents the heat source output at node i; τ ji Q represents the heat transfer delay (in minutes) from node j to node i; jiQ represents the thermal energy flowing from node j into node i. load,i (t) represents the load of node i at time t; Q ik (t) represents the heat energy power flowing from node i to k; N i Let i be the set of all nodes connected to node i.

[0167] E25: Heat source output limits and pipeline heat flow limits ensure the stable operation of heat sources and pipelines under safe conditions, which can be specifically expressed as:

[0168]

[0169] in, Q gen,i (t) and Minimum heat source power at node i, maximum heat source power at time t; Q ij (t) and These represent the calorific value transfer power from i to j at time t and the maximum value of calorific value transfer through the pipeline, respectively; ζ heat This represents the set of normal operating states of the thermal network.

[0170] Specifically, establishing gas network system constraints in step E2 means that the gas source of the natural gas network comes partly from methane produced after water electrolysis and partly from natural gas supply. The constraints, besides the gas source, primarily come from the pipeline network and gas pressure. Natural gas network balance constraints, through the pressure-flow equation, ensure real-time matching between gas supply, pipeline transmission, and load demand, preventing pressure exceeding limits from causing equipment damage or gas supply interruptions. This includes the following steps E26-E27:

[0171] E26: Node flow balance constraints ensure the balance of inflow and outflow at gas network nodes, guaranteeing the supply-demand balance and stable operation of the gas network. Specifically, this can be expressed as:

[0172]

[0173] Among them, F injection,i (t) represents the gas injection amount at node i; F load,i (t) represents the gas load at node i; F ji (t) and F ik (t) represents the inflow and outflow gas flow rates at time t, respectively; Ni For all nodes connected to node i, use the set of nodes.

[0174] E27: To ensure that the flow rate and pressure in the gas network pipeline are within a suitable range, the following constraints are established for the gas pressure-flow rate relationship (Weymouth equation):

[0175]

[0176] Where, pi (t) and p j (t) represents the air pressure (kPa) at nodes i and j; K ij F is the pipe characteristic constant. ji (t) represents the inflow air flow rate at time t.

[0177] The safe range of air pressure can be specifically expressed as:

[0178]

[0179] in, Let be the lower limit of the air pressure safety for node i; N represents the upper limit of air pressure safety for node i; gas This is the set of normal operating conditions for the gas network.

[0180] It should be noted that, in addition to the operational constraints within each energy network, the coupling constraints between energy sources are a key focus of this invention. In this invention, an electrothermal-gas coupling matrix is ​​established to describe the coupling relationship between various energy sources. The carbon flow interaction between various energy sources is reflected through equipment coupling relationships. Therefore, the following equipment coupling constraints are established to further realize the low-carbon optimized scheduling of the system. The electrothermal coupling constraints of the cogeneration equipment and the energy conversion constraints of the power-to-gas equipment are the key to achieving synergistic carbon reduction of the electricity-heat-gas system.

[0181] Specifically, step E2 establishes cross-energy coupling constraints, including the following steps E28-E29:

[0182] E28: Based on the electrothermal efficiency, the relationship between power output and heat output is determined, and the electrothermal coupling constraint of the cogeneration equipment is obtained.

[0183] E29: Based on the conversion efficiency, the relationship between power input and natural gas output is determined, and the constraints of the power-to-gas equipment are obtained.

[0184] Furthermore, the electrothermal coupling constraint of the cogeneration equipment in step E28 refers to describing the multi-energy supply characteristics of the cogeneration equipment through electrothermal coupling equations and allocating carbon emission responsibilities based on efficiency parameters. Specifically, the electrothermal coupling relationship can be expressed as:

[0185]

[0186] The carbon emission allocation relationship can be specifically represented as follows:

[0187]

[0188] Among them, Q CHP (t) represents the thermal output power of the CHP device; η h The coupling coefficient; P CHP (t) and These represent the lower limit of CHP device power output, the output power of CHP device at time t, and the upper limit of CHP device power output, respectively; B e (i,t) and B h (i,t) represent the carbon emissions from electrical energy and thermal energy of the CHP equipment, respectively; η e and η h These are the electrical and thermal coupling coefficients of the CHP equipment, respectively; EF gas The carbon emission factor for CHP equipment fuel.

