Regional electric power carbon emission factor measuring and calculating method and application

Abstract

The invention provides a regional power carbon emission factor measuring and calculating method and application, and belongs to the technical field of electronic information. The regional electric power carbon emission factor measuring and calculating method comprises the following steps: step 1, data acquisition: acquiring regional related data of which carbon emission needs to be calculated through a data acquisition channel in a data center, and storing the regional related data into a full-amount data table of a source attaching layer; 2, cleaning and processing the data, and performing data processing on missing data and abnormal data in sequence; step 3, calculating a regional power generation carbon emission factor, and sequentially calculating a thermal power generation carbon emission factor and a regional comprehensive power generation carbon emission factor; and 4, calculating regional power carbon emission factors, sequentially calculating regional power transfer electric quantity, constructing a regional carbon flow balance equation and solving the power carbon emission factors of each region. According to the regional electric power carbon emission factor calculation method, electric power carbon emission factor calculation with finer granularity in time and space is realized, and the calculation accuracy and practicability are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of electronic information technology, and in particular to a method for calculating a regional electricity carbon emission factor and its application. Background Art

[0002] Against the backdrop of global climate change, accurate measurement of carbon emission factors is crucial for effectively controlling and reducing carbon emissions. At present, the main methods for carbon emission accounting are calculation method and measurement method. In the power system, carbon emissions mainly come from power generation and transmission and transformation. Among them, carbon emissions in the power generation link mainly come from the direct combustion of fossil energy fuels by coal-fired, gas-fired and other types of generator sets during the power generation process, and carbon emissions in the transmission and transformation link mainly come from the leakage of insulating gases with greenhouse effects such as SF6 in electrical equipment, but this part of emissions only accounts for about 1% of power generation combustion emissions. Therefore, current research mainly focuses on direct carbon emissions generated by power generation fuel consumption.

[0003] Due to the significant differences in the structure of installed power capacity and power generation in different regions, the carbon emissions of the power sector have obvious temporal and spatial differences. Calculating regional power carbon emission factors is conducive to more accurate calculation of regional power carbon emissions. In terms of temporal differences, affected by the differences in the timing of clean energy power generation and the fluctuations in the timing of power supply and demand, the output size and output ratio of different types of power sources at different times will change all the time. For example, during periods of high clean energy generation and low load levels, the emission intensity and total amount will be significantly lower than during periods of high thermal power generation and high load levels. In terms of spatial differences, affected by the differences in the spatial distribution of power sources and loads, the carbon emissions of power generation in different regions are significantly different. For example, the carbon emission intensity and total amount in regions with rich clean energy and low load are significantly lower than those in regions with large total thermal power installed capacity and high load.

[0004] At present, the carbon emission factors of power grids are mainly divided into two categories according to different usage scenarios and control purposes: the first category is used to calculate greenhouse gas emissions, and the emission factor used is the annual average emission factor of the power grid, which mainly includes national power grid emission factor, regional power grid emission factor and provincial power grid emission factor; the second category is used to calculate greenhouse gas emission reductions, and the emission factor used is the regional power grid baseline emission factor.

[0005] Specific:

[0006] 1. The national grid average emission factor represents the emission level of each unit of electricity supply and consumption within the coverage of the national grid, and is used to calculate the indirect electricity emissions of enterprises included in the national carbon market. The calculation idea is to determine the average emission level through the fossil fuel carbon emissions of power plants across the country and related factors;

[0007] 2. Regional power grid average emission factor, the national power grid is divided into six regional power grids for calculation. Specifically, the sum of the carbon emissions of fossil fuels of all local power plants in the regional power grid and the carbon emissions contained in the net power transfer and net import power is divided by the total power supply of the regional power grid to reflect the differences in carbon emissions of power users in different regions;

[0008] 3. The provincial power grid average emission factor is used to divide the national power grid according to the provincial administrative boundaries. Its calculation is roughly the same as the regional power grid emission factor, and is mainly used to calculate the carbon emissions implied by the power transferred in and out of each province, as well as the emissions implied by the purchased power of some provincial and municipal enterprises;

