Accounting evaluation method and system for carbon verification and power data fusion

Abstract

The application relates to the technical field of carbon emission, and particularly discloses a carbon verification and power data fusion accounting evaluation method and system. Carbon verification data and power operation data of a target accounting unit are acquired; direct carbon emission parameters and indirect carbon emission parameters are acquired to obtain an initial accounting evaluation value; power efficiency parameters and power quality parameters are acquired; a plurality of reactive powers and a plurality of active powers in a preset accounting period are acquired; and an efficiency influence factor is acquired; the initial accounting evaluation value, the efficiency influence factor and the power quality influence factor are fused to obtain a comprehensive accounting evaluation value; and the comprehensive accounting evaluation value is taken as an accounting evaluation result of carbon verification and power data fusion. By introducing power system dynamic operation characteristics, the accuracy and comprehensiveness of carbon emission accounting are improved.

Description

Technical Field

[0001] This invention relates to the field of carbon emission technology, and in particular to a method and system for calculating and evaluating carbon verification and electricity data integration. Background Technology

[0002] As global climate change becomes increasingly severe, carbon emission reduction has become a crucial issue of common concern to the international community. Accurately calculating carbon emissions is fundamental to implementing effective carbon reduction measures. The core of carbon emission accounting is accurately quantifying the greenhouse gas emission levels of a target unit. As a key link between energy consumption and carbon emissions, the energy efficiency and power quality of the power system directly affect carbon emission intensity and control effectiveness. In various accounting scenarios, such as industrial production and public buildings, the carbon emissions of a target unit include both direct emissions from its own stationary combustion equipment and indirect emissions from purchased electricity. Furthermore, the power consumption characteristics and power quality stability of the power system during operation indirectly alter carbon emission levels by affecting energy utilization efficiency. However, existing carbon accounting methods are usually based on the analysis of collected fixed data, which fails to fully integrate the dynamic operating characteristics of the power system. There is a separation between carbon verification data and power operation data, which leads to the accounting results not fully reflecting the actual carbon emission situation, thus affecting the accuracy of carbon emission accounting. Therefore, a new accounting and assessment method that integrates carbon verification and power data is needed to solve the above problems. Summary of the Invention

[0003] The purpose of this invention is to comprehensively reflect the actual carbon emission situation and provide a calculation and assessment method that integrates carbon verification and electricity data, including: S1. Obtain carbon verification data and power operation data of the target accounting unit; S2. Obtain direct carbon emission parameters and indirect carbon emission parameters based on the carbon verification data, and obtain an initial accounting assessment value based on the direct carbon emission parameters and the indirect carbon emission parameters; S3. Obtain power efficiency parameters and power quality parameters based on the power operation data, and obtain multiple reactive power and multiple active power within a preset calculation period based on the power efficiency parameters, and obtain energy efficiency impact factors based on reactive power and active power. S4. Obtain the power quality impact factor based on the power quality parameters and the energy efficiency impact factor; S5. The initial accounting assessment value, the energy efficiency impact factor, and the power quality impact factor are integrated to obtain a comprehensive accounting assessment value, which is then used as the accounting assessment result of the carbon verification and power data fusion.

[0004] Optionally, step S2 includes: S21. Obtain the types and net consumption of fossil fuels consumed by stationary combustion equipment based on the direct carbon emission parameters. S22. Obtain the fuel calorific value, carbon content per unit calorific value, and default value of carbon oxidation rate for each of the aforementioned fossil fuels, and obtain the direct carbon emission intensity based on the fuel calorific value, the carbon content per unit calorific value, the default value of carbon oxidation rate, and the net consumption. S23. Obtain the total consumption of purchased electricity and the dynamic marginal emission factor for a preset time period based on the indirect carbon emission parameters, and obtain the indirect carbon emission intensity based on the total consumption and the dynamic marginal emission factor. S24. Obtain carbon emission accounting correction factors based on the direct carbon emission intensity and indirect carbon emission intensity; S25. Obtain the corresponding historical carbon emission baseline value based on the target accounting unit, and obtain the initial accounting assessment value based on the historical carbon emission baseline value and the carbon emission accounting correction factor.

[0005] Optionally, step S3 includes: S31. Obtain the active power time series and reactive power time series within the target calculation period based on the power efficiency parameters, and obtain multiple reactive powers based on the reactive power time series, and obtain the average reactive power based on the reactive power. S32. Obtain multiple active powers based on the active power time series, obtain the average active power based on the active power, and obtain the average power factor based on the average active power and the average reactive power. S33. Obtain the standard deviation of active power based on the active power time series, and obtain the load fluctuation coefficient based on the ratio of the standard deviation of active power to the average active power. S34. Obtain the rated operating parameters of the transformer and the rated operating parameters of the motor of the target accounting unit, and obtain the comprehensive average load rate based on the active power time series, the rated operating parameters of the transformer and the rated operating parameters of the motor. S35. Obtain the energy efficiency state sequence within the target calculation period based on the reactive power time series, wherein the energy efficiency state sequence includes a high-efficiency state sequence, a normal state sequence, and an inefficient state sequence. S36. Obtain the energy efficiency state transition probability based on the high-efficiency state sequence, the normal state sequence, and the low-efficiency state sequence, and obtain the energy efficiency stability index by weighting the energy efficiency state transition probability, the load fluctuation coefficient, and the comprehensive average load rate. S37. Based on the preset average power factor weight value, the average power factor and the energy efficiency stability index are weighted and summed to obtain the energy efficiency impact factor.

[0006] Optionally, the average reactive power is obtained based on the historical reactive power time series, and the relationship between the reactive power and the average reactive power is determined: when the reactive power is lower than 70% of the average reactive power, it is determined to be in an efficient state; when the reactive power is in the range of 70% to 130% of the average reactive power, it is determined to be in a normal state; when the reactive power is higher than 130% of the average reactive power, it is determined to be inefficient.

[0007] Optionally, step S4 includes: S41. Obtain the voltage deviation rate, frequency deviation rate, and total harmonic distortion rate based on the power quality parameters; S42. Obtain the allowable limit of the power quality standard, and obtain the frequency of voltage deviation exceeding the standard within the preset calculation period based on the allowable limit and the voltage deviation rate; S43. Obtain the frequency of frequency deviation exceeding the standard within the preset calculation period based on the allowable limit and the frequency deviation rate; S44. Obtain the frequency of harmonic distortion exceeding the standard within the preset calculation period based on the allowable limit and the total harmonic distortion rate. S45. Obtain the power quality degradation index based on the frequency of voltage deviation exceeding the standard, the frequency of frequency deviation exceeding the standard, and the frequency of harmonic distortion exceeding the standard. S46. Obtain the nonlinear load ratio and sensitive load capacity ratio based on the power quality parameters, obtain the quality-energy efficiency coupling coefficient based on the nonlinear load ratio and the sensitive load capacity ratio, and obtain the power quality influence factor based on the quality-energy efficiency coupling coefficient and the power quality degradation index.

[0008] Optionally, step S5 includes: S51. Obtain the energy efficiency benchmark value corresponding to the energy efficiency impact factor, and obtain the energy efficiency deviation based on the energy efficiency benchmark value and the energy efficiency impact factor; S52. Obtain the power quality benchmark value corresponding to the power quality influence factor, and obtain the power quality deviation based on the power quality benchmark value and the power quality influence factor; S53. The energy efficiency deviation is processed by a preset mathematical mapping function to obtain the energy efficiency adjustment coefficient; S54. The power quality deviation is processed by a preset mathematical mapping function to obtain the quality adjustment coefficient; S55. Based on the initial accounting evaluation value, the energy efficiency adjustment coefficient, and the quality adjustment coefficient, a comprehensive accounting evaluation value is obtained through weighted correction calculation.

