Dynamic metering method for carbon emission of electric power system

Abstract

The invention discloses a dynamic metering method for carbon emission of an electric power system, relates to the technical field of electric power systems, solves the problem that dynamic data cannot be effectively processed and data features cannot be accurately identified, and locks an optimal determined section as an energy consumption feature through variance screening and clustering analysis of the energy consumption feature. Abnormal data interference is effectively eliminated; meanwhile, standard verification intervals are divided based on variance processing of the carbon emission mean value, so that data in the same kind of value segments are more homogeneous, it is ensured that the carbon emission metering result of each interval is accurate and reliable, the error rate is reduced, a solid data basis is provided for follow-up carbon emission monitoring, the carbon emission mean value is quantized to the interval of [0, 1], and the accuracy of carbon emission monitoring is improved. According to the method, the characteristic segments are selected according to the preset quantization range, the interval characteristics are finally determined, the data dimension is unified through the processing mode, comparison and analysis of different data are facilitated, and the core characteristics of all the standard verification intervals can be effectively extracted.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for dynamically measuring carbon emissions from power systems. Background Art

[0002] A unified whole consisting of power generation, power supply (transmission, transformation, distribution), power consumption facilities, and secondary facilities such as regulation and control, relay protection and safety automatic devices, metering devices, dispatching automation, power communications, etc. required to ensure their normal operation; The power system is an electric energy production and consumption system consisting of power plants, transmission and transformation lines, power distribution stations and power users. Its function is to convert primary energy from nature into electric energy through power generation devices, and then supply the electric energy to various users through transmission, transformation and distribution. To achieve this function, the power system also has corresponding information and control systems at various links and levels to measure, regulate, control, protect, communicate and dispatch the electric energy production process to ensure that users obtain safe and high-quality electricity.

[0003] With the advancement of the global "dual carbon" goals, the power system, as the main area of ​​carbon emissions, has become the key to energy conservation and emission reduction by accurately measuring its carbon emissions. Existing power system carbon emissions measurement methods mostly use static emission factors, which are difficult to dynamically reflect the impact of unit load fluctuations, energy structure adjustments and changes in grid operating status on carbon emissions. Some methods based on real-time monitoring have problems such as rough data processing and large interference from outliers, resulting in a high error rate in measurement results.

[0004] In addition, traditional metering methods lack in-depth data mining and feature extraction, and cannot meet the high-precision metering requirements in scenarios such as real-time optimization and scheduling of power systems and carbon trading compliance.