[0009] 4. Regional grid baseline emission factor, used to calculate the actual emission reductions generated by CDM and CCER projects. It is calculated by weighted average of the marginal emission factor of electricity and the marginal emission factor of capacity in the regional power grid. Among them, the marginal emission factor of electricity is equal to the weighted average of the unit emission factor of power plants and the unit emission factor of net imported electricity in the local power system in the past three years, with electricity as the weight; the marginal emission factor of capacity is the weighted average of the power supply emission factors of several selected new unit samples, with electricity as the weight.

[0010] However, the existing methods for calculating carbon emissions from electricity have many shortcomings: traditional calculation methods are usually too macroscopic and cannot be refined to the prefecture-level level, making it difficult to meet the increasingly refined carbon emission management needs. At the same time, the existing methods have difficulties in data acquisition and processing, and the incompleteness and inaccuracy of the data lead to large deviations in the calculation results. Moreover, the consideration of the amount of power transfer is not sufficient, and it is impossible to accurately reflect the impact of power flow between regions on carbon emissions. In addition, the existing calculation model fails to fully integrate various types of power data, such as prefecture-level city power consumption data, thermal power data, renewable energy power, etc., so that the calculation results cannot fully reflect the actual situation. Specifically, the data is not updated in time and lags behind the low-carbon transformation process of the power system: the existing technology generally relies on the regional power grid emission factors released by the state in the accounting of corporate greenhouse gas emissions. However, these official data often have serious lags, and the static data used is several years ago, and the update cycle is long. This lag makes it impossible for the calculation results to timely reflect the latest progress and actual results of my country's power system in the green and low-carbon transformation. Due to the failure to update the data in a timely manner, it is difficult to accurately capture the impact of key factors such as changes in the power structure, energy efficiency improvement, and the growth of renewable energy installed capacity on carbon emissions. This not only leads to a distorted assessment of my country's carbon emission reduction effectiveness, but also affects the scientific advancement of carbon emission reduction work and hinders the formulation and implementation of more accurate and effective emission reduction policies. The temporal and spatial resolution is coarse and it is difficult to meet the needs of refined management: the existing power grid carbon emission factors are usually released on an annual basis and only provide data at the provincial level and above. This coarse temporal and spatial resolution is difficult to meet the increasingly refined carbon emission management needs. Annual data cannot reflect the fluctuations and changes in the power system within a year, such as seasonal peak electricity consumption and intermittent power generation from new energy sources, resulting in inaccurate calculation results. At the same time, only providing data at the provincial level and above cannot go deep into the city level for analysis and management, and it is difficult to identify the characteristics and differences of carbon emissions in different regions, let alone provide data support for local governments to formulate targeted emission reduction policies.

[0011] In summary, these shortcomings seriously restrict the accurate calculation and effective management of electricity carbon emissions, and bring huge challenges to achieving low-carbon development goals. Summary of the invention

[0012] The purpose of the embodiments of the present invention is to provide a method and application for calculating a regional electricity carbon emission factor, which realizes the calculation of electricity carbon emission factors at a finer granularity in time and space, greatly improving the accuracy and practicality of the calculation.

[0013] In order to achieve the above-mentioned object, the present invention provides a method and application for calculating the regional electricity carbon emission factor, and the method comprises:

[0014] Step 1: Collect data. Collect the relevant data of the area where carbon emissions need to be calculated through the data collection channel of the data center and store it in the full data table of the source layer;

[0015] Step 2: Clean and process the data, and process the missing data and abnormal data in turn;

[0016] Step 3: Calculate the regional power generation carbon emission factor, and calculate the thermal power generation carbon emission factor and the regional comprehensive power generation carbon emission factor in turn;

[0017] Step 4: Calculate the regional electricity carbon emission factor, calculate the regional electricity transfer quantity in turn, construct the regional carbon flow balance equation and solve the regional electricity carbon emission factor.

