Greenhouse gas carbon emission estimation method and system based on Bayesian algorithm

Abstract

The invention relates to the technical field of greenhouse gas monitoring, and particularly discloses a greenhouse gas carbon emission estimation method and system based on a Bayesian algorithm, and the method comprises the steps: collecting the transportation data, power generation equipment data and power supply area data of each energy output node; processing the transportation data to obtain an energy transportation efficiency evaluation index of each energy output node, and performing comprehensive analysis to obtain a carbon emission intensity evaluation value of each energy output node; processing the data of the power supply area to obtain a power supply demand evaluation index, and performing primary carbon emission early warning; and processing to obtain a regional predicted carbon emission intensity evaluation value and carrying out secondary carbon emission early warning. According to the greenhouse gas carbon emission estimation method and system based on the Bayesian algorithm provided by the invention, the carbon emission condition of each energy output node can be determined, so that measures can be taken to reduce the carbon emission intensity, the continuous development of the energy technology can be promoted, the energy utilization efficiency and environmental protection performance can be improved, and the market competitiveness can be enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of greenhouse gas monitoring, and in particular to a method and system for estimating greenhouse gas carbon emissions based on a Bayesian algorithm. Background Art

[0002] At present, carbon emission estimation methods have evolved from simple to complex and from qualitative to quantitative. Due to technical and data limitations in the early days, carbon emission estimation mainly relied on simple estimation methods and empirical formulas. With the development of science and technology and the improvement of data collection methods, carbon emission estimation methods have gradually become more accurate and reliable. Through accurate carbon emission estimation, we can understand the carbon emissions of different industries and regions, and provide a scientific basis for formulating targeted emission reduction policies and measures.

[0003] For example, the invention patent with announcement number CN115984069B is a carbon emission data processing and analysis method based on a carbon metering edge integrated machine. The specific steps include: Step 1: Obtain the main target for carbon emission data collection, and install the carbon metering edge integrated machine according to the obtained main target. The carbon metering edge integrated machine is used to collect carbon emission data; Step 2: Select the corresponding greenhouse gas emission accounting standard for application according to the actual application scenario; Step 3: Collect carbon emission data through the carbon metering edge integrated machine, and preliminarily process the collected carbon emission data to obtain initial data; Step 4: Upload the obtained initial data to the chain based on blockchain technology; Step 5: Visualize the initial data based on 3D visualization technology to obtain a visualization data display model.

[0004] For example, the invention patent with announcement number CN118839843A is a carbon emission remote sensing monitoring system and method for urban and park scales. This method is based on the spatial autocorrelation of atmospheric CO2 concentration and uses the statistical results of the local Moran's I index to automatically and objectively identify the XCO2 enhancement signal caused by human emissions on the satellite observation strip; the carbon emission estimation method simulated by the XCO2 enhancement value constraint model of satellite observation, established the mapping relationship between atmospheric CO2 concentration and human emissions through the WRF-Chem atmospheric transmission model, and quantified the impact of atmospheric transmission on emission estimation; the framework of the top-down quantitative estimation of carbon emissions method simulated by the artificial XCO2 signal constraint model of satellite observation.

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application discovered that the above technology has at least the following technical problems: the current greenhouse gas carbon emission estimation method focuses more on the precise measurement of carbon emissions at the micro level, but due to the uncertainty of the data and the complexity of the emission sources, the carbon emission estimation results may have large errors, and the accuracy of the estimation results is difficult to guarantee. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a method and system for estimating greenhouse gas carbon emissions based on a Bayesian algorithm, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a greenhouse gas carbon emission estimation method based on the Bayesian algorithm, including: marking statistical regional power stations as various energy output nodes, and collecting transportation data, power generation equipment data, production capacity data and power supply area data of each energy output node.

[0008] The transportation data is processed to obtain the energy transportation efficiency evaluation index of each energy output node, and the power generation equipment data is processed to obtain the standard coal consumption evaluation index for power generation of each energy output node. According to the energy transportation efficiency evaluation index, standard coal consumption evaluation index for power generation and production capacity data of each energy output node, a comprehensive analysis is performed to obtain the carbon emission intensity assessment value of each energy output node.

[0009] The power supply area data is processed to obtain a power supply demand assessment index, and based on the power supply demand assessment index, an updated carbon emission intensity assessment threshold is obtained and a carbon emission early warning is issued.

[0010] Statistics are collected on the historical regional load and the carbon emission intensity assessment values ​​of each energy output node. The regional predicted load is obtained at the same time. The regional predicted carbon emission intensity assessment value is processed and a secondary carbon emission warning is carried out.

[0011] As a further method, the transportation data is processed to obtain the energy transportation efficiency evaluation index of each energy output node. The specific processing process is: the transportation data includes the fuel sulfur content, energy transportation volume and energy transportation path length of each transportation.

[0012] The critical fuel sulfur content, reference standard energy transportation volume, allowable deviation energy transportation volume and critical energy transportation path length are extracted from the carbon emission database, and the energy transportation efficiency evaluation index of each energy output node is obtained through comprehensive analysis. The energy transportation efficiency evaluation index is used to quantify the efficiency of energy transportation.

[0013] As a further method, the power generation equipment data is processed to obtain the standard coal consumption evaluation index of each energy output node. The specific processing process is: the power generation equipment data includes the fuel consumption, surface temperature, main steam pressure and generator speed of each power generation equipment.

[0014] The critical fuel consumption, reference standard surface temperature, allowable deviation surface temperature, critical main steam pressure, reference standard generator speed and allowable deviation generator speed are extracted from the carbon emission database. The standard coal consumption assessment index for power generation at each energy output node is obtained through comprehensive analysis. Based on the standard coal consumption assessment index for power generation, it is determined whether frequency conversion transformation of the equipment is required.

[0015] As a further method, the comprehensive analysis obtains the carbon emission intensity assessment value of each energy output node. The specific analysis process is as follows: the production capacity data includes the total electricity output value and the clean energy electricity output value.