[0009] In addition, this invention also proposes an accounting and evaluation system that integrates carbon verification and electricity data, including: The first acquisition module is used to acquire carbon verification data and power operation data of the target accounting unit; The second acquisition module is used to acquire direct carbon emission parameters and indirect carbon emission parameters based on the carbon verification data, and to acquire an initial accounting assessment value based on the direct carbon emission parameters and the indirect carbon emission parameters. The third acquisition module is used to acquire power efficiency parameters and power quality parameters based on the power operation data, and to acquire multiple reactive power and multiple active power within a preset calculation period based on the power efficiency parameters, and to acquire energy efficiency impact factors based on the reactive power and active power. The fourth acquisition module is used to acquire the power quality impact factor based on the power quality parameters and the energy efficiency impact factor; The fusion module is used to fuse the initial accounting assessment value, the energy efficiency impact factor, and the power quality impact factor to obtain a comprehensive accounting assessment value, and to use the comprehensive accounting assessment value as the accounting assessment result of the fusion of carbon verification and power data.

[0010] Optionally, the second acquisition module includes: The first acquisition unit is used to acquire the types and net consumption of fossil fuels consumed by stationary combustion equipment based on the direct carbon emission parameters. The second acquisition unit is used to acquire the calorific value, carbon content per unit calorific value, and default value of carbon oxidation rate for each type of fossil fuel, and to acquire the direct carbon emission intensity based on the calorific value, the carbon content per unit calorific value, the default value of carbon oxidation rate, and the net consumption. The third acquisition unit is used to acquire the total consumption of purchased electricity and the dynamic marginal emission factor for a preset time period based on the indirect carbon emission parameters, and to acquire the indirect carbon emission intensity based on the total consumption and the dynamic marginal emission factor. The fourth acquisition unit is used to acquire carbon emission accounting correction factors based on the direct carbon emission intensity and the indirect carbon emission intensity. The fifth acquisition unit is used to acquire the corresponding historical carbon emission baseline value based on the target accounting unit, and to acquire the initial accounting assessment value based on the historical carbon emission baseline value and the carbon emission accounting correction factor.

[0011] In addition, the present invention also proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above method.

[0012] In addition, the present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above method.

[0013] The beneficial effects of this application are as follows: Based on fuel consumption and purchased electricity data, the invention calculates the initial carbon emission value by combining emission factors. Secondly, it generates an energy efficiency impact factor by using active / reactive power time series analysis of average power factor, load fluctuation, and energy efficiency stability. Simultaneously, it calculates the power quality degradation index based on voltage, frequency, and harmonic distortion exceeding the standard frequency, and obtains the power quality impact factor by combining the load structure sensitivity coefficient and the energy efficiency impact factor. Finally, it weights and fuses the initial carbon emission value, energy efficiency, and the normalized deviation of the power quality factor to form a comprehensive accounting and evaluation result. This method improves the accuracy and comprehensiveness of carbon emission accounting by introducing the dynamic operating characteristics of the power system. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of a method flow according to an embodiment of this application.

[0015] Figure 2 This is a schematic diagram of the system structure according to an embodiment of this application. Detailed Implementation

[0016] The following detailed description, in conjunction with the accompanying drawings and specific embodiments, provides a further detailed explanation of the carbon verification and power data fusion accounting and evaluation method and system proposed in this invention. The advantages and features of this invention will become clearer from the following description. It should be noted that the drawings are in a very simplified form and use non-precise proportions, used only to facilitate and clearly illustrate the purpose of the embodiments of this invention. Please refer to the drawings to make the objectives, features, and advantages of this invention more apparent and understandable. It should be understood that the structures, proportions, sizes, etc., depicted in the accompanying drawings are only for illustrative purposes to aid those skilled in the art and are not intended to limit the implementation conditions of this invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to the size, without affecting the effects and objectives achieved by this invention, should still fall within the scope of the technical content disclosed in this invention.

[0017] like Figures 1-2 As shown, this application provides an accounting and assessment method for integrating carbon verification and electricity data, including: S1. Obtain carbon verification data and power operation data of the target accounting unit; S2. Obtain direct carbon emission parameters and indirect carbon emission parameters based on the carbon verification data, and obtain an initial accounting assessment value based on the direct carbon emission parameters and the indirect carbon emission parameters; S3. Obtain power efficiency parameters and power quality parameters based on the power operation data, and obtain multiple reactive power and multiple active power within a preset calculation period based on the power efficiency parameters, and obtain energy efficiency impact factors based on reactive power and active power. S4. Obtain the power quality impact factor based on the power quality parameters and the energy efficiency impact factor; S5. The initial accounting assessment value, the energy efficiency impact factor, and the power quality impact factor are integrated to obtain a comprehensive accounting assessment value, which is then used as the accounting assessment result of the carbon verification and power data fusion.

[0018] As described in steps S1-S5 above, existing carbon accounting methods typically analyze collected fixed data, failing to fully integrate the dynamic operating characteristics of the power system. This results in a separation between carbon verification data and power operation data, leading to incomplete accounting results that fail to fully reflect actual carbon emissions and thus affecting the accuracy of carbon emission accounting. Therefore, this application obtains carbon verification data and power operation data from the target accounting unit, which forms the foundation of the entire accounting and assessment process. Carbon verification data can be obtained through the target accounting unit's energy consumption ledgers, environmental monitoring reports, etc., covering core information such as fuel consumption records of stationary combustion equipment and purchased electricity settlement data. Power operation data is collected through the target accounting unit's power monitoring system, smart meters, transformer and motor operation monitoring devices, including real-time operating parameters such as active power, reactive power, voltage, and frequency. This acquisition of basic data establishes the data source support for the accounting and assessment. Only by comprehensively acquiring both types of core data can the integrated analysis of carbon verification and power data be achieved, avoiding incomplete accounting results due to data gaps and providing a complete data foundation for subsequent analysis.

[0019] Based on carbon verification data, direct and indirect carbon emission parameters are obtained, and an initial assessment value is derived accordingly. Direct carbon emission parameters include the types of fossil fuels consumed by stationary combustion equipment (such as coal, natural gas, and heavy oil) and their net consumption. Indirect carbon emission parameters cover the total consumption of purchased electricity and the dynamic marginal emission factor for a preset time period (this factor can be obtained through dynamic data released by the regional power grid carbon emission monitoring platform, reflecting the carbon emission intensity corresponding to purchased electricity). The implementation process is as follows: First, based on the direct carbon emission parameters, combined with the calorific value of each fossil fuel, the carbon content per unit calorific value (obtained based on a preset fuel comparison table), and the default carbon oxidation rate, the value is calculated using the formula q(d) = Where q(d) is the direct carbon emission intensity, Net fuel consumption The calorific value of the fuel. Carbon content per unit of calorific value The carbon oxidation rate is used to calculate the direct carbon emission intensity. Then, based on indirect carbon emission parameters, the indirect carbon emission intensity is calculated using the formula: "Indirect carbon emission intensity = Total purchased electricity consumption × Dynamic marginal emission factor." Subsequently, a carbon emission accounting correction factor is determined based on the weighting of direct and indirect carbon emission intensity. Finally, combined with the historical baseline carbon emission value of the target accounting unit (obtained from the average of the unit's carbon emission accounting data over the past 3-5 years), an initial value is obtained using the formula: "Initial accounting assessment value = Historical baseline carbon emission value × Carbon emission accounting correction factor." This allows for basic quantification of carbon emissions, focusing on the two core sources of direct and indirect emissions, providing a benchmark value for subsequently incorporating factors affecting power operation.

