CN115879962A - Metal processing industry carbon emission monitoring method and system based on electric power big data - Google Patents
Metal processing industry carbon emission monitoring method and system based on electric power big data Download PDFInfo
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Abstract
The invention relates to the field of carbon emission detection, in particular to a method and a system for monitoring carbon emission in the metal processing industry based on electric power big data. The method comprises the following steps of 1: calculating the direct carbon emission of the metal processing industry of the area i caused by fossil fuel consumption, and calculating the indirect carbon emission of the area i caused by power calling by using a network analysis method; step 2: obtaining the electric carbon conductivity coefficient of the metal processing industry of the region i according to the direct carbon emission and the indirect carbon emission; and step 3: and collecting the power consumption big data of the metal processing industry of the region i in real time, and measuring the carbon emission condition of different enterprises of the metal processing industry of the region i at each time scale by combining the electric carbon conductivity coefficient. The carbon emission detection system is constructed based on the electric power big data, and the time granularity of the data obtained through monitoring is low, objectivity is strong, and precision is high.
Description
Technical Field
The invention relates to the field of carbon emission monitoring, in particular to a metal processing industry carbon emission monitoring method and system based on electric power big data.
Background
At present, global warming caused by climate change becomes a consensus to be urgently solved in the world, and the problems of frequent extreme weather, rising sea level and the like caused by the global warming cause serious negative effects on the world economy and the life safety of people. Greenhouse gas emission is a main cause of global warming, and accurate monitoring of greenhouse gas emission has important significance in implementing carbon peak-reaching carbon neutralization as soon as possible in future emission reduction strategies. Currently, monitoring methods for carbon emissions include continuous monitoring methods and accounting methods based on annual statistics of fossil fuel consumption.
A continuous monitoring method, also called a continuous monitoring system (CEMS), which cannot distinguish between various fuel types and account individually, and which monitors only direct carbon emissions and ignores indirect carbon emissions, results in low objectivity of the monitoring data obtained by the method.
The data used by the accounting method based on the fossil fuel consumption annual statistical data is the annual consumption of the fossil fuel, and indirect carbon emission is also ignored by the method, so the monitoring data obtained by the method has the problems of large time granularity, poor real-time performance and low objectivity of the data.
Disclosure of Invention
The invention provides a metal processing industry carbon emission monitoring method and system based on electric power big data, aiming at solving the problems of large time granularity, poor real-time performance and low objectivity of data obtained by monitoring carbon emission by using the prior art, and the specific technical scheme is as follows:
the invention provides a metal processing industry carbon emission monitoring method based on electric power big data, which is used for monitoring the carbon emission of the metal processing industry, and comprises the following steps: step 1: calculating direct carbon emission generated by fossil fuel consumption in the metal processing industry of the region i, and calculating indirect carbon emission caused by calling out electric power in the region i by using a network analysis method; step 2: obtaining the electric carbon conductivity coefficient of the metal processing industry of the region i according to the direct carbon emission and the indirect carbon emission; and 3, step 3: and collecting the power consumption big data of the metal processing industry of the region i in real time, and measuring the carbon emission condition of different enterprises of the metal processing industry of the region i at each time scale by combining the electric carbon conductivity coefficient.
Preferably, calculating the direct carbon emission in step 1 comprises: step 1.1: collecting different fossil energy consumption of the metal processing industry in the region i; step 1.2: by the formula
Calculating carbon emission factors of different types of fossil energy sources, wherein v k Represents the average lower calorific value of the kth fossil fuel, f k Represents the carbon content per calorific value, o, of the kth fossil fuel k Indicating the oxidation rate level of the kth fossil fuel in power generation, and 44/12 indicating the constant conversion coefficient between carbon and CO 2; and 1.3, calculating the direct carbon emission of the area i by using the consumption amount of different fossil energy sources of the metal processing industry of the area i and the corresponding carbon emission factor of the fossil fuel.
Preferably, calculating the direct carbon emission in step 1.3 further comprises: by the formula
Calculating said direct carbon emissions for region i, wherein ec k Denotes the consumption of the kth fossil fuel, ef, of the metalworking industry k Representing the CO2 emission factor of the kth fossil fuel.
