Rapid wind power plant operation period carbon emission accounting method and system

Abstract

The embodiment of the invention relates to the technical field of carbon emission management, and discloses a rapid wind power plant operation period carbon emission accounting method, which comprises the following steps: receiving basic data and operation data of a wind power plant input by a user; carbon emission parameters associated with the energy consumption and the equipment replacement records are retrieved according to the constructed equipment parameter library; inputting the energy consumption, the equipment replacement record and the retrieved carbon emission parameters into a carbon emission model to calculate the total carbon emission of the wind power plant in the operation stage; and outputting the corresponding total carbon emission. According to the scheme of the embodiment of the invention, the carbon emission of the wind power plant can be more comprehensively and accurately quantified, the accounting deviation caused by only considering a single factor is avoided, and compared with a traditional method, the accuracy of the accounting result is improved.

Description

Technical Field

[0001] This invention relates to the field of carbon emission management technology, specifically to a rapid method and system for calculating carbon emissions during the operation of a wind farm. Background Technology

[0002] Currently, the carbon emission accounting system for wind farms has significant limitations. Research and practice primarily focus on the equipment manufacturing and project construction phases, while the methods for calculating carbon emissions during the operational phase remain relatively rudimentary. Existing operational phase accounting largely relies on manual operations, requiring the manual retrieval of relevant parameters from scattered factor libraries in Excel, PDF, and other formats, and cross-platform matching of industry classification codes with emission source data. This process is not only time-consuming and labor-intensive but also highly susceptible to human error, severely impacting accounting efficiency and accuracy.

[0003] At the accounting tool level, existing methods generally employ static formula calculation tools (such as simple calculators or fixed templates), lacking complete traceability of data sources, conversion processes, and calculation logic, making it difficult to verify accounting results. Furthermore, existing systems generally do not pay sufficient attention to the timeliness and regional differences of emission factors, nor do they systematically quantify the uncertainties of data and models, further weakening the credibility and audit traceability of accounting results.

[0004] However, with the continuous and rapid growth of wind farm installed capacity in my country, carbon emission accounting and reduction management during the operation phase has become increasingly important strategically. Therefore, designing a scheme capable of accurate carbon emission accounting has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] To address the aforementioned shortcomings, this invention discloses a rapid method for calculating carbon emissions during the operation of wind farms, which quickly enables the calculation of carbon emissions during the operation of wind farms.

[0006] The first aspect of this invention discloses a rapid method for calculating carbon emissions during the operation of a wind farm, including: Receive basic and operational data of the wind farm input by the user; the operational data includes energy consumption and equipment replacement records; Based on the constructed equipment parameter database, carbon emission parameters associated with the energy consumption and equipment replacement records are retrieved, wherein the carbon emission parameters include material composition data, material carbon emission factor, energy carbon emission factor and transportation carbon emission factor. The energy consumption, equipment replacement records, and retrieved carbon emission parameters are input into the carbon emission model to calculate the total carbon emissions of the wind farm during the operation phase. The total carbon emissions include implicit carbon caused by equipment replacement, operational carbon caused by energy consumption, and transportation carbon caused by transportation activities. Output the corresponding total carbon emissions.

[0007] As an optional implementation, in a first aspect of the present invention, the step of retrieving carbon emission parameters associated with the energy consumption and equipment replacement records based on a constructed equipment parameter library includes: The corresponding energy source name and equipment name are determined based on the energy consumption and equipment replacement records. The system automatically retrieves the raw material composition and composition ratio from the constructed equipment parameter library based on the energy name and equipment name. For the raw materials identified in the search, their corresponding carbon emission factors are determined, and the data sources are labeled. The carbon emission factors are the average values ​​of parameters from one or more data sources selected from different data sources. Determine the appropriate carbon emission parameters.

[0008] As an optional implementation, in the first aspect of the present invention, the total carbon emissions include at least the sum of the following three parts: The hidden carbon emissions from replacing major components and fluids; Operational carbon emissions from daily energy consumption; Carbon emissions from transportation caused by the replacement of major components and fluids.

