Electricity-carbon coupled regional multi-energy complementary system carbon emission analysis method and system

Abstract

The invention discloses an electricity-carbon coupled regional multi-energy complementary system carbon emission analysis method and system, and relates to the technical field of energy system optimization, and the method comprises the steps: converging energy data in a multi-energy complementary system; establishing a network load flow calculation model, and solving a load flow result; measuring and calculating the power carbon emission of the power generation side of the multi-energy complementary system; and constructing a carbon emission analysis model of the regional multi-energy complementary system. According to the regional multi-energy complementary power system data fusion method provided by the invention, the problem that the algorithm precision is influenced by inaccurate data caused by bad data is reduced, and then a network load flow calculation model of a multi-energy complementary power system containing renewable energy sources is established; according to the method, the power flow results of the power generation side, the network side and the power utilization side of the system can be solved quickly, and the power flow direction of renewable energy of the power generation side can be tracked.

Description

Technical Field

[0001] The present invention relates to the technical field of energy system optimization, and in particular to a carbon emission analysis method and system for a regional multi-energy complementary system coupled with electricity and carbon. Background Art

[0002] With the transformation of the global energy structure and the advancement of the low-carbon economy, regional multi-energy complementary systems play an important role in improving energy efficiency and promoting the consumption of renewable energy. The core of this system is to integrate various forms of energy, such as wind energy and solar energy, to achieve the diversification and complementarity of energy supply. In this context, electricity-carbon coupling analysis, as an important means of evaluating the environmental impact of energy systems, has become a hot topic in the energy field. Existing technologies establish an energy data aggregation platform, use network flow calculation models to analyze the operating status of the power system, and to a certain extent, measure carbon emissions on the power generation side.

[0003] However, the existing electricity-carbon coupling analysis technology has obvious shortcomings when applied to regional multi-energy complementary systems. First, traditional methods often lack comprehensiveness and accuracy in aggregating energy data, and fail to fully consider the data characteristics of different energy types in the multi-energy complementary system, resulting in deviations in subsequent analysis results. Secondly, the existing network flow calculation model fails to effectively consider the volatility and uncertainty of new energy when dealing with multi-energy complementary systems, which affects the accuracy and reliability of the flow results. Furthermore, for the measurement of carbon emissions on the power generation side, the existing technology often ignores the dynamic distribution characteristics of carbon emissions, and fails to effectively allocate carbon emissions on the power generation side to the network side and the power consumption side, making it impossible to comprehensively evaluate the carbon emission level of the system. The carbon emission analysis method for regional multi-energy complementary systems with electricity-carbon coupling proposed by our invention is aimed at these shortcomings. By constructing a more accurate carbon emission analysis model, it is expected to provide strong technical support for the low-carbon operation of regional multi-energy complementary systems. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the current carbon emission management of various industries is relatively extensive and has not yet been refined. At the same time, most of the current methods are based on the power system dominated by fossil energy as the research object for analysis. The above methods do not consider the green low-carbon multi-energy system composed of multiple energy sources such as wind power and photovoltaics, and do not meet the country's strategic needs for building a new energy system and a new power system. The purpose of this invention is to improve the above shortcomings and propose a carbon emission analysis method for a multi-energy complementary system under the background of new power system construction.

[0006] In order to solve the above technical problems, the present invention provides the following technical solution: a method for analyzing carbon emissions of a regional multi-energy complementary system coupled with electricity and carbon, which comprises the following steps:

[0007] Gather energy data in the multi-energy complementary system; establish a network flow calculation model and solve the flow results; calculate the carbon emissions of electricity on the power generation side of the multi-energy complementary system; and construct a regional multi-energy complementary system carbon emission analysis model.

[0008] As a preferred scheme of the carbon emission analysis method of the regional multi-energy complementary system coupled with electricity and carbon described in the present invention, the energy data in the multi-energy complementary system includes wind power generation data, photovoltaic power generation data, power grid flow data, and power grid data.

[0009] As a preferred solution of the carbon emission analysis method of the regional multi-energy complementary system coupled with electricity and carbon described in the present invention, the energy data in the multi-energy complementary system needs to be preprocessed before being aggregated, specifically including reading the measurement data, selecting the initial value of the state estimation iterative calculation, performing the state estimation iterative calculation, judging whether it converges, and if so, calculating the measurement residual, performing bad data detection, and if there is no bad data, identifying the suspicious data and correcting the corresponding data, and if there is bad data, eliminating the bad data.