[0189] Furthermore, the constraint on the electricity-to-gas conversion equipment in step E29 refers to quantifying the electricity-to-gas conversion relationship through an efficiency model. Its carbon emission factor can be adjusted based on whether carbon capture technology is integrated, and can be specifically expressed as follows:

[0190]

[0191] Among them, LHV gas Low calorific value of natural gas (kWh / m³) 3 Typical value ≈ 10 kWh / m³ 3 ;F P2G (t) represents the flow rate of natural gas produced (m³) 3 / h); η P2G P is the electro-pneumatic conversion coefficient for P2G equipment; P2G (t) and The power output and maximum power output of the P2G device at time t are given.

[0192] Specifically, in step E3, establishing carbon intensity constraints based on time-varying node carbon intensity refers to using time-varying node carbon intensity threshold constraints to force the carbon intensity of time-varying nodes to not exceed the policy-permitted value, and guiding energy storage and demand-side resources to participate in low-carbon dispatch through dynamic carbon price signals. This can be expressed as follows:

[0193]

[0194] Among them, TV_NCI i (t) and Let N be the time-varying nodal carbon intensity of node i at time t and the system's time-varying nodal carbon intensity threshold; critical This is the set of system nodes.

[0195] In this embodiment of the invention, step S400, which combines a low-carbon optimization scheduling model and key nodes of carbon flow propagation to determine a low-carbon scheduling scheme for the integrated energy system, can specifically involve:

[0196] Construct a mixed-integer linear programming model and use the branch and bound method to handle integer variables.

[0197] The dual simplex method is combined to accelerate the solution of linear programming subproblems.

[0198] The model is solved using the GLPK solver to obtain the optimal low-carbon scheduling scheme.

[0199] It should be noted that the low-carbon optimization scheduling model based on time-varying node carbon intensity constructed in this invention achieves a coordinated unity between minimizing carbon emissions and economic efficiency, solving the problem of conflict between carbon emission reduction targets and economic operation targets, and the difficulty in achieving multi-objective balance optimization in existing scheduling methods. Traditional economic scheduling models mainly aim to minimize operating costs, with insufficient consideration for carbon emission constraints. Simple carbon constraints often lead to a significant increase in operating costs and a deterioration in economic efficiency. This invention establishes an objective function that includes time-varying node carbon intensity, weightedly optimizes carbon emission costs and operating costs, and establishes a complete constraint system covering power systems, thermal systems, natural gas networks, and cross-energy couplings. This ensures that the scheduling scheme is technically feasible and economically reasonable. Furthermore, by considering the electrothermal coupling characteristics of cogeneration equipment and the energy conversion characteristics of power-to-gas equipment, it can fully leverage the advantages of multi-energy complementarity, achieving carbon emission reduction while ensuring the safe and stable operation of the system.

[0200] In summary, this invention proposes a time-varying node carbon intensity model, which can be updated on a minute-level timescale, demonstrating the impact of new energy units on carbon flow changes and providing a more accurate evaluation index for low-carbon dispatch and planning of power systems. By utilizing the carbon flow betweenness centrality to calculate the shortest flow path of carbon flow and the nodes and lines it passes through, it demonstrates the impact of grid structure on carbon flow propagation paths, which can be applied to identify high-carbon nodes and lines in power systems, providing a new consideration index for low-carbon grid planning. By constructing a model with time-varying node carbon intensity and economic indicators as objective functions, the granularity of node carbon intensity time changes is refined, strengthening the impact of carbon flow changes on the low-carbon dispatch of integrated energy systems. This can effectively reduce carbon emissions of integrated energy systems while reducing the increase in operating costs, thus improving the effectiveness of low-carbon economic dispatch of integrated energy systems.