[0018] Preferably, the above-mentioned data platform is used for data cleaning, storage, processing and analysis, including:

[0019] The data access module is used to convert data into structured data and access the structured data scenario using ETL, data bus, and data interaction components to store the collected source data;

[0020] Structured data scenarios and offline processing modules, where the structured data scenarios are divided into the source layer, the sharing layer, and the analysis layer. They are used to first enter the source layer and store them in the MaxCompute full table and incremental table according to the data type and source. Secondly, the data in the source layer is cleaned and processed offline according to the process of data exploration and analysis, duplicate data processing, missing data processing, and abnormal data processing to obtain a standardized data table, which is then stored in the standard table of the sharing layer. Then, the standard table data in the sharing layer is trained through offline processing to obtain the corresponding model table, and the corresponding model is stored in the model table. Finally, according to the needs of the business scenario, the data in the standard table and the model in the model table are called for data analysis, and the results are stored in the dimension table, fact table, and result table in the analysis layer.

[0021] Data analysis module, used to provide data visualization, report analysis and autonomous analysis;

[0022] The data application module is used to analyze and calculate relevant scenario indicators and models, encapsulate the analysis and calculation results into data service APIs, publish and register them to the API gateway, and use the internal Internet regional data isolation device to allow external self-service analysis tools to call data calculation results and perform visual display;

[0023] And the integration service module is used to provide user permission integration, page integration and data integration services in the intranet and extranet.

[0024] Preferably, the data collected in step 1 include historical energy consumption, historical electricity consumption, regional GDP, regional clean energy share, regional major industrial product output, regional major industrial product electricity consumption and major industrial product supply index.

[0025] Preferably, in step 2, the following operations are taken for missing data:

[0026] Search for missing data in the collected data obtained from the full data table of the source layer. When missing data is found, fill the missing data with the previous and next average values. If the missing data is the last data, fill it with the previous value; if the missing data is the first data, fill it with the next value.

[0027] Preferably, in step 2, the following operations are performed for the abnormal data after the missing data processing:

[0028] The IQR abnormal data analysis method was used for data analysis. For abnormal data, the front and back average method was used to replace the abnormal values. If the abnormal value was the last value, the previous value was used to fill it. If the missing data was the first data, the latter value was used to fill it.

[0029] The IQR abnormal data analysis method performs abnormal numerical analysis on the characteristic data one by one:

[0030]

[0031] Among them, Upper represents the upper limit value, and Lower represents the lower limit value;

[0032] The specific analysis steps include:

[0033] First, calculate the quartiles of the data, arrange the data from small to large, and find its Q1, median Q2, and Q3;

[0034] Secondly, calculate IQR, IQR = Q3-Q1;

[0035] Then, determine the range of the outliers, and calculate the upper limit Upper and the lower limit Lower according to the formula Upper = Q3 + 1.5IQR and Lower = Q1 - 1.5IQR;

[0036] Finally, detect outliers and consider data that is greater than Upper or less than Lower as outliers.

[0037] Preferably, step 3 includes:

[0038] Step 3.1: Calculate the carbon emission factor of thermal power generation. Use the standard coal consumption of power supply in the power statistics data released by the China Electricity Council to convert the fossil energy consumption of thermal power generation. The calculation formula is:

[0039]

[0040] Among them, E h is the carbon emission of thermal power generation, P h is the thermal power generation, F h is the standard coal consumption per kWh of thermal power generation, T h is the standard raw coal conversion factor, EF is the raw coal carbon emission factor;

[0041] Step 3.2: Calculate the regional comprehensive power generation carbon emission factor by dividing the power generation carbon emission by the total power generation. The calculation formula is:

[0042]

[0043] Among them, EF p is the carbon emission factor for power generation, P i is the power generation of the i-th power generation type.