[0010] As a preferred solution of the carbon emission analysis method of the regional multi-energy complementary system of electricity-carbon coupling described in the present invention, wherein: the network flow calculation model is expressed as:

[0011]

[0012] Among them, P i is the active power injected into node i, Q i is the reactive power injected into node i, δ i is the phase angle of node i, U i is the voltage at node i, G ij With B ij are the impedance and admittance of the line between node i and node j respectively.

[0013] As a preferred solution of the carbon emission analysis method of the regional multi-energy complementary system of electricity-carbon coupling described in the present invention, wherein: the calculation of the carbon emission of electricity on the power generation side of the multi-energy complementary system includes obtaining relevant data of the thermal power units that generate carbon dioxide on the power generation side, obtaining the distribution scatter data of CO2 emissions of the thermal power units at different load rates, performing feature engineering on all data of carbon emissions from thermal power plants, and forming a feature sample set The independent variables and dependent variables in the feature sample set are normalized separately, and then substituted into the algorithm measurement model through the training set to calculate the carbon emissions on the power generation side.

[0014] As a preferred solution of the carbon emission analysis method of the regional multi-energy complementary system of electricity-carbon coupling described in the present invention, wherein: the calculation of carbon emissions on the power generation side is expressed as,

[0015] E k =ω k ·P k +α

[0016] Among them, E k represents the carbon emissions of thermal power units on the power generation side of the regional multi-energy complementary system, ω k The algorithm calculates the model parameters, and α represents the deviation coefficient.

[0017] As a preferred solution of the carbon emission analysis method of the regional multi-energy complementary system of electricity-carbon coupling described in the present invention, the carbon emission analysis model of the regional multi-energy complementary system adopts the carbon flow theory to allocate the carbon emission of electricity on the power generation side to the network side and the power consumption side, wherein the carbon emission on the power consumption side is expressed as,

[0018] A u P=P k

[0019] Among them, it means A u The traceability matrix, P, represents the power vector of all nodes, P k Represents the power vector on the generator side.

[0020] The node power on the power consumption side is expressed as:

[0021] P load =BP

[0022] Among them, P load represents the load power vector on the electricity consumption side, and B represents the load factor vector.

[0023] The power consumption of the load on the power consumption side is expressed as:

[0024]

[0025] The carbon emission of the electricity side load is expressed as:

[0026]

[0027] Among them, E k / P k Indicates carbon emission intensity.

[0028] The carbon emission of network side load is expressed as,

[0029]

[0030] Among them, B line is the branch coefficient vector.

[0031] Another object of the present invention is to provide a carbon emission analysis system for a regional multi-energy complementary system coupled with electricity and carbon, which can efficiently aggregate and preprocess the energy data in the multi-energy complementary system, establish an accurate network flow calculation model, and accurately solve the flow results, thereby solving the shortcomings of the prior art in data processing and model calculation.

[0032] In order to solve the above technical problems, the present invention provides the following technical solutions: a carbon emission analysis system for a regional multi-energy complementary system coupled with electricity and carbon, comprising a data aggregation and processing module, a network flow calculation module, and an electric power carbon emission measurement module;

[0033] The data aggregation and processing module is responsible for collecting and preprocessing energy data in the multi-energy complementary system.

[0034] The network power flow calculation module is used to simulate and calculate the power flow in the power grid.

[0035] The electricity carbon emission calculation module is used to calculate the carbon dioxide emissions generated by the thermal power units on the power generation side.

[0036] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the carbon emission analysis method of the regional multi-energy complementary system coupled with electricity and carbon are implemented as described above.

[0037] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the carbon emission analysis method for a regional multi-energy complementary system coupled with electricity and carbon as described above.

[0038] The beneficial effects of the present invention are as follows: the present invention constructs a carbon emission analysis method for a regional multi-energy complementary system considering electricity-carbon coupling. Firstly, a data fusion method for a regional multi-energy complementary power system is proposed to reduce the problem of inaccurate data caused by bad data and affecting the accuracy of the algorithm. Secondly, a network flow calculation model for a multi-energy complementary power system including renewable energy is established. The method can quickly solve the flow results on the power generation side, network side and power consumption side of the system, and track the flow direction of renewable energy on the power generation side. A calculation of power carbon emissions on the power generation side of a multi-energy complementary power system based on a machine learning algorithm is proposed, and the power carbon emission levels of thermal power generating units under different load levels are calculated, so that the power carbon emissions of the entire multi-energy complementary power system are more accurate. Finally, a carbon emission analysis model for a regional multi-energy complementary system with electricity-carbon coupling based on carbon flow theory is constructed to accurately analyze the power carbon emission intensity on the network side and the power consumption side, so that the calculation of indirect carbon emissions is more fair and reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0040] Figure 1 This is an overall flow chart of the carbon emission analysis method for the regional multi-energy complementary system of electricity-carbon coupling provided in the first embodiment of the present invention.