[0016] The critical total electricity output value, the reference clean energy electricity output ratio and the allowable deviation clean energy electricity output ratio are extracted from the carbon emission database. Based on the energy transportation efficiency evaluation index, the standard coal consumption evaluation index for power generation, the electricity output value and the clean energy electricity output ratio, a comprehensive analysis is performed to obtain the carbon emission intensity assessment value of each energy output node.

[0017] As a further method, the power supply area data is processed to obtain a power supply demand assessment index, and the specific processing process is: the power supply area data includes the area, voltage level and population density of each power supply area.

[0018] The critical area, critical voltage level and critical population density are extracted from the carbon emission database, and a power supply demand assessment index is obtained through comprehensive analysis. The power supply demand assessment index is used to quantify the degree of power supply demand.

[0019] As a further method, the updated carbon emission intensity assessment threshold is obtained according to the power supply demand assessment index. The specific processing process is: the power supply demand assessment index is input into the carbon emission database to match the carbon emission intensity assessment threshold correction value corresponding to each power supply demand assessment index interval, the carbon emission intensity assessment threshold is extracted from the carbon emission database, and the carbon emission intensity assessment threshold is added to the carbon emission intensity assessment threshold correction value to obtain the updated carbon emission intensity assessment threshold.

[0020] As a further method, a carbon emission warning is carried out, and the specific process is: summing up the carbon emission intensity assessment values ​​of each energy output node to obtain a regional carbon emission intensity assessment value, and comparing the regional carbon emission intensity assessment value with the updated carbon emission intensity assessment threshold. If the regional carbon emission intensity assessment value is less than the updated carbon emission intensity assessment threshold, no additional operation is performed; if the regional carbon emission intensity assessment value is greater than or equal to the updated carbon emission intensity assessment threshold, a warning feedback is performed.

[0021] As a further method, the processing obtains a regional predicted carbon emission intensity assessment value and performs a secondary carbon emission warning. The specific process is: a Bayesian model is trained based on the historical regional load and the historical carbon emission intensity assessment values ​​of each energy output node, the regional predicted load is input into the Bayesian model to obtain the regional predicted carbon emission intensity assessment value, and the regional predicted carbon emission intensity assessment value is compared with the updated carbon emission intensity assessment threshold. If the regional predicted carbon emission intensity assessment value is greater than or equal to the updated carbon emission intensity assessment threshold, the relevant staff is notified to issue a warning. If the regional predicted carbon emission intensity assessment value is less than the updated carbon emission intensity assessment threshold, no additional operation is performed.

[0022] As a further method, the carbon emission intensity assessment value of each energy output node is specifically expressed as:

[0023]

[0024] Among them, TP j represents the carbon emission intensity assessment value of the j-th energy output node, EN j represents the energy transport efficiency evaluation index of the jth energy output node, FM j represents the standard coal consumption evaluation index of power generation at the j-th energy output node, DP j represents the power output value of the jth energy output node, DP0 represents the critical power output value, R j represents the proportion of clean energy electricity output of the j-th energy output node, R0 represents the proportion of clean energy electricity output of the reference standard, θ1 represents the carbon emission intensity assessment impact factor corresponding to the set energy transportation efficiency assessment index, θ2 represents the carbon emission intensity assessment impact factor corresponding to the set power generation standard coal consumption assessment index, θ3 represents the carbon emission intensity assessment impact factor corresponding to the set electricity output value, θ4 represents the carbon emission intensity assessment impact factor corresponding to the set clean energy electricity output proportion, j represents the number of each energy output node, j=1,2,3,...,n, n represents the total number of energy output nodes.

[0025] The second aspect of the present invention provides a greenhouse gas carbon emission estimation system based on the Bayesian algorithm, including: a data acquisition module for statistically marking regional power stations as various energy output nodes, and collecting transportation data, power generation equipment data, production capacity data and power supply area data of each energy output node.

[0026] The carbon emission intensity analysis module is used to process the transportation data to obtain the energy transportation efficiency evaluation index of each energy output node, and to process the power generation equipment data to obtain the standard coal consumption evaluation index of each energy output node. Based on the energy transportation efficiency evaluation index, standard coal consumption evaluation index and production capacity data of each energy output node, a comprehensive analysis is performed to obtain the carbon emission intensity evaluation value of each energy output node.

[0027] A primary carbon emission warning module is used to process the power supply area data to obtain a power supply demand assessment index, and to update the carbon emission intensity assessment threshold based on the power supply demand assessment index and issue a primary carbon emission warning.

[0028] The secondary carbon emission warning module is used to count the historical regional loads and the carbon emission intensity assessment values ​​of each historical energy output node, and at the same time obtain the regional predicted load, process the regional predicted carbon emission intensity assessment value and conduct secondary carbon emission warning.

[0029] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0030] (1) The present invention provides a greenhouse gas carbon emission estimation method and system based on the Bayesian algorithm, which can clarify the carbon emission situation of each energy output node, help take measures to reduce carbon emission intensity, further promote the continuous development of energy technology, improve energy utilization efficiency and environmental protection performance, and at the same time help formulate more scientific and reasonable emission reduction plans and enhance market competitiveness.

[0031] (2) The present invention can reveal the energy efficiency bottleneck in the energy transportation process by evaluating the energy transportation efficiency evaluation index of each energy output node, thereby providing a clear direction for optimizing energy transportation, directly reducing energy consumption, and reducing carbon emissions caused by energy consumption, which helps to promote the green development of the energy industry, further optimize the energy structure, and improve the overall efficiency of the energy system.

[0032] (3) The present invention helps to optimize the energy structure by evaluating the standard coal consumption evaluation index of each energy output node, thereby improving the overall energy utilization efficiency. It can also identify problems of energy waste and emission emissions, and take corresponding measures to improve them. It helps to promote energy conservation and emission reduction, and can also help power generation companies optimize resource allocation and enhance market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0034] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0035] Figure 2 It is a schematic diagram of system module connection of the present invention.