[0005] Therefore, there is an urgent need for a dynamic measurement method for carbon emissions in power systems that can effectively process dynamic data and accurately identify data characteristics, so as to achieve real-time, accurate monitoring and scientific management of carbon emissions. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a dynamic measurement method for carbon emissions in an electric power system, which solves the problem of failing to effectively process dynamic data and accurately identify data features.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for dynamically measuring carbon emissions in a power system, comprising the following steps: From historical cloud data, identify the energy consumption characteristics associated with power generation at different time units, and determine the standard calibration interval based on the different carbon emissions associated with different energy consumption within these energy consumption characteristics. The specific methods for confirming energy consumption characteristics are as follows: The power generation per unit time is calibrated as F i , where i represents different values, and the single power generation F i The associated multiple groups of different energy consumption are confirmed and calibrated as feature sets, and the energy consumption associated in the feature sets are reordered from small to large values. The variance of the reordered feature sets is confirmed: a value segment is randomly selected from the feature set, and several groups of values ​​associated with the selected value segment are processed for variance to confirm the standard deviation. If the standard deviation is ≤ Y1, the selected value segment is recorded as the standard value segment. If the standard deviation is greater than Y1, no calibration is performed. From the determined groups of standard value segments, the standard value segment with the largest number of values ​​is selected as the determined segment. If there is only one group of determined segments, this group of determined segments is recorded as the energy consumption characteristic of the power generation per unit time. If there are multiple groups of determined segments, the mean values ​​associated with the corresponding determined segments are locked. From the different confirmed mean values, the determined segment associated with the maximum value is selected, and this group of determined segments is recorded as the energy consumption characteristic of the power generation per unit time. The specific method is: Based on the energy consumption characteristics associated with the corresponding power generation per unit time, the different carbon emissions associated with different energy consumptions are identified, and multiple groups of carbon emissions associated with a single energy consumption are averaged to determine the average carbon emissions associated with the corresponding energy consumption; According to the internal sorting method of the energy consumption characteristics, the carbon emission means associated with each energy consumption are sorted, and the carbon emission mean sequence is confirmed. Starting from the minimum value in the carbon emission mean sequence, the relevant carbon emission means are gradually selected, and the multiple selected carbon emission means are subjected to variance processing to confirm the check value. If the check value is less than Y2, the carbon emission means are continuously selected until the check value is greater than or equal to Y2. The currently selected carbon emission mean is eliminated, and the multiple groups of carbon emission means selected in this stage are regarded as a similar value segment, and the eliminated carbon emission mean is used as the initial value of the next value segment, and the specific confirmation of the subsequent similar value segments is carried out; According to the energy consumption associated with the corresponding similar value segment, the standard calibration interval associated with the corresponding energy consumption is determined, wherein the minimum value and the maximum value of the standard calibration interval are the minimum value and the maximum value of the energy consumption associated with the corresponding similar value segment, and each standard calibration interval is associated with a different similar value segment; Based on the confirmed standard calibration interval, quantify the similar value segments associated with each interval. From the quantification process, confirm and record the interval characteristics associated with each interval. The specific method is as follows: Based on the similar value segments associated with the standard calibration interval, the carbon emission mean values ​​associated with the similar value segments are numerically quantified, and the corresponding numerical values ​​are quantified to [0, 1]. The minimum value of the carbon emission mean values ​​associated with the similar value segments is quantified to 0, and the maximum value of the carbon emission mean values ​​is quantified to 1. According to the specific quantification process, the quantitative values ​​associated with each carbon emission mean value are confirmed; Based on the different quantitative values ​​associated with each similar value segment, a numerical segment is selected from different quantitative values ​​through a preset quantitative range of 0.6. The difference between the minimum and maximum values ​​of the selected numerical segment shall not exceed 0.6. Based on several groups of numerical segments confirmed within the similar value segment, a group of numerical segments with the largest total quantitative value is locked, and the locked numerical segment is recorded as the characteristic segment of the corresponding standard verification interval. The average values ​​of several groups of carbon emissions associated with the characteristic segment are again averaged, and the average value obtained by the processing is recorded as the interval characteristic of the corresponding standard verification interval. If there are multiple groups of numerical segments with the largest total quantitative value, the average values ​​of the multiple groups of numerical segments are confirmed, and the numerical segment with the mean value at the middle position is selected as the numerical segment, and the specific confirmation of the interval characteristic is performed; The power generation associated with the power system per unit time is monitored, and based on the monitoring process, the energy consumption per unit time is confirmed. Then, through the numerical comparison process, the carbon emissions generated per unit time are locked. The specific method is as follows: The power generation per unit time in the power system is recorded as FD k , where k represents different unit time, based on the confirmed FD k , confirm the recorded energy consumption characteristics; Then the energy consumption associated with the unit time k is confirmed and calibrated as BH k , confirm the associated standard calibration interval from the energy consumption characteristics, and identify BH k The standard calibration interval to which it belongs, and the interval characteristics associated with the standard calibration interval are determined, and the corresponding interval characteristics are recorded as the carbon emissions associated with the current unit time; Based on the actual numerical monitoring process, the carbon emissions associated with each subsequent unit time are recorded, and a carbon emissions change curve associated with the corresponding monitoring process is generated.

