Hybrid multi-energy system carbon flow multi-temporal-spatial-scale analysis method

Abstract

The invention provides a hybrid multi-energy system carbon flow multi-temporal-spatial-scale analysis method, which comprises the following steps of: firstly, performing coupling analysis on energy flow and carbon flow of a comprehensive energy system, and then performing carbon flow analysis under a whole-body period to obtain historical carbon emission and energy carbon emission analysis under a carbon emission coefficient to obtain current carbon emission; constructing nodes and channels of the carbon network model by means of the network model, and finally constructing the carbon network model according to the energy characteristics of the nodes. According to the energy-carbon flow coupling analysis method based on the integrated energy system, by means of coupling analysis of the energy flow and the carbon flow, the carbon flow is obtained through decoupling to construct the carbon network model, and the defect that the relation between the energy flow and the carbon flow is not clear and the defect of a carbon network construction method of the integrated energy system can be overcome. The carbon emission condition of the comprehensive energy system is mastered quantitatively and accurately, and meanwhile subsequent carbon emission reduction optimization for the comprehensive energy system is simplified.

Description

Technical Field

[0001] The present invention belongs to the field of comprehensive energy analysis, and specifically is a multi-temporal and spatial scale analysis method for carbon flow in a hybrid multi-energy system. The present invention calculates carbon flow based on the energy-carbon flow coupling method and considers carbon flow over the entire life cycle. Finally, the carbon flow characteristics under the coupling of energy-carbon flow are comprehensively considered to construct and analyze the carbon network. Background Art

[0002] Carbon dioxide emissions are an important cause of global warming, and the use of energy produces a large amount of carbon emissions. The integrated energy system can absorb clean energy and optimize the scheduling of energy sources, which can effectively reduce carbon emissions and generate benefits.

[0003] In the prior art, the analysis of integrated energy systems is mostly focused on the separation analysis of energy flow and carbon flow. There is no clear analysis of the coupling relationship between energy and carbon flow, and there are still deficiencies in the construction of carbon networks. A few studies have taken into account the carbon flow in photovoltaic systems, such as a carbon emission flow analysis method disclosed in Chinese Patent 202211161861.9, which considers the impact of the uncertainty of photovoltaic and storage combined power generation on the carbon emission flow of the power system to obtain the influence factor of the carbon flow rate of the node and branch of the balancing unit under the distributed photovoltaic output power, so as to achieve the purpose of quantitatively analyzing the impact of photovoltaic and storage combined power generation on the carbon emission flow of the power system. However, this method mainly focuses on the role of photovoltaic access in reducing carbon emissions in the power grid, and does not explain the relationship between its energy and carbon flow. At the same time, the selected objects have not been expanded to integrated energy systems. Therefore, a method that considers energy flow and carbon flow and their coupling relationship is needed to construct a carbon network to better serve energy conservation and carbon reduction. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-temporal and spatial scale analysis method for carbon flow in a hybrid multi-energy system, which can provide an analysis and construction method for a carbon network for an integrated energy system.

[0005] The present invention proposes a method for analyzing carbon flow at multiple spatiotemporal scales in a hybrid multi-energy system. The method is obtained by performing energy flow analysis, carbon flow analysis, and energy-carbon flow coupling analysis on a comprehensive energy system, and comprises the following steps:

[0006] (1) Calculate the selected integrated energy system to obtain the characteristics of the integrated energy system and the energy flow load characteristics;

[0007] (2) Based on the integrated energy system characteristics obtained in step (1), an energy-carbon flow coupling analysis is performed on the integrated energy system to establish a coupling relationship between energy flow and carbon flow;

[0008] (2.1) Based on the comprehensive energy system characteristics obtained in step (1), the corresponding energy production and transmission characteristics are obtained, and the historical carbon flow analysis and calculation are performed with the help of the life cycle analysis method (LCA) to provide carbon flow data for the construction of the carbon network model;

[0009] (2.2): Based on the energy flow load characteristics of the integrated energy system obtained in step (1), the corresponding energy transfer information is obtained, and the current carbon emission is analyzed and calculated with the help of the energy carbon emission factor to provide carbon flow data for the construction of the carbon network model;

[0010] (3): Based on the comprehensive energy system characteristics obtained in step (1), a carbon network model is constructed with the help of the analysis and calculation method of the network model;