Preferably, calculating the amount of indirect carbon emissions in step 1 comprises: step 1.4: collecting local power generation data p, local power consumption data c and power flow data t among different regions of n regions; step 1.5: respectively calculating the total power flow x of n regions by using the collected data p, c and t, and obtaining an n x n-dimensional power flow according to the power flow data t among different regionsA dynamic matrix T, which is defined by the total power flow x of each region and the power flow matrix T to obtain a power outflow matrix B, wherein the (i, j) th element in the power outflow matrix B represents the proportion of the total electric quantity of the region i flowing to the region j, wherein i is less than or equal to n, and j is less than or equal to n; step 1.6: defining a power consumption structure matrix H based on the power flow matrix B; step 1.7: respectively calculating direct carbon emission generated by power generation in n regions, and forming the data of the carbon emission generated by power generation in the n regions into a vector E G Using said vector E G Defining an indirect carbon emission matrix E with the power consumption structural matrix H C Matrix E C The (i, j) th element of (a) represents the indirect carbon emission of the region i caused by calling out electric power to the region j, wherein i is less than or equal to n, j is less than or equal to n, and the formula e is utilized C ＝[1,L,1]E C And obtaining indirect carbon emission caused by calling out power in the region i.
Preferably, the step 1.5 of calculating the total power flow x of n regions respectively includes: using formulas
Calculating the total power flow of the ith area, wherein the values of i represent different areas, and x represents different areas i Representing the total power flow, p, of region i i Representing local power generation of area i, c i Indicating local power consumption, t ij Indicates the amount of electricity, t, flowing from region i to region j ji Indicating the amount of power flowing from region j to region i.
Preferably, in the step 1.5, a power outflow matrix B is defined by using the total power flow x and the power flow matrix T of each region, where the power outflow matrix B is:
wherein x is n For the total power flow of region n, T n,m The amount of electricity flowing from region n to region m.
Preferably, said vector E is used in said step 1.7 G And the electricityDefining an indirect carbon emission matrix E by the force consumption structure matrix H C The indirect carbon emission matrix E C Comprises the following steps:
wherein, the matrix E C The (i, j) th element of (a)
And the indirect carbon emission of the area i caused by calling power to the area j is shown, wherein i is less than or equal to n, and j is less than or equal to n.
Preferably, the step 2 includes: step 2.1: adding the direct carbon emission of the area i and the indirect carbon emission of the area i to obtain the total carbon emission of the area i; step 2.2: collecting the power consumption of the metal processing industry of the corresponding year in the region i; step 2.3: and calculating by using the total carbon emission of the area i and the power consumption of the metal processing industry in the corresponding year of the area i to obtain the electrical carbon conductivity coefficient of the metal processing industry in the area i.
Preferably, the step 3 includes: step 3.1: collecting big power consumption data of the metal processing industry in the region i in real time; step 3.2: calculating the carbon emission of the metal processing industry enterprise u at the time t by using the electric carbon conductivity coefficient of the metal processing industry in the area i and the electric power consumption big data of the metal processing industry in the area i collected in real time according to a formula C, wherein the formula C is C ut ＝E ut Xcoefe; step 3.3: integrating the formula C to obtain
Thereby measuring the carbon emission of different enterprises in the metal processing industry at each time scale; wherein, C ut Representing the carbon emission at time t, E of the Metal working Enterprise u of region i ut Coefe is the conductivity coefficient of electrical carbon in the metal working industry, corresponding to the power consumption of an enterprise at the same time.
The application provides a metal processing industry carbon emission monitoring system based on electric power big data based on the same inventive concept, which is used for monitoring the carbon emission of the metal processing industry, and the system comprises: direct carbon emission calculation unit: calculating the direct carbon emission generated by fossil fuel consumption in the metal processing industry of the region i; an indirect carbon emission amount calculation unit: calculating indirect carbon emission caused by power dispatching in the area i by using a network analysis method; an electrical carbon conductivity calculation unit: obtaining the electric carbon conductivity coefficient of the metal processing industry of the area i according to the direct carbon emission of the area i and the indirect carbon emission of the area i; a carbon emission measurement unit based on a time scale: and collecting the power consumption big data of the metal processing industry of the area i in real time, and measuring the carbon emission condition of different enterprises of the metal processing industry of the area i at each time scale by combining the electrical carbon conductivity coefficient of the metal processing industry of the area i.