[0044] Preferably, step 4 includes:

[0045] Step 4.1, calculate the regional power transfer amount. According to the requirements of the power grid balance relationship, the power generation and power consumption need to be in a balanced relationship. If the power generation is greater than the power consumption, it is necessary to transfer the power from outside the region; otherwise, the excess power is transported to the area with insufficient power generation. The calculation formula for regional transfer power is:

[0046]

[0047] Among them, P t is the regional transfer power, P u The total electricity consumption of the region;

[0048] Step 4.2: Construct the regional carbon flow balance equation. In the power system, the node carbon flow balance algorithm is based on the power flow distribution of the power system, combined with the power generation carbon emission factor of each node and the power consumption of the node, to construct the node carbon flow balance equation. The calculation formula is:

[0049] EF i (E i +∑P j,i )=C i +ΣP j,i EF j

[0050] Among them, EF i is the electricity carbon emission factor of the i-th region, E i is the power generation of the ith region, P j,i is the amount of electricity transferred from area j to area i, C iis the carbon emissions from thermal power generation in region i;

[0051] Step 4.3: Solve the carbon emission factor of electricity in each region:

[0052] First, the carbon flow balance equations of the nodes in each region are combined to construct the balance equation group:

[0053]

[0054] Secondly, the matrix is ​​simplified through matrix operations to define the regional electricity carbon emission matrix M:

[0055]

[0056] Finally, solve the regional electricity carbon emission factor:

[0057]

[0058] Preferably, it also includes calculating the regional electricity carbon emissions using the regional electricity carbon emission factor calculated by the regional electricity carbon emission factor calculation method.

[0059] Preferably, the calculation of regional electricity carbon emissions includes: calculating regional electricity transfer carbon emissions using a regional electricity carbon emission factor, the formula being:

[0060] E Z =EF N P t

[0061] Among them, E Z Carbon emissions for regional electricity transfer, EF N Transfer power to the region.

[0062] Preferably, the calculation of regional electricity carbon emissions further includes: calculating regional electricity net carbon emissions using regional electricity transfer carbon emissions, the formula is:

[0063] E J =E h -EF N P t

[0064] Among them, E J is the net carbon emissions of regional electricity, E h Carbon emissions from regional power generation.

[0065] According to the above technical scheme, firstly, by hierarchically decoupling multi-dimensional data such as prefecture-level city electricity consumption data, thermal power data, renewable energy power, and provincial thermal power standard coal consumption and carbon emission factors, the limitations of traditional calculation methods, such as single and incomplete data, are broken. Secondly, the power generation and consumption data and hierarchical structure characteristics of prefecture-level cities are fully considered, and the power transfer between prefecture-level power grids and provincial power grids is accurately calculated, providing key support for the accurate calculation of carbon emissions. Furthermore, thermal power data is used to specifically calculate the carbon emission factor of thermal power in prefecture-level cities, making the calculation of thermal power carbon emissions more accurate. At the same time, renewable energy power data is used to obtain the regional power generation structure to fully reflect the composition of power supply. Finally, based on the power transfer of provincial and municipal power grids and the power generation and consumption of prefecture-level cities, a new model for calculating carbon emission factors of prefecture-level power is constructed, which realizes the calculation of power carbon emission factors with finer granularity in time and space, greatly improving the accuracy and practicality of the calculation.

[0066] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0068] Figure 1 It is a flow chart of the method for calculating the regional electricity carbon emission factor provided by the present invention. DETAILED DESCRIPTION

[0069] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0070] See also Figure 1 The present invention provides a method and application for calculating a regional electricity carbon emission factor, and the method comprises:

[0071] Step 1: Collect data. Collect the relevant data of the area where carbon emissions need to be calculated through the data collection channel of the data center and store it in the full data table of the source layer;

[0072] Step 2: Clean and process the data, and process the missing data and abnormal data in turn;

[0073] Step 3: Calculate the regional power generation carbon emission factor, and calculate the thermal power generation carbon emission factor and the regional comprehensive power generation carbon emission factor in turn;

[0074] Step 4: Calculate the regional electricity carbon emission factor, calculate the regional electricity transfer quantity in turn, construct the regional carbon flow balance equation and solve the regional electricity carbon emission factor.

