Carbon emission data detection method and detection platform

Abstract

The invention provides a carbon emission data detection method and a carbon emission data detection platform. The detection method comprises the following steps: acquiring carbon emission data of the generator set, wherein the carbon emission data comprises as received basis element carbon content, low calorific value, boiler efficiency, turbine efficiency, heat supply carbon emission intensity, heat supply coal consumption, power generation carbon emission intensity and power generation coal consumption; based on a preset calculation formula, calculating design values corresponding to heat supply carbon emission intensity, heat supply coal consumption, power generation carbon emission intensity and power generation coal consumption according to the obtained as received basis element carbon content, the obtained low heating value, the obtained boiler efficiency and the obtained steam turbine efficiency; comparing the corresponding design value with the obtained corresponding carbon emission data to obtain a corresponding comparison result; and according to the corresponding comparison result, judging the rationality of the obtained corresponding carbon emission data. The method can help a power enterprise to check the reasonability of the carbon emission data by themselves.

Description

TECHNICAL FIELD

[0001] The present application relates to an environmental protection technology, in particular to a carbon emission data detection method and a detection platform. BACKGROUND

[0002] In the field of carbon emissions, according to relevant national regulations, power enterprises need to report greenhouse gas emissions and calculate carbon emissions annually. However, the existing technology mainly focuses on the calculation and reporting of carbon emissions, and lacks in-depth analysis of the rationality and optimization potential of carbon emission data.

[0003] In addition, with the high attention of the country to the double carbon goal, power enterprises also face increasingly stringent requirements in carbon emission management, especially in data quality and rationality. At the same time, due to the large difference in actual production process of each power plant, practitioners need a tool to further analyze relevant carbon emission data to help power enterprises self-check the rationality of carbon emission data.

[0004] Therefore, how to detect the rationality of carbon emission data and identify the opportunity for emission reduction and optimization is one of the urgent problems to be solved in the industry. SUMMARY

[0005] One of the purposes of the present application is to provide a carbon emission data detection method and a detection platform, which can help power enterprises to self-check the rationality of carbon emission data.

[0006] In order to realize a carbon emission data detection method, the method comprises the following steps:

[0007] Obtain carbon emission data of a generator set, the carbon emission data including received base element carbon content, low calorific value, boiler efficiency, steam turbine efficiency, heating carbon emission intensity, heating coal consumption, power generation carbon emission intensity, and power generation coal consumption;

[0008] Based on a preset calculation formula, according to the received base element carbon content, the low calorific value, the boiler efficiency, and the steam turbine efficiency, the design values of the heating carbon emission intensity, the heating coal consumption, the power generation carbon emission intensity, and the power generation coal consumption corresponding to the generator set are calculated;

[0009] Compare the corresponding design values with the corresponding carbon emission data obtained, to obtain corresponding comparison results;

[0010] According to the corresponding comparison results, the rationality of the corresponding carbon emission data obtained is judged.

[0011] In some embodiments of the present application, the calculation formula includes:

[0012] Measurement formula 1:

[0013] Heat supply coal consumption / heat supply carbon emission intensity = 1 / (measurement default factor x unit calorific value carbon content x 99% x 44 / 12)

[0014] Measurement formula 2:

[0015] Power generation coal consumption / power generation carbon emission intensity = 1 / (measurement default factor x unit calorific value carbon content x 99% x 44 / 12);

[0016] Measurement formula 3:

[0017] Unit calorific value carbon content = received base element carbon content / low calorific value

[0018] Measurement formula 4:

[0019] Heat supply carbon emission intensity = (0.03412 x measurement default factor x 44 / 12) x unit calorific value carbon content / boiler efficiency

[0020] Measurement formula 5:

[0021] Power generation carbon emission intensity = 3.6 x 44 / 12 x unit calorific value carbon content / (boiler efficiency x steam turbine efficiency)

[0022] Wherein, 99% is the carbon oxidation rate, 44 / 12 is the molecular weight ratio of carbon dioxide and carbon, 0.03412 is the standard coal conversion coefficient, and 3.6 is the conversion coefficient of degrees and joules;

[0023] Wherein, the measurement default factor is the empirical value of the low calorific value;

[0024] Wherein, the units and scientific notation in the measurement formula have been offset.