[0009] As an optional implementation, in the first aspect of the present invention, the carbon emission model includes a first calculation model, a second calculation model, and a third calculation model; The carbon emission model is as follows: in, Total carbon emissions Carbon emissions from replacing large components and fluids. Carbon emissions from daily operational energy consumption; Carbon emissions from transportation caused by the replacement of large components and fluids; The first calculation model is: in, Carbon emissions from replacing large components and fluids. Let the mass of the i-th material be... Let be the carbon emission coefficient of the i-th material; n is the type of material used in the manufacture of the replacement major components and oil; and i is the i-th material used in the manufacture of the replacement major components and oil. The second calculation model is: in, denoted as the carbon emissions from daily operation energy consumption, m represents the type of energy used during the operation phase, and i represents the i-th type of energy used during the operation phase. Let be the consumption of the i-th type of energy. Let be the carbon emission coefficient of the i-th energy source; The third calculation model is: in, The carbon emissions from transportation caused by the replacement of large components and fluids, where k is the type of equipment or material that needs to be transported during the operation phase; and i is the i-th type of equipment or material that needs to be transported during the operation phase. Let i be the transport volume of the i-th type of equipment or material. Let i be the transportation distance for the i-th type of equipment or material. The carbon emission coefficient for the mode of transportation used for the i-th type of equipment or material.

[0010] As an optional implementation, in the first aspect of the present invention, the carbon emission accounting method further includes: The input parameters on which the calculation of total carbon emissions depends are defined with a range of numerical fluctuations; the input parameters include at least one of material carbon emission factor, energy carbon emission factor, transportation carbon emission factor, material mass ratio, and transportation distance. Multiple random samplings are performed within the numerical fluctuation range, and the carbon emission calculation steps are repeated based on the parameter set obtained from each sampling, thereby generating a probability distribution of total carbon emissions. Output the probability distribution and the classified carbon emission results.

[0011] As an optional implementation, in the first aspect of the present invention, multiple random sampling is performed using the Monte Carlo simulation method.

[0012] As an optional implementation, in the first aspect of the present invention, the multiple random sampling is performed using Monte Carlo simulation, including: Perform a predetermined number of simulation iterations; In each iteration, for each numerical fluctuation range of the input parameter, random sampling is performed in its corresponding probability distribution to generate a set of parameter sample values. In each iteration, a predefined carbon emission model is executed using a set of parameter sample values ​​to calculate sample values ​​of carbon emissions; After all iterations are completed, a probability distribution of the total carbon emissions is generated based on the set of all calculated carbon emission sample values.

[0013] The second aspect of this invention discloses a rapid carbon emission accounting system for wind farm operation, comprising: Receiving module: Used to receive basic and operational data of the wind farm input by the user; the operational data includes energy consumption and equipment replacement records; The retrieval module is used to retrieve carbon emission parameters associated with the energy consumption and equipment replacement records based on the constructed equipment parameter library. The carbon emission parameters include material composition data, material carbon emission factors, energy carbon emission factors, and transportation carbon emission factors. Calculation module: Input the energy consumption, equipment replacement records, and retrieved carbon emission parameters into the carbon emission model to calculate the total carbon emissions of the wind farm during the operation phase. The total carbon emissions include implicit carbon caused by equipment replacement, operational carbon caused by energy consumption, and transportation carbon caused by transportation activities. Output module: Used to output the corresponding total carbon emissions.

[0014] A third aspect of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calling the executable program code stored in the memory to execute the rapid carbon emission accounting method for wind farm operation period disclosed in the first aspect of the present invention.

[0015] A fourth aspect of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute the rapid carbon emission accounting method for wind farm operation period disclosed in the first aspect of the present invention.