[0020] Power efficiency parameters and power quality parameters are obtained from power operation data, and the power efficiency influencing factor is derived based on the power efficiency parameters. Power efficiency parameters include active power time series and reactive power time series within the target calculation period, while power quality parameters cover indicators such as voltage deviation rate, frequency deviation rate, and total harmonic distortion rate. The implementation process is as follows: First, the active power time series and reactive power time series are extracted from the power efficiency parameters, and the average active power and average reactive power are calculated. Then, the average power factor is calculated using the formula: Average Power Factor = Average Active Power / √(Average Active Power). 2 +Average reactive power 2The process begins by obtaining the average power factor, reflecting the efficiency level of power utilization. Next, the standard deviation of the active power time series is calculated and divided by the average active power to obtain the load fluctuation coefficient, which directly reflects the relative fluctuation range of the active load. Simultaneously, rated operating parameters such as transformer rated capacity and motor rated power of the target accounting unit are obtained. The comprehensive average load rate is calculated using the formula: "Comprehensive Average Load Rate = Actual Average Active Power / (Transformer Rated Capacity × Power Factor + Motor Rated Power × Power Factor)," reflecting the degree of load matching for the power equipment. Finally, based on the reactive power time series, and according to preset thresholds (e.g., reactive power below 30% of the rated value indicates high efficiency, 30%-70% indicates normal operation, and above 70% indicates low efficiency), the system is categorized into high-efficiency, normal, and low-efficiency states. The three inefficient energy efficiency state sequences are analyzed. The energy efficiency state transition probabilities between different states are calculated using a Markov chain model. These probabilities are then weighted by the load fluctuation coefficient and the overall average load rate (weights can be set according to industry characteristics, e.g., 0.3 for the load fluctuation coefficient, 0.3 for the overall average load rate, and 0.4 for the state transition probability for industrial enterprises), resulting in an energy efficiency stability index. Finally, based on a preset average power factor weight value (e.g., 0.6), the final energy efficiency impact factor is calculated using the formula: "Energy Efficiency Impact Factor = Average Power Factor × Weight Value + Energy Efficiency Stability Index × (1 - Weight Value)". This step is significant because it quantifies the indirect impact of power operation energy efficiency on carbon emissions. Higher power efficiency and more stable operation lead to higher energy utilization efficiency and lower carbon emissions per unit of output.

[0021] Power quality influencing factors are obtained based on power quality parameters and energy efficiency influencing factors. Voltage deviation rate, frequency deviation rate, and total harmonic distortion (THD) rate among the power quality parameters can be collected and calculated in real time by power monitoring equipment (e.g., voltage deviation rate = (actual voltage - rated voltage) / rated voltage × 100%). The implementation process is as follows: First, obtain the allowable limits for voltage deviation, frequency deviation, and THD rate specified in the preset power quality parameters; then, count the number of times voltage deviation exceeds the allowable limit (voltage deviation exceeding frequency), frequency deviation exceeds the allowable limit (frequency exceeding frequency), and THD rate exceeds the allowable limit (harmonic distortion exceeding frequency) within the preset calculation period. Based on these three types of exceeding frequencies, the power quality degradation index is calculated using the formula: "Power Quality Degradation Index = (Voltage Deviation Exceeding Frequency × 0.4 + Frequency Deviation Exceeding Frequency × 0.3 + Harmonic Distortion Exceeding Frequency × 0.3) / Total Monitoring Times." The higher the index, the worse the power quality. Simultaneously, the proportion of nonlinear loads (such as frequency converters and rectifiers) to the total load capacity (nonlinear load proportion) and the proportion of sensitive loads (such as precision instruments and automated control systems) to the total load capacity are extracted from power quality parameters (sensitive load capacity proportion). The quality-energy efficiency coupling coefficient is calculated using the formula: "Quality-Energy Efficiency Coupling Coefficient = (Nonlinear Load Proportion + Sensitive Load Capacity Proportion) / 2". This coefficient characterizes the sensitivity of power quality degradation to the impact on system energy efficiency. Finally, the degree to which power quality indirectly affects carbon emissions by influencing energy efficiency is quantified using the formula: "Power Quality Influence Factor = Quality-Energy Efficiency Coupling Coefficient × Power Quality Deterioration Index". The larger the power quality influence factor, the more significant the indirect promoting effect of power quality degradation on carbon emissions.

[0022] The initial calculated assessment value, energy efficiency impact factor, and power quality impact factor are integrated to obtain a comprehensive calculated assessment value. The implementation process is as follows: First, obtain the industry-recognized energy efficiency benchmark value (e.g., an industry average energy efficiency impact factor of 0.9) and power quality benchmark value (e.g., an industry average power quality impact factor of 0.01); then calculate the energy efficiency deviation = (actual energy efficiency impact factor - energy efficiency benchmark value) / energy efficiency benchmark value, and the power quality deviation = (actual power quality impact factor - power quality benchmark value) / power quality benchmark value, reflecting the degree of difference between the actual value and the industry benchmark; subsequently, the energy efficiency deviation and power quality deviation are processed separately using preset mathematical mapping functions, such as using a dual... The tangent function (tanh) is used for range mapping: energy efficiency adjustment coefficient = tanh(energy efficiency deviation / θ1), quality adjustment coefficient = -tanh(power quality deviation / θ2), where θ1 and θ2 are adjustable scale parameters, and the negative sign is used to convert power quality deviation (the smaller the better) into a positive adjustment contribution. Finally, weights α and β are set according to the industry characteristics of the target accounting unit (e.g., α=0.2, β=0.2), and the weighted correction calculation is completed through "comprehensive accounting assessment value = initial accounting assessment value × (1 + α × energy efficiency adjustment coefficient + β × quality adjustment coefficient)". The physical significance of this step is to achieve the organic integration of basic carbon emission data and dynamic influencing factors of power operation, forming a comprehensive and accurate accounting assessment result. This result reflects both the basic level of direct and indirect carbon emissions and incorporates the influence of power energy efficiency and power quality, which is more in line with the actual carbon emission situation. At the same time, it constructs an accounting system that takes into account both basic carbon verification data and dynamic power operation data, thus comprehensively and accurately reflecting the actual carbon emission situation.

[0023] In one embodiment, step S2, which involves obtaining an initial accounting assessment value based on the direct carbon emission parameters and the indirect carbon emission parameters, includes: S21. Obtain the types and net consumption of fossil fuels consumed by stationary combustion equipment based on the direct carbon emission parameters. S22. Obtain the fuel calorific value, carbon content per unit calorific value, and default value of carbon oxidation rate for each of the aforementioned fossil fuels, and obtain the direct carbon emission intensity based on the fuel calorific value, the carbon content per unit calorific value, the default value of carbon oxidation rate, and the net consumption. S23. Obtain the total consumption of purchased electricity and the dynamic marginal emission factor for a preset time period based on the indirect carbon emission parameters, and obtain the indirect carbon emission intensity based on the total consumption and the dynamic marginal emission factor. S24. Obtain carbon emission accounting correction factors based on the direct carbon emission intensity and indirect carbon emission intensity; S25. Obtain the corresponding historical carbon emission baseline value based on the target accounting unit, and obtain the initial accounting assessment value based on the historical carbon emission baseline value and the carbon emission accounting correction factor.