[0025] In some embodiments of the present application, the step of obtaining carbon emission data of the power generating unit comprises:

[0026] The user inputs the carbon emission data through an input interface;

[0027] Wherein, the input carbon emission data is annual detection or monthly detection data.

[0028] In some embodiments of the present application, the comparison result is fluctuation data reflecting the fluctuation between the corresponding design value and the corresponding obtained carbon emission data.

[0029] In some embodiments of the present application, the step of judging the rationality of the corresponding obtained carbon emission data comprises:

[0030] determining that the obtained corresponding carbon emission data is abnormal when the fluctuation data is within the first fluctuation interval;

[0031] determining that the obtained corresponding carbon emission data is reasonable when the fluctuation data is within the second fluctuation interval;

[0032] determining that the obtained corresponding carbon emission data is abnormal and has a high risk of error when the fluctuation data is within the third fluctuation interval;

[0033] determining that the obtained corresponding carbon emission data is abnormal and has a very high risk of error when the fluctuation data is within the fourth fluctuation interval;

[0034] The first fluctuation interval, the second fluctuation interval, the third fluctuation interval, and the fourth fluctuation interval are intervals that are small to large and continuous.

[0035] In some embodiments of the present application, the step of determining the reasonableness of the obtained corresponding carbon emission data further comprises:

[0036] determining that the obtained carbon emission data corresponding to the heat supply carbon emission intensity and the heat supply coal consumption is reasonable when the corresponding fluctuation data corresponding to the heat supply carbon emission intensity and the heat supply coal consumption is simultaneously within the same interval and differs by less than a first threshold value;

[0037] determining that the obtained carbon emission data corresponding to the power generation carbon emission intensity and the power generation coal consumption is reasonable when the corresponding fluctuation data corresponding to the power generation carbon emission intensity and the power generation coal consumption is simultaneously within the same interval and differs by less than the first threshold value;

[0038] determining that the obtained carbon emission data corresponding to the heat supply carbon emission intensity and the heat supply coal consumption is abnormal when the corresponding fluctuation data corresponding to the heat supply carbon emission intensity and the heat supply coal consumption differs by more than a second threshold value;

[0039] determining that the obtained carbon emission data corresponding to the power generation carbon emission intensity and the power generation coal consumption is abnormal when the corresponding fluctuation data corresponding to the power generation carbon emission intensity and the power generation coal consumption differs by more than the second threshold value.

[0040] In some embodiments of the present application, the method for detecting carbon emission data further comprises:

[0041] displaying the fluctuation data, or displaying a code corresponding to the fluctuation data and an explanation corresponding to the code, through a display interface;

[0042] The fluctuation data is percentage data.

[0043] In some embodiments of the present application, the carbon emission data further comprises at least one of unit information, heat supply, power generation, and load output coefficient of the power generator set, and the unit information comprises at least one of unit type, cooling mode, installed capacity, and whether to be combined.

[0044] In some embodiments of the present application, the calculated default factor is obtained by weighted average calculation according to historical data of multiple power plants.

[0045] To achieve the above-mentioned purpose, the present application further provides a carbon emission data detection platform, comprising:

[0046] a processor configured to execute the carbon emission data detection method as described above; and

[0047] a user interface connected to the processor and comprising an input interface for user to input data and a display interface for displaying the results processed by the processor.

[0048] The present application provides a tool easy for enterprises and practitioners to use based on the disclosed data and calculation logic in the related documents, which not only meets the basic requirements of the state for carbon emission report, but also further strictly tests and improves the quality and rationality of the carbon emission report, so that the enterprises can more accurately judge and manage their own carbon emission situation, which facilitates the carbon emission management of the enterprises and guarantees the healthy and orderly development of the carbon emission right trading market. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 Fig. 1 is a flowchart of the carbon emission data detection method of the present application;

[0050] Figure 2 Fig. 2 is a structural schematic diagram of the carbon emission data detection platform of the present application;

[0051] Figure 3 Fig. 3 is a schematic diagram of the input interface for user to input the carbon emission data detected by year in the detection platform built according to a preferred embodiment of the present application;