[0016] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The method in this invention comprehensively considers multiple carbon emission sources during the wind farm operation phase, including embodied carbon from equipment replacement, operational carbon from energy consumption, and transportation carbon from transportation activities. By constructing an equipment parameter database and retrieving carbon emission parameters associated with energy consumption and equipment replacement records, such as material composition data, material carbon emission factors, energy carbon emission factors, and transportation carbon emission factors, the carbon emissions of wind farms can be quantified more comprehensively and accurately. This avoids calculation biases caused by considering only a single factor and improves the accuracy of the calculation results compared to traditional methods. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in 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.

[0018] Figure 1 This is a flowchart illustrating the rapid carbon emission accounting method for wind farm operation period disclosed in an embodiment of the present invention; Figure 2This is a schematic diagram of the process for retrieving carbon emission parameters disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a rapid carbon emission accounting system for wind farm operation provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that the terms first, second, third, fourth, etc., in the specification and claims of this invention are used to distinguish different objects, not to describe a specific order. The terms used in the embodiments of this invention include and have, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0021] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating the rapid carbon emission accounting method for wind farm operation disclosed in this invention. The execution entity of the method described in this embodiment is a software and / or hardware-based entity that can receive relevant information via wired or / or wireless means and send certain instructions. It may also have processing and storage capabilities. This entity can control multiple devices, such as remote physical servers or cloud servers and related software, or local hosts or servers and related software that perform operations on equipment located in a specific location. In some scenarios, it can also control multiple storage devices, which may be located in the same or different locations as the equipment. Figure 1 As shown, this rapid carbon emission accounting method for wind farm operation includes the following steps: S101: Receive basic and operational data of the wind farm input by the user; the operational data includes energy consumption and equipment replacement records; S102: Retrieve carbon emission parameters associated with the energy consumption and equipment replacement records based on the constructed equipment parameter library, wherein the carbon emission parameters include material composition data, material carbon emission factor, energy carbon emission factor and transportation carbon emission factor; S103: Input the energy consumption, equipment replacement records, and retrieved carbon emission parameters into the carbon emission model to calculate the total carbon emissions of the wind farm during the operation phase. The total carbon emissions include implicit carbon caused by equipment replacement, operational carbon caused by energy consumption, and transportation carbon caused by transportation activities. S104: Output the corresponding total carbon emissions.

[0022] In this embodiment of the invention, by receiving basic and operational data of the wind farm input by the user, the data can be quickly matched with carbon emission parameters in the equipment parameter library and input into the carbon emission model for calculation. This enables rapid data processing and analysis, improves the efficiency of carbon emission accounting, meets the needs of real-time or periodic carbon emission accounting during the operation of the wind farm, and provides timely data support for the operation and management of the wind farm.

[0023] Accurate carbon emission accounting results can provide a comprehensive basis for wind farm operation decisions. Wind farm managers can understand the contribution of different operating activities to carbon emissions based on the accounting results, and thus take targeted energy-saving and emission-reduction measures, such as optimizing equipment replacement plans, reducing energy consumption, and improving transportation methods, to achieve low-carbon operation and sustainable development of wind farms.

[0024] With increasing attention to climate change and rising requirements for carbon emission management, wind farms need to accurately calculate their carbon emissions. The method described in this invention adapts to industry trends, providing strong technical support for wind farms to participate in carbon emission trading and meet relevant policy and regulatory requirements, thus helping wind farms enhance their competitiveness in a low-carbon economy.

[0025] More preferably, such as Figure 2 As shown, the step of retrieving carbon emission parameters associated with energy consumption and equipment replacement records based on the constructed equipment parameter library includes: S1021: Determine the corresponding energy name and equipment name based on the energy consumption and equipment replacement records; S1022: Automatically retrieve the raw material composition and composition ratio from the constructed equipment parameter library based on the energy name and equipment name; S1023: For the raw materials identified in the search, determine their corresponding carbon emission factors and label the data sources, wherein the carbon emission factors are the average values ​​of parameters from one or more data sources selected from different data sources; S1024: Determine the corresponding carbon emission parameters.