[0036] Figure 3 It is a schematic diagram of the functional relationship between the carbon emission intensity assessment value of each energy output node and the power generation standard coal consumption assessment index of each energy output node of the present invention. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0038] Reference Figure 1 As shown, the first aspect of the present invention provides a greenhouse gas carbon emission estimation method based on the Bayesian algorithm, including: marking statistical regional power stations as energy output nodes, and collecting transportation data, power generation equipment data, production capacity data and power supply area data of each energy output node.

[0039] The transportation data is processed to obtain the energy transportation efficiency evaluation index of each energy output node, and the power generation equipment data is processed to obtain the standard coal consumption evaluation index for power generation of each energy output node. According to the energy transportation efficiency evaluation index, standard coal consumption evaluation index for power generation and production capacity data of each energy output node, a comprehensive analysis is performed to obtain the carbon emission intensity assessment value of each energy output node.

[0040] The power supply area data is processed to obtain a power supply demand assessment index, and based on the power supply demand assessment index, an updated carbon emission intensity assessment threshold is obtained and a carbon emission early warning is issued.

[0041] Statistics are collected on the historical regional load and the carbon emission intensity assessment values ​​of each energy output node. The regional predicted load is obtained at the same time. The regional predicted carbon emission intensity assessment value is processed and a secondary carbon emission warning is carried out.

[0042] Specifically, the transportation data is processed to obtain the energy transportation efficiency evaluation index of each energy output node. The specific processing process is: the transportation data includes the fuel sulfur content, energy transportation volume and energy transportation path length of each transportation.

[0043] The critical fuel sulfur content, reference standard energy transportation volume, allowable deviation energy transportation volume and critical energy transportation path length are extracted from the carbon emission database, and the energy transportation efficiency evaluation index of each energy output node is obtained through comprehensive analysis. The energy transportation efficiency evaluation index is used to quantify the efficiency of energy transportation.

[0044] In a specific embodiment, the sulfur content of fuel refers to the average sulfur content of various types of fuels, which can be obtained by a sulfur meter; the energy transportation volume refers to the space occupied by energy during transportation. A reasonable energy transportation volume can improve the loading efficiency of transportation tools, reduce the time difference between empty and full loads, thereby improving the overall efficiency of energy transportation, which can be measured by a 3D scanner; the energy transportation path length refers to the length of the route that energy passes from the output node to the destination. A shorter energy transportation path length can shorten the transportation time of energy from the output node to the destination, improve the timeliness and stability of energy supply, and can be obtained through map software.

[0045] Furthermore, the energy transport efficiency evaluation index of each energy output node is expressed as follows:

[0046]

[0047] Among them, EN j represents the energy transport efficiency evaluation index of the jth energy output node, Rh jx represents the sulfur content of the fuel transported for the xth time at the jth energy output node, Rh0 represents the critical fuel sulfur content, V jx represents the energy transport volume of the xth transport of the jth energy output node, V0 represents the reference standard energy transport volume, ΔV represents the allowable deviation energy transport volume, and Ys jx represents the energy transport path length of the xth transport of the jth energy output node, Ys0 represents the critical energy transport path length, Indicates the impact factor of energy transportation efficiency assessment corresponding to the set fuel sulfur content, Indicates the energy transportation efficiency assessment impact factor corresponding to the set energy transportation volume, It represents the energy transportation efficiency assessment influencing factor corresponding to the set energy transportation path length, x represents the number of each transportation time, x=1,2,3,...,y, y represents the total number of transportation times, j represents the number of each energy output node, j=1,2,3,...,n, n represents the total number of energy output nodes.

[0048] The algorithm of this embodiment combines the fuel sulfur content, energy transportation volume and energy transportation path length of each transportation, and comprehensively analyzes to obtain the energy transportation efficiency evaluation index of each energy output node. The higher the sulfur content of the fuel, the more stringent supervision and safety measures are required during transportation, which indirectly affects the transportation volume; the fuel sulfur content may affect the path selection of energy transportation. Some areas may have strict restrictions on fuel sulfur content, which may also force energy output nodes to choose longer transportation paths to bypass these restricted areas; the energy transportation volume usually affects the choice of transportation path length. For example, for larger volumes of energy transportation, it may be necessary to choose wider and flatter roads or waterways to ensure the safety and efficiency of transportation, which in turn affects the length of the transportation path. Comprehensive analysis can obtain a more comprehensive energy transportation efficiency evaluation index.

[0049] It should be explained that three key factors are considered in this embodiment, namely, the sulfur content of the fuel, the energy transportation volume and the energy transportation path length of each transportation, which can significantly reduce the emission of pollutants such as sulfur oxides generated during energy transportation, can reduce fuel consumption, time cost and labor cost during transportation, help shorten transportation time, improve the loading efficiency and utilization rate of transportation tools, thereby improving overall transportation efficiency, and can also reduce safety risks during transportation, and promote the development of clean energy and renewable energy. By standardizing the sulfur content of the fuel, the energy transportation volume and the energy transportation path length of each transportation, ensuring that they are compared at the same level, the fairness and comparability of the evaluation are improved, and at the same time, the setting of Rh0, V0 and Ys0 can avoid the transportation efficiency problems caused by excessive sulfur content in the fuel, unreasonable energy transportation volume and excessive energy transportation path length. By weighting the influence of the sulfur content of the fuel, the energy transportation volume and the energy transportation path length of each transportation, it reflects their relative importance in the evaluation index, and the weights of different factors can be adjusted according to different needs, so that the formula has good adaptability. It is not difficult to see that the smaller the fuel sulfur content or energy transportation volume deviation or energy transportation path length, the greater the energy transportation efficiency evaluation index. By evaluating the energy transportation efficiency evaluation index of each energy output node, it is possible to reveal the energy efficiency bottleneck in the energy transportation process, thereby providing a clear direction for optimizing energy transportation, which can directly reduce energy consumption and energy costs, while reducing carbon emissions caused by energy consumption, helping to promote the green development of the energy industry, reduce the impact on the environment, further optimize the energy structure, and improve the overall efficiency of the energy system.