[0052] Figure 4 Fig. 4 is a schematic diagram of the display interface for displaying the detection results obtained after calculation by year;

[0053] Figure 5 Fig. 5 is a schematic diagram of the input interface for user to input the carbon emission data detected by month in the detection platform built according to a preferred embodiment of the present application;

[0054] Figure 6 Fig. 6 is a schematic diagram of the input interface for user to input enterprise information and principal information in the detection platform built according to a preferred embodiment of the present application; and

[0055] Figure 7 Fig. 3 is a schematic diagram of a query interface for querying historical records in the detection platform. DETAILED DESCRIPTION

[0056] Example implementations are now described with reference to the drawings; however, these implementations are merely examples of implementations and are not intended to be limiting. Rather, these implementations are presented as understood by one of ordinary skill in the art to fully and completely disclose the example implementations to one of ordinary skill in the art. Like reference numbers in the figures indicate like components or features, where appropriate.

[0057] The words "a" or "an" encompass both a and one or more items. The terms "comprises," "comprising," "includes," "including" and "has" or "having," are intended to be open-ended terms that do not exclude additional elements or steps. The terms "first," "second" and the like do not denote any ordinal, quantity, or importance, but are used to distinguish one element from another.

[0058] It is to be understood that the phraseology or terminology herein is for the purpose of description and not of limitation, such that the terminology or phraseology of the present specification is to be interpreted by those skilled in relevant art in light of the teachings and / or disclosures of the present specification.

[0059] Different embodiments or examples of the inventive subject matter provided herein can be implemented in different ways. Of course, it is to be understood that specific embodiments or examples of the inventive subject matter provided herein can be implemented in various manners, and that these are merely examples and are not intended to be limiting. Also, the inventive subject matter can be used in each of the various embodiments or examples provided herein, and this repetition is for the sake of brevity and clarity. Furthermore, the inventive subject matter can be used in other embodiments or examples that are not provided herein.

[0060] As shown in FIG. 10, it illustrates a flow of a carbon emission data detection method 10 provided by the present application. The detection method 10 provided by the present application can include the following steps: Figure 1

[0061] In step S11, carbon emission data of a generator set is acquired, wherein the carbon emission data includes received base element carbon content, low calorific value, boiler efficiency, steam turbine efficiency, heating carbon emission intensity, heating coal consumption, power generation carbon emission intensity, and power generation coal consumption.

[0062] ​Step S12, based on a preset calculation formula, the received base element carbon content, the low calorific value, the boiler efficiency, and the steam turbine efficiency are used to calculate the design value of the heat supply carbon emission intensity, the heat supply coal consumption, the power generation carbon emission intensity, and the power generation coal consumption corresponding to the generator set;

[0063] Step S13, the corresponding design value is compared with the corresponding carbon emission data obtained to obtain a corresponding comparison result;

[0064] Step S14, according to the corresponding comparison result, the rationality of the corresponding carbon emission data obtained is judged.

[0065] As shown in Figure 2 , which shows the structure of the carbon emission data detection platform 20 of the present application. Among them, the detection platform 20 of the present application can include a processor 21 and a user interface 22. The processor 21 is configured to execute the carbon emission data detection method of the present application. The user interface 22 is connected with the processor 21, and includes an input interface (such as the interface shown in Figure 3 、 Figure 5 , or Figure 6 ) for the user to input data, and a display interface (such as the interface shown in Figure 4 or Figure 7 ) for displaying the results processed by the processor 21. In some embodiments of the present application, the connection between the user interface 22 and the processor 21 is, for example, a communication connection. In the present application, the carbon emission data detection platform 20 of the present application can be built through a program language, and the built detection platform can be in the form of a web page or an application program, which does not limit the present application.