[0075] Specifically, the regional electricity carbon emission factor calculation method provided by the present invention relies on the data middle platform to build data products, and performs data collection, storage, calculation and other services based on the data middle platform. It can also call the relevant analysis result data of the data middle platform to perform large-screen visualization configuration display of business scenarios, so as to realize the full process solution from data collection to data application.

[0076] Throughout the entire process, the data center is used for data cleaning, storage, processing and analysis, including the following functions:

[0077] 1. Data access: Since data collection comes from multiple different business systems or channels, ETL, data bus, data interaction and other components are needed to convert data into structured data and access structured data scenarios to store the collected source data;

[0078] 2. Structured data scenarios and offline processing: Structured data scenarios are divided into source layer, sharing layer and analysis layer. Because data collected from multiple sources often have inconsistent data dimensions, calibers, types, etc., the multi-source data accessed from the data access function includes the following steps:

[0079] a. Enter the source layer and store them in the MaxCompute full table and incremental table according to the data type and source;

[0080] b. Perform data cleaning on the source layer according to the data exploration and analysis, duplicate data processing, missing data processing, abnormal data processing and other processes, and process them offline to obtain a standardized data table, which is then stored in the standard table of the shared layer;

[0081] c. The standard table data in the shared layer is trained by offline processing to obtain a corresponding model table, and the corresponding model is stored in the model table (the electricity-carbon calculation model in the present invention is trained in the offline processing link);

[0082] d. Call the data in the standard table and the model in the model table to perform data analysis according to the needs of the business scenario, and store the results in the dimension table, fact table, and result table in the analysis layer.

[0083] 3. Data analysis: Provide data services such as data visualization, report analysis, and autonomous analysis.

[0084] 4. Data application: Analyze and calculate relevant scenario indicators and models in the data center, encapsulate the analysis and calculation results into a data service API, publish and register it to the API gateway, and use the internal Internet regional data isolation device to allow external self-service analysis tools to call the data calculation results for visual display.

[0085] 5. Integration services: The intranet realizes user authority integration with the State Grid’s unified authority management platform and page integration with the Beijing Energy Big Data Center basic platform. The external network realizes page integration and data integration services with the Haidian City Brain IOCC.

[0086] In this embodiment, the data collected in the above step 1 include historical energy consumption, historical electricity consumption, regional GDP, regional clean energy share, regional major industrial product output, regional major industrial product electricity consumption and major industrial product supply index. When collecting data, it is necessary to integrate marketing business systems, electricity information collection systems, government data, water, gas, heat and other multi-source data.

[0087] Through the offline processing function provided by the above data center, the collected data is extracted and cleaned. Specifically, in step 2, the following operations are taken for missing data:

[0088] Search for missing data in the collected data obtained from the full data table of the source layer. When missing data is found, fill the missing data with the previous and next average values. If the missing data is the last data, fill it with the previous value; if the missing data is the first data, fill it with the next value.

[0089] Take the following actions for abnormal data after missing data processing:

[0090] The IQR abnormal data analysis method was used for data analysis. For abnormal data, the front and back average method was used to replace the abnormal values. If the abnormal value was the last value, the previous value was used to fill it. If the missing data was the first data, the latter value was used to fill it.

[0091] The IQR abnormal data analysis method performs abnormal numerical analysis on the characteristic data one by one:

[0092]

[0093] Among them, Upper represents the upper limit value, and Lower represents the lower limit value;

[0094] The specific analysis steps include:

[0095] First, calculate the quartiles of the data, arrange the data from small to large, and find its Q1, Q2 (median), and Q3;

[0096] Secondly, calculate IQR, IQR = Q3-Q1;

[0097] Then, determine the range of the outliers, and calculate the upper limit Upper and the lower limit Lower according to the formula Upper = Q3 + 1.5IQR and Lower = Q1 - 1.5IQR;

[0098] Finally, detect outliers and consider data that is greater than Upper or less than Lower as outliers.