[0024] As described in steps S21-S25 above, this application obtains the types and net consumption of fossil fuels consumed by stationary combustion equipment based on direct carbon emission parameters. Direct carbon emission parameters are derived from the carbon verification data of the target accounting unit. This data can be obtained by reviewing the enterprise's energy consumption ledger, fuel purchase invoices, and stationary combustion equipment operation records. For example, the fuel consumption records of stationary combustion equipment such as boilers and kilns in industrial enterprises will detail the type of fuel used (e.g., anthracite, liquefied natural gas) and the actual net consumption (effective consumption after deducting losses). The significance of this step lies in clarifying the material basis of direct carbon emissions. Only by first determining the fuel type and net consumption can the direct carbon emission intensity be calculated subsequently based on the carbon content characteristics of the fuel itself, avoiding inaccurate direct emission accounting due to unclear fuel parameters. Taking a heating company as an example, its stationary combustion equipment is a gas-fired boiler. Through carbon verification data, the annual net consumption of liquefied natural gas by this boiler is extracted to be 5 million cubic meters, clearly identifying liquefied natural gas as the fuel type. This data provides the core input for subsequent calculation of direct carbon emission intensity.

[0025] Obtain the default values ​​for calorific value, carbon content per unit calorific value, and carbon oxidation rate for each fossil fuel, and calculate the direct carbon emission intensity based on these parameters and net consumption. Calorific value refers to the heat released by the complete combustion of a unit mass or volume of fuel. Carbon content per unit calorific value refers to the mass of carbon in a unit of heat of fuel. This data can be referenced from industry default values ​​in authoritative documents such as the IPCC National Greenhouse Gas Inventory Guidelines. For example, the carbon content per unit calorific value of coal is approximately 26.3 tons of carbon per teracogram, and that of natural gas is approximately 15.3 tons of carbon per teracogram. The default carbon oxidation rate reflects the proportion of carbon converted to carbon dioxide during fuel combustion. The default carbon oxidation rate for most fossil fuels can be set to 0.99 (i.e., 99% of carbon is oxidized to carbon dioxide), which can be fine-tuned according to the technical level of the fuel combustion equipment. The direct carbon emission intensity is calculated using the formula q(d) = Where q(d) is the direct carbon emission intensity, Net fuel consumption The calorific value of the fuel. Carbon content per unit of calorific value The carbon oxidation rate is the formula used to convert fuel consumption into actual carbon emissions, reflecting the quantitative relationship between fuel consumption and direct carbon emissions. Here, (44 / 12) is the molecular weight conversion coefficient from carbon to carbon dioxide.

[0026] The total consumption of purchased electricity and the dynamic marginal emission factor for a preset time period are obtained based on indirect carbon emission parameters, and the indirect carbon emission intensity is obtained accordingly. Indirect carbon emission parameters also come from carbon verification data. The total consumption of purchased electricity can be obtained from the electricity bills of the target accounting unit and the annual cumulative data of smart meters. For example, a shopping mall's annual total consumption of purchased electricity is 8 million kWh. The dynamic marginal emission factor for a preset time period refers to the carbon emissions generated by the newly added unit electricity supply of the regional power grid within a specific time period (such as monthly or quarterly). This data can be obtained from the carbon emission monitoring reports released by the regional power grid company and the dynamic emission factor platform published by the provincial ecological and environmental departments. Its value will be dynamically adjusted according to the change in the proportion of clean energy (wind power, photovoltaic) in the power grid. For example, the dynamic marginal emission factor of a province's power grid in the second quarter was 0.45 tons / 10,000 kWh, which decreased to 0.42 tons / 10,000 kWh in the third quarter due to the increase in wind power installations. The indirect carbon emission intensity is calculated as follows: Indirect carbon emission intensity = Total purchased electricity consumption × Dynamic marginal emission factor. This quantifies the carbon emissions indirectly generated by a target accounting unit due to the use of purchased electricity, reflecting the relationship between electricity consumption and indirect carbon emissions. Taking this shopping mall as an example, if its total purchased electricity consumption of 8 million kWh corresponds to a dynamic marginal emission factor of 0.43 tons / 10,000 kWh, then the indirect carbon emission intensity = 8 million kWh × 0.43 tons / 10,000 kWh = 344 tons of CO2 equivalent. This result accurately reflects the level of indirect carbon emissions from purchased electricity, compensating for the shortcomings of only calculating direct emissions while ignoring indirect emissions.

[0027] The carbon emission accounting correction factor is obtained based on direct and indirect carbon emission intensity. The purpose of the carbon emission accounting correction factor is to balance the weights of direct and indirect emissions in total carbon emissions, avoiding deviations in the initial accounting value from reality due to differences in the proportions of the two types of emissions. Its calculation requires setting weights based on the industry characteristics of the target accounting unit. For example, high-energy-consuming industrial enterprises (such as steel and chemical industries) have a high proportion of direct emissions, so the direct carbon emission intensity weight can be set to 0.6 and the indirect carbon emission intensity weight to 0.4; commercial buildings have a high proportion of indirect emissions, so the direct carbon emission intensity weight can be set to 0.3 and the indirect carbon emission intensity weight to 0.7. The correction factor = (direct carbon emission intensity × direct emission weight + indirect carbon emission intensity × indirect emission weight) / (direct carbon emission intensity + indirect carbon emission intensity). If the weights of direct and indirect emissions are equal (both 0.5), then the correction factor is (direct carbon emission intensity + indirect carbon emission intensity) / (direct carbon emission intensity + indirect carbon emission intensity) = 1. In this case, the correction factor only serves a standardization function. By adjusting the weights, the subsequent initial accounting assessment values ​​are made more closely aligned with the actual emission structure of the target accounting unit.

[0028] The initial assessment value is obtained by acquiring the corresponding historical carbon emission baseline value for the target accounting unit and combining this baseline value with the carbon emission accounting correction factor. The historical carbon emission baseline value refers to the average carbon emissions of the target accounting unit over the past three years, calculated by an authoritative body. This value can be obtained through the company's past carbon emission verification reports and data filed with the ecological and environmental departments. It reflects the long-term carbon emission benchmark level of the target accounting unit and avoids the impact of abnormal fluctuations in a single year (such as a sudden decrease in fuel consumption due to equipment maintenance) on the accuracy of the calculation. The initial assessment value is calculated as follows: Initial Assessment Value = Historical Carbon Emission Baseline Value × Carbon Emission Accounting Correction Factor. By combining the historical baseline value with the current direct and indirect emission structure characteristics, an initial accounting result that reflects both long-term trends and the current emission structure is obtained. This result retains the stability of historical data while incorporating changes in the current emission structure through the correction factor, providing a reliable baseline value for subsequent comprehensive accounting by integrating power operation data.

[0029] In one embodiment, step S3, which involves obtaining multiple reactive power and multiple active power within a preset calculation period based on the power efficiency parameters, and obtaining the energy efficiency impact factor based on the reactive power and active power, includes: S31. Obtain the active power time series and reactive power time series within the target calculation period based on the power efficiency parameters, and obtain multiple reactive powers based on the reactive power time series, and obtain the average reactive power based on the reactive power. S32. Obtain multiple active powers based on the active power time series, obtain the average active power based on the active power, and obtain the average power factor based on the average active power and the average reactive power. S33. Obtain the standard deviation of active power based on the active power time series, and obtain the load fluctuation coefficient based on the ratio of the standard deviation of active power to the average active power. S34. Obtain the rated operating parameters of the transformer and the rated operating parameters of the motor of the target accounting unit, and obtain the comprehensive average load rate based on the active power time series, the rated operating parameters of the transformer and the rated operating parameters of the motor. S35. Obtain the energy efficiency state sequence within the target calculation period based on the reactive power time series, wherein the energy efficiency state sequence includes a high-efficiency state sequence, a normal state sequence, and an inefficient state sequence. S36. Obtain the energy efficiency state transition probability based on the high-efficiency state sequence, the normal state sequence, and the low-efficiency state sequence, and obtain the energy efficiency stability index by weighting the energy efficiency state transition probability, the load fluctuation coefficient, and the comprehensive average load rate. S37. Based on the preset average power factor weight value, the average power factor and the energy efficiency stability index are weighted and summed to obtain the energy efficiency impact factor.