[0050] In a specific embodiment, the value range of the energy transportation efficiency assessment influencing factor corresponding to the fuel sulfur content, energy transportation volume and energy transportation path length is between 0 and 1, which represents the numerical value of the influence degree of the fuel sulfur content, energy transportation volume and energy transportation path length on the energy transportation efficiency assessment index. Each energy transportation efficiency assessment influencing factor can be obtained from the carbon emission database. By adjusting the value of the influencing factor, the influence degree of different factors on the final energy transportation efficiency assessment index can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the fuel sulfur content, energy transportation volume and energy transportation path length form a mapping set with the weight factors corresponding to the fuel sulfur content, energy transportation volume and energy transportation path length preset in the carbon emission database, and the real-time fuel sulfur content, energy transportation volume and energy transportation path length are brought into the mapping set to obtain the weight factors corresponding to the fuel sulfur content, energy transportation volume and energy transportation path length. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0051] Specifically, the power generation equipment data is processed to obtain the power generation standard coal consumption evaluation index of each energy output node. The specific processing process is: the power generation equipment data includes the fuel consumption, surface temperature, main steam pressure and generator speed of each power generation equipment.

[0052] The critical fuel consumption, reference standard surface temperature, allowable deviation surface temperature, critical main steam pressure, reference standard generator speed and allowable deviation generator speed are extracted from the carbon emission database. The standard coal consumption assessment index for power generation at each energy output node is obtained through comprehensive analysis. Based on the standard coal consumption assessment index for power generation, it is determined whether frequency conversion transformation of the equipment is required.

[0053] In a specific embodiment, fuel consumption refers to the amount of fuel consumed by the power generation equipment per hour under specific conditions, which can be directly measured by a fuel meter; the surface temperature of the equipment is an important indicator reflecting the operating status of the equipment. By real-time monitoring and analysis of the surface temperature, problems such as equipment overheating and poor cooling can be discovered in a timely manner, and the surface temperature of the power generation equipment can be measured by an infrared thermometer; the main steam pressure refers to the pressure in the main steam pipeline of the power generation equipment, which can be measured by a pressure sensor; the generator speed refers to the rotation speed of the generator rotor, which can be measured by a speed sensor.

[0054] Furthermore, the specific numerical expression of the standard coal consumption evaluation index of each energy output node is:

[0055]

[0056] Among them, FM j represents the standard coal consumption evaluation index of the j-th energy output node, e represents the natural constant, Rxji represents the fuel consumption of the i-th power generation equipment at the j-th energy output node, Rx0 represents the critical fuel consumption, Hn ji represents the surface temperature of the i-th power generation equipment at the j-th energy output node, Hn0 represents the reference standard surface temperature, ΔHn represents the allowable deviation surface temperature, and Y ji represents the main steam pressure of the i-th power generation equipment at the j-th energy output node, Y0 represents the critical main steam pressure, Ch ji represents the generator speed of the ith power generation equipment at the jth energy output node, Ch0 represents the reference standard generator speed, ΔCh represents the allowable deviation generator speed, α1 represents the power generation standard coal consumption assessment influence factor corresponding to the set fuel consumption, α2 represents the power generation standard coal consumption assessment influence factor corresponding to the set surface temperature, α represents the power generation standard coal consumption assessment influence factor corresponding to the set main steam pressure, α4 represents the power generation standard coal consumption assessment influence factor corresponding to the set generator speed, i represents the number of each power generation equipment, i=1,2,3,...,m, m represents the total number of power generation equipment, j represents the number of each energy output node, j=1,2,3,...,n, n represents the total number of energy output nodes.

[0057] The algorithm of this embodiment combines the fuel consumption, surface temperature, main steam pressure and generator speed of each power generation equipment, and comprehensively analyzes to obtain the standard coal consumption evaluation index of each energy output node. As the main steam pressure increases, the work capacity of the steam is enhanced, thereby improving the efficiency of the thermal system and reducing fuel consumption; the impact of surface temperature on fuel consumption is mainly reflected in equipment efficiency and heat loss. For example, if the surface temperature of the generator or steam turbine is too high, it may cause increased heat loss, thereby reducing equipment efficiency and increasing fuel consumption; changes in main steam pressure may affect the surface temperature of the equipment. For example, an increase in main steam pressure may increase the temperature and pressure of the steam inside the equipment, thereby causing an increase in the surface temperature of the equipment; at the same time, within a certain range, an increase in the generator speed may increase the output power, but it will also increase additional consumption such as friction loss and air resistance, so the fuel consumption may increase. Comprehensive analysis can obtain a more comprehensive standard coal consumption evaluation index for power generation.

[0058] It should be explained that in this embodiment, four key factors are considered, namely, the fuel consumption, surface temperature, main steam pressure and generator speed of each power generation equipment, so that the problem of energy waste can be discovered in time and corresponding optimization measures can be taken to ensure that steam can effectively work in the turbine, reduce energy loss, thereby improving energy utilization efficiency, helping to avoid equipment failure and damage, improving equipment performance and stability, and reducing emissions of pollutants such as carbon dioxide and nitrogen oxides, and reducing pollution and damage to the environment. By standardizing the fuel consumption, surface temperature, main steam pressure and generator speed of each power generation equipment, ensuring that they are compared at the same level, the fairness and comparability of the evaluation are improved, and the setting of Y0 can avoid unit safety problems caused by overpressure. By weighting the influence of the fuel consumption, surface temperature, main steam pressure and generator speed of each power generation equipment, it reflects their relative importance in the evaluation index, and the weights of different factors can be adjusted according to different needs, so that the formula has good adaptability. It is not difficult to see that the greater the fuel consumption or the greater the surface temperature deviation or the smaller the main steam pressure or the greater the generator speed deviation, the greater the power generation standard coal consumption evaluation index. By evaluating the standard coal consumption assessment index for power generation at each energy output node, it is helpful to optimize the energy structure and allocate more resources to nodes with high energy utilization efficiency, thereby improving the overall energy utilization efficiency. It can also identify problems of energy waste and emissions, and take corresponding measures to improve them. This will help promote energy conservation and emission reduction, reduce environmental pollution, achieve sustainable development, and help power generation companies optimize resource allocation and enhance market competitiveness.