[0008] The present invention provides a method for dynamically measuring carbon emissions from power systems. Compared with existing technologies, it has the following advantages: The present invention uses variance screening and cluster analysis of energy consumption characteristics to lock in the optimal segment as the energy consumption feature, effectively eliminating abnormal data interference. At the same time, the standard calibration interval is divided based on the variance processing of the carbon emission mean, making the data within the same value segment more homogeneous, ensuring the accuracy and reliability of the carbon emission measurement results in each interval, reducing the error rate, and providing a solid data foundation for subsequent carbon emission monitoring. The mean carbon emissions are quantized to the interval [0,1], and characteristic segments are selected using a preset quantization range to ultimately determine interval characteristics. This processing method not only unifies the data dimensions, facilitating comparison and analysis between different data, but also effectively extracts the core characteristics of each standard calibration interval, making complex carbon emissions data more concise and intuitive, providing strong support for dynamic analysis and decision-making of carbon emissions in power systems. By real-time monitoring of power generation and energy consumption per unit time, quickly locking in the corresponding carbon emissions and generating a change curve, the dynamic trend of carbon emissions in the power system can be intuitively presented; this real-time, dynamic metering mode helps power companies and regulatory authorities to promptly detect abnormal carbon emissions, accurately trace the source of carbon emissions, and provide a scientific basis for optimizing power production scheduling and formulating emission reduction strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 Schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0010] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0011] First embodiment See also Figure 1 , this application provides a method for dynamic measurement of carbon emissions in a power system, comprising the following steps: Step 1: From the cloud historical data, identify the energy consumption characteristics associated with power generation per unit time. Based on the different carbon emissions associated with different energy consumption characteristics, determine the standard calibration interval for value selection in subsequent related steps. The calibration interval is the numerical interval associated with the corresponding carbon emissions, and the corresponding numerical interval belongs to a relatively clustered interval. The specific methods for confirming energy consumption characteristics are as follows: Different unit time power generation (that is, different unit values, the unit time is generally 1 minute, and there may be different power generation values ​​within the corresponding 1 minute) are calibrated as F i , where i represents different values, and the single power generation F i The associated multiple groups of different energy consumption are confirmed and calibrated as a feature set, and the energy consumption associated in the feature set is re-sorted in ascending order of value. The variance of the re-sorted feature set is confirmed: a value segment is randomly selected from the feature set, and several groups of values ​​associated with the selected value segment are processed for variance to confirm the standard deviation. If the standard deviation is ≤ Y1, the selected value segment is recorded as the standard value segment. If the standard deviation is > Y1, no calibration is performed. Y1 is a preset value, and its specific value is determined by the operator based on experience. From the determined groups of standard value segments, the standard value segment with the largest number of values ​​is selected as the determined segment. If there is only one group of determined segments, this group of determined segments is recorded as the energy consumption characteristic of the power generation per unit time. If there are multiple groups of determined segments, the mean values ​​associated with the corresponding determined segments are locked. From the different confirmed mean values, the determined segment associated with the maximum value is selected, and this group of determined segments is recorded as the energy consumption characteristic of the power generation per unit time. Specifically, a single unit of time power generation has several different energy consumption values. Based on the total number of specific values ​​of the corresponding energy consumption values, several groups of values ​​are clustered and analyzed to confirm the corresponding cluster characteristics, thereby locking the optimal determination segment associated with the corresponding power generation. From the locked optimal determination segment, the specific power consumption characteristics are confirmed to facilitate subsequent analysis and confirmation. Among them, the specific method for confirming the standard calibration interval is: Based on the energy consumption characteristics associated with the corresponding power generation per unit time, the different carbon emissions associated with different energy consumptions are identified, and multiple groups of carbon emissions associated with a single energy consumption are averaged to determine the average carbon emissions associated with the corresponding energy consumption; According to the internal sorting method of the energy consumption characteristics (that is, the original sorting method from small to large), the carbon emission means associated with each energy consumption are sorted, and the carbon emission mean sequence is confirmed. Starting from the minimum value in the carbon emission mean sequence, the relevant carbon emission means are gradually selected (that is, gradually selected from small to large). The multiple selected carbon emission means are subjected to variance processing to confirm the check value. If the check value is less than Y2, the carbon emission means are continuously selected until the check value is greater than or equal to Y2. The currently selected carbon emission mean is eliminated, and the multiple groups of carbon emission means selected in this stage are regarded as a similar value segment, and the eliminated carbon emission mean is used as the initial value of the next value segment, and the specific confirmation of the subsequent similar value segments is carried out. Y2 is a preset value, and its specific value is determined by the operator based on experience; According to the energy consumption associated with the corresponding similar value segment, the standard calibration interval associated with the corresponding energy consumption is confirmed. The minimum and maximum values ​​of the standard calibration interval are the minimum and maximum values ​​of the energy consumption associated with the corresponding similar value segment. Each standard calibration interval is associated with a different similar value segment. Specifically, different parameters (power generation per unit time) have an energy consumption feature. This feature is a data column, which includes multiple different energy consumptions. The energy consumption has different carbon emission means. According to the specific processing process, the energy consumption is sorted in the process, and the corresponding associated carbon emission means are also sorted synchronously. Therefore, according to the value segment confirmation process of the corresponding carbon emission mean, the specific confirmation of the calibration value can be carried out, and the specific confirmation of the similar value segment can be carried out, which is convenient for feature confirmation, so as to ensure that the specific emissions corresponding to the subsequent carbon emission process are sufficiently accurate. Step 2: Based on the confirmed standard calibration interval, quantify the similar value segments associated with each interval. From the quantization process, confirm and record the interval characteristics associated with each interval. The specific method of quantization is as follows: Based on the similar value segments associated with the standard calibration interval, the carbon emission mean values ​​associated with the similar value segments are numerically quantified, and the corresponding numerical values ​​are quantified to [0, 1]. The minimum value of the carbon emission mean values ​​associated with the similar value segments is quantified to 0, and the maximum value of the carbon emission mean values ​​is quantified to 1. According to the specific quantification process, the quantitative values ​​associated with each carbon emission mean value are confirmed; Based on the different quantitative values ​​associated with each similar value segment, a numerical segment is selected from different quantitative values ​​using a preset quantitative range of 0.6. The difference between the minimum and maximum values ​​of the selected numerical segment shall not exceed 0.6. Based on the multiple groups of numerical segments confirmed within the similar value segment, the group of numerical segments with the largest total quantitative value is locked, and the locked numerical segment is recorded as the characteristic segment of the corresponding standard calibration interval. The average values ​​of the multiple groups of carbon emissions associated with the characteristic segment are again averaged, and the average value obtained is recorded as the interval characteristic of the corresponding standard calibration interval. If there are multiple groups of numerical segments with the largest total quantitative value (that is, the total number is the same), the average values ​​of the multiple groups of numerical segments are confirmed, and the numerical segment with the average value at the most middle position is selected as the numerical segment, and the specific interval characteristic is confirmed. The so-called numerical segment with the average value at the most middle position means that the corresponding mean is in the middle position. From the corresponding average value, the middle value can be confirmed. Then, from the multiple average values, the mean closest to the middle value is confirmed, so that the specific interval characteristic can be confirmed. Specifically, its quantization interval is [0, 1], and the proposed similar value segment is calibrated as {20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70}, then 20 is quantized to 0, 70 is quantized to 1, then 25 is quantized to 0.1, 30 is quantized to 0.2, and 65 is quantized to 0.9, so the quantization range is 0.6, and several groups of value segments can be confirmed within the similar value segment, and the quantization range of each value segment does not exceed the corresponding 0.6, so the value segment with the mean at the middle position can be confirmed, that is, {30, 35, 40, 45, 50, 55, 60}, and its mean is 45; Step 3: Monitor the power generation associated with the power system per unit time, and based on the monitoring process, confirm the energy consumption per unit time. Then, through the numerical comparison process, lock the carbon emissions generated per unit time. The specific method for locking is: The power generation per unit time in the power system is recorded as FD k , where k represents different unit time, based on the confirmed FD k , confirm the recorded energy consumption characteristics; Then the energy consumption associated with the unit time k is confirmed and calibrated as BH k , confirm the associated standard calibration interval from the energy consumption characteristics, and identify BH k The standard calibration interval to which it belongs, and the interval characteristics associated with the standard calibration interval are determined, and the corresponding interval characteristics are recorded as the carbon emissions associated with the current unit time; Based on the actual numerical monitoring process, the carbon emissions associated with each subsequent unit time are recorded, and a carbon emissions change curve associated with the corresponding monitoring process is generated.

