A method and system for verifying the quality of carbon emission data from coal-fired power plants

Abstract

This invention discloses a method and system for verifying the quality of coal-fired carbon emission data in thermal power plants. The method includes: S1) collecting key indicator data of thermal power units; S2) constructing quality verification indicators based on the key indicator data; S3) comprehensively analyzing and evaluating various quality verification indicators, and issuing early warnings based on the comprehensive analysis and evaluation; S4) formulating a verification plan based on the comprehensive analysis and evaluation results of various quality verification indicators in S3) and the early warning output results, and verifying the coal-fired carbon emission data of thermal power plants according to the verification plan. This invention provides a method for analyzing and evaluating the reliability of monitoring data such as key indicators of coal-fired carbon emissions, carbon content per unit calorific value of coal, and comprehensive sample representativeness, thereby improving the internal data quality monitoring capabilities of enterprises. Through the implementation of the early warning plan, it is possible to effectively trace and verify problematic links in data anomalies and correct and improve them in a timely manner, reducing the adverse risks caused by data quality and non-compliance in data verification by relevant departments.

Description

Technical Field

[0001] This invention belongs to the technical field of quality supervision and quality management of coal-fired carbon emission accounting data of thermal power enterprises, and specifically relates to a method and system for quality verification of coal-fired carbon emission data of thermal power enterprises. Background Technology

[0002] Against the backdrop of achieving "carbon peaking" and "carbon neutrality," and with the full launch of the national carbon emissions trading system, the Ministry of Ecology and Environment requires key power generation companies to submit monthly and annual reports on greenhouse gas emissions from power generation facilities using fossil fuels such as coal. The Ministry is also organizing verification of greenhouse gas emissions and related data from these key companies. If any data irregularities or inconsistencies are found during the verification process, the relevant emission indicators will be treated at the maximum limit, resulting in significant economic losses for enterprises in carbon trading.

[0003] Coal-fired carbon emission accounting for thermal power plants is the main carbon emission activity of key emission units in the power generation industry. In the past two years, there have been cases of abnormal carbon element detection data and abnormal fluctuations in carbon content per unit calorific value in the carbon emission reporting data of enterprises. The quality is uneven, making it difficult to grasp the reliability of the reported data, which affects the accuracy of carbon emission accounting and the compliance of verification.

[0004] Therefore, data quality is an important foundation for the healthy development of national carbon emission management and the carbon emission trading rights market. Improving the supervision and management of data quality through methodological means is an important measure to solve existing technical problems. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for verifying the quality of coal-fired carbon emission data of thermal power plants, which is used to solve the problem of thermal power plants controlling the rationality and quality of coal-fired carbon emission accounting data, reducing the adverse risks caused by data quality and non-compliance with data verification by relevant departments; at the same time, it fills the gap in data quality supervision and verification.

[0006] The technical solution of the present invention:

[0007] A method for verifying the quality of carbon emission data from coal-fired power plants includes:

[0008] S1) Collect key indicator data of thermal power units;

[0009] S2) Construct quality verification indicators based on key indicator data;

[0010] S3) Conduct a comprehensive analysis and evaluation of all quality inspection indicators, and issue early warnings based on the comprehensive analysis and evaluation.

[0011] S4) Based on the comprehensive analysis and evaluation results of the various quality verification indicators in S3) and the early warning output results, formulate a verification plan and verify the coal-fired carbon emission data of thermal power enterprises according to the verification plan.

[0012] Preferably, S1) is to receive key indicator data of thermal power units reported by thermal power enterprises, including coal consumption; calorific value of coal; carbon and hydrogen elements in coal; and carbon content per unit calorific value of coal.

[0013] Preferably, the quality verification indicators in S2) include the comprehensive sample representativeness error (SRE), the fluctuation of carbon content per unit calorific value (CCV), the rationality of carbon content per unit calorific value (CCR), and the regularity of fluctuations between different coal standards (FRR).

[0014] Preferably, the comprehensive sample representativeness error (SRE) is a measure of the representativeness of the monthly comprehensive coal sample, using the consistency between the lower heating value of the monthly comprehensive coal sample and the monthly weighted average lower heating value of the coal fed into the furnace as the standard. The calculation method is as follows:

[0015] in: Q is the monthly weighted calorific value of the coal fed into the furnace; net，月综合 This represents the lower calorific value of the monthly composite sample.

[0016] Preferably, the carbon content fluctuation of unit calorific value (CCV) is obtained from the unit calorific value carbon content statistics module of the sub-unit, but is not limited to the monthly comprehensive coal sample carbon content difference, range, and average data. Specifically, the carbon content fluctuation of unit calorific value (CCV) is analyzed in the following two ways.

[0017] The first method: Analyze the fluctuation of carbon content per unit calorific value (CCV) using calculations.

[0018] The calculation method is as follows:

[0019] Among them: CC max This represents the maximum carbon content per unit calorific value for the month within the statistical period.

[0020] CC min This represents the minimum carbon content per unit of calorific value for the month within the statistical period.