[0011] (4) selecting the construction and analysis of the carbon network model of the integrated energy system based on the carbon flow data of the carbon network model of the integrated energy system obtained in step (2) and the carbon network model obtained in step (3);

[0012] C out =V*φ out (5)

[0013] Where: C out is the carbon flow output of the node, φ out is the energy flow output of the node, V represents the carbon potential energy of the current node;

[0014]

[0015] Where: τ is the carbon utilization rate of clean energy, based on carbon emissions, C clean use is the total carbon emissions from using clean energy, C use Carbon emissions from all energy used;

[0016]

[0017] Where: ξ is the energy carbon utilization rate in the carbon dimension. While horizontally comparing the energy utilization rates of different nodes to form a systematic network node utilization rate, it can also judge the access energy advancement; C loss represents the carbon emission conversion of energy loss, C is the overall carbon emission of the energy system;

[0018]

[0019] in: is the carbon flow from node a to node b, is the energy flow from node a to node b, is the carbon resistance of the path between a and b;

[0020]

[0021] Where: V m is the carbon potential of node m, Enter the carbon potential for the m-node, Output carbon potential for node m, Transmit energy flow to m nodes.

[0022] In the present invention, the analysis and calculation of energy-carbon flow coupling is performed on the integrated energy system in step (2):

[0023] ρ=ρ his +ρ cur (1)

[0024] Where: ρ is the unit emission coefficient of energy, ρ his is the historical carbon emission coefficient of carbon dioxide emissions during the life cycle of energy equipment, ρ cur The carbon emission coefficient of the current carbon emissions of energy equipment.

[0025] In the present invention, the energy flow analysis and calculation of the entire life cycle of the integrated energy system described in step (2.1) is performed, and the historical carbon flow is calculated using the LCA analysis method:

[0026]

[0027] Where: C LCA is the carbon emission of energy equipment over its entire life cycle, ρ his is the historical carbon emission coefficient of carbon dioxide emissions during the life cycle of energy equipment, φ total The energy output of energy equipment over its entire life cycle.

[0028] In the present invention, the current carbon flow analysis of the integrated energy system is performed in combination with the load characteristics in step (2.2), and the calculation formula is as follows:

[0029] C cur =φ cur *ρ cur (3)

[0030] Where: C cur is the current energy carbon flow, φ cur is the current energy flow, ρ cur The carbon emission coefficient of the current carbon emissions of the energy equipment is used to calculate the carbon flow for the selected energy equipment and its operating conditions.

[0031] In the present invention, the carbon emission factor selection standard for indirect carbon emission verification described in step (2.1) and step (2.2) is the ISO14067 international standard.

[0032] In the present invention, the carbon network model is constructed based on the energy characteristics of the comprehensive energy in step (1) in step (3), and the calculation formula is as follows:

[0033] C line =R*φ line (4)

[0034] Where: C line is the carbon flow in the link, indicating the carbon flow under the current link; R is the link carbon resistance, indicating the carbon emission strength of the current transmission energy flow; φ line is the energy flow of the current link.

[0035] The beneficial effects of the present invention are as follows: the present invention proposes the concept of energy-carbon flow coupling to integrate different types of energy, and proposes a spatiotemporal dynamic analysis method of carbon flow with the help of the idea of ​​power flow analysis. The actual scenario is characterized by a network model, with carbon emission as the dynamic flow medium, carbon potential energy as the carbon flow driver on the node side, and carbon resistance as the carbon flow accumulation in the path. In addition, the LCA method is used to subdivide the carbon flow into historical carbon emissions under the whole life cycle and current carbon emissions during the energy flow process, among which the carbon flow accounting of electric energy is improved by 0.89%, the accounting of gas is improved by 10.12%, and the accuracy of heat generation accounting is improved by 11.45%. A method for analyzing the clean energy consumption rate and energy carbon utilization rate from a carbon perspective is proposed, and the energy level analysis is given while also providing carbon emission analysis in the process of energy substitution. A virtual IES14 carbon network model is established in an industrial park in Nanjing for case verification and analysis. The results show that the model and analysis method can effectively and accurately quantify the spatiotemporal distribution of carbon emissions. The study also reveals the impact of renewable energy node consumption and energy access with different utilization rates on system carbon emissions, laying the foundation for subsequent multi-energy network evaluation and low-carbon optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic flow chart of the multi-temporal and spatial scale analysis method for carbon flow in a hybrid multi-energy system provided by the present invention.