[0041] Figure 2 A flow chart for detecting bad data in a carbon emission analysis method for a regional multi-energy complementary system coupled with electricity and carbon provided in the first embodiment of the present invention.

[0042] Figure 3 The overall framework diagram of the carbon emission analysis system of the regional multi-energy complementary system of electricity-carbon coupling provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0044] Example 1, reference Figure 1-Figure 2 , which is an embodiment of the present invention, provides a carbon emission analysis method for a regional multi-energy complementary system coupled with electricity and carbon, characterized in that:

[0045] S1: Aggregating energy data in multi-energy complementary systems.

[0046] The energy data in the multi-energy complementary system includes wind power generation data, photovoltaic power generation data, power grid flow data, and power grid data.

[0047] Before aggregating energy data in a multi-energy complementary system, preprocessing is required, including reading measurement data, selecting initial values ​​for iterative calculation of state estimation, performing iterative calculation of state estimation, judging whether it has converged, and if so, calculating measurement residuals, performing bad data detection, and if there is no bad data, identifying suspicious data and correcting the corresponding data; if there is bad data, eliminating the bad data.

[0048] In actual applications, the weights of electricity consumption data samples in the source field of the power system may decrease while the weights of data samples in the application field continue to increase. This weight difference will seriously affect the accuracy of multi-source heterogeneous data analysis.

[0049] The source field electricity consumption data that is different from the target field electricity data is initially filtered out. The process of multi-energy complementary power system data fusion is as follows: Figure 2 shown.

[0050] Input the multi-source heterogeneous data set in the source domain and the data set in the target domain. Then, initialize the multi-source heterogeneous data, fuse various data in the fields of renewable energy power generation and electricity consumption, and classify the fused data set. Then, analyze and iterate the data to filter the source domain data set and the target domain data set. Using the computing advantages of big data technology, calculate the distance between the two domains and eliminate the data that is not in the target domain, so as to improve the computing efficiency of the data fusion algorithm.

[0051] S2: Establish a network power flow calculation model and solve the power flow results.

[0052] The network power flow calculation model is expressed as:

[0053]

[0054] Among them, P i is the active power injected into node i, Q i is the reactive power injected into node i, δ i is the phase angle of node i, U i is the voltage at node i, G ij With B ij are the impedance and admittance of the line between node i and node j respectively.

[0055] When node i is a renewable energy power generation node, the power flow value injected into each branch connected to it can be calculated according to formula (1).

[0056] Furthermore, in S2, the flow results of all generators on the power generation side injected into each branch are calculated, and a flow tracing method based on proportional sharing is adopted, that is, the flow out of the branch is provided by each node inflowing into the branch in proportion, and the flow into the starting node of the branch is also proportionally distributed by the outflow flow of each outgoing branch. Using this method, based on the flow calculation results calculated by formula (1), the flow results of the system power generation side, network side, and power consumption side can be solved and decomposed, and the flow composition and distribution of the power consumption side, network side, and power consumption side can be traced.

[0057] S3: Calculate the carbon emissions of electricity on the power generation side of the multi-energy complementary system;

[0058] Furthermore, in S3, the carbon emissions of electricity on the power generation side of the multi-energy complementary power system are calculated based on the machine learning algorithm. First, the relevant data of the thermal power units that produce carbon dioxide on the power generation side are obtained, including the power generation data of the thermal power units, the volume, gas flow, gas temperature, pressure and other data of CO2 monitored by the online monitoring system of the unit's flue, and the carbon emissions of the thermal power units are obtained based on this.

[0059] Furthermore, in step 3, the scatter data of CO2 emission distribution of thermal power units at different load rates are obtained. In actual working conditions, different load rates of the units and different environmental conditions will cause changes in the CO2 emission intensity of the units. The CO2 emission levels of thermal power units at different load rates vary greatly. In order to accurately calculate the time-sharing carbon emission intensity of thermal power units, a power carbon emission model of thermal power units is modeled based on a machine learning algorithm.