[0059] In a specific embodiment, the value range of the standard coal consumption assessment influencing factor corresponding to the fuel consumption, surface temperature, main steam pressure and generator speed is between 0 and 1, which represents the numerical value of the influence of the fuel consumption, surface temperature, main steam pressure and generator speed on the standard coal consumption assessment index of power generation. Each standard coal consumption assessment influencing factor can be obtained from the carbon emission database. By adjusting the value of the influencing factor, the influence of different factors on the final standard coal consumption assessment index of power generation can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the fuel consumption, surface temperature, main steam pressure and generator speed form a mapping set with the weight factors corresponding to the fuel consumption, surface temperature, main steam pressure and generator speed preset in the carbon emission database. The real-time fuel consumption, surface temperature, main steam pressure and generator speed are brought into the mapping set to obtain the weight factors corresponding to the fuel consumption, surface temperature, main steam pressure and generator speed. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0060] Furthermore, whether to perform frequency conversion transformation on the equipment is determined according to the standard coal consumption evaluation index for power generation. The specific process is: extracting the standard coal consumption evaluation threshold for power generation from the carbon emission database, comparing the standard coal consumption evaluation index for power generation of each energy output node with the standard coal consumption evaluation threshold for power generation; if the standard coal consumption evaluation index for power generation of an energy output node is greater than or equal to the standard coal consumption evaluation threshold for power generation, it is necessary to perform frequency conversion transformation on the equipment at the node, that is, adjust the frequency of the inverter until the standard coal consumption evaluation index for power generation of the energy output node is less than the standard coal consumption evaluation threshold for power generation; if the standard coal consumption evaluation index for power generation of an energy output node is less than the standard coal consumption evaluation threshold for power generation, no additional operation is performed.

[0061] Specifically, a comprehensive analysis is performed to obtain the carbon emission intensity assessment value of each energy output node. The specific analysis process is as follows: the production capacity data includes the total electricity output value and the clean energy electricity output value.

[0062] The critical total electricity output value, the reference clean energy electricity output ratio and the allowable deviation clean energy electricity output ratio are extracted from the carbon emission database. Based on the energy transportation efficiency evaluation index, the standard coal consumption evaluation index for power generation, the electricity output value and the clean energy electricity output ratio, a comprehensive analysis is performed to obtain the carbon emission intensity assessment value of each energy output node.

[0063] In a specific embodiment, the total electricity output value refers to the total amount of all electric energy produced by the energy output node during the monitoring period, which is directly measured by the electric energy metering equipment installed on the energy output node; the clean energy electricity output value refers to the total amount of electric energy produced by the energy output node using clean energy during the monitoring period, which is measured by installing corresponding electric energy metering equipment on different types of clean energy power generation equipment, such as turbines, wind turbines and solar panels.

[0064] Furthermore, the carbon emission intensity assessment value of each energy output node is expressed as follows:

[0065]

[0066] Among them, TP j represents the carbon emission intensity assessment value of the j-th energy output node, EN j represents the energy transport efficiency evaluation index of the jth energy output node, FM j represents the standard coal consumption evaluation index of power generation at the j-th energy output node, DP j represents the power output value of the jth energy output node, DP0 represents the critical power output value, R jrepresents the proportion of clean energy electricity output of the j-th energy output node, R0 represents the proportion of clean energy electricity output of the reference standard, θ1 represents the carbon emission intensity assessment impact factor corresponding to the set energy transportation efficiency assessment index, θ2 represents the carbon emission intensity assessment impact factor corresponding to the set power generation standard coal consumption assessment index, θ3 represents the carbon emission intensity assessment impact factor corresponding to the set electricity output value, θ4 represents the carbon emission intensity assessment impact factor corresponding to the set clean energy electricity output proportion, j represents the number of each energy output node, j=1,2,3,...,n, n represents the total number of energy output nodes.

[0067] like Figure 3 As shown, in a specific embodiment, θ1=θ3=0.3, θ2=θ4=0.4, j=n=1, DP j =20 kWh, DP0 =50 kWh, R j =51%, R0=37%, when EN j =0.1, the functional relationship between the carbon emission intensity assessment value of each energy output node and the power generation standard coal consumption assessment index of each energy output node is shown in curve a; when EN j =0.5, the functional relationship between the carbon emission intensity assessment value of each energy output node and the power generation standard coal consumption assessment index of each energy output node is shown in curve b; when EN j =1, the functional relationship between the carbon emission intensity assessment value of each energy output node and the power generation standard coal consumption assessment index of each energy output node is shown in curve c.

[0068] The algorithm of this embodiment combines the energy transportation efficiency assessment index, the standard coal consumption assessment index for power generation, the electricity output value and the proportion of clean energy electricity output, and comprehensively analyzes to obtain the carbon emission intensity assessment value of each energy output node. The more efficient the energy transportation, the less energy loss in the transmission process, thereby improving energy utilization and electricity output value; clean energy usually has lower carbon emissions and environmental pollution, so increasing the proportion of clean energy electricity output helps to reduce the standard coal consumption in the power generation process; the lower the standard coal consumption for power generation, the higher the power generation efficiency, the less energy consumed under the same output, and the higher the electricity output value. Comprehensive analysis can obtain a more comprehensive carbon emission intensity assessment value.

[0069] It should be explained that four key factors are considered in this embodiment, namely, the energy transportation efficiency evaluation index, the standard coal consumption evaluation index for power generation, the power output value, and the proportion of clean energy power output. It helps to pay more attention to the use of clean energy while ensuring power output, and can comprehensively evaluate and optimize energy output nodes, which helps to find the best energy utilization and power generation plan to maximize benefits. By weighting the impact of the energy transportation efficiency evaluation index, the standard coal consumption evaluation index for power generation, the power output value, and the proportion of clean energy power output, it reflects their relative importance in the evaluation index. The weights of different factors can be adjusted according to different needs, making the formula very adaptable. It is not difficult to see that when the energy transportation efficiency evaluation index is smaller or the standard coal consumption evaluation index for power generation is larger or the power output value is larger or the proportion of clean energy power output is smaller, the carbon emission intensity evaluation value is larger. By evaluating the carbon emission intensity assessment value of each energy output node, the carbon emission situation of each energy output node can be clarified, thereby promoting relevant parties to take measures to reduce carbon emission intensity and promote low-carbon development. It will help promote the continuous development of energy technology, improve the efficiency of energy utilization and environmental protection performance, and promote relevant parties to formulate more scientific and reasonable emission reduction plans and enhance market competitiveness.