[0037] Figure 2 This is a diagram of the analysis results of Example 1 of the present invention under the IEEE14 network.

[0038] Figure 3 This is a diagram showing the results of building a carbon network for the integrated energy system of Example 1 of the present invention.

[0039] Figure 4 This is a node carbon potential energy diagram under energy-carbon flow coupling of Example 1 of the present invention. DETAILED DESCRIPTION

[0040] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the symbols used have the same meaning throughout. The embodiments described below are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0041] Embodiment 1:

[0042] The present invention is aimed at the integrated energy system and proposes a carbon flow multi-space scale analysis method for a hybrid multi-energy system through carbon flow analysis, energy flow analysis, and energy-carbon flow coupling analysis.

[0043] In order to have a clearer understanding of the present invention, a brief description is given here. The present invention includes two basic steps: Step 1, carbon flow and energy flow analysis of the integrated energy system; Step 2, carbon flow network construction of the integrated energy system.

[0044] Specifically, Figure 1 The figure is a flow chart of a monitoring method for selecting points and integrating carbon verification based on three-dimensional space-time carbon monitoring in the sky and on the ground according to an embodiment of the present invention, which includes the following steps:

[0045] S1: Calculate the selected integrated energy system to obtain the characteristics of the integrated energy system and the energy flow load characteristics, and provide energy model support for the coupling of energy flow and carbon flow in subsequent work;

[0046] P Gi -P Li =U i ∑U j (R ij cosθ ij +B ij sinθ ij )#(10)

[0047] Q Gi -Q Li =U i ∑U j (R ij cosθ ij -B ij sinθ ij )#(11)

[0048] E=S×H×η×t#(12)

[0049]

[0050] P e-h =P e *η e-h #(15)

[0051] P h-e =P h *ηh-e #(16)

[0052] P h =2.58*P e #(17)

[0053]

[0054] Formulas (10) and (11) are general energy flow calculation models for power grids. Gi is the active power generation at node i, P Li is the active load power at node i; Q Gi is the reactive power generated at node i, Q Li is the reactive load power at node i, where U j is the voltage at node j, R and B are the real and imaginary parts of the node admittance matrix, θ ij is the voltage phase difference between i and j. Equation (12) is a photovoltaic system, where E is the energy produced per unit time (kWh), S is the irradiated area (m 2 ), H is the amount of sunlight (kWh / m 2 ),η is the photoelectric conversion efficiency, t is the illumination time (s). The factors affecting the power generation effect in a certain area are the irradiation area and the conversion efficiency. The irradiation area is related to the installation angle, and different installation methods will have different irradiation effects. The conversion efficiency depends on the material of the solar photovoltaic panel. Since polycrystalline silicon has the advantages of good light transmittance, low cost, and good sealing, the present invention selects polycrystalline silicon as the research object. The conversion efficiency of existing polycrystalline silicon solar photovoltaic panels can reach 20%. As shown in the above formulas (13)-(17), it is a thermoelectric coupling model, where η e-h is the conversion efficiency of electricity to heat, η h-e E is the efficiency of converting heat energy into electrical energy; h is the energy of thermal energy, E e is the energy of electrical energy; P e-h is the energy conversion power from electricity to heat, P h-e P is the energy conversion power from heat to electricity; e is the electrical power, P h is the thermal power. Formula (18) is the thermal power output of the gas boiler. GB is the air intake volume at time t, in m 3 ,η is the thermal efficiency of the gas boiler, and β is the calorific value of natural gas.

[0055] S2: According to the characteristics of the integrated energy system obtained in step S1, analyze the energy-carbon flow coupling of the integrated energy system, sort out the coupling relationship between energy flow and carbon flow, obtain the corresponding energy production and transmission characteristics according to the characteristics of the integrated energy system obtained in step S1, and use the life cycle (LCA) analysis method to analyze and calculate the historical carbon flow, providing data support for the subsequent construction of the carbon network model, and according to the energy flow load characteristics of the integrated energy system obtained in step S1, obtain the corresponding energy transfer information, use the energy carbon emission factor to analyze and calculate the current carbon emissions, and provide data support for the subsequent construction of the carbon network model.