[0026] The solution in this invention uses a dual-dimensional search based on energy name and equipment name, clarifying the core basis for parameter retrieval and avoiding parameter deviations caused by fuzzy matching. Simultaneously, it accurately locates the composition and proportion of raw materials, ensuring that the subsequent selection of carbon emission factors is highly consistent with the actual equipment and energy characteristics, thus improving the quality of basic data for carbon emission accounting from the source.

[0027] In practice, by labeling the data sources of carbon emission factors, key parameters in the accounting process become traceable, facilitating verification and validation. It also supports flexible modes such as selecting a single data source or averaging multiple data sources, meeting the accuracy requirements of different scenarios while enhancing the credibility of the accounting results through transparency of data sources.

[0028] By breaking down parameter retrieval into standardized steps—name determination, raw material retrieval, matching factors, and parameter confirmation—a streamlined operational logic is formed. This eliminates the need for manual parameter screening, enabling automated and accurate retrieval of carbon emission parameters, further reducing manual intervention costs and improving the overall efficiency of the accounting process.

[0029] It supports selecting carbon emission factors based on different data availability (single / multiple data sources), adapting to wind farm scenarios of different scales and data recording levels. Simultaneously, the precise retrieval of raw material composition and proportions can also adapt to the accounting needs of different types of equipment and different energy categories, broadening the applicability of the method.

[0030] More preferably, the total carbon emissions include at least the sum of the following three parts: The hidden carbon emissions from replacing major components and fluids; Operational carbon emissions from daily energy consumption; Carbon emissions from transportation caused by the replacement of major components and fluids.

[0031] Specifically, total carbon emissions are clearly broken down into three categories: carbon from major component and fluid replacement, carbon from daily operation energy consumption, and carbon from related transportation. This accurately covers the core carbon emission scenarios during the wind farm's operation. This avoids the ambiguity of sources caused by general calculations, and makes the quantification of each type of emission more closely aligned with the actual operation of the wind farm, thus improving the accuracy of the calculation results.

[0032] After clearly classifying emission types, wind farms can directly identify the carbon emission share of different stages. For example, by clarifying whether the implicit carbon from the replacement of major components or transportation carbon is a major emission source, targeted emission reduction measures can be formulated (such as optimizing the replacement cycle of major components and choosing low-carbon transportation methods), so that the accounting results truly serve emission reduction decisions and enhance the practical value of the method.

[0033] This invention clarifies the summation rules for the three types of emissions, forming a standardized accounting approach and avoiding misunderstandings among different users regarding the total carbon emission range. It also simplifies the classification and statistical steps in the accounting process, eliminating the need for additional emission attribution definitions, reducing operational complexity, and improving the consistency and repeatability of the accounting.

[0034] This invention focuses on high-percentage emissions during the wind farm operation period (implicit carbon from major component replacement, transportation carbon, and daily energy consumption operation carbon) to ensure no core emissions are omitted. Compared to a general calculation, this classification is more in line with the operational characteristics of the wind power industry, making the calculation results more comprehensive and meaningful.

[0035] More preferably, the carbon emission model includes a first calculation model, a second calculation model, and a third calculation model; The carbon emission model is as follows: in, Total carbon emissions Carbon emissions from replacing large components and fluids. Carbon emissions from daily operational energy consumption; Carbon emissions from transportation caused by the replacement of large components and fluids; The first calculation model is: in, Carbon emissions from replacing large components and fluids. Let the mass of the i-th material be... Let be the carbon emission coefficient of the i-th material; n is the type of material used in the manufacture of the replacement major components and oil; and i is the i-th material used in the manufacture of the replacement major components and oil. The second calculation model is: in, denoted as the carbon emissions from daily operation energy consumption, m represents the type of energy used during the operation phase, and i represents the i-th type of energy used during the operation phase. Let be the consumption of the i-th type of energy. Let be the carbon emission coefficient of the i-th energy source; The third calculation model is: in, The carbon emissions from transportation caused by the replacement of large components and fluids, where k is the type of equipment or material that needs to be transported during the operation phase; and i is the i-th type of equipment or material that needs to be transported during the operation phase. Let i be the transport volume of the i-th type of equipment or material. Let i be the transportation distance for the i-th type of equipment or material. The carbon emission coefficient for the mode of transportation used for the i-th type of equipment or material.