[0012] Specifically, based on several sets of recorded numerical features, carbon emissions can be monitored in real time, and according to the monitored carbon emissions, a dynamic curve can be constructed to facilitate the corresponding dynamic metering process of the corresponding power system.

[0013] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0014] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for dynamic measurement of carbon emissions in a power system, characterized in that: The following steps are involved: From historical cloud data, identify the energy consumption characteristics associated with power generation at different time units, and determine the standard calibration interval based on the different carbon emissions associated with different energy consumption within these energy consumption characteristics. Based on the confirmed standard calibration interval, quantify the similar value segments associated with each interval. From the quantification process, confirm and record the interval characteristics associated with each interval. The power generation associated with the power system per unit time is monitored, and based on the monitoring process, the energy consumption per unit time is confirmed. Then, through the numerical comparison process, the carbon emissions generated per unit time are locked.

2. A method for dynamic measurement of carbon emissions from a power system according to claim 1, characterized in that: The specific method for confirming the energy consumption characteristics is: The power generation per unit time is calibrated as F i , where i represents different values, and the single power generation F i The associated multiple groups of different energy consumption are confirmed and calibrated as feature sets. The energy consumption associated with the feature set is reordered from small to large values. The variance of the reordered feature set is confirmed: a value segment is randomly selected from the feature set, and several groups of values ​​associated with the selected value segment are processed for variance to confirm the standard deviation. If the standard deviation is ≤ Y1, the selected value segment is recorded as the standard value segment. From the determined groups of standard value segments, a standard value segment with the largest number of values ​​is selected as the determined segment. If there is only one group of determined segments, this group of determined segments is recorded as the energy consumption characteristics of the corresponding power generation per unit time. If there are multiple groups of determined segments selected, the mean associated with the corresponding determined segment is locked, and from the different confirmed mean values, the determined segment associated with the maximum value is selected, and this group of determined segments is recorded as the energy consumption characteristics of the corresponding power generation per unit time.