[0056] ρ=ρ his +ρ cur (1)

[0057]

[0058] C cur =φ cur *ρ cur (3)

[0059] C line =R*φ line (4)

[0060] Where: ρ is the unit emission coefficient of energy, ρ his is the historical carbon emission coefficient of carbon dioxide emissions during the life cycle of energy equipment, ρ cur is the carbon emission coefficient of the current carbon emission of energy equipment. LCA is the carbon emissions of energy equipment over its entire life cycle, total It is the energy output of energy equipment in its entire life cycle. cur is the current energy carbon flow, φ cur The current energy flow; carbon flow accounting is performed based on the selected energy equipment and operation conditions. line is the carbon flow in the link, indicating the carbon flow under the current link; R is the link carbon resistance, indicating the carbon emission strength of the current transmission energy flow; φ line is the energy flow of the current link.

[0061] S3: Based on the comprehensive energy system characteristics obtained in step S1, the carbon network is constructed with the help of the analysis and calculation method of the network model to provide model support for the construction of the carbon network model in subsequent work;

[0062] C out =V*φ out (5)

[0063]

[0064] Where: C out is the carbon flow output of the node, φout is the energy flow output of the node, V represents the carbon potential of the current node. τ is the carbon utilization rate of clean energy, which is based on carbon emissions. ξ is the energy carbon utilization rate under the carbon dimension. While horizontally comparing the energy utilization rates of different nodes to form a systematic network node utilization rate, it can also judge the access energy advancement. loss It represents the carbon emission conversion of energy loss, and C is the overall carbon emission of the energy system. is the carbon flow from node a to node b, is the energy flow from node a to node b, is the carbon resistance of the path between a and b. m is the carbon potential of node m, Enter the carbon potential for the m-node, Output carbon potential for node m, Transmit energy flow to m nodes.

[0065] S4: Based on the carbon flow data of the integrated energy system in step S2 and the carbon network model construction method in step S3, construct and analyze the carbon network model of the selected integrated energy system.

[0066] like Figure 2 The figure shows the detailed structure of the IEEE14 power network model, in which each node and branch is accurately labeled to fully reflect the carbon characteristics of the network. The number marked next to the node represents the current carbon potential state of the node, which intuitively reflects the accumulated carbon impact potential of the node during power transmission. The current size and direction indication (indicated by positive and negative signs) marked above the branch reveal the flow path of electricity in the network; and the number below directly quantifies the size of the carbon flow accompanying the flow of electricity, providing key data for evaluating the carbon footprint of the network.

[0067] In order to more accurately assess the contribution of different energy types to the carbon characteristics of the network, Figure 2 A clear distinction is made between node types: Node 1 is defined as a thermal power output node, highlighting its reliance on fossil fuels and relatively high carbon emissions; while Node 2 is designated as a photovoltaic power generation node, symbolizing a clean and low-carbon energy supply method.

[0068] also, Figure 2 The historical and real-time carbon emission data of nodes have also been expanded to fully reflect the evolution of the carbon characteristics of the power grid. Historical carbon emissions are calculated by calculating the ratio of the total carbon emissions of equipment or energy facilities over their entire life cycle to the annual power generation, providing a quantitative benchmark for long-term carbon footprint management. Real-time carbon emissions are closely related to the carbon emission factors of specific energy types, ensuring the timeliness and accuracy of the assessment results.

[0069] In terms of the calculation process, Figure 2The steps of the above method are followed: First, the energy flow distribution of each branch is determined by using the traditional power network flow calculation method, and the carbon potential distribution diagram is derived based on this; then, starting from a specific node (such as node 1), combined with its known carbon resistance characteristics and the carbon potential value obtained by the flow calculation, the carbon flow of each branch is calculated layer by layer through the radial network model. For the case of unknown branch carbon flow, the model adopts the method of substituting the unknown carbon resistance of the upstream node, and iterates the solution in combination with the radiation effect of the node until the complete carbon resistance and carbon flow information of all nodes and branches in the entire network is obtained.

[0070] This example is an intuitive demonstration of the carbon characteristics of the IEEE14 power network model and a demonstration of the evaluation method described in the present invention and its superiority. By introducing the concepts of carbon potential and carbon flow, combined with the differentiated definition of node types and comprehensive carbon emission data expansion, this model provides technical support and decision-making basis for the green transformation and optimization of the power network.