The beneficial effect of this application does: a carbon emission detection system of a metal processing industry enterprise is constructed based on real-time collected electric power consumption big data, and the monitoring system comprises direct carbon emission and indirect carbon emission, so that continuous dynamic monitoring of the carbon emission of the enterprise is realized, and the time granularity of the monitored data is low and the objectivity is strong.
Drawings
Fig. 1 is a flow chart of a method for monitoring carbon emissions in the metal processing industry based on power big data according to an embodiment of the present application.
FIG. 2 is a flow chart of calculating direct carbon emissions according to one embodiment of the present application.
FIG. 3 is a flow chart of calculating indirect carbon emissions according to one embodiment of the present application.
Fig. 4 is a flow chart of calculating an electrical carbon conductivity coefficient provided according to one embodiment of the present application.
FIG. 5 is a flow chart of a method for calculating carbon emissions at various time scales for different enterprises in the metal processing industry according to one embodiment of the present application.
FIG. 6 is a block diagram of a power big data based metalworking industry carbon emission monitoring system according to one embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, the method specifically includes the following steps:
step S1: and (3) calculating the direct carbon emission caused by fossil fuel consumption in the metal processing industry of the region i, and calculating the indirect carbon emission caused by the electric power calling out in the region i by using a network analysis method.
Step S2: and obtaining the electric carbon conductivity coefficient of the metal processing industry of the region i according to the direct carbon emission and the indirect carbon emission.
And step S3: and collecting the power consumption big data of the metal processing industry of the area i in real time, and measuring the carbon emission condition of different enterprises of the metal processing industry of the area i at each time scale by combining the electric carbon conductivity coefficient.
The carbon emission monitoring system established through the steps comprises direct carbon emission and indirect carbon emission, the carbon emission condition of an enterprise is monitored based on the real-time collected electric power consumption big data, the method is utilized to realize continuous dynamic monitoring of the carbon emission of the enterprise, and the time granularity of the monitoring data is low, and the objectivity is strong.
One embodiment of the present application provides a flow chart for calculating direct carbon emissions, see fig. 2, specifically including the following steps:
s1.1: collecting different fossil energy consumption of the metal processing industry of the region i.
S1.2: and calculating carbon emission factors of different types of fossil energy.
Specifically, by the formula
Calculating carbon emission factors of different types of fossil energy sources, wherein v k Represents the average lower calorific value of the kth fossil fuel, f k Represents the carbon content per calorific value, o, of the kth fossil fuel k Indicating the oxidation rate level of the kth fossil fuel at power generation, and 44/12 indicating the constant conversion coefficient between carbon and CO 2.
S1.3: and calculating the direct carbon emission of the area i by using the different fossil energy consumption amounts and the corresponding fossil fuel carbon emission factors of the metal processing industry of the area i.
In particular, by the formula
Calculating said direct carbon emissions for region i, wherein ec k Denotes the consumption of the kth fossil fuel, ef, of the metalworking industry k Representing the CO2 emission factor of the kth fossil fuel.
Through the steps S1.1-S1.3, the method and the device can distinguish and independently account a plurality of fossil fuel types consumed in one region, and refine the fossil fuel consumption condition of the region.
One embodiment of the present application provides a flow chart for calculating indirect carbon emissions, see fig. 3, specifically including the following steps:
s1.4: and collecting local power generation amount data p, local power consumption amount data c and power flow data t among different regions of the n regions.
S1.5: and calculating the total power flow x of the n regions according to the collected data to generate a power flow matrix T, and defining a power flow matrix B according to the x and the T.
Specifically, the collected data p, c, and T are used to calculate total power flow x of n regions respectively, then an n × n dimensional power flow matrix T is obtained according to the power flow data T between different regions, and a power outflow matrix B is obtained by using the total power flow x of each region and the power flow matrix T definition, where the (i, j) th element in the power outflow matrix B represents the proportion of the total power of the region i flowing to the region j, where i is not more than n, and j is not more than n.
In practical applications, the calculating the total power flow x of the n regions respectively specifically includes: using formulas
Calculating the total power flow of the ith area, wherein the values of i represent different areas, and x represents different areas i Representing the total power flow, p, of region i i Representing local power generation of area i, c i Indicating local power consumption, t ij Indicates the amount of electricity flowing from region i to region j, t ji Indicating the amount of power that region j flows to region i.