[0036] In this embodiment of the invention, total carbon emissions are broken down into three sub-items, and a specific calculation formula is designed for each sub-item, achieving a precise transformation from qualitative classification to quantitative calculation. For example: The first model directly quantifies the implicit carbon emissions from equipment replacement by multiplying the material mass by the carbon emission coefficient, thus avoiding fuzzy estimations of material carbon emissions. The third model introduces the product of transport volume, distance, and transport mode coefficients to accurately capture the differences in carbon emissions in the transport process (such as the differences in emissions from different equipment weights, transport distances, and transport vehicles), making the calculation results more consistent with the actual scenario and reducing the error of empirical estimation.

[0037] In this invention, each sub-model adopts a clear variable definition and a unified summation formula structure, forming a standardized calculation framework. Regardless of the wind farm size, equipment type, or operation mode, the model can be directly applied to input data for calculation, avoiding differences in results caused by inconsistent accounting logic, and improving the comparability and repeatability of accounting results between different wind farms and at different times.

[0038] Each sub-model's variables (such as material type n, energy type m, and transportation category k) directly correspond to actual operational data (material information, energy consumption, transportation records, etc. in equipment replacement logs). Users can collect and input data according to the model's requirements without complex data conversion or additional processing. This direct mapping relationship between data, model, and results simplifies the operational process, reduces reliance on the professional skills of accounting personnel, and facilitates wider application within the industry.

[0039] By calculating independently from each sub-model, the carbon emission proportions of the three stages—equipment replacement, material replacement, daily energy consumption, and transportation—can be quantified separately.

[0040] More preferably, the carbon emission accounting method further includes: The input parameters on which the calculation of total carbon emissions depends are defined with a range of numerical fluctuations; the input parameters include at least one of material carbon emission factor, energy carbon emission factor, transportation carbon emission factor, material mass ratio, and transportation distance. Multiple random samplings are performed within the numerical fluctuation range, and the carbon emission calculation steps are repeated based on the parameter set obtained from each sampling, thereby generating a probability distribution of total carbon emissions. Output the probability distribution and the classified carbon emission results.

[0041] In this invention, the input parameters (such as carbon emission factors and transportation distances) naturally fluctuate due to the data source and actual scenario, and traditional fixed-parameter calculations cannot reflect this uncertainty. By defining the numerical fluctuation range and performing multiple samplings, a probability distribution of total carbon emissions is generated, which clearly presents the fluctuation range and confidence level of the results. This makes the calculation no longer a single absolute value, but a more realistic probabilistic result, reducing the bias risk caused by fixed parameters and improving the reliability of the results.

[0042] Probability distribution results directly reflect the degree of influence of key parameters on total emissions. For example, if fluctuations in transportation carbon emission factors lead to a large range in the probability distribution of total emissions, it indicates that this parameter is a sensitive factor, and the transportation process needs to be optimized. If fluctuations in material carbon emission factors have little impact on the results, the data collection accuracy of this parameter can be appropriately simplified. This kind of sensitivity implicit analysis can help managers predict the risks in carbon emission accounting, formulate more targeted control measures, and enhance the value of decision-making.

[0043] In actual wind farm operation, some parameters (such as material mass ratio and estimated transportation distance) may not be accurately obtained, and only a general range can be determined. This method allows calculations based on fluctuation ranges, without requiring precise parameters. It adapts to real-world scenarios with incomplete or inaccurate data, lowers the data collection threshold, and makes the method easier to implement and promote.

[0044] In addition to the categorized carbon emission results, the output includes probability distributions (such as the probability of emissions falling into different ranges), which can meet the application needs of different scenarios. For example, in carbon emission trading, the probability distribution can be used to determine conservative / optimistic accounting values, improving the adaptability and application value of the accounting results.