[0071] like Figure 3 As shown in the figure, a 14-node carbon network model under the integrated energy system (IES) is proposed. The model integrates the IEEE14 power network, the optimized Belgian 20-node gas network model (in the construction process, in view of the large number of original nodes in the Belgian gas model, some nodes were reasonably merged and simplified to maintain a balance between model complexity and computational efficiency), and an 8-node thermal model, together forming a highly integrated multi-energy carbon footprint assessment framework.

[0072] In the IES14 node carbon network, each node not only represents the key point of energy transmission and conversion, but also carries rich carbon characteristic information. The node load type is intuitively distinguished by standardized icons, mainly divided into residential type and industrial type. The small figure is specially marked next to the residential type node to roughly represent the scale or energy consumption level of the residential area, which enhances the readability and practicality of the model.

[0073] In particular, Node 1 is designed as the core energy supply node of the entire system, representing the energy input from the source side of the large power grid, and is one of the main sources of power for the system. In addition, electrical symbols are clearly marked above each node to accurately indicate the energy supply type of the node, including but not limited to thermal energy, electrical energy, gas energy, etc. These diverse energy supply forms reflect the complexity and flexibility of the IES system.

[0074] As for electricity production, the model clearly points out that electricity is mainly generated by nodes 1 and 2. Among them, node 1 is a traditional power production node, and its production capacity is set to 230MW, reflecting the dominant position of the large power grid in energy supply; while node 2 represents the introduction of a clean energy, specifically a 40MWp photovoltaic power generation system, demonstrating the potential and application value of renewable energy in IES.

[0075] To further improve the stability and economy of the system, the model is also equipped with a 130MW energy storage system. These energy storage resources are mainly distributed at key nodes within the system and are mainly used for peak shaving and valley filling operations, that is, releasing energy during peak electricity consumption and storing energy during valley hours, thereby effectively smoothing system load fluctuations, improving energy utilization efficiency, and reducing carbon emissions caused by supply and demand mismatch.

[0076] The IES14 node carbon network model proposed in this invention provides a powerful tool for evaluating and optimizing the carbon characteristics of integrated energy systems through refined node design, diversified energy supply and efficient energy storage configuration, which is of great significance for promoting the low-carbon transformation of the energy industry.

[0077] like Figure 4 The figure is a heat map of carbon potential energy at each node under the IES14 scenario. Figure 3 The specific scenario shown uses advanced analysis methods and calculation processes to accurately calculate and intuitively present the carbon potential intensity distribution of each node under different energy types and carbon attributes. The generation of this heat map not only reflects the excellent ability of the method in dealing with the carbon characteristics of complex power systems, but also further reveals the deep-level characteristics of the power network in terms of energy utilization and carbon emissions.

[0078] It can be clearly observed from the figure that electricity, as the main form of energy, has a significant proportion of carbon emissions in the overall network. This finding is consistent with the current situation that electricity production generally relies on fossil fuels, highlighting the severe challenges faced by the power industry in energy conservation and emission reduction. At the same time, the heat map vividly shows the horizontal comparison of carbon emissions of different nodes under the same energy type through the changes in color depth. This intuitive visualization method makes the differences in carbon emission scales between nodes clear at a glance, providing strong data support for decision makers.

[0079] It is worth noting that the carbon emission differences of electric energy are particularly prominently displayed in the heat map. This difference not only reflects the carbon emission efficiency of different nodes in the process of power generation, transmission and distribution, but may also be closely related to various factors such as the energy structure, load characteristics, and equipment efficiency of the nodes. Through in-depth analysis of these differences, high carbon emission nodes and potential optimization points in the network can be more accurately identified, providing a scientific basis for formulating targeted energy-saving and emission reduction measures.

[0080] In addition, the heat map also demonstrates the unique advantages of the method in the subdivision of carbon properties of energy types. By dividing the carbon potential intensity by energy type and presenting it independently, researchers can more clearly understand the specific contribution of different energy types to the carbon characteristics of the network, thereby providing strong support for the optimization and adjustment of the energy structure.

[0081] The heat map of carbon potential energy of each node under the IES14 scenario shown in the figure demonstrates the application effect of the method of the present invention, provides ideas for carbon emission analysis in the field of carbon characteristic assessment of power networks, and provides valuable reference and reference for the green transformation and sustainable development of the power industry.