In practical applications, the power outflow matrix B is defined by using the total power flow x and the power flow matrix T of each region, where the power outflow matrix B is:
wherein x is n For the total power flow of region n, T n,m The amount of electricity flowing from region n to region m.
S1.6: and defining a power consumption structure matrix H based on the power outflow matrix B.
In practical application, the power consumption structure matrix H is specifically defined as:
wherein the content of the first and second substances,
is a diagonal matrix composed of regional power consumption, G = (I-B) -1 ＝I+B+B 2 +B 3 + L, I is the identity matrix, B indicates that power flows directly between two nodes without passing through a transit area, B 2 Representing the flow of electricity through a transit area, B 3 Representing the flow of electricity through two transit areas and so on. The matrix H links power production and power consumption in different regions, and the element H in the matrix ij Representing the total amount of electricity consumed by region j per unit of electricity generated by region i.
S1.7: calculating direct carbon emission generated by power generation in n regions, forming vectors of the direct carbon emission into vectors, defining an indirect carbon emission matrix by using the vectors and the power consumption structure matrix H, and calculating the indirect carbon emission caused by power calling in the region i by using a formula.
Specifically, direct carbon emission generated by power generation in n regions is calculated respectively, and the carbon emission data generated by power generation in the n regions is formed into a vector E G Using said vector E G Defining an indirect carbon emission matrix E with the power consumption structure matrix H C Matrix E C The (i, j) th element of (a) represents indirect carbon emission of the region i caused by calling power to the region j, wherein i is less than or equal to n, j is less than or equal to n, and the formula e is utilized C ＝[1,L,1]E C And obtaining indirect carbon emission caused by power calling in the region i.
In practical application, the vector E is utilized G Defining an indirect carbon emission matrix E with the power consumption structure matrix H C The indirect carbon emission matrix E C Comprises the following steps:
wherein, the matrix E C The (i, j) th element of (a)
And the indirect carbon emission of the area i caused by calling power to the area j is shown, wherein i is less than or equal to n, and j is less than or equal to n.
Through the steps S1.4-S1.7, the method can calculate the indirect carbon emission caused by the electric power calling of the area i. This application brings the indirect carbon emission that causes by electric power call out in the carbon emission monitoring system in the region, can improve the objectivity of monitoring data.
One embodiment of the present application provides a flowchart for calculating the electrical carbon conductivity of the metalworking industry, see fig. 4, specifically including the steps of:
s2.1: and adding the direct carbon emission of the area i and the indirect carbon emission of the area i to obtain the total carbon emission of the area i.
S2.2: the collection area i corresponds to the annual metalworking electricity consumption.
S2.3: and calculating by using the total carbon emission of the area i and the power consumption of the metal processing industry in the corresponding year of the area i to obtain the electrical carbon conductivity coefficient of the metal processing industry in the area i.
One embodiment of the present application provides a flow chart for calculating carbon emissions at various time scales for different enterprises in the metal processing industry, see fig. 5, which specifically includes the following steps:
s3.1: and collecting the power consumption big data of the metal processing industry in the region i in real time.
S3.2: and calculating the carbon emission of the metal processing industry enterprise u at the time t by using the metal processing industry electrical carbon conductivity coefficient of the area i and the metal processing industry power consumption big data of the area i collected in real time through a formula C.
Specifically, the formula C is C ut ＝E ut ×coefe。
S3.3: and integrating the formula C, thereby measuring the carbon emission of different enterprises in the metal processing industry at various time scales.
Specifically, the formula C is integrated to obtain
The carbon emission of different enterprises in the metal processing industry at various time scales is measured.
Wherein, C ut Representing the carbon emission at time t, E of the Metal working Enterprise u of region i ut Coefe is the conductivity of electrical carbon in the metal processing industry, corresponding to the power consumption of an enterprise at the same time.
Through the steps S3.1-S3.3, the carbon emission conditions of different enterprises under various time scales can be measured based on real-time large power consumption data, the time granularity of carbon emission monitoring data is reduced, and the data fineness is improved.