[0060] Furthermore, in S3, all data on carbon emissions from thermal power plants are subjected to feature engineering to form a feature sample set k=1,...m, further modeling is performed.

[0061] Furthermore, in S3, the independent variables and dependent variables in the feature sample set are respectively normalized so that the dependent variable and the independent variable data are in the interval [0,1].

[0062] Furthermore, in S3, 70% of the sample data is selected as the training set for machine learning, and 30% is selected as the test set. The loss function ε is used to guide the training of machine learning. The training set is substituted into the algorithm measurement model to optimize the model parameters.

[0063] Furthermore, in S3, the model output results of the training set are compared with the actual values, and the mean square error is calculated. If the error value is small, the model is used as the optimal model. If the difference is large compared with the actual value, the loss function threshold is readjusted until the error value is controlled within the set range.

[0064] Furthermore, in step 3, the model is verified and tested, and after passing the performance verification, the model is used to calculate carbon emissions on the power generation side, as shown in formula (2).

[0065] E k =ω k ·P k +α(2)

[0066] In the formula, ω k is the model parameter and α is the bias coefficient.

[0067] Furthermore, in S3, E kThe carbon emissions of thermal power units on the generation side of the regional multi-energy complementary power system are obtained by applying the above model to all thermal power units in the system. The carbon emissions of the entire system can be obtained, and the carbon emission intensity is E k / P k .

[0068] S4: Construct a carbon emission analysis model for regional multi-energy complementary systems.

[0069] The carbon emission analysis model of the regional multi-energy complementary system adopts the carbon flow theory to allocate the carbon emissions of electricity on the power generation side to the network side and the power consumption side, where the carbon emissions on the power consumption side are expressed as:

[0070] A u P=P k

[0071] Among them, it means A u The traceability matrix, P, represents the power vector of all nodes, P k Represents the power vector on the generator side.

[0072] The node power on the power consumption side is expressed as:

[0073] P load =BP

[0074] Among them, P load represents the load power vector on the electricity consumption side, and B represents the load factor vector.

[0075] The power consumption of the load on the power consumption side is expressed as:

[0076]

[0077] The carbon emission of the electricity side load is expressed as:

[0078]

[0079] Among them, E k / P k Indicates carbon emission intensity.

[0080] The carbon emission of network side load is expressed as,

[0081]

[0082] Among them, B line is the branch coefficient vector.

[0083] Example 2, reference Figure 3 , which is an embodiment of the present invention, provides a system for carbon emission analysis method of regional multi-energy complementary system with electricity-carbon coupling, characterized by: including a data aggregation and processing module 100, a network flow calculation module 200, and an electricity carbon emission measurement module 300.

[0084] The data aggregation and processing module is responsible for collecting and preprocessing the energy data in the multi-energy complementary system.

[0085] The network power flow calculation module is used to simulate and calculate the power flow in the power grid.

[0086] The electricity carbon emission calculation module is used to calculate the carbon dioxide emissions generated by thermal power units on the power generation side.

[0087] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0088] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0089] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0090] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0091] Example 3: In this example, in order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments. This example conducts experiments on the existing traditional method and the method of this example, as shown in Table 1.

[0092] Traditional methods usually collect energy data manually or semi-automatically, including wind power, photovoltaic power generation, etc. Use basic power flow calculation models to simulate the power grid, ignoring the volatility and uncertainty of new energy. Make a rough estimate of carbon emissions on the power generation side based on a fixed carbon emission factor. Distribute the total carbon emissions evenly to each part of the power grid without considering the specific distribution of carbon emissions.

[0093] In the field of carbon emission analysis of energy systems, traditional methods usually rely on simple data aggregation and preliminary power flow calculation to estimate carbon emissions, but because they fail to fully consider the complexity and dynamic changes of multi-energy complementary systems, they have certain limitations in accuracy and efficiency. The method of the present invention aims to improve the overall performance of the analysis system by improving the data aggregation, power flow calculation and carbon emission measurement process.

[0094] Table 1 Experimental effect comparison chart

[0095]

[0096] The data aggregation efficiency of the method of the present invention is significantly improved, which is attributed to the automated data collection and preprocessing technology.