Claims

1. Multi-temporal and spatial scale analysis method of carbon flow in hybrid multi-energy system, It is characterized in that The method is obtained by performing energy flow analysis, carbon flow analysis, and energy-carbon flow coupling analysis on an integrated energy system, and comprises the following steps: (1) performing calculations on the selected integrated energy system to obtain the characteristics of the integrated energy system and the energy flow load characteristics; (2) performing energy-carbon flow coupling analysis on the integrated energy system according to the characteristics of the integrated energy system obtained in step (1), and establishing a coupling relationship between energy flow and carbon flow; (2.1) Based on the comprehensive energy system characteristics obtained in step (1), the corresponding energy production and transmission characteristics are obtained, and the historical carbon flow analysis and calculation are performed with the help of the life cycle analysis method (LCA) to provide carbon flow data for the construction of the carbon network model; (2.2): Based on the energy flow load characteristics of the integrated energy system obtained in step (1), the corresponding energy transfer information is obtained, and the current carbon emission is analyzed and calculated with the help of the energy carbon emission factor to provide carbon flow data for the construction of the carbon network model; (3): Based on the comprehensive energy system characteristics obtained in step (1), a carbon network model is constructed with the help of the analysis and calculation method of the network model; (4) selecting the construction and analysis of the carbon network model of the integrated energy system based on the carbon flow data of the carbon network model of the integrated energy system obtained in step (2) and the carbon network model obtained in step (3); C out =V*φ out (5) Where: C out is the carbon flow output of the node, φ out is the energy flow output of the node, V represents the carbon potential energy of the current node; Where: τ is the carbon utilization rate of clean energy, based on carbon emissions, C cleanuse is the total carbon emissions from using clean energy, C use Carbon emissions from all energy used; Where: ξ is the energy carbon utilization rate in the carbon dimension. While horizontally comparing the energy utilization rates of different nodes to form a systematic network node utilization rate, it can also judge the access energy advancement; C loss represents the carbon emission conversion of energy loss, C is the overall carbon emission of the energy system; in: is the carbon flow from node a to node b, is the energy flow from node a to node b, is the carbon resistance of the path between a and b; Where: V m is the carbon potential of node m, Enter the carbon potential for the m-node, Output carbon potential for node m, Transmit energy flow to m nodes.

2. The multi-temporal and spatial scale analysis method for carbon flow in hybrid multi-energy systems according to claim 1, It is characterized in that The analysis and calculation of energy-carbon flow coupling for the integrated energy system described in step (2) is as follows: p=p his +r cur (1) Where: ρ is the unit emission coefficient of energy, ρ his is the historical carbon emission coefficient of carbon dioxide emissions during the life cycle of energy equipment, ρ cur The carbon emission coefficient of the current carbon emissions of energy equipment.

3. The multi-temporal and spatial scale analysis method for carbon flow in hybrid multi-energy systems according to claim 1, It is characterized in that In step (2.1), the energy flow analysis and calculation of the entire life cycle of the integrated energy system is carried out, and the historical carbon flow is calculated using the LCA analysis method: Where: C LCA is the carbon emission of energy equipment over its entire life cycle, ρ his is the historical carbon emission coefficient of carbon dioxide emissions during the life cycle of energy equipment, φ total The energy output of energy equipment over its entire life cycle.

4. The multi-temporal and spatial scale analysis method for carbon flow in hybrid multi-energy systems according to claim 1, It is characterized in that The current carbon flow analysis of the integrated energy system is performed in combination with the load characteristics as described in step (2.2). The calculation formula is as follows: C cur =φ cur *r cur (3) Where: C cur is the current energy carbon flow, φ cur is the current energy flow, ρ cur The carbon emission coefficient of the current carbon emissions of the energy equipment is used to calculate the carbon flow for the selected energy equipment and its operating conditions.

5. The multi-temporal and spatial scale analysis method for carbon flow in hybrid multi-energy systems according to claim 1, It is characterized in that The carbon emission factor selection standard for indirect carbon emission verification described in step (2.1) and step (2.2) is the ISO14067 international standard.

6. The multi-temporal and spatial scale analysis method for carbon flow in hybrid multi-energy systems according to claim 1, It is characterized in that In step (3), the carbon network model is constructed based on the energy characteristics of the comprehensive energy in step (1), and the calculation formula is as follows: C line =R*φ line (4) Where: C line is the carbon flow in the link, indicating the carbon flow under the current link; R is the link carbon resistance, indicating the carbon emission strength of the current transmission energy flow; φ line is the energy flow of the current link.

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