[0066] In some embodiments of the present application, the calculation formula can include, for example:

[0067] Calculation formula 1:

[0068] Heat supply coal consumption / heat supply carbon emission intensity = 1 / (calculation default factor × unit calorific value carbon content × 99% × 44 / 12);

[0069] Calculation formula 2:

[0070] Power generation coal consumption / power generation carbon emission intensity = 1 / (calculation default factor × unit calorific value carbon content × 99% × 44 / 12);

[0071] Calculation formula 3:

[0072] Unit calorific value carbon content = received base element carbon content / low calorific value;

[0073] Calculation formula 4:

[0074] The heat supply carbon emission intensity = (0.03412 x the calculated default factor x 44 / 12) x the carbon content per unit heat value / the boiler efficiency;

[0075] The calculation formula 5 is:

[0076] The power generation carbon emission intensity = 3.6 x 44 / 12 x the carbon content per unit heat value / (the boiler efficiency x the steam turbine efficiency);

[0077] Wherein, 99% is the carbon oxidation rate, 44 / 12 is the molecular weight ratio of carbon dioxide and carbon, 0.03412 is the standard coal conversion coefficient (for example, from the national standard GB / T 28398-2023 Coal Enterprise Energy Consumption Statistics Specification), 3.6 is the conversion coefficient of degree and joule (i.e., 1 degree = 1 kilowatt x 1 hour = 1000 watts x 3600 seconds = 3.6 x 10 6 Joule, wherein 10 6 will be offset in the calculation process);

[0078] Wherein, the calculation default factor is the empirical value of low heat value;

[0079] Wherein, the units and scientific notation in the calculation formula have been offset.

[0080] In some embodiments of the present application, the calculation default factor can be calculated by weighted average according to the historical data of multiple power plants. For example, the calculation default factor in the calculation formula 1, 2 and 4 can be calculated by weighted average according to the historical data related to low heat value of multiple power plants.

[0081] Preferably, in step S11, the step of obtaining the carbon emission data of the power generating unit may, for example, include that the user inputs the carbon emission data through an input interface. Wherein, the input carbon emission data may, for example, be annual detection or monthly detection data.

[0082] Preferably, in step S13, the comparison result may, for example, be fluctuation data reflecting the fluctuation between the corresponding design value and the corresponding carbon emission data obtained. For example, including but not limited to, fluctuation data reflecting the fluctuation of the heat supply carbon emission intensity of the power generating unit, fluctuation data reflecting the fluctuation of the heat supply coal consumption of the power generating unit, fluctuation data reflecting the fluctuation of the power generation carbon emission intensity of the power generating unit, fluctuation data reflecting the fluctuation of the power generation coal consumption of the power generating unit, etc.

[0083] Preferably, in step S14, the step of judging the rationality of the obtained corresponding carbon emission data can include, for example: when the fluctuation data is within a first fluctuation range (for example, but not limited to, <-3%), it is determined that the obtained corresponding carbon emission data is abnormal; when the fluctuation data is within a second fluctuation range (for example, but not limited to, -3%~5%), it is determined that the obtained corresponding carbon emission data is reasonable; when the fluctuation data is within a third fluctuation range (for example, but not limited to, 16%~5%), it is determined that the obtained corresponding carbon emission data is abnormal and has a high risk of error; when the fluctuation data is within a fourth fluctuation range (for example, but not limited to, >16%), it is determined that the obtained corresponding carbon emission data is abnormal and has a very high risk of error. The first fluctuation range, the second fluctuation range, the third fluctuation range, and the fourth fluctuation range are consecutive ranges from small to large.

[0084] More preferably, in step S14, the step of judging the rationality of the obtained corresponding carbon emission data can further include:

[0085] when the corresponding fluctuation data corresponding to the heating carbon emission intensity and the heating coal consumption is in the same range and differs by less than a first threshold value, it is determined that the obtained carbon emission data corresponding to the heating carbon emission intensity and the heating coal consumption is reasonable;

[0086] when the corresponding fluctuation data corresponding to the power generation carbon emission intensity and the power generation coal consumption is in the same range and differs by less than a first threshold value, it is determined that the obtained carbon emission data corresponding to the power generation carbon emission intensity and the power generation coal consumption is reasonable;

[0087] when the corresponding fluctuation data corresponding to the heating carbon emission intensity and the heating coal consumption differs by more than a second threshold value, it is determined that the obtained carbon emission data corresponding to the heating carbon emission intensity and the heating coal consumption is abnormal;

[0088] when the corresponding fluctuation data corresponding to the power generation carbon emission intensity and the power generation coal consumption differs by more than a second threshold value, it is determined that the obtained carbon emission data corresponding to the power generation carbon emission intensity and the power generation coal consumption is abnormal.