[0030] As described in steps S31-S37 above, since power efficiency is a core indicator reflecting the energy utilization efficiency of a power system, and the energy efficiency status of a power system is directly related to carbon emission levels, when the operating efficiency of power equipment is high (e.g., power factor close to 1, small load fluctuations, and equipment operating at high efficiency), the energy consumption per unit output is lower, indirectly reducing carbon emissions; conversely, inefficient operation leads to energy waste and increases carbon emissions. In real-world scenarios, power efficiency is affected by various dynamic factors, such as real-time changes in electricity load causing fluctuations in active and reactive power, mismatched load rates of core equipment such as transformers and motors reducing energy utilization efficiency, and switching between different operating states affecting overall energy efficiency stability. This application obtains the active power time series and reactive power time series within the target calculation period based on power efficiency parameters, and obtains multiple reactive power values ​​and average reactive power values ​​based on the reactive power time series. The power efficiency parameters are derived from the power operation data of the target accounting unit. This data can be collected in real-time through intelligent power monitoring terminals, SCADA systems (data acquisition and monitoring control systems), or power quality analyzers within the plant area. The collection frequency can be set according to the accounting period (e.g., hourly, minute-level; if the accounting period is monthly, hourly power data can be collected), forming time-series data. The active power time series records the active power values ​​at different times within the accounting period (reflecting the actual power done by the equipment), while the reactive power time series records the reactive power values ​​at different times (reflecting the power required for the equipment to establish a magnetic field; it does not directly do work but affects energy utilization efficiency). The average reactive power is calculated by summing all data in the reactive power time series and then dividing by the number of data collection points. This average reactive power reflects the average level of reactive power within the accounting period, providing a basis for subsequent calculations of power factor and load fluctuation coefficient. Furthermore, the average reactive power directly reflects the overall reactive power consumption level within the period, avoiding the impact of abnormal reactive power values ​​at a single moment on subsequent analysis.

[0031] Multiple active power data points and the average active power are obtained from the active power time series. The average power factor is then derived by combining the average active power and the average reactive power. The calculation of average active power is consistent with that of average reactive power; that is, the summation of the active power time series data is divided by the number of data collection points. Its physical meaning reflects the average power actually performed by the equipment and is a core indicator for measuring the effective utilization of energy. The average power factor is calculated using the formula: "Average Power Factor = Average Active Power / √(Average Active Power)". 2 +Average reactive power 2The formula, ))”, reflects the proportion of active power in the total power (the vector sum of active and reactive power). The closer the average power factor is to 1, the less reactive power is consumed and the higher the effective utilization rate of electrical energy.

[0032] The load fluctuation coefficient is obtained based on the average reactive power and average power factor. The load fluctuation coefficient measures the stability of the power load within a calculation period. It is calculated as follows: first, calculate the standard deviation σ_P of the active power time series; then, calculate the load fluctuation coefficient as σ_P / average active power. This parameter is the coefficient of variation of active power. Its physical meaning is: the smaller the load fluctuation coefficient, the smaller the relative fluctuation of active power within the period, the more stable the power load operation, the less frequent the equipment needs to adjust its operating state, and the more stable the energy utilization efficiency; conversely, excessive fluctuation can lead to frequent equipment start-ups and shutdowns or deviations of operating parameters from optimal values, increasing energy loss.

[0033] Obtain the rated operating parameters of the transformer and the rated operating parameters of the motor for the target accounting unit, and calculate the comprehensive average load rate based on the active power time series and these two types of rated parameters. The rated operating parameters of the transformer (such as rated capacity and rated power factor) and the rated operating parameters of the motor (such as rated power and rated load rate) can be obtained from the equipment manufacturer's technical specifications and the equipment ledger of the target accounting unit, reflecting the optimal operating parameters designed for the equipment. The calculation of the comprehensive average load rate needs to consider the coordinated operation of the transformer and the motor: first, calculate the average load rate of the transformer (actual average active power of the transformer / rated capacity of the transformer × rated power factor of the transformer) and the average load rate of the motor (actual average active power of the motor / rated power of the motor). Then, set weights according to the power proportion of the two types of equipment (e.g., if the transformer accounts for 60% of the power, the weight is 0.6; if the motor accounts for 40%, the weight is 0.4), and obtain the comprehensive average load rate by weighted summation. The overall average load factor reflects the degree of matching between the actual load and the rated load of the core equipment of the power system. If the overall average load factor is too high (close to or exceeding 100%), it will lead to equipment overload operation, increasing energy consumption and failure risk; if it is too low, it will cause equipment to be idle and energy utilization efficiency to be low.

[0034] The energy efficiency status sequence within the target accounting period is obtained based on the reactive power time series. This sequence includes high-efficiency, normal, and inefficient states. The classification of energy efficiency states is based on a threshold set by comparing reactive power with the optimal operating parameters of the equipment (average reactive power obtained from historical reactive power time series). For example, when reactive power is below 70% of the average reactive power, it is considered a high-efficiency state (low reactive power consumption, high energy efficiency); when reactive power is between 70% and 130% of the average reactive power, it is considered a normal state (normal energy efficiency); and when reactive power is above 130% of the average reactive power, it is considered an inefficient state (excessive reactive power consumption, low energy efficiency). Subsequently, each data point in the reactive power time series is categorized according to the above thresholds, forming an energy efficiency status sequence composed of "high-efficiency - normal - inefficient." Its physical meaning is to intuitively present the change process of the power system's energy efficiency status within the accounting period, providing data support for subsequent analysis of state transition patterns.

[0035] The energy efficiency state transition probabilities are obtained from the high-efficiency state sequence, normal state sequence, and low-efficiency state sequence. These probabilities, along with the load fluctuation coefficient and the overall average load rate, are then weighted to calculate the energy efficiency stability index. The calculation of the energy efficiency state transition probability uses a Markov chain model: first, the number of transitions from one state to another in the state sequence is counted (e.g., the number of transitions from high efficiency to normal, from normal to low efficiency, etc.), then divided by the total number of transitions to obtain the transition probability between each state (e.g., the probability of transitioning from high efficiency to normal, and from normal to low efficiency). This probability reflects the frequency and trend of energy efficiency state switching; a higher probability of maintaining a high-efficiency state and a lower probability of transitioning to a low-efficiency state indicates better energy efficiency stability. The weighted calculation of the energy efficiency stability index requires setting weights based on industry characteristics (e.g., industrial enterprises are more sensitive to load fluctuations, so the load fluctuation coefficient has a weight of 0.3; commercial buildings are more concerned about state transitions, so the state transition probability has a weight of 0.4). For example, the weights can be set as follows: energy efficiency state transition probability (the difference between the probability of maintaining high efficiency and the probability of transitioning to low efficiency) 0.4, load fluctuation coefficient 0.3, and overall average load rate 0.3. The index is calculated using the formula: "Energy Efficiency Stability Index = (Probability of Maintaining High Efficiency - Probability of Transitioning to Low Efficiency) × 0.4 + (1 - Load Fluctuation Coefficient) × 0.3 + Overall Average Load Rate × 0.3" (where "1 - Load Fluctuation Coefficient" is used to convert the fluctuation coefficient into a positive indicator; the smaller the fluctuation, the larger this value). The energy efficiency stability index comprehensively measures the stability and rationality of the power system's energy efficiency. The closer the index is to 1, the more stable the energy efficiency state, the more reasonable the load matching, and the better the sustainability of energy utilization efficiency.