[0201] Example 3, referring to Figures 5-9 As an embodiment of the present invention, a low-carbon scheduling method for an integrated energy system is provided. To verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0202] This embodiment uses the IEEE 33 standard node system as an example to perform low-carbon optimized scheduling of a single-energy flow system, verifying the effectiveness of the time-varying node carbon intensity and carbon flow betweenness centrality proposed in this invention in reducing carbon emissions and identifying high-carbon nodes. It also uses an extended electro-thermal-gas coupled system based on the IEEE 33 node as an example to verify the advantages of the scheduling strategy based on time-varying node carbon intensity and carbon flow betweenness centrality. The equipment parameters used in this embodiment are as follows: 200kW photovoltaic, 150kW wind power, diesel generator set carbon emission factor 650gCO2 / kWh; CHP unit (electrical efficiency 40%, thermal efficiency 50%), P2G equipment (conversion efficiency 60%). Time-varying node carbon intensity describes the time-varying characteristics of carbon intensity, and carbon flow betweenness centrality helps identify high-carbon nodes in the power grid system. To demonstrate the effectiveness of these two indicators in the low-carbon strategy of the power grid system, this embodiment sets up the following two scenarios for verification: Scenario 1: Time-varying node carbon intensity shows the carbon reduction effect; Scenario 2: Carbon flow betweenness centrality identifies high-carbon nodes and lines.

[0203] In displaying the carbon reduction effect at time-varying nodes, calculations are performed at node 5 of the IEEE 33-node system based on the time-varying node carbon intensity definition and update mechanism proposed in this invention. Figure 5 As shown, before 7:00 AM, when the photovoltaic (PV) units were not outputting power, the carbon intensity of the node remained unchanged. After 7:00 AM, when the PV units began to output power, the carbon intensity of the node was affected and began to decrease until the output of the PV units exceeded the load, and the carbon intensity of the node approached 0. After 6:00 PM, the output of the PV units decreased, and the carbon intensity of the node began to rise until it returned to the initial value. This indicates that the time-varying carbon intensity of the node is affected by the PV output. Compared with the fixed carbon intensity, it can more scientifically display the carbon intensity of the node and provide a better tool for the low-carbon scheduling of the system.

[0204] In identifying high-carbon nodes and circuits using carbon flow betweenness centrality, such as Figure 6 and Figure 7 The results shown are the calculation results of the carbon flow betweenness centrality of nodes and lines in the IEEE 33-bus system. Figure 7 It can be seen that nodes 4, 7, 8, 21, and 32 are the nodes with the highest carbon flow betweenness centrality, and lines numbered 2-4, 4-5, 14-15, 25-26, and 28-29 are the lines with the highest carbon flow betweenness centrality. By conducting a blocking experiment (connecting to new energy units) on the nodes with the highest carbon flow betweenness centrality, the system's carbon emissions and new energy absorption rate were significantly improved. The improvement results are shown in Table 1. As can be seen from the table, after the blocking experiment, the system's curtailment rate decreased by 34.2%, the average carbon reduction rate was also greatly improved, carbon emissions decreased, and the utilization rate of new energy was also improved, verifying the control role of high carbon flow betweenness centrality nodes on carbon propagation paths.

[0205] Table 1. Experimental Results of High Carbon Flow Betweenness Centrality Blocking

[0206] Indicator Before blocking After blocking Change rate Average power rejection rate 18.7％ 12.3％ -34.2％ Average carbon reduction rate 15.2％ 28.7％ +88.8％ Total carbon emissions 1850 kg CO2 / day 1320 kg CO2 / day -28.6％ New energy utilization rate 81.3％ 87.7％ +7.9％