[0089] The first threshold value and the second threshold value can be the same value, for example, 3%, but this is not a limitation of the present application. In other embodiments, the first threshold value and the second threshold value can also be other threshold values, for example, 4% or 5%, which can be designed according to different requirements.

[0090] In some embodiments of the present application, the detection method 10 of the present application further includes: displaying the fluctuation data through a display interface, or displaying a code corresponding to the fluctuation data and an explanation corresponding to the code. The fluctuation data can be, for example, percentage data.

[0091] In some embodiments of the present application, the carbon emission data of the present application can further include at least one of unit information, heat supply, power generation, and load capacity factor of the power generating unit. The unit information can include at least one of unit type, cooling method, installed capacity, and whether it is a combined unit. Of course, it can be understood that the carbon emission data of the present application is not limited to the above-mentioned data or information, and can further include other data or information, which is not limited to the present application.

[0092] The different interfaces in the detection platform 20 according to a preferred embodiment of the present application, and the detection method of the present application will be described in detail below. Figures 3 to 7 The detection platform 20 can be, for example, an application program platform built by a WeChat mini program.

[0093] As shown in Figure 3 , an input interface 221 for inputting the annual detection carbon emission data of the built detection platform 20 is shown, and the user can input the annual detection carbon emission data through the input interface 221 as shown in Figure 3 .

[0094] In the present application, the carbon emission data used is generally the data used by the power enterprise when reporting the national report, and the enterprise does not need to process the data again. The format of the data can have unit and decimal place requirements, but the system can automatically adjust the decimal places of the data to the appropriate result. In the present application, the carbon emission data used can include but is not limited to data or information related to the power generating unit and the production of the enterprise, such as including but not limited to the following data or information:

[0095] 1. Unit information, including: unit type (for example: conventional coal-fired unit 300 MV or more), cooling method (for example: air cooling), installed capacity, and whether it is a combined unit. According to the basic unit information such as cooling method, installed capacity, and whether it is a combined unit, the unit type of the power generating unit can be confirmed.

[0096] 2. Fuel data, including: received base element carbon, low calorific value, boiler efficiency, steam turbine efficiency, heat supply carbon emission intensity, heat supply coal consumption, power generation carbon emission intensity, and power generation coal consumption.

[0097] 3. Overall data: heat supply, power generation, and load capacity factor.

[0098] The relevant unit information can be obtained from the machine nameplate of the corresponding generator unit. The relevant fuel data, such as the received base element carbon and low calorific value, can be obtained from the report required by the state to detect the fuel coal quality. The data such as boiler efficiency and steam turbine efficiency are data used by the power enterprise when calculating energy consumption. The data such as heat carbon emission intensity, heat coal consumption, power carbon emission intensity and power coal consumption are data that must be recorded by the power enterprise in production. The relevant overall data, such as heat supply, power generation, load output coefficient, are data that must be recorded by the power enterprise in production. Therefore, all the data required in the present application do not deviate from the production of the power enterprise, and the power enterprise does not need to process the data. The relevant data in the production daily report or other report can be directly inputted to achieve the requirement, for example, the corresponding data "0.099" can be directly inputted in the "heat carbon emission intensity (tCO2 / GJ)" column of the input interface 221 in Figure 3 . The platform built in the present application is very simple for the user as a whole, and the user can use it without further learning.

[0099] When the user clicks the "start detection" button in the input interface 221 in Figure 3 , the annual detection results obtained after calculation can be displayed through the display interface 222 shown in Figure 4 . In a specific embodiment shown in Figure 4 , the numbers behind the intensity data and coal consumption data displayed in the following four lines are the percentage fluctuation data obtained after calculation and comparison of the above-mentioned calculation formulas 1-5 of the present application. These percentage fluctuation data can reflect the fluctuation between the corresponding design value and the obtained corresponding carbon emission data. In some embodiments, for example, if the obtained comparison result is negative, it means that there is an error in the data reported by the enterprise. If the obtained comparison result is positive and the fluctuation exceeds 5%, it means that the capacity of the corresponding generator unit can be greatly improved.