[0036] The energy efficiency impact factor is obtained based on the average power factor and the energy efficiency stability index. According to the preset average power factor weight value wpf (0≤wpf≤1), the energy efficiency impact factor is calculated by weighted summation: Energy Efficiency Impact Factor = wpf × Average Power Factor + (1-wpf) × Energy Efficiency Stability Index. This formula combines "static energy efficiency level" (energy utilization efficiency reflected by the average power factor) with "dynamic energy efficiency stability" (continuous operation capability reflected by the energy efficiency stability index) to form a quantitative indicator that comprehensively reflects the indirect impact of power energy efficiency on carbon emissions. The larger the energy efficiency impact factor, the higher the energy efficiency level and the better the stability of the power system, and the more significant the effect on inhibiting carbon emissions. The figures mentioned above in this scheme are all quantified figures and will not be described in detail here.

[0037] In one embodiment, step S4, which involves obtaining the power quality impact factor based on the power quality parameters and the energy efficiency impact factor, includes: S41. Obtain the voltage deviation rate, frequency deviation rate, and total harmonic distortion rate based on the power quality parameters; S42. Obtain the allowable limit of the power quality standard, and obtain the frequency of voltage deviation exceeding the standard within the preset calculation period based on the allowable limit and the voltage deviation rate; S43. Obtain the frequency of frequency deviation exceeding the standard within the preset calculation period based on the allowable limit and the frequency deviation rate; S44. Obtain the frequency of harmonic distortion exceeding the standard within the preset calculation period based on the allowable limit and the total harmonic distortion rate. S45. Obtain the power quality degradation index based on the frequency of voltage deviation exceeding the standard, the frequency of frequency deviation exceeding the standard, and the frequency of harmonic distortion exceeding the standard. S46. Obtain the nonlinear load ratio and sensitive load capacity ratio based on the power quality parameters, obtain the quality-energy efficiency coupling coefficient based on the nonlinear load ratio and the sensitive load capacity ratio, and obtain the power quality influence factor based on the quality-energy efficiency coupling coefficient and the power quality degradation index.

[0038] As described in steps S41-S46 above, this application obtains voltage deviation rate, frequency deviation rate, and total harmonic distortion rate based on power quality parameters. The power quality parameters are derived from the power operation data of the target accounting unit and can be collected in real time through a power quality analyzer or intelligent monitoring terminal. The collection frequency must meet the requirements of dynamic power quality monitoring (e.g., minute-level collection to ensure the capture of short-term fluctuations). The voltage deviation rate is calculated as: (actual monitored voltage value - rated voltage value) / rated voltage value × 100%, reflecting the degree of deviation between the actual voltage and the standard voltage. The frequency deviation rate is calculated as: (actual monitored frequency value - rated frequency value) / rated frequency value × 100%. The total harmonic distortion rate is the ratio of the root mean square value of all harmonic currents (or voltages) to the root mean square value of the fundamental current (or voltage), reflecting the severity of harmonic pollution in the power system. It is usually obtained by decomposing and calculating the monitored current / voltage signal through Fourier transform. By extracting the three core quantitative indicators of power quality, basic data is provided for subsequent assessment of the degree of power quality degradation, avoiding one-sided quality assessment due to missing indicators. These data provide key inputs for subsequent analysis of the impact of power quality on the energy efficiency of production equipment.

[0039] Based on the permissible limits of power quality standards, and combined with voltage deviation rate, frequency deviation rate, and total harmonic distortion rate, the frequency of voltage deviation exceeding the standard, frequency of frequency deviation exceeding the standard, and frequency of harmonic distortion exceeding the standard within a preset calculation period are obtained. The permissible limits of power quality standards must be determined according to industry standards. The frequency of exceeding the standards is calculated as follows: within the preset calculation period (e.g., monthly), the indicator value at each monitoring time is compared with the permissible limit. If the indicator value exceeds the limit range, it is recorded as one instance of exceeding the standard. The total number of instances exceeding the standard within the final calculation period is then calculated. For example, a shopping mall has a pre-set accounting period of one month, monitoring for a total of 720 hours (one data point per hour). The allowable limit for voltage deviation rate is ±7%. Statistics show that the voltage deviation rate exceeded this range at 12 times, resulting in 12 instances of voltage deviation exceeding the limit. The allowable limit for frequency deviation rate is ±0.4%, and it exceeded this limit at 5 times, resulting in 5 instances of frequency deviation exceeding the limit. The allowable limit for total harmonic distortion rate is 4%, and it exceeded this limit at 8 times, resulting in 8 instances of harmonic distortion exceeding the limit. This step converts the "numerical deviation" of power quality indicators into "frequency statistics," intuitively reflecting the frequency of power quality exceeding the limits within the accounting period. This provides a quantitative basis for subsequent calculations of the degradation index, avoiding bias caused by evaluating only the maximum or average value. For example, even if the average value of an indicator does not exceed the limit, frequent short-term exceedances can still adversely affect equipment operation; the frequency of exceedances can capture such problems.

[0040] The power quality degradation index is obtained based on the frequency of voltage deviation exceeding the standard, the frequency of ...

[0041] The proportion of nonlinear loads and the proportion of sensitive loads are obtained based on power quality parameters. From these, the quality-energy efficiency coupling coefficient is derived. This coefficient, combined with the power quality degradation index and energy efficiency impact factor, yields the power quality impact factor. Specifically, the proportion of nonlinear loads refers to the ratio of the total rated capacity of nonlinear loads (such as frequency converters, rectifiers, and electric arc furnaces) in the target accounting unit to the total rated capacity of the total electrical load. The proportion of sensitive loads refers to the ratio of the total rated capacity of sensitive loads (such as precision instruments, PLC control systems, and data servers) to the total rated capacity of the total electrical load. These two parameters can be obtained from the electrical equipment ledger and load statistics reports of the target accounting unit. The quality-energy efficiency coupling coefficient is calculated as follows: Considering that the higher the proportion of nonlinear loads and the higher the proportion of sensitive load capacity, the more significant the impact of power quality degradation on energy efficiency, the coupling coefficient = (proportion of nonlinear loads + proportion of sensitive load capacity) / 2. The physical meaning of this formula is to balance the impact of the two types of loads on the quality-energy efficiency relationship, forming a coefficient that can reflect the synergistic effect of the two. This coefficient represents the "sensitivity or amplification factor of the impact of power quality degradation on the overall energy efficiency of the system". The final calculation of the power quality impact factor is: Power quality impact factor = quality-energy efficiency coupling coefficient × power quality degradation index. This formula can quantify the degree to which power quality indirectly affects carbon emissions by influencing energy efficiency. The larger the power quality impact factor, the more significant the indirect promoting effect of power quality degradation on carbon emissions (or the more significant the indirect inhibiting effect of power quality optimization on carbon emissions).

[0042] In one embodiment, step S5, which integrates the initial calculated evaluation value, the energy efficiency impact factor, and the power quality impact factor to obtain a comprehensive calculated evaluation value, includes: S51. Obtain the energy efficiency benchmark value corresponding to the energy efficiency impact factor, and obtain the energy efficiency deviation based on the energy efficiency benchmark value and the energy efficiency impact factor; S52. Obtain the power quality benchmark value corresponding to the power quality influence factor, and obtain the power quality deviation based on the power quality benchmark value and the power quality influence factor; S53. The energy efficiency deviation is processed by a preset mathematical mapping function to obtain the energy efficiency adjustment coefficient; S54. The power quality deviation is processed by a preset mathematical mapping function to obtain the quality adjustment coefficient; S55. Based on the initial accounting evaluation value, the energy efficiency adjustment coefficient, and the quality adjustment coefficient, a comprehensive accounting evaluation value is obtained through weighted correction calculation.