[0070] In a specific embodiment, the carbon emission intensity assessment influencing factors corresponding to the energy transportation efficiency assessment index, the power generation standard coal consumption assessment index, the electricity output value and the clean energy electricity output ratio are in the range of 0 to 1, indicating the numerical value of the influence of the energy transportation efficiency assessment index, the power generation standard coal consumption assessment index, the electricity output value and the clean energy electricity output ratio on the carbon emission intensity assessment value. Each carbon emission intensity assessment influencing factor can be obtained from the carbon emission database. By adjusting the value of the influencing factor, the influence of different factors on the final carbon emission intensity assessment value can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship, for example The energy transportation efficiency evaluation index, the standard coal consumption evaluation index for power generation, the electricity output value and the proportion of clean energy electricity output are connected to the weight factors corresponding to the energy transportation efficiency evaluation index, the standard coal consumption evaluation index for power generation, the electricity output value and the proportion of clean energy electricity output preset in the carbon emission database to form a mapping set. The real-time energy transportation efficiency evaluation index, the standard coal consumption evaluation index for power generation, the electricity output value and the proportion of clean energy electricity output are brought into the mapping set to obtain the weight factors corresponding to the energy transportation efficiency evaluation index, the standard coal consumption evaluation index for power generation, the electricity output value and the proportion of clean energy electricity output. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0071] Specifically, the power supply area data is processed to obtain the power supply demand assessment index, and the specific processing process is: the power supply area data includes the area, voltage level and population density of each power supply area.

[0072] The critical area, critical voltage level and critical population density are extracted from the carbon emission database, and a power supply demand assessment index is obtained through comprehensive analysis. The power supply demand assessment index is used to quantify the degree of power supply demand.

[0073] In a specific embodiment, the area of ​​the power supply area can be obtained through a map measurement tool; the voltage level is an important parameter in the power system, which determines the transmission efficiency of electric energy and the safety of the power grid, and can be directly obtained through power equipment; population density refers to the number of people per square kilometer in the power supply area, which is an important indicator reflecting the load density, electricity demand and power facility planning of the power supply area, and can be obtained through a geographic information system.

[0074] Furthermore, the specific numerical expression of the power supply demand assessment index is:

[0075]

[0076] Among them, GN represents the power supply demand assessment index, S a represents the area of ​​the ath power supply area, S0 represents the critical area, Ud a represents the voltage level of the ath power supply area, Ud0 represents the critical voltage level, Hu a represents the population density of the ath power supply area, Hu0 represents the critical population density, τ1 represents the power supply demand assessment impact factor corresponding to the set area, τ2 represents the power supply demand assessment impact factor corresponding to the set voltage level, τ3 represents the power supply demand assessment impact factor corresponding to the set population density, a represents the number of each power supply area, a=1,2,3,...,b, b represents the total number of power supply areas.

[0077] The algorithm of this embodiment combines the regional area, voltage level and population density of each power supply area, and comprehensively analyzes to obtain the power supply demand assessment index. The larger the regional area, the higher the power supply demand, because a wider geographical area needs to be covered. To meet this demand, a higher voltage level needs to be selected to transmit electric energy to reduce power loss and improve transmission efficiency. At the same time, the regional area also determines the layout and scale of the power grid. In a power supply area with a larger area, it may be necessary to set up multiple substations and transmission lines, and use a higher voltage level to connect these facilities to ensure stable transmission and distribution of electric energy. The higher the population density, the higher the load density. In order to meet these high load demands, a higher voltage level needs to be selected to provide sufficient electric energy and ensure the stability and reliability of the power grid. At the same time, in areas with higher population density, the optimization and upgrading of the power grid are more frequent. By increasing the voltage level, optimizing the power grid structure and other measures, the power supply capacity and efficiency of the power grid can be improved to meet the growing electricity demand. A more comprehensive power supply demand assessment index can be obtained through comprehensive analysis.

[0078] It should be explained that in this embodiment, three key factors are considered, namely, the regional area, voltage level and population density of each power supply area, which can plan the layout of the power grid more scientifically, ensure that the geographical distribution of power grid facilities is more reasonable, reduce the loss in the power transmission process, improve the overall efficiency of the power grid, ensure the effective allocation of power resources in the power supply area, meet the power demand of different regions, help identify the weak links in the power grid, and take corresponding measures to strengthen it, thereby enhancing the resilience of the power grid and improving the reliability and stability of power supply. By standardizing the regional area, voltage level and population density of each power supply area, ensuring that they are compared at the same magnitude, the fairness and comparability of the evaluation are improved. By weighting the influence of the regional area, voltage level and population density of each power supply area, it reflects their relative importance in the evaluation index, and the weights of different factors can be adjusted according to different needs, so that the formula has good adaptability. It is not difficult to see that the larger the regional area, voltage level or population density, the larger the power supply demand evaluation index. By evaluating the power supply demand assessment index, we can more scientifically determine the reasonable range of carbon emission intensity, thereby setting a more accurate assessment threshold, which will help ensure the accuracy and fairness of the carbon emission intensity assessment and provide strong support for subsequent emission reduction work.