[0100] In some specific embodiments, Figure 4 , the four "positive or negative percentage fluctuation data" displayed in the following four lines of the display interface 222 correspond to the fluctuation data of "heat carbon emission intensity, heat coal consumption, power carbon emission intensity, power coal consumption", respectively.

[0101] Of course, it can be understood that in some other embodiments, the display interface 222 can also not display specific fluctuation data, but display codes corresponding to the fluctuation data and explanations corresponding to the codes. As shown in Table 1 below, different display modes, different fluctuation intervals, and explanations corresponding to different results corresponding to data such as heat supply carbon emission intensity, heat supply coal consumption, power generation carbon emission intensity, power generation coal consumption, etc. are shown.

[0102] Table 1:

[0103]

[0104]

[0105] In Table 1, “A”, “B”, “C”, “D” in the “display as” in the second row may, for example, represent percentage fluctuation data corresponding to “heat supply carbon emission intensity”, “heat supply coal consumption”, “power generation carbon emission intensity”, “power generation coal consumption”, etc. respectively (for example, may correspond to the display in the lower four rows of the display interface 222 as shown in Figure 4 Cases 1-4 represent different preset fluctuation intervals and explanations corresponding to different fluctuation intervals, respectively. Case 5 represents that when the result difference between A and B, C and D is small, it can be considered that the data is reasonable. Case 6 represents that when the result difference between A and B, C and D is large, it can be considered that the data is abnormal, i.e. there is a high risk that the reported data is problematic, and further explanation or checking is required.

[0106] For example, taking the percentage fluctuation data (for example, "A") corresponding to the "heating carbon emission intensity" as an example, "A" may be, for example, the ratio of the reported data corresponding to the "heating carbon emission intensity" input by the user of the power enterprise and the design value corresponding to the "heating carbon emission intensity" calculated by the preset calculation formula of the application. Case 1 is, for example, corresponding to the first interval (that is, "<-3%"), when "A" is within the first interval, it indicates that the data is abnormal, and the corresponding result explanation may be, for example, "The unit performance is better than the design case, please confirm the data rationality"; Case 2 is, for example, corresponding to the second interval (that is, "-3%~5%"), when "A" is within the second interval, it indicates that the data is reasonable, and the corresponding result explanation may be, for example, "The unit performance is excellent, and it is recommended to keep it"; Case 3 is, for example, corresponding to the third interval (that is, "16%~5%"), when "A" is within the third interval, it indicates that the data is abnormal, and the corresponding result explanation may be, for example, "The unit performance is lower than the design case, and the data error risk is high, please confirm the data rationality"; Case 4 is, for example, corresponding to the fourth interval (that is, ">16%"), when "A" is within the fourth interval, it indicates that the data is abnormal, and the corresponding result explanation may be, for example, "The unit performance is lower than the design case, and the data error risk is extremely high, please confirm the data rationality", and the like.

[0107] For example, case 5 may indicate that when the corresponding fluctuation data (for example, "A", "B") corresponding to the heating carbon emission intensity and the heating coal consumption are in the same interval and differ by less than a first threshold value (for example, "3%", which is not limited by the application), it is determined that the obtained carbon emission data corresponding to the heating carbon emission intensity and the heating coal consumption is reasonable, and the corresponding result explanation may be, for example, "The data is reasonable". Alternatively, it may indicate that when the corresponding fluctuation data (for example, "C", "D") corresponding to the power generation carbon emission intensity and the power generation coal consumption are in the same interval and differ by less than the first threshold value (for example, "3%", which is not limited by the application), it is determined that the obtained carbon emission data corresponding to the power generation carbon emission intensity and the power generation coal consumption is reasonable, and the corresponding result explanation may be, for example, "The data is reasonable".

[0108] In case 6, for example, it can be determined that the carbon emission data corresponding to the heat carbon emission intensity and heat coal consumption has an abnormality and a high risk of data error when the corresponding fluctuation data (for example, "A" and "B") of the heat carbon emission intensity and heat coal consumption differ by more than a second threshold value (for example, "3%", without being limited to this), and the corresponding result can be interpreted as "Please check the reported data, which has a high risk of data error".