[0021] This represents the average monthly carbon content per unit of calorific value during the statistical period.

[0022] The second method is to take the average carbon content per unit calorific value over a period of time as the center line, and use ±3S of the center line as the warning line to statistically analyze the distribution of carbon content per unit calorific value of monthly comprehensive coal samples, where S is the standard deviation of carbon content per unit calorific value.

[0023] Preferably, the rationality of carbon content per unit calorific value (CCR) is measured by the consistency between the measured value of carbon content per unit calorific value of the comprehensive coal sample and the recommended value of the coal type to be fed into the furnace. The measured value of carbon content per unit calorific value of the coal sample is obtained from the carbon content per unit calorific value statistical module. The statistical theoretical value of carbon content per unit calorific value of coal in the power industry is used as the upper and lower warning lines. The recommended value of different coal types is used as the reference line for statistical analysis. When the value exceeds the adjacent reference line, an early warning is issued. When the value exceeds the upper and lower warning lines, an abnormality is indicated.

[0024] Preferably, the fluctuation regularity of coal between different standards is based on the correlation between the two key indicators of coal calorific value and carbon and hydrogen elements. Combining the fluctuation trends of all these key indicators, the rationality of the change regularity of each key indicator data is analyzed through the following two methods.

[0025] (1) Pre-set the relationship model between carbon content per unit calorific value of coal and volatile matter and ash content, obtain the difference between the predicted value and the measured value of carbon content per unit calorific value of coal, and statistically analyze the standard deviation S of carbon content per unit calorific value. The average difference ±3S is used as the warning line.

[0026] (2) Analyze the consistency between the changes in calorific value of coal and the trends in carbon and hydrogen, and set two early warning conditions: ① carbon and hydrogen decrease while calorific value increases; ② carbon and hydrogen increase while calorific value decreases.

[0027] The trends of increase or decrease in the three indicators of carbon, hydrogen, and calorific value are calculated using the following formula:

[0028] ΔC=C 本月 -C 上月 ; ΔH=H 本月 -H 上月 ; ΔQ=Q 本月 -Q 上月 ;

[0029] ΔC represents the change in carbon content, C 本月 The carbon element value for the current month, C 上月 This is the carbon element value from the previous month;

[0030] ΔH represents the change in hydrogen element content, H 本月 H represents the hydrogen element value for the current month. 上月 This is the hydrogen element value from last month;

[0031] ΔQ is the change in calorific value, Q 本月 Q represents the calorific value for the current month. 上月 This is the calorie value from the previous month.

[0032] When ΔC < 0, ΔH < 0 and ΔQ > 0, it is judged to meet the warning condition ①;

[0033] When ΔC > 0, ΔH > 0 and ΔQ < 0, it is judged to meet the warning condition ②.

[0034] Preferably, step S3) specifically involves: setting up an early warning system with three levels, represented by red, yellow, and green colors according to the severity of the warning. At the same time, the warning for each level is output based on the comprehensive analysis results of carbon emission data quality verification indicators, and the warning output results are used to guide the subsequent verification of abnormal indicators.

[0035] The types of warnings include:

[0036] The first type: When the comprehensive sample representativeness error (SRE), carbon content fluctuation per unit calorific value (CCV), carbon content rationality per unit calorific value (CCR), and the fluctuation regularity between different coal standards (FRR) are all within the statistical period, i.e., not exceeding the warning line, the warning system's identification color is green, and the warning system's output type is no output.

[0037] The second type: When the fluctuation of carbon content per unit calorific value (CCV), the rationality of carbon content per unit calorific value (CCR), and the regularity of fluctuation between different coal standards (FRR) are all within the statistical period and do not exceed the warning line, and the comprehensive sample representative error (SRE) exceeds the warning line, the warning system will be marked in yellow and the warning system will output a comprehensive sample retention warning.

[0038] The third type: When the comprehensive sample representativeness error (SRE), the rationality of carbon content per unit calorific value (CCR), and the regularity of fluctuations between different coal standards (FRR) are all within the statistical period and do not exceed the warning line, and the fluctuation of carbon content per unit calorific value (CCV) exceeds the warning line, the warning system will be marked in yellow and the warning system will output a warning for fluctuations in the quality of coal entering the furnace.

[0039] The fourth type: When the comprehensive sample representativeness error (SRE) and the fluctuation regularity of coal between different standards (FRR) are both within the statistical period and do not exceed the warning line, and the fluctuation of carbon content per unit calorific value (CCV) and the rationality of carbon content per unit calorific value (CCR) both exceed the warning line, the warning system will be marked in yellow and the warning system output type will be a detection link warning.

[0040] Fifth type: When the comprehensive sample representativeness error (SRE) and the reasonableness of carbon content per unit calorific value (CCR) are both within the statistical period and do not exceed the warning line, and the fluctuation of carbon content per unit calorific value (CCV) and the regularity of fluctuation between different coal standards (FRR) both exceed the warning line, the warning system will be marked in yellow and the warning system output type will be a detection link warning.