[0079] In a specific embodiment, the value range of the power supply demand assessment influencing factor corresponding to the regional area, voltage level and population density is between 0 and 1, which represents the numerical value of the influence of the regional area, voltage level and population density on the power supply demand assessment index. Each power supply demand assessment influencing factor can be obtained from the carbon emission database. By adjusting the value of the influencing factor, the influence of different factors on the final power supply demand assessment index can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the regional area, voltage level and population density form a mapping set with the weight factors corresponding to the regional area, voltage level and population density preset in the carbon emission database, and the real-time regional area, voltage level and population density are brought into the mapping set to obtain the weight factors corresponding to the regional area, voltage level and population density. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0080] Furthermore, an updated carbon emission intensity assessment threshold is obtained according to the power supply demand assessment index. The specific processing process is: the power supply demand assessment index is input into the carbon emission database to match the carbon emission intensity assessment threshold correction value corresponding to each power supply demand assessment index interval, the carbon emission intensity assessment threshold is extracted from the carbon emission database, and the carbon emission intensity assessment threshold is added to the carbon emission intensity assessment threshold correction value to obtain an updated carbon emission intensity assessment threshold.

[0081] Specifically, a carbon emission early warning is carried out, and the specific process is as follows: the carbon emission intensity assessment values ​​of each energy output node are summed up to obtain the regional carbon emission intensity assessment value, and the regional carbon emission intensity assessment value is compared with the updated carbon emission intensity assessment threshold. If the regional carbon emission intensity assessment value is less than the updated carbon emission intensity assessment threshold, no additional operation is performed; if the regional carbon emission intensity assessment value is greater than or equal to the updated carbon emission intensity assessment threshold, an early warning feedback is performed, that is, relevant staff are notified to reduce emissions in a timely manner.

[0082] Furthermore, the regional predicted carbon emission intensity assessment value is processed and a secondary carbon emission warning is carried out. The specific process is: a Bayesian model is trained according to the historical regional load and the historical carbon emission intensity assessment values ​​of each energy output node, and the regional predicted load is input into the Bayesian model to obtain the regional predicted carbon emission intensity assessment value, wherein the input of the Bayesian model is the regional predicted load, and the output is the regional predicted carbon emission intensity assessment value, wherein the regional predicted load refers to the average value of the historical regional load, and the regional predicted carbon emission intensity assessment value is compared with the updated carbon emission intensity assessment threshold. If the regional predicted carbon emission intensity assessment value is greater than or equal to the updated carbon emission intensity assessment threshold, the relevant staff is notified to issue a warning. If the regional predicted carbon emission intensity assessment value is less than the updated carbon emission intensity assessment threshold, no additional operation is performed.

[0083] Reference Figure 2 As shown, the second aspect of the present invention provides a greenhouse gas carbon emission estimation system based on the Bayesian algorithm, including: a data acquisition module, used to count regional power stations marked as various energy output nodes, and collect transportation data, power generation equipment data, production capacity data and power supply area data of each energy output node.

[0084] The carbon emission intensity analysis module is used to process the transportation data to obtain the energy transportation efficiency evaluation index of each energy output node, and to process the power generation equipment data to obtain the standard coal consumption evaluation index of each energy output node. Based on the energy transportation efficiency evaluation index, standard coal consumption evaluation index and production capacity data of each energy output node, a comprehensive analysis is performed to obtain the carbon emission intensity evaluation value of each energy output node.

[0085] A primary carbon emission warning module is used to process the power supply area data to obtain a power supply demand assessment index, and to update the carbon emission intensity assessment threshold based on the power supply demand assessment index and issue a primary carbon emission warning.

[0086] The secondary carbon emission warning module is used to count the historical regional loads and the carbon emission intensity assessment values ​​of each historical energy output node, and at the same time obtain the regional predicted load, process the regional predicted carbon emission intensity assessment value and conduct secondary carbon emission warning.

[0087] The carbon emission database is used to store carbon emission related data, including: critical fuel sulfur content, reference standard energy transportation volume, allowable deviation energy transportation volume, critical energy transportation path length, critical fuel consumption, reference standard boundary surface temperature, allowable deviation surface temperature, critical main steam pressure, reference standard generator speed, allowable deviation generator speed, critical total electricity output value, reference clean energy electricity output proportion, allowable deviation clean energy electricity output proportion, critical area, critical voltage level, critical population density, carbon emission intensity assessment influencing factor corresponding to the set energy transportation efficiency assessment index, carbon emission intensity assessment influencing factor corresponding to the set standard coal consumption assessment index for power generation, carbon emission intensity assessment influencing factor corresponding to the set electricity output value, carbon emission intensity assessment influencing factor corresponding to the set clean energy electricity output proportion and carbon emission intensity assessment threshold correction value and other indicators.

[0088] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A greenhouse gas carbon emission estimation method based on Bayesian algorithm, characterized in that: include: The power stations in the statistical area are marked as energy output nodes, and the transportation data, power generation equipment data, production capacity data and power supply area data of each energy output node are collected; The transportation data is processed to obtain the energy transportation efficiency evaluation index of each energy output node, and the power generation equipment data is processed to obtain the power generation standard coal consumption evaluation index of each energy output node. Based on the energy transportation efficiency evaluation index, power generation standard coal consumption evaluation index and production capacity data of each energy output node, a comprehensive analysis is performed to obtain the carbon emission intensity evaluation value of each energy output node; The power supply area data is processed to obtain the power supply demand assessment index, and the carbon emission intensity assessment threshold is updated according to the power supply demand assessment index, and a carbon emission early warning is carried out; Statistics are collected on the historical regional load and the carbon emission intensity assessment values ​​of each energy output node. The regional predicted load is obtained at the same time. The regional predicted carbon emission intensity assessment value is processed and a secondary carbon emission warning is carried out.

2. The method for estimating greenhouse gas carbon emissions based on the Bayesian algorithm according to claim 1, characterized in that: The energy transportation efficiency evaluation index of each energy output node is obtained by processing the transportation data. The specific processing process is as follows: The transportation data includes the fuel sulfur content, energy transportation volume and energy transportation route length of each transportation; The critical fuel sulfur content, reference standard energy transportation volume, allowable deviation energy transportation volume and critical energy transportation path length are extracted from the carbon emission database, and the energy transportation efficiency evaluation index of each energy output node is obtained through comprehensive analysis. The energy transportation efficiency evaluation index is used to quantify the efficiency of energy transportation.