[0109] As shown in Figure 5 , it shows the input interface 223 in the detection platform 20 for the user to input the monthly detected carbon emission data. The user can input the monthly detected carbon emission data through the input interface 223 as shown in Figure 5 .

[0110] After the user clicks the "start detection" button in the input interface 223 as shown in Figure 5 , the monthly detection result obtained after calculation can be displayed through the display interface 222 as shown in Figure 4 .

[0111] In the present application, the principle and calculation formula of "monthly detection" are consistent with "annual detection", and the obtained result is four positive or negative percentage fluctuation data of the filled monthly data (for example, the last four rows shown in Figure 4 ). In the present application, several months of results can be obtained by filling in several months of monthly data in the detection platform 20, and different "results" represent the rationality of the data of the enterprise and different data abnormality conditions.

[0112] As shown in Figure 6 , it shows the input interface 224 in the detection platform 20 for the user to input the enterprise information and the information of the person in charge. The user can input the enterprise information and the information of the person in charge through the input interface 224 as shown in Figure 6 , for example, "enterprise information" including the enterprise name, tax number, enterprise address, and industry to which the enterprise belongs, and "information of the person in charge" including the name, department, position, and telephone number of the person in charge, so as to collect the necessary enterprise information for the detection platform 20.

[0113] As shown in Figure 7 , it shows the query interface 225 in the detection platform 20 for the user to query the historical records. The user can query the historical records through the query interface 225 as shown inFigure 7 The input interface 225 shown makes a query of the history record, for example including for the enterprise / user to view the history record of his / her own query.

[0114] The carbon emission data detection method and detection platform of the present application can calculate the design value of the heat supply carbon emission intensity, heat supply coal consumption, power generation carbon emission intensity, power generation coal consumption and other carbon emission data of the power generating unit based on the preset calculation formula according to the input data of the received base element carbon content, low calorific value, boiler efficiency, steam turbine efficiency and the like input through the user interface. By comparing the design value with the heat supply carbon emission intensity, heat supply coal consumption, power generation carbon emission intensity, power generation coal consumption and other reporting data filled in by the enterprise (or user) independently, it can be identified whether the reporting data of the enterprise is reasonable.

[0115] The present application provides a tool easy for enterprises and practitioners to use based on the disclosed data and calculation logic in the relevant documents (for example the formula calculation logic in the E.3 section "Calculation method of power generation carbon emission intensity and heat supply carbon emission intensity" of the "Guidelines for Accounting and Reporting of Greenhouse Gas Emissions of Enterprises - Power Generation Facilities"), not only meets the basic requirements of the state for carbon emission reporting, but also further strictly tests and improves the quality and reasonableness of the carbon emission reporting, so that the enterprise can more accurately judge and manage its own carbon emission situation, facilitates the carbon emission management of the enterprise, and guarantees the healthy and orderly development of the carbon emission right trading market.

[0116] The exemplary embodiments of the present application are specifically shown and described above. It should be understood that the present application is not limited to the disclosed embodiments, rather the present application is intended to encompass various modifications and equivalent arrangements within the spirit and scope of the appended claims.

Claims

1. A method for detecting carbon emission data, characterized in that, Includes the following steps: Acquire carbon emission data of generator sets, including carbon content of basic elements, lower heating value, boiler efficiency, turbine efficiency, heating carbon emission intensity, heating coal consumption, power generation carbon emission intensity, and power generation coal consumption; Based on the preset calculation formula, and according to the obtained carbon content of the received basic element, the lower heating value, the boiler efficiency, and the turbine efficiency, the design values ​​of the heating carbon emission intensity, the heating coal consumption, the power generation carbon emission intensity, and the power generation coal consumption corresponding to the generator set are calculated. The corresponding design value is compared with the corresponding carbon emission data obtained to obtain the corresponding comparison result; Based on the corresponding comparison results, the rationality of the obtained carbon emission data is determined.