[0041] The sixth type: When the carbon content per unit calorific value (CCR) and the fluctuation regularity between different coal standards (FRR) are both within the statistical period and do not exceed the warning line, and the comprehensive sample representativeness error (SRE) and the carbon content per unit calorific value (CCV) both exceed the warning line, the warning system will be marked in yellow and the warning system will output a comprehensive sample coal quality fluctuation warning.

[0042] The seventh type: When the comprehensive sample representativeness error (SRE), carbon content fluctuation per unit calorific value (CCV), and the fluctuation regularity between different coal standards (FRR) are all within the statistical period and do not exceed the warning line, and the carbon content per unit calorific value rationality (CCR) exceeds the warning line, the warning system will be marked in red and the warning system output type will be a comprehensive warning for sample retention and testing.

[0043] The eighth type: When the comprehensive sample representativeness error (SRE), carbon content fluctuation per unit calorific value (CCV), carbon content rationality per unit calorific value (CCR), and the fluctuation regularity between different coal standards (FRR) all exceed the warning line, the warning system's identifier color is red, and the warning system's output type is "overall data out of control warning".

[0044] Preferably, step S4) involves developing a verification plan based on the corresponding early warning output results, including:

[0045] The first approach is to verify the carbon emission data of coal-fired power plants by checking the sample retention plan and records and retrieving the sample retention work monitoring when the early warning system outputs a comprehensive sample retention early warning.

[0046] The second approach is to verify the coal-fired carbon emission data of thermal power plants when the early warning system outputs a warning about fluctuations in the quality of coal entering the furnace. This involves checking the composition of coal quality and quantity for each value entering the furnace, comparing the fluctuations in the quality of coal entering the plant, and analyzing the rationality of the fluctuation differences.

[0047] The third approach is to verify the carbon emission data of coal-fired power plants by adopting methods such as retesting existing samples or commissioning third-party testing when the early warning system outputs a warning at the detection stage.

[0048] The fourth approach is to verify the coal-fired carbon emission data of thermal power plants by adopting a scheme of comprehensive sample retesting, coal quality testing in the furnace, and comprehensive sample retention record verification when the early warning system outputs a comprehensive sample coal quality fluctuation warning.

[0049] The fifth approach is to verify the carbon emission data of coal-fired power plants by checking the accuracy of data statistics, whether samples are mixed up, and sending samples for retesting when the early warning system outputs an overall data loss warning.

[0050] A system for verifying the quality of carbon emission data from coal-fired power plants includes:

[0051] Data acquisition and receiving module: Receives key indicator data of thermal power units reported by thermal power enterprises, including coal consumption; calorific value of coal; carbon and hydrogen content of coal; and carbon content per unit calorific value of coal.

[0052] Quality verification index module: Construct quality verification indexes based on key indicator data, including comprehensive sample representativeness error (SRE), carbon content fluctuation per unit calorific value (CCV), carbon content rationality per unit calorific value (CCR), and fluctuation regularity between different coal standards (FRR), and calculate the above indicators;

[0053] The comprehensive analysis, evaluation, and monitoring result early warning output module: It performs individual evaluations based on the representativeness error of the comprehensive sample (SRE), the fluctuation of carbon content per unit calorific value (CCV), the rationality of carbon content per unit calorific value (CCR), and the abnormal fluctuation patterns of coal between different standards (FRR), and provides an evaluation output list. Based on the list results, it conducts comprehensive analysis and sets up a corresponding early warning system.

[0054] Data Verification Module: Based on the details of the comprehensive analysis and evaluation list and the corresponding early warning output type output by the comprehensive analysis and evaluation and supervision result early warning output module, select the appropriate supplementary supervision method to trace, verify and rectify the data, and provide suggested solutions.

[0055] The beneficial effects of this invention compared to the prior art are as follows:

[0056] 1. A method and system for verifying the quality of carbon emission data from coal-fired power plants were proposed, filling a gap in data quality supervision.

[0057] 2. A method for analyzing and evaluating the reliability of monitoring data for key indicators of coal-fired carbon emissions, such as the unit calorific value of coal, carbon content, and comprehensive sample representativeness, is provided to improve the internal data quality monitoring capabilities of enterprises.

[0058] 3. By implementing the early warning plan, the problematic links of data anomalies can be effectively traced, verified, and corrected and improved in a timely manner, reducing the adverse risks caused by data quality and non-compliance in data verification by relevant departments. Attached Figure Description

[0059] Figure 1 This is a flowchart of the method for verifying the quality of carbon emission data from coal-fired power plants according to the present invention;

[0060] Figure 2 This is a graph showing the statistical results of the SRE index in the embodiment;

[0061] Figure 3 This is a graph showing the statistical results of the CCV1 index in the embodiment;

[0062] Figure 4 This is a curve showing the statistical results of the CCV2 index in the embodiment;

[0063] Figure 5 This is a graph showing the statistical results of the CCR index in the embodiment. Detailed Implementation

[0064] The invention will now be described in detail with reference to the accompanying drawings. A method for verifying the quality of carbon emission data from coal-fired power plants is described below. Figure 1 As shown, it includes the following steps:

[0065] S1) Collect key indicator data of thermal power units, specifically receiving key indicator data of thermal power units reported by thermal power enterprises, including coal consumption; calorific value of coal; carbon and hydrogen elements in coal; and carbon content per unit calorific value of coal.