3. The method for estimating greenhouse gas carbon emissions based on the Bayesian algorithm according to claim 1, characterized in that: The power generation equipment data is processed to obtain the power generation standard coal consumption evaluation index of each energy output node. The specific processing process is: The power generation equipment data includes fuel consumption, surface temperature, main steam pressure and generator speed of each power generation equipment; The critical fuel consumption, reference standard surface temperature, allowable deviation surface temperature, critical main steam pressure, reference standard generator speed and allowable deviation generator speed are extracted from the carbon emission database. The standard coal consumption assessment index for power generation at each energy output node is obtained through comprehensive analysis. Based on the standard coal consumption assessment index for power generation, it is determined whether frequency conversion transformation of the equipment is required.

4. The method for estimating greenhouse gas carbon emissions based on the Bayesian algorithm according to claim 3, characterized in that: The comprehensive analysis obtains the carbon emission intensity assessment value of each energy output node. The specific analysis process is as follows: The production capacity data includes the total electricity output value and the clean energy electricity output value; The critical total electricity output value, the reference clean energy electricity output ratio and the allowable deviation clean energy electricity output ratio are extracted from the carbon emission database. Based on the energy transportation efficiency evaluation index, the standard coal consumption evaluation index for power generation, the electricity output value and the clean energy electricity output ratio, a comprehensive analysis is performed to obtain the carbon emission intensity assessment value of each energy output node.

5. The method for estimating greenhouse gas carbon emissions based on the Bayesian algorithm according to claim 1, characterized in that: The power supply area data is processed to obtain the power supply demand assessment index, and the specific processing process is as follows: The power supply area data includes the area, voltage level and population density of each power supply area; The critical area, critical voltage level and critical population density are extracted from the carbon emission database, and a power supply demand assessment index is obtained through comprehensive analysis. The power supply demand assessment index is used to quantify the degree of power supply demand.

6. The method for estimating greenhouse gas carbon emissions based on the Bayesian algorithm according to claim 5, characterized in that: The updated carbon emission intensity assessment threshold is obtained by processing the power supply demand assessment index, and the specific processing process is as follows: The power supply demand assessment index is input into the carbon emission database to match the carbon emission intensity assessment threshold correction value corresponding to each power supply demand assessment index interval, the carbon emission intensity assessment threshold is extracted from the carbon emission database, and the carbon emission intensity assessment threshold is added to the carbon emission intensity assessment threshold correction value to obtain an updated carbon emission intensity assessment threshold.

7. The method for estimating greenhouse gas carbon emissions based on the Bayesian algorithm according to claim 6, characterized in that: The specific process of conducting a carbon emission early warning is as follows: The carbon emission intensity assessment values ​​of each energy output node are summed up to obtain the regional carbon emission intensity assessment value, and the regional carbon emission intensity assessment value is compared with the updated carbon emission intensity assessment threshold. If the regional carbon emission intensity assessment value is less than the updated carbon emission intensity assessment threshold, no additional operation is performed; if the regional carbon emission intensity assessment value is greater than or equal to the updated carbon emission intensity assessment threshold, an early warning feedback is performed.

8. The method for estimating greenhouse gas carbon emissions based on the Bayesian algorithm according to claim 4, characterized in that: The processing obtains the regional predicted carbon emission intensity assessment value and performs a secondary carbon emission warning. The specific process is as follows: A Bayesian model is trained based on the historical regional load and the historical carbon emission intensity assessment values ​​of each energy output node. The regional predicted load is input into the Bayesian model to obtain the regional predicted carbon emission intensity assessment value, and the regional predicted carbon emission intensity assessment value is compared with the updated carbon emission intensity assessment threshold. If the regional predicted carbon emission intensity assessment value is greater than or equal to the updated carbon emission intensity assessment threshold, the relevant staff will be notified for early warning. If the regional predicted carbon emission intensity assessment value is less than the updated carbon emission intensity assessment threshold, no additional operation will be performed.

9. The method for estimating greenhouse gas carbon emissions based on the Bayesian algorithm according to claim 4, characterized in that: The carbon emission intensity assessment value of each energy output node is specifically expressed as follows: Among them, TP j represents the carbon emission intensity assessment value of the j-th energy output node, EN j represents the energy transport efficiency evaluation index of the jth energy output node, FM j represents the standard coal consumption evaluation index of the j-th energy output node, DP j represents the power output value of the jth energy output node, DP0 represents the critical power output value, R j represents the proportion of clean energy electricity output of the j-th energy output node, R0 represents the proportion of clean energy electricity output of the reference standard, θ1 represents the carbon emission intensity assessment impact factor corresponding to the set energy transportation efficiency assessment index, θ2 represents the carbon emission intensity assessment impact factor corresponding to the set power generation standard coal consumption assessment index, θ3 represents the carbon emission intensity assessment impact factor corresponding to the set electricity output value, θ4 represents the carbon emission intensity assessment impact factor corresponding to the set clean energy electricity output proportion, j represents the number of each energy output node, j=1,2,3,...,n, n represents the total number of energy output nodes.

10. A system for applying the method for estimating greenhouse gas carbon emissions based on the Bayesian algorithm as claimed in any one of claims 1 to 9, characterized in that: include: The data collection module is used to count the regional power stations marked as various energy output nodes, and collect the transportation data, power generation equipment data, production capacity data and power supply area data of each energy output node; The carbon emission intensity analysis module is used to process the transportation data to obtain the energy transportation efficiency evaluation index of each energy output node, and to process the power generation equipment data to obtain the power generation standard coal consumption evaluation index of each energy output node. Based on the energy transportation efficiency evaluation index, power generation standard coal consumption evaluation index and production capacity data of each energy output node, a comprehensive analysis is performed to obtain the carbon emission intensity evaluation value of each energy output node; A primary carbon emission warning module is used to process the power supply area data to obtain a power supply demand assessment index, and to update the carbon emission intensity assessment threshold according to the power supply demand assessment index and issue a primary carbon emission warning; The secondary carbon emission warning module is used to count the historical regional loads and the carbon emission intensity assessment values ​​of each historical energy output node, and at the same time obtain the regional predicted load, process the regional predicted carbon emission intensity assessment value and conduct secondary carbon emission warning.

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