2. The method for detecting carbon emission data according to claim 1, characterized in that, The calculation formula includes: Calculation Formula 1: Heating coal consumption / Heating carbon emission intensity = 1 / (Calculation default factor × Carbon content per unit calorific value × 99% × 44 / 12) Calculation Formula 2: Coal consumption for power generation / Carbon emission intensity for power generation = 1 / (Default calculation factor × Carbon content per unit calorific value × 99% × 44 / 12) Calculation Formula 3: Carbon content per unit calorific value = Carbon content of received basic element / Lower heating value Calculation formula 4: Heating carbon emission intensity = (0.03412 × default calculation factor × 44 / 12) × carbon content per unit calorific value / boiler efficiency Calculation Formula 5: Carbon emission intensity of power generation = 3.6 × 44 / 12 × carbon content per unit calorific value / (boiler efficiency × turbine efficiency) where 99% is the carbon oxidation rate, 44 / 12 is the ratio of the molecular weight of carbon dioxide to carbon, 0.03412 is the standard coal conversion factor, and 3.6 is the conversion factor between degrees and joules. The default calculation factor is an empirical value of the lower heating value; The units and scientific notation in the calculation formula have been offset.

3. The method for detecting carbon emission data according to claim 1, characterized in that, The steps for obtaining carbon emission data from the generator set include: Users input the carbon emission data through the input interface; The carbon emission data input is either annually or monthly.

4. The method for detecting carbon emission data according to claim 3, characterized in that, The comparison result is fluctuation data reflecting the fluctuation between the corresponding design value and the corresponding carbon emission data obtained.

5. The method for detecting carbon emission data according to claim 4, characterized in that, The step of determining the reasonableness of the obtained carbon emission data includes: When the fluctuation data is within the first fluctuation range, it is determined that the corresponding carbon emission data obtained is abnormal; When the fluctuation data is within the second fluctuation range, the corresponding carbon emission data obtained is deemed reasonable. When the fluctuation data is within the third fluctuation range, it is determined that the corresponding carbon emission data obtained is abnormal and has a high risk of error. When the fluctuation data is within the fourth fluctuation range, it is determined that the corresponding carbon emission data obtained is abnormal and has a very high risk of error. The first fluctuation range, the second fluctuation range, the third fluctuation range, and the fourth fluctuation range are consecutive ranges that increase in size.

6. The method for detecting carbon emission data according to claim 5, characterized in that, The step of determining the reasonableness of the obtained carbon emission data further includes: When the corresponding fluctuation data for heating carbon emission intensity and heating coal consumption are simultaneously in the same range and the difference is less than a first threshold, the obtained carbon emission data corresponding to heating carbon emission intensity and heating coal consumption are determined to be reasonable. When the corresponding fluctuation data for the power generation carbon emission intensity and the power generation coal consumption are simultaneously in the same range and the difference is less than the first threshold, the obtained carbon emission data corresponding to the power generation carbon emission intensity and the power generation coal consumption are determined to be reasonable. When the difference between the fluctuation data corresponding to the heating carbon emission intensity and the heating coal consumption is greater than a second threshold, it is determined that the obtained carbon emission data corresponding to the heating carbon emission intensity and the heating coal consumption is abnormal. When the difference between the fluctuation data corresponding to the power generation carbon emission intensity and the power generation coal consumption is greater than the second threshold, it is determined that the obtained carbon emission data corresponding to the power generation carbon emission intensity and the power generation coal consumption is abnormal.

7. The method for detecting carbon emission data according to claim 6, characterized in that, Also includes: The fluctuation data is displayed through a display interface, or the code corresponding to the fluctuation data and the explanation corresponding to the code are displayed. The fluctuation data is percentage data.

8. The method for detecting carbon emission data according to claim 3, characterized in that, The carbon emission data also includes at least one of the generator set's unit information, heat supply, power generation, and load output coefficient. The unit information includes at least one of the unit type, cooling method, installed capacity, and whether it is combined with another unit.

9. The method for detecting carbon emission data according to claim 2, characterized in that, The default factor is calculated by weighted averaging based on historical data from multiple power plants.

10. A carbon emission data detection platform, characterized in that, include: The processor is configured to perform a method for detecting carbon emission data as described in any one of claims 1 to 9 above; as well as The user interface is connected to the processor and includes an input interface for the user to input data and a display interface for displaying the results processed by the processor.

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