[0045] More preferably, the multiple random sampling is performed using the Monte Carlo simulation method, including: Perform a predetermined number of simulation iterations; In each iteration, for each numerical fluctuation range of the input parameter, random sampling is performed in its corresponding probability distribution to generate a set of parameter sample values. In each iteration, a predefined carbon emission model is executed using a set of parameter sample values ​​to calculate sample values ​​of carbon emissions; After all iterations are completed, a probability distribution of the total carbon emissions is generated based on the set of all calculated carbon emission sample values.

[0046] Specifically, the Monte Carlo simulation method uses a preset number of iterations (e.g., hundreds to thousands) to randomly sample the fluctuation range of each input parameter according to its probability distribution (e.g., normal distribution, uniform distribution, etc.), avoiding the bias that may be caused by simple random sampling. Its mathematical logic ensures that the impact of parameter fluctuations on total emissions is fully covered, and the generated probability distribution of total carbon emissions is closer to the real uncertainty, upgrading the accounting results from empirical estimates to statistically reliable inferences, thus greatly improving their scientific rigor.

[0047] A standardized process is established, specifying the predetermined number of iterations, parameter sampling in each round, model calculation in each round, and the aggregation of probability distributions. This eliminates subjective arbitrariness in the sampling process (such as the ambiguity of the number of samplings and parameter distribution assumptions). Different users can obtain consistent probability distribution trends by using the same parameter fluctuation range and number of iterations, ensuring the reproducibility and comparability of the accounting results and meeting the needs of standardized accounting in the industry.

[0048] Input parameters for wind farm carbon emissions (such as material factors and transport distance) may exhibit implicit correlations. Monte Carlo simulations, through numerous iterations, can naturally capture the combined impact of these multi-parameter interactions on total emissions. For example, the contribution of the extreme combination of high material carbon emission factors and long transport distances to total emissions is difficult to detect through single-parameter sensitivity analysis alone. However, this method can accurately reveal this contribution through the tail characteristics of the probability distribution (such as the probability of high emission intervals), providing a basis for risk management in extreme scenarios.

[0049] Monte Carlo simulations can flexibly adapt to different types of input parameters (such as factors with known mean and standard deviation, or transport distances with only known range) and their probability distribution types (normal distribution, uniform distribution, etc.) without simplifying parameter characteristics. This flexibility enables it to cope with real-world scenarios in wind farm operation where parameters come from diverse sources and have complex characteristics, thus expanding the applicability of the method.

[0050] Based on a large number of iteratively generated probability distributions (such as the mean, median, 95% confidence interval, etc.), richer decision-making information can be provided: for example, managers can refer to the total emissions not exceeding X tons with a 90% probability to formulate conservative emission reduction targets, or plan daily operation strategies based on the most likely value Y tons, so that uncertainty analysis can directly serve actual decision-making and improve the application value of accounting results.

[0051] The method in this invention comprehensively considers multiple carbon emission sources during the wind farm operation phase, including embodied carbon from equipment replacement, operational carbon from energy consumption, and transportation carbon from transportation activities. By constructing an equipment parameter database and retrieving carbon emission parameters associated with energy consumption and equipment replacement records, such as material composition data, material carbon emission factors, energy carbon emission factors, and transportation carbon emission factors, the carbon emissions of wind farms can be quantified more comprehensively and accurately. This avoids calculation biases caused by considering only a single factor and improves the accuracy of the calculation results compared to traditional methods.

[0052] Example 2 Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of the rapid carbon emission accounting system for wind farm operation disclosed in an embodiment of the present invention. Figure 3 As shown, this rapid carbon emission accounting system for wind farm operation may include: Receiving module 21: Used to receive basic data and operational data of the wind farm input by the user; the operational data includes energy consumption and equipment replacement records; Search module 22: used to search for carbon emission parameters associated with the energy consumption and equipment replacement records based on the constructed equipment parameter library, wherein the carbon emission parameters include material composition data, material carbon emission factor, energy carbon emission factor and transportation carbon emission factor; Calculation module 23: Inputs the energy consumption, equipment replacement records, and retrieved carbon emission parameters into the carbon emission model to calculate the total carbon emissions of the wind farm during the operation phase. The total carbon emissions include implicit carbon caused by equipment replacement, operational carbon caused by energy consumption, and transportation carbon caused by transportation activities. Output module 24: Used to output the corresponding total carbon emissions.