[0066] S2) Construct quality verification indicators based on key indicator data, including comprehensive sample representativeness error (SRE), carbon content fluctuation per unit calorific value (CCV), carbon content rationality per unit calorific value (CCR), and fluctuation regularity between different coal standards (FRR).

[0067] Among them, the comprehensive sample representativeness error (SRE) is a measure of the representativeness of the monthly comprehensive sample, which uses the consistency between the lower heating value of the monthly comprehensive coal sample and the monthly weighted average lower heating value of the coal fed into the furnace as a measure. The calculation method is as follows:

[0068] in: Q is the monthly weighted calorific value of the coal fed into the furnace; net，月综合 This represents the lower calorific value of the monthly composite sample.

[0069] The carbon content fluctuation (CCV) per unit calorific value is obtained from the unit calorific value carbon content statistics module for each sub-unit, but is not limited to the monthly comprehensive coal sample carbon content difference, range, and average data. Specifically, the CCV per unit calorific value carbon content fluctuation is analyzed in the following two ways.

[0070] The CCV calculated using the first method is denoted as CCV1: The calculation method for CCV1 is as follows:

[0071] Among them: CC max This represents the maximum carbon content per unit calorific value for the month within the statistical period.

[0072] CC min This represents the minimum carbon content per unit of calorific value for the month within the statistical period.

[0073] This represents the average monthly carbon content per unit of calorific value during the statistical period.

[0074] The second method of calculating CCV is denoted as CCV2: CCV2 is based on the average carbon content per unit calorific value over a period of time as the center line, with ±3S of the center line as the warning line. It statistically analyzes the distribution of carbon content per unit calorific value of monthly comprehensive coal samples, where S is the standard deviation of carbon content per unit calorific value.

[0075] The rationality of carbon content per unit calorific value (CCR) is measured by the consistency between the measured value of carbon content per unit calorific value of the comprehensive coal sample and the recommended value for the type of coal to be fed into the furnace. The measured value of carbon content per unit calorific value of the coal sample is obtained from the carbon content per unit calorific value statistical module. The statistical theoretical value of carbon content per unit calorific value of coal in the power industry is used as the upper and lower warning lines. The recommended value of different coal types is used as the reference line for statistical analysis. When the value exceeds the adjacent reference line, an early warning is issued. When the value exceeds the upper or lower warning line, an abnormality is indicated.

[0076] The regularity of fluctuations among various standards in coal is based on the correlation between two key indicators: calorific value and carbon and hydrogen content. Combining the fluctuation trends of all these key indicators, the rationality of the variation patterns among the key indicator data is analyzed through the following two methods.

[0077] (1) Pre-set the relationship model between carbon content per unit calorific value of coal and volatile matter and ash content, obtain the difference between the predicted value and the measured value of carbon content per unit calorific value of coal, and statistically analyze the standard deviation S of carbon content per unit calorific value. The average difference ±3S is used as the warning line.

[0078] (2) To analyze the consistency between the changes in calorific value of coal and the trends in carbon and hydrogen content, two early warning conditions were set: ① carbon and hydrogen decrease while calorific value increases; ② carbon and hydrogen increase while calorific value decreases. The trends of the three indicators of carbon, hydrogen, and calorific value are calculated using the following formula:

[0079] ΔC=C 本月 -C 上月 ; ΔH=H 本月 -H 上月 ; ΔQ=Q 本月 -Q 上月 ;

[0080] ΔC represents the change in carbon content, C 本月 The carbon element value for the current month, C 上月 This is the carbon element value from the previous month;

[0081] ΔH represents the change in hydrogen element content, H 本月 H represents the hydrogen element value for the current month. 上月 This is the hydrogen element value from last month;

[0082] ΔQ is the change in calorific value, Q 本月 Q represents the calorific value for the current month. 上月 This is the calorie value from the previous month.

[0083] When ΔC < 0, ΔH < 0 and ΔQ > 0, it is judged to meet condition ①;

[0084] When ΔC > 0, ΔH > 0 and ΔQ < 0, it is judged to meet condition ②.

[0085] In step S2), the control limits (i.e. warning lines) of each quality verification indicator serve as the core of data supervision and quality management. Their selection is based on long-term, extensive survey data and obtained through statistical analysis. The indicator limits for each quality verification indicator are shown in Table 1 below.