Based on the same inventive concept, one embodiment of the present application provides a metal processing industry carbon emission monitoring system based on power big data. Referring to fig. 6, the system specifically includes:
direct carbon emission calculation unit: calculating the direct carbon emission generated by fossil fuel consumption in the metal processing industry of the region i;
an indirect carbon emission amount calculation unit: calculating indirect carbon emission caused by power dispatching in the area i by using a network analysis method;
an electrical carbon conductivity calculation unit: obtaining the electrical carbon conductivity coefficient of the metal processing industry of the area i according to the direct carbon emission of the area i and the indirect carbon emission of the area i;
a time scale-based carbon emission measurement unit: and collecting the power consumption big data of the metal processing industry of the region i in real time, and measuring the carbon emission condition of different enterprises of the metal processing industry of the region i at each time scale by combining the electric carbon conductivity coefficient of the metal processing industry of the region i.
In summary, the carbon emission detection system of the metal processing industry enterprise is constructed based on the real-time collected electric power consumption big data, so that the carbon emission conditions of different enterprises under various time scales can be measured, the continuous dynamic monitoring of the carbon emission of the enterprises is realized, the time granularity of the carbon emission monitoring data is reduced, and the data fineness is improved; in addition, the monitoring means of the method is based on the existing electric power big data, an additional gas monitoring instrument is not needed, the method has sufficient economy compared with other methods, a monitoring system comprises direct carbon emission and indirect carbon emission, and the objectivity of the carbon emission monitoring data is strong.
Those of ordinary skill in the art will appreciate that the elements of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations thereof, and that the components of the examples have been described above generally in terms of their functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present application, it should be understood that the division of a unit is only one logical function division, and in actual implementation, there may be another division manner, for example, multiple units may be combined into one unit, one unit may be split into multiple units, or some features may be omitted.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. A metal working industry carbon emission monitoring method based on electric power big data is used for monitoring metal working industry carbon emission, and is characterized by comprising the following steps:
step 1: calculating the direct carbon emission of the metal processing industry of the area i caused by fossil fuel consumption, and calculating the indirect carbon emission of the area i caused by power calling by using a network analysis method;
and 2, step: obtaining the electric carbon conductivity coefficient of the metal processing industry of the region i according to the direct carbon emission and the indirect carbon emission;
and 3, step 3: and collecting the power consumption big data of the metal processing industry of the area i in real time, and measuring the carbon emission condition of different enterprises of the metal processing industry of the area i at each time scale by combining the electric carbon conductivity coefficient.
2. The power big data-based carbon emission monitoring method according to claim 1, wherein calculating the direct carbon emission amount in the step 1 comprises:
step 1.1: collecting different fossil energy consumption of the metal processing industry of the region i;
step 1.2: by the formula
Calculating carbon emission factors of different types of fossil energy sources, wherein v k Represents the average lower calorific value, f, of the kth fossil fuel k Represents the carbon content per calorific value, o, of the kth fossil fuel k Represents the oxidation rate level of the kth fossil fuel in power generation, and 44/12 represents the constant conversion coefficient between carbon and CO 2;
step 1.3: and calculating the direct carbon emission of the area i by using the different fossil energy consumption amounts and the corresponding fossil fuel carbon emission factors of the metal processing industry of the area i.
3. The method for monitoring carbon emissions based on power big data according to claim 2, wherein calculating the direct carbon emissions in step 1.3 further comprises:
by the formula
Calculating said direct carbon emissions for region i, wherein ec k Denotes the consumption of the kth fossil fuel, ef, of the metal processing industry k Representing the CO2 emission factor of the kth fossil fuel.
4. The method for monitoring carbon emission based on power big data according to claim 1, wherein calculating the indirect carbon emission in step 1 comprises:
step 1.4: collecting local power generation data p, local power consumption data c and power flow data t among different regions of n regions;
step 1.5: respectively calculating total power flow x of n regions by using the collected data p, c and T, obtaining an n x n-dimensional power flow matrix T according to the power flow data T among different regions, and obtaining a power outflow matrix B by using the total power flow x of each region and the power flow matrix T, wherein the (i, j) th element in the power outflow matrix B represents the proportion of the total electric quantity of the region i to the region j, wherein i is less than or equal to n, and j is less than or equal to n;
step 1.6: defining a power consumption structure matrix H based on the power outflow matrix B;
step 1.7: respectively calculating the direct carbon emission generated by power generation in n regions, and forming a vector E by the carbon emission data generated by power generation in the n regions G Using said vector E G Defining an indirect carbon emission matrix E with the power consumption structure matrix H C Matrix E C The (i, j) th element of (a) represents indirect carbon emission of the region i caused by calling power to the region j, wherein i is less than or equal to n, j is less than or equal to n, and the formula e is utilized C ＝[1,L,1]E C Obtaining the district i from the electric powerResulting in indirect carbon emissions.