[0099] In this embodiment, step 3 includes:

[0100] Step 3.1: Calculate the carbon emission factor of thermal power generation. Use the standard coal consumption of power supply in the power statistics data released by the China Electricity Council to convert the fossil energy consumption of thermal power generation. The calculation formula is:

[0101]

[0102] Among them, E h is the carbon emissions from thermal power generation, P h is the thermal power generation, F h is the standard coal consumption per kWh of thermal power generation, T h is the standard raw coal conversion factor, EF is the raw coal carbon emission factor;

[0103] Step 3.2: Calculate the regional comprehensive power generation carbon emission factor by dividing the power generation carbon emission by the total power generation. The calculation formula is:

[0104]

[0105] Among them, EF p is the carbon emission factor for power generation, P i is the power generation of the i-th power generation type.

[0106] In this embodiment, step 4 includes:

[0107] Step 4.1, calculate the regional power transfer amount. According to the requirements of the power grid balance relationship, the power generation and power consumption need to be in a balanced relationship. If the power generation is greater than the power consumption, it is necessary to transfer the power from outside the region; otherwise, the excess power is transported to the area with insufficient power generation. The calculation formula for regional transfer power is:

[0108]

[0109] Among them, P t is the regional transfer power, P u The total electricity consumption of the region;

[0110] Step 4.2: Construct the regional carbon flow balance equation. In the power system, the node carbon flow balance algorithm is based on the power flow distribution of the power system, combined with the power generation carbon emission factor of each node and the power consumption of the node, to construct the node carbon flow balance equation. The calculation formula is:

[0111] EF i (E i +∑P j,i )=C i +∑P j,i EF j

[0112] Among them, EF i is the electricity carbon emission factor of the i-th region, E i is the power generation of the ith region, P j,i is the amount of electricity transferred from area j to area i, C i is the carbon emissions from thermal power generation in region i;

[0113] Step 4.3: Solve the carbon emission factor of electricity in each region:

[0114] First, the carbon flow balance equations of the nodes in each region are combined to construct the balance equation group:

[0115]

[0116] Secondly, the matrix is ​​simplified through matrix operations to define the regional electricity carbon emission matrix M:

[0117]

[0118] Finally, solve the regional electricity carbon emission factor:

[0119]

[0120] Through the above technical scheme, the regional electricity carbon emission factor calculation method provided by the present invention first breaks the limitations of single and incomplete data of traditional calculation methods by hierarchically decoupling multi-dimensional data such as prefecture-level city electricity consumption data, thermal power data, renewable energy power, and provincial thermal power electricity standard coal consumption and carbon emission factors. Secondly, the power generation and power consumption data and hierarchical structure characteristics of prefecture-level cities are fully considered, and the power transfer between prefecture-level power grids and provincial power grids is accurately calculated, which provides key support for the accurate calculation of carbon emissions. Furthermore, thermal power data is used to specifically calculate the carbon emission factor of thermal power in prefecture-level cities, making the calculation of thermal power carbon emissions more accurate. At the same time, the regional power generation structure is obtained using renewable energy power data to fully reflect the composition of power supply. Finally, based on the power transfer of provincial and municipal power grids and the power generation and consumption of prefecture-level cities, a new prefecture-level power carbon emission factor calculation model is constructed, which realizes the calculation of power carbon emission factors with finer granularity in time and space, greatly improving the accuracy and practicality of the calculation.

[0121] The present invention also provides a method for calculating regional electricity carbon emissions based on the above-mentioned regional electricity carbon emission factor calculation method. The method for calculating regional electricity carbon emissions uses the regional electricity carbon emission factor calculated by the method for calculating regional electricity carbon emission factor to calculate regional electricity carbon emissions.