[0086] Table 1. Selection and Application Instructions for Indicator Limits

[0087]

[0088]

[0089] S3) Comprehensively analyze and evaluate various quality inspection indicators, and issue early warnings based on the comprehensive analysis and evaluation. Specifically, an early warning system is set up with three levels, represented by red, yellow, and green colors respectively according to the degree of warning (see Table 2 for level classification). At the same time, the early warning for each level is output according to the comprehensive analysis results of the quality inspection indicators, and the early warning output results are used to guide the subsequent verification of abnormal indicators.

[0090] Table 2 Explanation of Warning Level Color Indicators

[0091] Warning sign colors Number of abnormal indicators green 0 yellow [1，2] red (2，4]

[0092] In Table 2, 0 indicates no abnormal indicators; [1, 2] and (2, 4] represent the intervals of the number of abnormal indicators. The corresponding warning indicator color is output according to the interval range of the number of abnormal indicators.

[0093] The types of warnings include:

[0094] The first type: When the comprehensive sample representativeness error (SRE), carbon content fluctuation per unit calorific value (CCV), carbon content rationality per unit calorific value (CCR), and the fluctuation regularity between different coal standards (FRR) are all within the statistical period, i.e., not exceeding the warning line, the warning system's identification color is green, and the warning system's output type is no output.

[0095] The second type: When the fluctuation of carbon content per unit calorific value (CCV), the rationality of carbon content per unit calorific value (CCR), and the regularity of fluctuation between different coal standards (FRR) are all within the statistical period and do not exceed the warning line, and the comprehensive sample representative error (SRE) exceeds the warning line, the warning system will be marked in yellow and the warning system will output a comprehensive sample retention warning.

[0096] The third type: When the comprehensive sample representativeness error (SRE), the rationality of carbon content per unit calorific value (CCR), and the regularity of fluctuations between different coal standards (FRR) are all within the statistical period and do not exceed the warning line, and the fluctuation of carbon content per unit calorific value (CCV) exceeds the warning line, the warning system will be marked in yellow and the warning system will output a warning for fluctuations in the quality of coal entering the furnace.

[0097] The fourth type: When the comprehensive sample representativeness error (SRE) and the fluctuation regularity of coal between different standards (FRR) are both within the statistical period and do not exceed the warning line, and the fluctuation of carbon content per unit calorific value (CCV) and the rationality of carbon content per unit calorific value (CCR) both exceed the warning line, the warning system will be marked in yellow and the warning system output type will be a detection link warning.

[0098] Fifth type: When the comprehensive sample representativeness error (SRE) and the reasonableness of carbon content per unit calorific value (CCR) are both within the statistical period and do not exceed the warning line, and the fluctuation of carbon content per unit calorific value (CCV) and the regularity of fluctuation between different coal standards (FRR) both exceed the warning line, the warning system will be marked in yellow and the warning system output type will be a detection link warning.

[0099] The sixth type: When the carbon content per unit calorific value (CCR) and the fluctuation regularity between different coal standards (FRR) are both within the statistical period and do not exceed the warning line, and the comprehensive sample representativeness error (SRE) and the carbon content per unit calorific value (CCV) both exceed the warning line, the warning system will be marked in yellow and the warning system will output a comprehensive sample coal quality fluctuation warning.

[0100] The seventh type: When the comprehensive sample representativeness error (SRE), carbon content fluctuation per unit calorific value (CCV), and the fluctuation regularity between different coal standards (FRR) are all within the statistical period and do not exceed the warning line, and the carbon content per unit calorific value rationality (CCR) exceeds the warning line, the warning system will be marked in red and the warning system output type will be a comprehensive warning for sample retention and testing.

[0101] The eighth type: When the comprehensive sample representativeness error (SRE), carbon content fluctuation per unit calorific value (CCV), carbon content rationality per unit calorific value (CCR), and the fluctuation regularity between different coal standards (FRR) all exceed the warning line, the warning system's identifier color is red, and the warning system's output type is "overall data out of control warning".

[0102] That is, the above eight typical evaluation lists and corresponding early warning system settings are shown in Table 3.

[0103] Table 3 Typical Comprehensive Evaluation List and Corresponding Early Warning System Settings

[0104]

[0105]

[0106] Note: + indicates that the indicator exceeds the warning line, and - indicates that the indicator is normal within the statistical period.

[0107] S4) Based on the comprehensive analysis and evaluation results of the various quality verification indicators in S3) and the early warning output results, formulate a verification plan. Verify the coal-fired carbon emission data of thermal power plants according to the verification plan. The verification plan is formulated based on the corresponding early warning output results, including:

[0108] The first approach is to verify the carbon emission data of coal-fired power plants by checking the sample retention plan and records and retrieving the sample retention work monitoring when the early warning system outputs a comprehensive sample retention early warning.

[0109] The second approach is to verify the coal-fired carbon emission data of thermal power plants when the early warning system outputs a warning about fluctuations in the quality of coal entering the furnace. This involves checking the composition of coal quality and quantity for each value entering the furnace, comparing the fluctuations in the quality of coal entering the plant, and analyzing the rationality of the fluctuation differences.