3. A method for dynamic measurement of carbon emissions from a power system according to claim 2, characterized in that: If the standard deviation is greater than Y1, no calibration is performed.

4. A method for dynamic measurement of carbon emissions from a power system according to claim 1, characterized in that: The specific method for confirming the standard calibration interval is: Based on the energy consumption characteristics associated with the corresponding power generation per unit time, the different carbon emissions associated with different energy consumptions are identified, and multiple groups of carbon emissions associated with a single energy consumption are averaged to determine the average carbon emissions associated with the corresponding energy consumption; According to the internal sorting method of the energy consumption characteristics, the carbon emission means associated with each energy consumption are sorted, and the carbon emission mean sequence is confirmed. Starting from the minimum value in the carbon emission mean sequence, the relevant carbon emission means are gradually selected, and the multiple selected carbon emission means are subjected to variance processing to confirm the check value. If the check value is less than Y2, the carbon emission means are continuously selected until the check value is greater than or equal to Y2. The currently selected carbon emission mean is eliminated, and the multiple groups of carbon emission means selected in this stage are regarded as a similar value segment, and the eliminated carbon emission mean is used as the initial value of the next value segment, and the specific confirmation of the subsequent similar value segments is carried out; According to the energy consumption associated with the corresponding similar value segment, the standard calibration interval associated with the corresponding energy consumption is confirmed.

5. A method for dynamic measurement of carbon emissions from a power system according to claim 4, characterized in that: The minimum value and the maximum value of the standard verification interval are the minimum value and the maximum value of the energy consumption associated with the corresponding similar value segment, and each standard verification interval is associated with a different similar value segment.

6. A method for dynamic measurement of carbon emissions from a power system according to claim 1, characterized in that: The specific method of quantizing the same type of value segments is as follows: Based on the similar value segments associated with the standard calibration interval, the carbon emission mean values ​​associated with the similar value segments are numerically quantified, and the corresponding numerical values ​​are quantified to [0, 1]. The minimum value of the carbon emission mean values ​​associated with the similar value segments is quantified to 0, and the maximum value of the carbon emission mean values ​​is quantified to 1. According to the specific quantification process, the quantitative values ​​associated with each carbon emission mean value are confirmed; Based on the different quantitative values ​​associated with each similar value segment, numerical segments are selected from different quantitative values ​​through the preset quantitative range of 0.

6. The difference between the minimum and maximum values ​​of the selected numerical segments shall not exceed 0.

6. Based on several groups of numerical segments confirmed within the similar value segment, a group of numerical segments with the largest total number of quantitative values ​​is locked, and the locked numerical segments are recorded as the characteristic segments of the corresponding standard verification interval. The average values ​​of several groups of carbon emissions associated with the characteristic segments are again processed, and the processed average values ​​are recorded as the interval characteristics of the corresponding standard verification interval.

7. A method for dynamic measurement of carbon emissions from a power system according to claim 6, characterized in that: If there are multiple groups of numerical segments with the largest total number of quantized values, the mean values ​​of the multiple groups of numerical segments are confirmed, and the numerical segment with the mean value at the most middle position is selected as the numerical segment, and the specific confirmation of the interval characteristics is performed.

8. The method for dynamic measurement of carbon emissions from a power system according to claim 1, characterized in that: The specific method of locking the carbon emissions is as follows: The power generation per unit time in the power system is recorded as FD k , where k represents different unit time, based on the confirmed FD k , confirm the recorded energy consumption characteristics; Then the energy consumption associated with the unit time k is confirmed and calibrated as BH k , confirm the associated standard calibration interval from the energy consumption characteristics, and identify BH k The standard calibration interval to which it belongs, and the interval characteristics associated with the standard calibration interval are determined, and the corresponding interval characteristics are recorded as the carbon emissions associated with the current unit time; Based on the actual numerical monitoring process, the carbon emissions associated with each subsequent unit time are recorded, and a carbon emissions change curve associated with the corresponding monitoring process is generated.

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