[0122] Specifically, the method for calculating the regional electricity carbon emissions includes: using the regional electricity carbon emission factor to calculate the regional electricity transfer carbon emissions, the formula is:

[0123] E Z =EF N P t

[0124] Among them, E Z Carbon emissions for regional electricity transfer, EF N Transfer power to the region.

[0125] At the same time, the calculation method of regional electricity carbon emissions also includes: using regional electricity transfer carbon emissions to calculate regional electricity net carbon emissions, the formula is:

[0126] E J =E h -EF N P t

[0127] Among them, E J is the net carbon emissions of regional electricity, E h Carbon emissions from regional power generation.

[0128] The regional electricity carbon emissions calculated by the above method fully integrates various types of electricity data. The calculation results are more accurate and can fully reflect the actual situation. They can meet the increasingly refined carbon emission management needs and achieve comprehensive low-carbon development goals.

[0129] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0130] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0131] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0133] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0134] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0135] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0136] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0137] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method and application for calculating regional electricity carbon emission factors, characterized in that: The method for calculating the regional electricity carbon emission factor includes: Step 1: Collect data. Collect the relevant data of the area where carbon emissions need to be calculated through the data collection channel of the data center and store it in the full data table of the source layer; Step 2: Clean and process the data, and process the missing data and abnormal data in turn; Step 3: Calculate the regional power generation carbon emission factor, and calculate the thermal power generation carbon emission factor and the regional comprehensive power generation carbon emission factor in turn; Step 4: Calculate the regional electricity carbon emission factor, calculate the regional electricity transfer quantity in turn, construct the regional carbon flow balance equation and solve the regional electricity carbon emission factor.

2. The method and application for calculating the regional electricity carbon emission factor according to claim 1 is characterized in that: The data center is used for data cleaning, storage, processing and analysis, including: The data access module is used to convert data into structured data and access the structured data scenario using ETL, data bus, and data interaction components to store the collected source data; Structured data scenarios and offline processing modules, where the structured data scenarios are divided into the source layer, the sharing layer, and the analysis layer. They are used to first enter the source layer and store them in the MaxCompute full table and incremental table according to the data type and source. Secondly, the data in the source layer is cleaned and processed offline according to the process of data exploration and analysis, duplicate data processing, missing data processing, and abnormal data processing to obtain a standardized data table, which is then stored in the standard table of the sharing layer. Then, the standard table data in the sharing layer is trained through offline processing to obtain the corresponding model table, and the corresponding model is stored in the model table. Finally, according to the needs of the business scenario, the data in the standard table and the model in the model table are called for data analysis, and the results are stored in the dimension table, fact table, and result table in the analysis layer. Data analysis module, used to provide data visualization, report analysis and autonomous analysis; The data application module is used to analyze and calculate relevant scenario indicators and models, encapsulate the analysis and calculation results into data service APIs, publish and register them to the API gateway, and use the internal Internet regional data isolation device to allow external self-service analysis tools to call data calculation results and perform visual display; And the integration service module is used to provide user permission integration, page integration and data integration services in the intranet and extranet.

3. The method and application for calculating the regional electricity carbon emission factor according to claim 1 is characterized in that: The data collected in step 1 include historical energy consumption, historical electricity consumption, regional GDP, regional clean energy share, regional major industrial product output, regional major industrial product electricity consumption and major industrial product supply index.

4. The method and application for calculating the regional electricity carbon emission factor according to claim 1 is characterized in that: In step 2, take the following actions for missing data: Search for missing data in the collected data obtained from the full data table of the source layer. When missing data is found, fill the missing data with the previous and next average values. If the missing data is the last data, fill it with the previous value; if the missing data is the first data, fill it with the next value.