[0110] The third approach is to verify the carbon emission data of coal-fired power plants by adopting methods such as retesting existing samples or commissioning third-party testing when the early warning system outputs a warning at the detection stage.

[0111] The fourth approach is to verify the coal-fired carbon emission data of thermal power plants by adopting a scheme of comprehensive sample retesting, coal quality testing in the furnace, and comprehensive sample retention record verification when the early warning system outputs a comprehensive sample coal quality fluctuation warning.

[0112] The fifth approach is to verify the carbon emission data of coal-fired power plants by checking the accuracy of data statistics, whether samples are mixed up, and sending samples for retesting when the early warning system outputs an overall data loss warning.

[0113] That is, the five typical early warning types and their corresponding verification plans are shown in Table 4:

[0114] Table 4 Typical Warning Types and Corresponding Verification Plans

[0115]

[0116]

[0117] In addition, the present invention also provides a system for verifying the quality of carbon emission data from coal-fired power plants, comprising:

[0118] Data acquisition and receiving module: Receives key indicator data of thermal power units reported by thermal power enterprises, including coal consumption; calorific value of coal; carbon and hydrogen content of coal; and carbon content per unit calorific value of coal.

[0119] Quality verification index module: Construct quality verification indexes based on key indicator data, including comprehensive sample representativeness error (SRE), carbon content fluctuation per unit calorific value (CCV), carbon content rationality per unit calorific value (CCR), and fluctuation regularity between different coal standards (FRR), and calculate the above indicators;

[0120] The comprehensive analysis, evaluation, and monitoring result early warning output module: It performs individual evaluations based on the representativeness error of the comprehensive sample (SRE), the fluctuation of carbon content per unit calorific value (CCV), the rationality of carbon content per unit calorific value (CCR), and the abnormal fluctuation patterns of coal between different standards (FRR), and provides an evaluation output list. Based on the list results, it conducts comprehensive analysis and sets up a corresponding early warning system.

[0121] Data Verification Module: Based on the details of the comprehensive analysis and evaluation list and the corresponding early warning output type output by the comprehensive analysis and evaluation and supervision result early warning output module, select the appropriate supplementary supervision method to trace, verify and rectify the data, and provide suggested solutions.

[0122] One embodiment of the present invention is as follows:

[0123] Taking a coal-fired power plant as an example, this data quality supervision system and method were applied to its internal data management of carbon accounting data. The statistical results of relevant data indicators are shown in Table 5 below:

[0124] Table 5. Partial data on carbon accounting for a coal-fired power plant.

[0125]

[0126] Based on the data in Table 5, statistics were performed on SRE, CCV1, CCV2, and CCR respectively. The statistical results are as follows: Figure 2-5 As shown; among them, the SRE index value in June was 2.03%; the CCV1 index value was 1.59%. The company uses the same stable coal source, which exceeds the warning line.

[0127] The key indicator analysis and evaluation checklist and early warning output are shown in Table 6 below:

[0128] Table 6. Comprehensive Evaluation List and Early Warning Results for Data Quality Supervision

[0129]

[0130] Based on the comprehensive evaluation checklist and early warning output results provided by the system, the recommended verification plan was implemented, and the data was traced and verified. The results are shown in Table 7 below:

[0131] (1) Comprehensive sample collection and retesting

[0132] Table 7 Comparison of Retest Results of Comprehensive Sample Retention

[0133]

[0134] The results of the comprehensive sample retention retest met the requirements, indicating that the testing process was in compliance with the requirements.

[0135] (2) Verification of comprehensive sample collection records

[0136] A review of the comprehensive sample retention records revealed that the daily sample retention amount was inconsistent with the amount of coal fed into the furnace, causing the comprehensive sample to lose its representativeness.

[0137] The implementation of this system and method revealed anomalies in the SRE and CCV1 indicators. The early warning scheme's execution results showed that the June comprehensive sample collection was not strictly performed according to the coal input ratio, resulting in insufficient representativeness of the comprehensive sample and large fluctuations in the carbon content per unit calorific value. Through the implementation of relevant improvement measures, the management of carbon accounting data quality was effectively improved.

[0138] This invention proposes a method and system for verifying the quality of coal-fired carbon emission data in thermal power plants, filling a gap in data quality supervision. It provides methods for analyzing and evaluating the reliability of monitoring data for key indicators of coal-fired carbon emissions, such as the unit calorific value of coal, carbon content, and comprehensive sample representativeness, thereby improving the enterprise's internal data quality monitoring capabilities. Through the implementation of early warning schemes, it can effectively trace and verify problematic data points and promptly correct and improve them, reducing the adverse risks caused by discrepancies in data quality and data verification by relevant departments.