[0097] The power flow calculation error of the method of the present invention is significantly reduced, indicating that the model considering the volatility of new energy is more accurate.

[0098] The carbon emission calculation error of the method of the present invention is relatively small, which is due to the application of the dynamic carbon emission intensity calculation method.

[0099] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A carbon emission analysis method for a regional multi-energy complementary system of electricity-carbon coupling, characterized in that: include: Aggregate energy data in multi-energy complementary systems; Establish a network power flow calculation model and solve the power flow results; Calculate the carbon emissions of electricity generated by the multi-energy complementary system; Construct a carbon emission analysis model for regional multi-energy complementary systems.

2. The carbon emission analysis method for the regional multi-energy complementary system of electricity-carbon coupling according to claim 1, characterized in that: The energy data in the multi-energy complementary system includes wind power generation data, photovoltaic power generation data, power grid flow data, and power grid data.

3. The carbon emission analysis method of the regional multi-energy complementary system of electricity-carbon coupling according to claim 2 is characterized in that: Before the energy data in the multi-energy complementary system is aggregated, preprocessing is required, which specifically includes reading the measurement data, selecting the initial value of the state estimation iterative calculation, performing the state estimation iterative calculation, judging whether it has converged, and if so, calculating the measurement residual, performing bad data detection, and if there is no bad data, identifying the suspicious data and correcting the corresponding data, and if there is bad data, eliminating the bad data.

4. The carbon emission analysis method of the regional multi-energy complementary system of electricity-carbon coupling according to claim 3 is characterized by: The network power flow calculation model is expressed as: Among them, P i is the active power injected into node i, Q i is the reactive power injected into node i, δ i is the phase angle of node i, U i is the voltage at node i, G ij With B ij are the impedance and admittance of the line between node i and node j respectively.

5. The carbon emission analysis method of the regional multi-energy complementary system of electricity-carbon coupling according to claim 4, characterized in that: The calculation of the carbon emissions of electricity on the power generation side of the multi-energy complementary system includes obtaining relevant data of the thermal power units that generate carbon dioxide on the power generation side, obtaining the scattered data of the CO2 emissions distribution of the thermal power units at different load rates, and performing feature engineering on all the data of carbon emissions from thermal power plants to form a feature sample set. The independent variables and dependent variables in the feature sample set are normalized separately, and then substituted into the algorithm measurement model through the training set to calculate the carbon emissions on the power generation side.

6. The carbon emission analysis method of the regional multi-energy complementary system of electricity-carbon coupling according to claim 5, characterized in that: The calculation of carbon emissions on the power generation side is expressed as, E k =ω k ·P k +a Among them, E k represents the carbon emissions of thermal power units on the power generation side of the regional multi-energy complementary system, ω k The algorithm calculates the model parameters, and α represents the deviation coefficient.

7. The carbon emission analysis method of the regional multi-energy complementary system of electricity-carbon coupling according to claim 6, characterized in that: The regional multi-energy complementary system carbon emission analysis model adopts the carbon flow theory to allocate the carbon emissions of electricity on the power generation side to the network side and the power consumption side, where the carbon emissions on the power consumption side are expressed as: A u P=P k Among them, it means A u The traceability matrix, P, represents the power vector of all nodes, P k represents the power vector on the generating side; The node power on the power consumption side is expressed as: P load =BP Among them, P load represents the load power vector on the power consumption side, and B represents the load factor vector; The power consumption of the load on the power consumption side is expressed as: The carbon emission of the electricity side load is expressed as: Among them, E k / P k Indicates carbon emission intensity; The carbon emission of network side load is expressed as, Among them, B line is the branch coefficient vector.

8. A system using the carbon emission analysis method of the regional multi-energy complementary system of electricity-carbon coupling according to any one of claims 1 to 7, characterized in that: It includes a data aggregation and processing module (100), a network flow calculation module (200), and an electric power carbon emission calculation module (300); The data aggregation and processing module is responsible for collecting and preprocessing energy data in the multi-energy complementary system; The network power flow calculation module is used to simulate and calculate the power flow in the power grid; The electricity carbon emission calculation module is used to calculate the carbon dioxide emissions generated by the thermal power units on the power generation side.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the carbon emission analysis method for a regional multi-energy complementary system coupled with electricity and carbon described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the carbon emission analysis method for a regional multi-energy complementary system coupled with electricity and carbon as described in any one of claims 1 to 7 are implemented.

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