5. The method for monitoring carbon emission based on power big data according to claim 4, wherein the step 1.5 of calculating the total power flow x of n regions respectively comprises:
using formulas
Calculating the total power flow of the ith area, wherein the values of i represent different areas, and x represents different areas i Representing the total power flow, p, of region i i Representing local power generation of area i, c i Indicating local power consumption, t ij Indicates the amount of electricity, t, flowing from region i to region j ji Indicating the amount of power that region j flows to region i.
6. The method for monitoring carbon emission based on power big data according to claim 4, wherein in step 1.5, a power flow matrix B is defined by the total power flow x and the power flow matrix T of each region, and the power flow matrix B is:
wherein x is n For the total power flow of region n, T n,m The amount of electricity flowing from region n to region m.
7. The power big data-based carbon emission monitoring method according to claim 4, wherein the vector E is utilized in the step 1.7 G Defining an indirect carbon emission matrix E with the power consumption structural matrix H C The indirect carbon emission matrix E C Comprises the following steps:
wherein, the matrix E C The (i, j) th element of (c)
And the indirect carbon emission of the area i caused by calling power to the area j is represented, wherein i is less than or equal to n, and j is less than or equal to n.
8. The method for monitoring carbon emission based on power big data according to claim 1, wherein the step 2 comprises:
step 2.1: adding the direct carbon emission of the area i and the indirect carbon emission of the area i to obtain the total carbon emission of the area i;
step 2.2: collecting the power consumption of the metal processing industry of the corresponding year in the region i;
step 2.3: and calculating by using the total carbon emission of the area i and the power consumption of the metal processing industry in the corresponding year of the area i to obtain the electrical carbon conductivity coefficient of the metal processing industry in the area i.
9. The method for monitoring carbon emission based on power big data according to claim 1, wherein the step 3 comprises:
step 3.1: collecting big power consumption data of the metal processing industry in the region i in real time;
step 3.2: calculating the carbon emission of the metal processing industry enterprise u at the time t by using the electric carbon conductivity coefficient of the metal processing industry in the area i and the electric power consumption big data of the metal processing industry in the area i collected in real time according to a formula C, wherein the formula C is C ut ＝E ut ×coefe；
Step 3.3: integrating the formula C to obtain
Measuring the carbon emission of different enterprises in the metal processing industry at each time scale;
wherein, C ut Representing the carbon emission at time t, E of the Metal working Enterprise u of region i ut Coefe is the conductivity coefficient of electrical carbon in the metal working industry, corresponding to the power consumption of an enterprise at the same time.
10. A metalworking industry carbon emission monitoring system based on power big data, the system comprising:
direct carbon emission calculation unit: calculating the direct carbon emission of the metal processing industry of the region i caused by fossil fuel consumption;
an indirect carbon emission amount calculation unit: calculating indirect carbon emission caused by calling out electric power in the area i by using a network analysis method;
an electrical carbon conductivity calculation unit: obtaining the electrical carbon conductivity coefficient of the metal processing industry of the area i according to the direct carbon emission of the area i and the indirect carbon emission of the area i;
a time scale-based carbon emission measurement unit: and collecting the power consumption big data of the metal processing industry of the area i in real time, and measuring the carbon emission condition of different enterprises of the metal processing industry of the area i at each time scale by combining the electrical carbon conductivity coefficient of the metal processing industry of the area i.
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|---|---|---|---|---|
| CN116628410A (en) * | 2023-05-29 | 2023-08-22 | 北京西清能源科技有限公司 | Regional power system carbon emission accounting method and system |
| CN116628410B (en) * | 2023-05-29 | 2024-04-02 | 北京西清能源科技有限公司 | Regional power system carbon emission accounting method and system |
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