[0139] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for verifying the quality of carbon emission data from coal-fired power plants, characterized in that, include: S1) Collect key indicator data of thermal power units; S2) Construct quality verification indicators based on key indicator data: Quality verification indicators include comprehensive sample representativeness error (SRE), carbon content fluctuation per unit calorific value (CCV), carbon content rationality per unit calorific value (CCR), and fluctuation regularity among various indicators of coal (FRR). The Comprehensive Sample Representativeness Error (SRE) is a measure of the representativeness of the monthly comprehensive coal sample, using the consistency between the lower heating value of the monthly comprehensive coal sample and the monthly weighted average lower heating value of the coal fed into the furnace as the standard. The calculation method is as follows: , in: The weighted average lower calorific value of the coal fed into the furnace; The lower calorific value of the monthly composite sample; The carbon content fluctuation (CCV) per unit calorific value is obtained from the unit unit in the carbon content per unit calorific value statistics module. Specifically, the CCV per unit calorific value is analyzed in the following two ways. The first method: Analyze the fluctuation of carbon content per unit calorific value (CCV) using calculations. The calculation method is as follows: , That: This represents the maximum carbon content per unit calorific value for the month within the statistical period. This represents the minimum carbon content per unit of calorific value for the month within the statistical period. This represents the average monthly carbon content per unit of calorific value during the statistical period. The second method: take the average carbon content per unit calorific value over a period of time as the center line, and use ±3S of the center line as the warning line to statistically analyze the distribution of carbon content per unit calorific value of monthly comprehensive coal samples, where S is the standard deviation of carbon content per unit calorific value. The rationality of carbon content per unit calorific value (CCR) is measured by the consistency between the measured value of carbon content per unit calorific value of the comprehensive coal sample and the recommended value of the coal type to be fed into the furnace. The measured value of carbon content per unit calorific value of the coal sample is obtained from the carbon content per unit calorific value statistical module. The statistical theoretical value of carbon content per unit calorific value of coal in the power industry is used as the upper and lower warning lines. The recommended value of different coal types is used as the reference line for statistical analysis. When the value exceeds the adjacent reference line, an early warning is issued. When the value exceeds the upper and lower warning lines, an abnormality is indicated. The fluctuation regularity among various indicators of coal is based on the correlation between the two key indicators of coal calorific value and carbon and hydrogen elements. Combining the fluctuation trends of all these key indicators, the rationality of the change regularity among the key indicator data is analyzed through the following two methods. (1) Pre-set the relationship model between carbon content per unit calorific value of coal and volatile matter and ash content, obtain the difference between the predicted value and the measured value of carbon content per unit calorific value of coal, and statistically analyze the standard deviation S of carbon content per unit calorific value. The average difference ±3S is used as the warning line. (2) Analyze the consistency between the changes in calorific value of coal and the trends in carbon and hydrogen, and set two early warning conditions: ① carbon and hydrogen decrease while calorific value increases; ② carbon and hydrogen increase while calorific value decreases. The trends of increase or decrease in the three indicators of carbon, hydrogen, and calorific value are calculated using the following formula: ； ； ； △C represents the change in carbon content, C 本月 The carbon element value for the current month, C 上月 This is the carbon element value from the previous month; △H represents the change in hydrogen element content, H 本月 H represents the hydrogen element value for the current month. 上月 This refers to the hydrogen element value from the previous month. △Q represents the change in heat output, Q 本月 Q represents the calorific value for the current month. 上月 This is the calorie value from the previous month; When △C<0, △H<0 and △Q>0, it is judged to meet the warning condition ①; When △C>0, △H>0 and △Q<0, it is judged to meet the warning condition ②; S3) Conduct a comprehensive analysis and evaluation of all quality inspection indicators, and issue early warnings based on the comprehensive analysis and evaluation. S4) Based on the comprehensive analysis and evaluation results of the various quality verification indicators in S3) and the early warning output results, formulate a verification plan and verify the coal-fired carbon emission data of thermal power enterprises according to the verification plan.

2. The method for verifying the quality of coal-fired carbon emission data of thermal power plants according to claim 1, characterized in that, S1) is to receive key indicator data of thermal power units reported by thermal power enterprises, including coal consumption; calorific value of coal; carbon and hydrogen content of coal; and carbon content per unit calorific value of coal.