[0043] As described in steps S51-S55 above, this application obtains the energy efficiency benchmark value corresponding to the energy efficiency impact factor, and obtains the energy efficiency deviation based on the benchmark value and the energy efficiency impact factor. The energy efficiency benchmark value refers to the average energy efficiency impact factor of the industry in which the target accounting unit is located, or its historical best energy efficiency impact factor. This can be obtained through energy efficiency reports published by industry associations or power energy efficiency monitoring data of the target accounting unit over the past 3-5 years. The energy efficiency deviation is calculated as follows: Energy Efficiency Deviation = (Actual Energy Efficiency Impact Factor - Energy Efficiency Benchmark Value) / Energy Efficiency Benchmark Value. The energy efficiency deviation quantifies the relative difference between the actual energy efficiency impact factor and the benchmark level. If the result is positive, it indicates that the actual energy efficiency is better than the benchmark level, and the inhibitory effect on carbon emissions is stronger; if the result is negative, it indicates that the actual energy efficiency is lower than the benchmark level, and power operation needs to be optimized to reduce carbon emissions. For example, the actual energy efficiency impact factor of a textile factory is 0.88, while the energy efficiency benchmark value for the industry is 0.85. Substituting these values ​​into the formula, we can obtain the energy efficiency deviation = (0.88-0.85) / 0.85≈3.53%. This result indicates that the factory's power energy efficiency is better than the industry average, and its energy efficiency has an indirect effect on inhibiting carbon emissions that exceeds the industry benchmark, providing a quantitative basis for "positive adjustment" for subsequent integration.

[0044] Obtain the power quality benchmark value corresponding to the power quality impact factor, and calculate the power quality deviation based on this benchmark value and the power quality impact factor. The determination logic for the power quality benchmark value is consistent with that for the energy efficiency benchmark value. The industry average power quality impact factor or the historical best power quality impact factor for the target accounting unit can be selected. The power quality deviation is calculated as follows: Power Quality Deviation = (Actual Power Quality Impact Factor - Power Quality Benchmark Value) / Power Quality Benchmark Value. Since a smaller power quality impact factor is better, a negative result indicates that the actual power quality's adverse impact on carbon emissions is less than the benchmark level, indicating a better effect. A positive result indicates that power quality needs to be improved to reduce its indirect contribution to carbon emissions.

[0045] The energy efficiency deviation is normalized to obtain the energy efficiency adjustment coefficient. The purpose of normalization is to eliminate differences in the dimensions and numerical range of the energy efficiency deviation and map it to a suitable adjustment range. This step uses a mathematical function that does not rely on external industry extreme values, such as the hyperbolic tangent function (tanh): Energy efficiency adjustment coefficient = tanh(energy efficiency deviation / θ1), where θ1 is an adjustable scaling parameter used to control the sensitivity of the mapping. This function maps the deviation to the (-1,1) interval, preserving the sign (positive or negative adjustment direction) while avoiding the influence of extreme values.

[0046] The power quality deviation is normalized to obtain the quality adjustment coefficient. A similar mathematical function is used for processing: quality adjustment coefficient = -tanh(power quality deviation / θ2), where θ2 is an adjustable scaling parameter, and the negative sign is used to convert the power quality deviation (the smaller the better) into a positive contribution (i.e., the better the quality, the larger the adjustment coefficient).

[0047] The initial accounting assessment value, energy efficiency adjustment coefficient, and quality adjustment coefficient are weighted and adjusted to obtain the comprehensive accounting assessment value. The core of the weighted adjustment is to set the weight coefficients α and β for each adjustment item based on the industry characteristics of the target accounting unit (e.g., α=0.2, β=0.2), reflecting the logic of "basic carbon emissions as the main factor, dynamic impacts as a secondary factor." The comprehensive accounting assessment value is calculated as follows: Comprehensive accounting assessment value = Initial accounting assessment value × (1 + α × Energy efficiency adjustment coefficient + β × Quality adjustment coefficient). Taking a steel plant as an example, the initial accounting assessment value is 5000 tons of CO2 equivalent, α=0.2, β=0.2. Assuming the calculated energy efficiency adjustment coefficient is 0.1 (energy efficiency slightly better than the benchmark) and the quality adjustment coefficient is 0.05 (quality slightly better than the benchmark), substituting these values ​​into the formula, we get the comprehensive accounting assessment value = 5000 × (1 + 0.2 × 0.1 + 0.2 × 0.05) = 5000 × 1.03 = 5150 tons of CO2 equivalent. This result retains the fundamental status of the initial accounting assessment value, and through dynamic adjustments of energy efficiency and power quality, yields a more realistic carbon emission assessment result.

[0048] This application also provides an accounting and assessment system for addressing the fusion of carbon verification and electricity data, including: The first acquisition module is used to acquire carbon verification data and power operation data of the target accounting unit; The second acquisition module is used to acquire direct carbon emission parameters and indirect carbon emission parameters based on the carbon verification data, and to acquire an initial accounting assessment value based on the direct carbon emission parameters and the indirect carbon emission parameters. The third acquisition module is used to acquire power efficiency parameters and power quality parameters based on the power operation data, and to acquire multiple reactive power and multiple active power within a preset calculation period based on the power efficiency parameters, and to acquire energy efficiency impact factors based on the reactive power and active power. The fourth acquisition module is used to acquire the power quality impact factor based on the power quality parameters and the energy efficiency impact factor; The fusion module is used to fuse the initial accounting assessment value, the energy efficiency impact factor, and the power quality impact factor to obtain a comprehensive accounting assessment value, and to use the comprehensive accounting assessment value as the accounting assessment result of the fusion of carbon verification and power data.

[0049] In one embodiment, the second acquisition module includes: The first acquisition unit is used to acquire the types and net consumption of fossil fuels consumed by stationary combustion equipment based on the direct carbon emission parameters. The second acquisition unit is used to acquire the calorific value, carbon content per unit calorific value, and default value of carbon oxidation rate for each type of fossil fuel, and to acquire the direct carbon emission intensity based on the calorific value, the carbon content per unit calorific value, the default value of carbon oxidation rate, and the net consumption. The third acquisition unit is used to acquire the total consumption of purchased electricity and the dynamic marginal emission factor for a preset time period based on the indirect carbon emission parameters, and to acquire the indirect carbon emission intensity based on the total consumption and the dynamic marginal emission factor. The fourth acquisition unit is used to acquire carbon emission accounting correction factors based on the direct carbon emission intensity and the indirect carbon emission intensity. The fifth acquisition unit is used to acquire the corresponding historical carbon emission baseline value based on the target accounting unit, and to acquire the initial accounting assessment value based on the historical carbon emission baseline value and the carbon emission accounting correction factor.

[0050] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0051] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0052] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0053] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0054] In the description of this invention, it should be understood that the terms "center," "height," "thickness," "upper," "lower," "vertical," "horizontal," "top," "bottom," "inner," "outer," "axial," "radial," and "circumferential," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0055] In the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0056] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0057] Although the present invention has been described in detail through the preferred embodiments above, it should be understood that the above description should not be considered as a limitation of the present invention. Various modifications and substitutions to the present invention will be apparent to those skilled in the art after reading the above description. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A method for accounting and assessment that integrates carbon verification and electricity data, characterized in that, include: S1. Obtain carbon verification data and power operation data of the target accounting unit; S2. Obtain direct carbon emission parameters and indirect carbon emission parameters based on the carbon verification data, and obtain an initial accounting assessment value based on the direct carbon emission parameters and the indirect carbon emission parameters; S3. Obtain power efficiency parameters and power quality parameters based on the power operation data, and obtain multiple reactive power and multiple active power within a preset calculation period based on the power efficiency parameters, and obtain energy efficiency impact factors based on reactive power and active power. S4. Obtain the power quality impact factor based on the power quality parameters and the energy efficiency impact factor; S5. The initial accounting assessment value, the energy efficiency impact factor, and the power quality impact factor are integrated to obtain a comprehensive accounting assessment value, which is then used as the accounting assessment result of the carbon verification and power data fusion.