[0053] The method in this invention comprehensively considers multiple carbon emission sources during the wind farm operation phase, including embodied carbon from equipment replacement, operational carbon from energy consumption, and transportation carbon from transportation activities. By constructing an equipment parameter database and retrieving carbon emission parameters associated with energy consumption and equipment replacement records, such as material composition data, material carbon emission factors, energy carbon emission factors, and transportation carbon emission factors, the carbon emissions of wind farms can be quantified more comprehensively and accurately. This avoids calculation biases caused by considering only a single factor and improves the accuracy of the calculation results compared to traditional methods.

[0054] Example 3 Please see Figure 4 , Figure 4This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. The electronic device can be a computer, a server, etc. Of course, in certain cases, it can also be a mobile phone, tablet computer, monitoring terminal, or other smart device, as well as an image acquisition device with processing capabilities. Figure 4 As shown, the electronic device may include: Memory 510 storing executable program code; Processor 520 coupled to memory 510; The processor 520 calls the executable program code stored in the memory 510 to execute some or all of the steps in the rapid carbon emission accounting method for wind farm operation period in Embodiment 1.

[0055] This invention discloses a computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps in the rapid carbon emission accounting method for wind farm operation period described in Embodiment 1.

[0056] This invention also discloses a computer program product, wherein when the computer program product is run on a computer, the computer performs some or all of the steps in the rapid carbon emission accounting method for wind farm operation period in Embodiment 1.

[0057] This invention also discloses an application publishing platform, which is used to publish computer program products. When the computer program products are run on a computer, the computer performs some or all of the steps in the rapid carbon emission accounting method for wind farm operation period in Embodiment 1.

[0058] In various embodiments of the present invention, it should be understood that the sequence number of each process does not necessarily imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0059] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they can be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0060] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0061] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several requests to cause a computer device (which can be a personal computer, server, or network device, specifically a processor in the computer device) to execute some or all of the steps of the methods described in the various embodiments of the present invention.

[0062] In the embodiments provided by this invention, it should be understood that B corresponding to A means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information.

[0063] Those skilled in the art will understand that some or all of the steps in the various methods of the embodiments described can be implemented by a program instructing related hardware. This program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0064] The foregoing has provided a detailed description of the rapid carbon emission accounting method, system, electronic equipment, and storage medium for wind farm operation disclosed in the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A rapid method for calculating carbon emissions during the operation of a wind farm, characterized in that, include: Receive basic and operational data of the wind farm input by the user; the operational data includes energy consumption and equipment replacement records; Based on the constructed equipment parameter database, carbon emission parameters associated with the energy consumption and equipment replacement records are retrieved, wherein the carbon emission parameters include material composition data, material carbon emission factor, energy carbon emission factor and transportation carbon emission factor. The energy consumption, equipment replacement records, and retrieved carbon emission parameters are input into the carbon emission model to calculate the total carbon emissions of the wind farm during the operation phase. The total carbon emissions include implicit carbon caused by equipment replacement, operational carbon caused by energy consumption, and transportation carbon caused by transportation activities. Output the corresponding total carbon emissions.

2. The rapid carbon emission accounting method for wind farm operation as described in claim 1, characterized in that, The step of retrieving carbon emission parameters associated with energy consumption and equipment replacement records based on the constructed equipment parameter database includes: The corresponding energy source name and equipment name are determined based on the energy consumption and equipment replacement records. The system automatically retrieves the raw material composition and composition ratio from the constructed equipment parameter library based on the energy name and equipment name. For the raw materials identified in the search, their corresponding carbon emission factors are determined, and the data sources are labeled. The carbon emission factors are the average values ​​of parameters from one or more data sources selected from different data sources. Determine the appropriate carbon emission parameters.