[0207] Secondly, to verify the advantages of using the time-varying nodal carbon intensity proposed in this invention as the objective function for low-carbon optimal scheduling of integrated energy systems, this embodiment also sets up three scenarios for comparative verification experiments, including traditional economic scheduling, fixed carbon intensity scheduling, and the method of this invention. To further analyze the effectiveness of the indicators proposed in this invention for low-carbon optimal scheduling of integrated energy systems, comparative experiments were conducted on the three scenarios in terms of total carbon emissions, total cost, and renewable energy utilization rate. The specific results are as follows: Figure 8 As shown, compared with traditional economic dispatch and fixed carbon intensity dispatch, the overall carbon emissions of the integrated energy system under the dispatching method of the present invention are reduced by 24.79% and 19.22% respectively, while the total operating cost increases by 19.43% and 7.14% respectively, and the renewable energy absorption rate increases by 12.8% and 4.4% respectively. It can be concluded that the method of the present invention reduces carbon emissions and increases renewable energy absorption rate while the overall operating cost increases only slightly, effectively meeting the requirements of low-carbon economic dispatch of integrated energy systems.

[0208] To visually demonstrate the superiority of the method of this invention, this embodiment also calculates the Pareto front data for three methods. The Pareto front is a core concept in multi-objective optimization, representing the boundary between the two objectives of carbon emissions and cost where further optimization is not possible. Figure 9 Each point on the graph represents an optimal solution, namely, the optimal solution with the lowest economic cost, the optimal solution with the lowest carbon emissions, and the equilibrium solution; from Figure 9 As can be seen, traditional scheduling methods have the lowest cost but the highest carbon emissions; fixed carbon intensity scheduling methods reduce emissions by constraining carbon intensity, but the lack of dynamic optimization leads to high costs; the method of this invention has the lowest cost under the same carbon emissions, or the lowest carbon emissions under the same cost, and is closest to the optimal solution region, demonstrating the advantages of dynamic optimization.

[0209] Example 4 is an embodiment of the present invention, which provides a low-carbon dispatching system for an integrated energy system, comprising: a multi-energy flow carbon flow module, used to acquire power network data, heating network data, and natural gas network data of the integrated energy system, and to establish a multi-energy flow carbon flow dynamic model based on the power network data, heating network data, and natural gas network data; a carbon intensity module, used to calculate the time-varying node carbon intensity of each node based on the multi-energy flow carbon flow dynamic model; a carbon flow betweenness centrality module, used to calculate the carbon flow betweenness centrality based on the time-varying node carbon intensity and the network topology, and to identify key nodes for carbon flow propagation through the carbon flow betweenness centrality; and a low-carbon dispatching module, used to establish a low-carbon optimized dispatching model based on the time-varying node carbon intensity, and to determine the low-carbon dispatching scheme of the integrated energy system by combining the low-carbon optimized dispatching model and the key nodes for carbon flow propagation.

[0210] This embodiment also provides an electronic device applicable to a low-carbon scheduling method for an integrated energy system, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a low-carbon scheduling method for an integrated energy system as proposed in the above embodiment.

[0211] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a low-carbon scheduling method for an integrated energy system as proposed in the above embodiments.

[0212] The storage medium proposed in this embodiment belongs to the same inventive concept as the low-carbon scheduling method for an integrated energy system proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0213] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0214] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A low-carbon dispatching method for an integrated energy system, characterized in that: include, Acquire power network data, heating network data, and natural gas network data of the integrated energy system, and establish a multi-energy flow carbon flow dynamic model based on the power network data, heating network data, and natural gas network data; The time-varying nodal carbon intensity of each node is calculated based on the multi-energy flow carbon flow dynamic model. The carbon flow betweenness centrality is calculated based on the time-varying node carbon intensity and network topology, and key nodes for carbon flow propagation are identified through the carbon flow betweenness centrality. A low-carbon optimization scheduling model is established based on the time-varying node carbon intensity, and a low-carbon scheduling scheme for the integrated energy system is determined by combining the low-carbon optimization scheduling model with the key nodes of carbon flow propagation.