5. The method and application for calculating the regional electricity carbon emission factor according to claim 4 is characterized in that: In step 2, take the following actions for the abnormal data after missing data processing: The IQR abnormal data analysis method was used for data analysis. For abnormal data, the front and back average method was used to replace the abnormal values. If the abnormal value was the last value, the previous value was used to fill it. If the missing data was the first data, the latter value was used to fill it. The IQR abnormal data analysis method performs abnormal numerical analysis on the characteristic data one by one: Among them, Upper represents the upper limit value, and Lower represents the lower limit value; The specific analysis steps include: First, calculate the quartiles of the data, arrange the data from small to large, and find its Q1, median Q2, and Q3; Secondly, calculate IQR, IQR = Q3-Q1; Then, determine the range of the outliers, and calculate the upper limit value Upper and the lower limit value Lower according to the formula Upper = Q3 + 1.5IQR and Lower = Q1 - 1.5IQR; Finally, detect outliers and consider data that is greater than Upper or less than Lower as outliers.

6. The method and application for calculating the regional electricity carbon emission factor according to claim 1 is characterized in that: Step 3 includes: Step 3.1: Calculate the carbon emission factor of thermal power generation. Use the standard coal consumption of power supply in the power statistics data released by the China Electricity Council to convert the fossil energy consumption of thermal power generation. The calculation formula is: Among them, E h is the carbon emission of thermal power generation, P h is the thermal power generation, F h is the standard coal consumption per kWh of thermal power generation, T h is the standard raw coal conversion factor, EF is the raw coal carbon emission factor; Step 3.2: Calculate the regional comprehensive power generation carbon emission factor by dividing the power generation carbon emission by the total power generation. The calculation formula is: Among them, EF p is the carbon emission factor for power generation, P i is the power generation of the ith power generation type.

7. The method and application for calculating the regional electricity carbon emission factor according to claim 1 is characterized in that: Step 4 includes: Step 4.1, calculate the regional power transfer amount. According to the requirements of the power grid balance relationship, the power generation and power consumption need to be in a balanced relationship. If the power generation is greater than the power consumption, it is necessary to transfer the power from outside the region; otherwise, the excess power is transported to the area with insufficient power generation. The calculation formula for regional transfer power is: Among them, P t is the regional transfer power, P u The total electricity consumption of the region; Step 4.2: Construct the regional carbon flow balance equation. In the power system, the node carbon flow balance algorithm is based on the power flow distribution of the power system, combined with the power generation carbon emission factor of each node and the power consumption of the node, to construct the node carbon flow balance equation. The calculation formula is: EF i (E i +∑P j,i )=C i +ΣP j,i EF j Among them, EF i is the electricity carbon emission factor of the i-th region, E i is the power generation of the ith region, P j,i is the amount of electricity transferred from area j to area i, C i is the carbon emissions from thermal power generation in region i; Step 4.3: Solve the carbon emission factor of electricity in each region: First, the carbon flow balance equations of the nodes in each region are combined to construct the balance equation group: Secondly, the matrix is ​​simplified through matrix operations to define the regional electricity carbon emission matrix M: Finally, solve the regional electricity carbon emission factor:

8. The method and application for calculating the regional electricity carbon emission factor according to any one of claims 1 to 7, characterized in that: It also includes calculating regional electricity carbon emissions using the regional electricity carbon emission factor calculated using the regional electricity carbon emission factor calculation method.

9. The method and application for calculating the regional electricity carbon emission factor according to claim 8 is characterized in that: The calculation of regional electricity carbon emissions includes: using the regional electricity carbon emission factor to calculate the regional electricity transfer carbon emissions, the formula is: E Z =EF N P t Among them, E Z Carbon emissions for regional electricity transfer, EF N Transfer power to the region.

10. The method and application for calculating the regional electricity carbon emission factor according to claim 9 is characterized in that: The calculation of regional electricity carbon emissions also includes: using regional electricity transfer carbon emissions to calculate regional electricity net carbon emissions, the formula is: AND J =And h -EF N P t Among them, E J is the net carbon emissions of regional electricity, E h Carbon emissions from regional power generation.

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