3. The rapid carbon emission accounting method for wind farm operation as described in claim 1, characterized in that, The total carbon emissions shall include at least the sum of the following three parts: The hidden carbon emissions from replacing major components and fluids; Operational carbon emissions from daily energy consumption; Carbon emissions from transportation caused by the replacement of major components and fluids.

4. The rapid carbon emission accounting method for wind farm operation as described in claim 3, characterized in that, The carbon emission model includes a first calculation model, a second calculation model, and a third calculation model; The carbon emission model is as follows: in, Total carbon emissions Carbon emissions from replacing large components and fluids. Carbon emissions from daily operational energy consumption; Carbon emissions from transportation caused by the replacement of large components and fluids; The first calculation model is: in, Carbon emissions from replacing large components and fluids. Let the mass of the i-th material be... Let be the carbon emission coefficient of the i-th material; n is the type of material used in the manufacture of the replacement major components and oil; and i is the i-th material used in the manufacture of the replacement major components and oil. The second calculation model is: in, denoted as the carbon emissions from daily operation energy consumption, m represents the type of energy used during the operation phase, and i represents the i-th type of energy used during the operation phase. Let be the consumption of the i-th type of energy. Let be the carbon emission coefficient of the i-th energy source; The third calculation model is: in, The carbon emissions from transportation caused by the replacement of large components and fluids, where k is the type of equipment or material that needs to be transported during the operation phase; and i is the i-th type of equipment or material that needs to be transported during the operation phase. Let i be the transport volume of the i-th type of equipment or material. Let i be the transportation distance for the i-th type of equipment or material. The carbon emission coefficient for the mode of transportation used for the i-th type of equipment or material.

5. The rapid carbon emission accounting method for wind farm operation as described in claim 1, characterized in that, The carbon emission accounting method also includes: The input parameters on which the calculation of total carbon emissions depends are defined with a range of numerical fluctuations; the input parameters include at least one of material carbon emission factor, energy carbon emission factor, transportation carbon emission factor, material mass ratio, and transportation distance. Multiple random samplings are performed within the numerical fluctuation range, and the carbon emission calculation steps are repeated based on the parameter set obtained from each sampling, thereby generating a probability distribution of total carbon emissions. Output the probability distribution and the classified carbon emission results.

6. The rapid carbon emission accounting method for wind farm operation as described in claim 5, characterized in that, Multiple random samplings were performed using the Monte Carlo simulation method.

7. The rapid carbon emission accounting method for wind farm operation as described in claim 6, characterized in that, The multiple random samplings are performed using the Monte Carlo simulation method, including: Perform a predetermined number of simulation iterations; In each iteration, for each range of numerical fluctuations of the input parameters, random sampling is performed on the corresponding probability distribution to generate a set of parameter sample values. In each iteration, a predefined carbon emission model is executed using a set of parameter sample values ​​to calculate sample values ​​of carbon emissions; After all iterations are completed, a probability distribution of the total carbon emissions is generated based on the set of all calculated carbon emission sample values.

8. A rapid carbon emission accounting system for wind farm operation, characterized in that, include: Receiving module: Used to receive basic and operational data of the wind farm input by the user; the operational data includes energy consumption and equipment replacement records; The retrieval module is used to retrieve carbon emission parameters associated with the energy consumption and equipment replacement records based on the constructed equipment parameter library. The carbon emission parameters include material composition data, material carbon emission factors, energy carbon emission factors, and transportation carbon emission factors. Calculation module: Input the energy consumption, equipment replacement records, and retrieved carbon emission parameters into the carbon emission model to calculate the total carbon emissions of the wind farm during the operation phase. The total carbon emissions include implicit carbon caused by equipment replacement, operational carbon caused by energy consumption, and transportation carbon caused by transportation activities. Output module: Used to output the corresponding total carbon emissions.

9. An electronic device, characterized in that, include: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the rapid carbon emission accounting method for wind farm operation period as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program causes a computer to execute the rapid carbon emission accounting method for wind farm operation as described in any one of claims 1 to 7.

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