2. The accounting and evaluation method for carbon verification and electricity data fusion as described in claim 1, characterized in that, Step S2 includes: S21. Obtain the types and net consumption of fossil fuels consumed by stationary combustion equipment based on the direct carbon emission parameters. S22. Obtain the fuel calorific value, carbon content per unit calorific value, and default value of carbon oxidation rate for each of the aforementioned fossil fuels, and obtain the direct carbon emission intensity based on the fuel calorific value, the carbon content per unit calorific value, the default value of carbon oxidation rate, and the net consumption. S23. Obtain the total consumption of purchased electricity and the dynamic marginal emission factor for a preset time period based on the indirect carbon emission parameters, and obtain the indirect carbon emission intensity based on the total consumption and the dynamic marginal emission factor. S24. Obtain carbon emission accounting correction factors based on the direct carbon emission intensity and indirect carbon emission intensity; S25. Obtain the corresponding historical carbon emission baseline value based on the target accounting unit, and obtain the initial accounting assessment value based on the historical carbon emission baseline value and the carbon emission accounting correction factor.

3. The accounting and evaluation method for carbon verification and electricity data fusion as described in claim 1, characterized in that, Step S3 includes: S31. Obtain the active power time series and reactive power time series within the target calculation period based on the power efficiency parameters, and obtain multiple reactive powers based on the reactive power time series, and obtain the average reactive power based on the reactive power. S32. Obtain multiple active powers based on the active power time series, obtain the average active power based on the active power, and obtain the average power factor based on the average active power and the average reactive power. S33. Obtain the standard deviation of active power based on the active power time series, and obtain the load fluctuation coefficient based on the ratio of the standard deviation of active power to the average active power. S34. Obtain the rated operating parameters of the transformer and the rated operating parameters of the motor of the target accounting unit, and obtain the comprehensive average load rate based on the active power time series, the rated operating parameters of the transformer and the rated operating parameters of the motor. S35. Obtain the energy efficiency state sequence within the target calculation period based on the reactive power time series, wherein the energy efficiency state sequence includes a high-efficiency state sequence, a normal state sequence, and an inefficient state sequence. S36. Obtain the energy efficiency state transition probability based on the high-efficiency state sequence, the normal state sequence, and the low-efficiency state sequence, and obtain the energy efficiency stability index by weighting the energy efficiency state transition probability, the load fluctuation coefficient, and the comprehensive average load rate. S37. Based on the preset average power factor weight value, the average power factor and the energy efficiency stability index are weighted and summed to obtain the energy efficiency impact factor.

4. The accounting and evaluation method for carbon verification and power data fusion as described in claim 3, characterized in that, Step S35 includes: obtaining the average reactive power based on the historical reactive power time series, and determining the relationship between the reactive power and the average reactive power: when the reactive power is lower than 70% of the average reactive power, it is determined to be in an efficient state; when the reactive power is in the range of 70% to 130% of the average reactive power, it is determined to be in a normal state; when the reactive power is higher than 130% of the average reactive power, it is determined to be inefficient.

5. The accounting and evaluation method for carbon verification and electricity data fusion as described in claim 1, characterized in that, Step S4 includes: S41. Obtain the voltage deviation rate, frequency deviation rate, and total harmonic distortion rate based on the power quality parameters; S42. Obtain the allowable limit of the power quality standard, and obtain the frequency of voltage deviation exceeding the standard within the preset calculation period based on the allowable limit and the voltage deviation rate; S43. Obtain the frequency of frequency deviation exceeding the standard within the preset calculation period based on the allowable limit and the frequency deviation rate; S44. Obtain the frequency of harmonic distortion exceeding the standard within the preset calculation period based on the allowable limit and the total harmonic distortion rate. S45. Obtain the power quality degradation index based on the frequency of voltage deviation exceeding the standard, the frequency of frequency deviation exceeding the standard, and the frequency of harmonic distortion exceeding the standard. S46. Obtain the nonlinear load ratio and sensitive load capacity ratio based on the power quality parameters, obtain the quality-energy efficiency coupling coefficient based on the nonlinear load ratio and the sensitive load capacity ratio, and obtain the power quality influence factor based on the quality-energy efficiency coupling coefficient and the power quality degradation index.

6. The accounting and evaluation method for carbon verification and electricity data fusion as described in claim 1, characterized in that, Step S5 includes: S51. Obtain the energy efficiency benchmark value corresponding to the energy efficiency impact factor, and obtain the energy efficiency deviation based on the energy efficiency benchmark value and the energy efficiency impact factor; S52. Obtain the power quality benchmark value corresponding to the power quality influence factor, and obtain the power quality deviation based on the power quality benchmark value and the power quality influence factor; S53. The energy efficiency deviation is processed by a preset mathematical mapping function to obtain the energy efficiency adjustment coefficient; S54. The power quality deviation is processed by a preset mathematical mapping function to obtain the quality adjustment coefficient; S55. Based on the initial accounting evaluation value, the energy efficiency adjustment coefficient, and the quality adjustment coefficient, a comprehensive accounting evaluation value is obtained through weighted correction calculation.

7. A carbon verification and electricity data fusion accounting and evaluation system, characterized in that, include: The first acquisition module is used to acquire carbon verification data and power operation data of the target accounting unit; The second acquisition module is used to acquire direct carbon emission parameters and indirect carbon emission parameters based on the carbon verification data, and to acquire an initial accounting assessment value based on the direct carbon emission parameters and the indirect carbon emission parameters. The third acquisition module is used to acquire power efficiency parameters and power quality parameters based on the power operation data, and to acquire multiple reactive power and multiple active power within a preset calculation period based on the power efficiency parameters, and to acquire energy efficiency impact factors based on the reactive power and active power. The fourth acquisition module is used to acquire the power quality impact factor based on the power quality parameters and the energy efficiency impact factor; The fusion module is used to fuse the initial accounting assessment value, the energy efficiency impact factor, and the power quality impact factor to obtain a comprehensive accounting assessment value, and to use the comprehensive accounting assessment value as the accounting assessment result of the fusion of carbon verification and power data.

8. The carbon verification and power data fusion accounting and evaluation system as described in claim 7, characterized in that, The second acquisition module includes: The first acquisition unit is used to acquire the types and net consumption of fossil fuels consumed by stationary combustion equipment based on the direct carbon emission parameters. The second acquisition unit is used to acquire the calorific value, carbon content per unit calorific value, and default value of carbon oxidation rate for each type of fossil fuel, and to acquire the direct carbon emission intensity based on the calorific value, the carbon content per unit calorific value, the default value of carbon oxidation rate, and the net consumption. The third acquisition unit is used to acquire the total consumption of purchased electricity and the dynamic marginal emission factor for a preset time period based on the indirect carbon emission parameters, and to acquire the indirect carbon emission intensity based on the total consumption and the dynamic marginal emission factor. The fourth acquisition unit is used to acquire carbon emission accounting correction factors based on the direct carbon emission intensity and the indirect carbon emission intensity; The fifth acquisition unit is used to acquire the corresponding historical carbon emission baseline value based on the target accounting unit, and to acquire the initial accounting assessment value based on the historical carbon emission baseline value and the carbon emission accounting correction factor.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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