3. The method for verifying the quality of coal-fired carbon emission data of thermal power plants according to claim 2, characterized in that, Step S3) Specifically, it involves setting up an early warning system with three levels, represented by red, yellow, and green colors according to the severity of the warning. At the same time, the warning for each level is output based on the comprehensive analysis results of carbon emission data quality verification indicators. The warning output results are used to guide the subsequent verification of abnormal indicators. The types of warnings include: The first type: When the comprehensive sample representativeness error (SRE), carbon content fluctuation per unit calorific value (CCV), carbon content rationality per unit calorific value (CCR), and the fluctuation regularity among various indicators of coal are all within the statistical period, i.e., not exceeding the warning line, the warning system's identification color is green, and the warning system's output type is no output. The second type: When the fluctuation of carbon content per unit calorific value (CCV), the rationality of carbon content per unit calorific value (CCR), and the regularity of fluctuation among various indicators of coal (FRR) are all within the statistical period and do not exceed the warning line, and the comprehensive sample representative error (SRE) exceeds the warning line, the warning system is marked in yellow and the warning system output type is comprehensive sample retention warning. The third type: When the comprehensive sample representativeness error (SRE), the rationality of carbon content per unit calorific value (CCR), and the regularity of fluctuations among various indicators of coal are all within the statistical period, i.e., they have not exceeded the warning line, and the fluctuation of carbon content per unit calorific value (CCV) exceeds the warning line, the warning system will be marked in yellow, and the warning system will output a warning for fluctuations in the quality of coal entering the furnace. The fourth type: When the comprehensive sample representativeness error (SRE) and the fluctuation regularity among various indicators of coal are both within the statistical period and do not exceed the warning line, and the fluctuation of carbon content per unit calorific value (CCV) and the rationality of carbon content per unit calorific value (CCR) both exceed the warning line, the warning system will be marked in yellow and the warning system output type will be a detection link warning. Fifth type: When the comprehensive sample representativeness error (SRE) and the reasonableness of carbon content per unit calorific value (CCR) are both within the statistical period and do not exceed the warning line, and the fluctuation of carbon content per unit calorific value (CCV) and the regularity of fluctuation among various indicators of coal exceed the warning line, the warning system will be marked in yellow and the warning system output type will be a detection link warning. The sixth type: When the carbon content per unit calorific value (CCR) and the fluctuation regularity among various indicators of coal are both within the statistical period and do not exceed the warning line, and the comprehensive sample representative error (SRE) and the carbon content per unit calorific value (CCV) both exceed the warning line, the warning system will be marked in yellow and the warning system output type will be comprehensive sample coal quality fluctuation warning. The seventh type: When the comprehensive sample representativeness error (SRE), the fluctuation of carbon content per unit calorific value (CCV), and the fluctuation regularity among various indicators of coal (FRR) are all within the statistical period and do not exceed the warning line, and the reasonableness of carbon content per unit calorific value (CCR) exceeds the warning line, the warning system will be marked in red and the warning system output type will be a comprehensive sample retention and testing warning. The eighth type: When the comprehensive sample representativeness error (SRE), carbon content fluctuation per unit calorific value (CCV), carbon content rationality per unit calorific value (CCR), and fluctuation regularity among various indicators of coal all exceed the warning line, the warning system's identifier color is red, and the warning system's output type is "overall data out of control warning".

4. The method for verifying the quality of coal-fired carbon emission data of thermal power plants according to claim 3, characterized in that, Step S4) Develop a verification plan based on the corresponding early warning output results, including: The first approach is to verify the carbon emission data of coal-fired power plants by checking the sample retention plan and records and retrieving the sample retention work monitoring when the early warning system outputs a comprehensive sample retention early warning. The second approach is to verify the coal-fired carbon emission data of thermal power plants when the early warning system outputs a warning about fluctuations in the quality of coal entering the furnace. This involves checking the composition of coal quality and quantity for each value entering the furnace, comparing the fluctuations in the quality of coal entering the plant, and analyzing the rationality of the fluctuation differences. The third approach is to verify the carbon emission data of coal-fired power plants by adopting a solution of retesting existing samples, commissioning third-party testing, and comparing the results when the early warning system outputs a warning at the detection stage. The fourth approach is to verify the coal-fired carbon emission data of thermal power plants by adopting a scheme of comprehensive sample retesting, coal quality testing in the furnace, and comprehensive sample retention record verification when the early warning system outputs a comprehensive sample coal quality fluctuation warning. The fifth approach is to verify the carbon emission data of coal-fired power plants by checking the accuracy of data statistics, whether samples are mixed up, and sending samples for retesting when the early warning system outputs an overall data loss warning.

5. A system for verifying the quality of coal-fired carbon emission data in thermal power plants according to any one of claims 1 to 4, characterized in that, include: Data acquisition and receiving module: Receives key indicator data of thermal power units reported by thermal power enterprises, including coal consumption; calorific value of coal; and carbon and hydrogen content of coal. Carbon content per unit calorific value of coal; Quality verification index module: Construct quality verification indicators based on key indicator data, including comprehensive sample representativeness error (SRE), carbon content fluctuation per unit calorific value (CCV), carbon content rationality per unit calorific value (CCR), and fluctuation regularity among various indicators of coal (FRR), and calculate the above indicators; The comprehensive analysis, evaluation, and monitoring result early warning output module: It performs individual evaluations based on the representativeness error of the comprehensive sample (SRE), the fluctuation of carbon content per unit calorific value (CCV), the rationality of carbon content per unit calorific value (CCR), and the abnormal fluctuation patterns of various indicators of coal (FRR), and provides an evaluation output list. Based on the list results, it conducts comprehensive analysis and sets up a corresponding early warning system. Data Verification Module: Based on the details of the comprehensive analysis and evaluation list and the corresponding early warning output type output by the comprehensive analysis and evaluation and supervision result early warning output module, select the appropriate supplementary supervision method to trace, verify and rectify the data, and provide suggested solutions.

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