2. The low-carbon dispatching method for an integrated energy system as described in claim 1, characterized in that: A multi-energy flow carbon flow dynamic model is established based on the power network data, the heating network data, and the natural gas network data, including: Based on the power network data, the heat network data, and the natural gas network data, establish a power carbon flow model, a heat carbon flow model, and a natural gas carbon flow model; By connecting the power carbon flow model, the thermal carbon flow model, and the natural gas carbon flow model through a cross-energy coupling matrix, a multi-energy flow carbon flow dynamic model is obtained.

3. The low-carbon dispatching method for an integrated energy system as described in claim 2, characterized in that: The time-varying nodal carbon intensity of each node is calculated based on the aforementioned multi-energy flow carbon flow dynamic model, including: A rolling update mechanism for time-varying node carbon intensity is established based on a model predictive control framework; Acquire real-time data of the integrated energy system, and update the dynamic carbon flow matrix and carbon source vector in the multi-energy flow carbon flow dynamic model based on the real-time data and the rolling update mechanism; Solve the updated multi-energy flow carbon flow dynamic model to obtain the time-varying nodal carbon intensity at each node.

4. The low-carbon dispatching method for an integrated energy system as described in claim 3, characterized in that: The carbon flow betweenness centrality is calculated based on the time-varying node carbon intensity and network topology, including: Obtain the network topology of the integrated energy system; The carbon flow propagation weights of each line in the network topology are determined based on the time-varying node carbon intensity. The shortest path algorithm is used to determine the shortest carbon flow path between each pair of nodes; The carbon flow betweenness centrality of each node is calculated based on the number of shortest carbon flow paths passing through each node and the carbon flow rate.

5. The low-carbon dispatching method for an integrated energy system as described in claim 4, characterized in that: Key nodes in carbon flow propagation are identified through the carbon flow betweenness centrality, including: The nodes are sorted according to the carbon flow betweenness centrality to obtain the sorting results of each node; Based on the sorting results of the nodes, the key nodes for carbon flow propagation are selected.

6. The low-carbon dispatching method for an integrated energy system as described in claim 5, characterized in that: A low-carbon optimization scheduling model is established based on the time-varying node carbon intensity, including: An objective function is established based on the time-varying node carbon intensity, and the objective function includes total carbon emission cost and total operating cost. Establish constraints for the power system, the thermal system, the gas grid system, and cross-energy coupling; Carbon intensity constraints are established based on the time-varying node carbon intensity.

7. The low-carbon dispatching method for an integrated energy system as described in claim 6, characterized in that: Establishing the cross-energy coupling constraints includes: Based on the electrothermal efficiency, the relationship between power output and heat output is determined, and the electrothermal coupling constraint of the cogeneration equipment is obtained. The relationship between power input and natural gas output is determined based on conversion efficiency, thus obtaining constraints for the power-to-gas conversion equipment.

8. A low-carbon dispatching system for an integrated energy system, employing the low-carbon dispatching method for an integrated energy system as described in any one of claims 1 to 7, characterized in that, include: The multi-energy flow carbon flow module is used to acquire power network data, heating network data, and natural gas network data of the integrated energy system, and to establish a multi-energy flow carbon flow dynamic model based on the power network data, heating network data, and natural gas network data. The carbon intensity module is used to calculate the time-varying nodal carbon intensity of each node based on the multi-energy flow carbon flow dynamic model. The carbon flow betweenness centrality module is used to calculate the carbon flow betweenness centrality based on the time-varying node carbon intensity and network topology, and to identify key nodes for carbon flow propagation through the carbon flow betweenness centrality. The low-carbon scheduling module is used to establish a low-carbon optimized scheduling model based on the time-varying node carbon intensity, and to determine the low-carbon scheduling scheme of the integrated energy system by combining the low-carbon optimized scheduling model and the key nodes of carbon flow propagation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the low-carbon scheduling method for an integrated energy system as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the low-carbon scheduling method for an integrated energy system as described in any one of claims 1 to 7.

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