CN114742353A - Dynamic carbon emission monitoring and energy consumption optimizing method based on electric tax data combination - Google Patents

Dynamic carbon emission monitoring and energy consumption optimizing method based on electric tax data combination Download PDF

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CN114742353A
CN114742353A CN202210233868.0A CN202210233868A CN114742353A CN 114742353 A CN114742353 A CN 114742353A CN 202210233868 A CN202210233868 A CN 202210233868A CN 114742353 A CN114742353 A CN 114742353A
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董哲恒
颜沓鳌
王志军
甘纯
吴昊
茅俊夫
余凡
李怡
张引贤
王军辉
叶军
吴以薇
贺天杰
竺纯宇
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State Administration Of Taxation Zhoushan Taxation Bureau
State Grid Zhoushan Comprehensive Energy Service Co ltd
Zhoushan Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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State Grid Zhoushan Comprehensive Energy Service Co ltd
Zhoushan Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention discloses a dynamic carbon emission monitoring and energy consumption optimizing method based on electric tax data combination, which comprises the following steps: establishing an enterprise electric tax data collection model based on GIS information, extracting registration addresses in tax registration information, converting the registration addresses into longitude and latitude coordinates, and collecting tax and electric power big data with the same longitude and latitude addresses; establishing an index model based on a data set, and quantitatively evaluating the relationship between the enterprise electricity consumption data and tax data and the enterprise carbon emission; establishing an index model-based enterprise carbon emission real-time monitoring model, solving, and obtaining enterprise carbon emission real-time data according to a solving result; and establishing an energy consumption optimization model considering carbon emission constraints of the enterprise, solving, and obtaining the annual optimal energy consumption behavior of the enterprise according to a solving result. The method comprehensively considers the index association relationship of the power utilization data, the tax data and the carbon emission data of the enterprise, realizes dynamic monitoring of the carbon emission intensity of the enterprise, can feed back the energy consumption of the enterprise in real time, provides data support for optimizing the energy utilization structure of the enterprise and promoting the green upgrade of the industry, also provides decision basis for the government to more accurately make and implement carbon peak reaching, carbon neutral and related industry development policies, and is favorable for realizing differentiation and containment type coordination emission reduction.

Description

Dynamic carbon emission monitoring and energy consumption optimizing method based on electric tax data combination
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a dynamic carbon emission monitoring and energy consumption optimizing method based on electric tax data combination.
Background
Currently, with the proposal of a '3060' double-carbon target, how to effectively and dynamically monitor the carbon emission of a high-energy-consumption enterprise faces a huge challenge, and the GIS technology provides an important means for effectively solving the problems. At present, the domestic research carbon emission indexes mainly focus on energy consumption total amount, carbon emission total amount, energy consumption intensity and carbon emission intensity, wherein GDP data in the carbon emission intensity and energy consumption intensity indexes are published according to the year, and the carbon emission intensity and the energy consumption intensity indexes have hysteresis. Under the background, how to dynamically monitor the carbon emission of a high-energy-consumption enterprise and optimize the energy consumption condition of the enterprise becomes an important challenge for influencing energy conservation, emission reduction and energy consumption double control and realizing double-carbon treatment.
Disclosure of Invention
The invention aims to provide a dynamic carbon emission monitoring and energy consumption optimizing method based on electric tax data combination so as to solve the problems in the background technology.
In order to realize the purpose, the technical scheme adopted by the invention is as follows: the dynamic carbon emission monitoring and energy consumption optimizing method based on electric tax data combination comprises the following steps:
step 1, establishing an enterprise electric tax data collection model based on GIS information, extracting registration addresses in tax registration information, converting the registration addresses into longitude and latitude coordinates, and collecting tax and electric power big data with the same longitude and latitude addresses; extracting a registration address in the tax registration information, a production operation address and an ammeter installation address and an actual electricity utilization address in the electric power registration information; performing deep analysis calculation on four registration addresses of two departments by using an intelligent algorithm, and converting to generate a national standard four-level address of a city-county/district-street-doorplate with unified standard; converting the address information into digital longitude and latitude coordinates by using the national standard four-level address and an electronic map public positioning service interface; and carrying out statistics and induction collection on the tax and electric big data with the same longitude and latitude addresses, and constructing a data matching mode of the electric big data-GIS address information-tax big data to form a data set with the same standard.
Step 2, establishing an index model based on a data set, and quantitatively evaluating the relationship between the enterprise electricity consumption data and tax data and the enterprise carbon emission; and calculating the electricity-carbon index based on the carbon emission of the enterprise and the electricity consumption of the enterprise, and calculating the carbon average tax and tax average carbon emission index based on the carbon emission of the enterprise and the tax data.
And 3, establishing an index model-based enterprise carbon emission real-time monitoring model, solving, and obtaining enterprise carbon emission real-time data according to a solving result. And calculating the carbon emission of the enterprise according to the tax-average carbon emission and the electric carbon index, solving two emission variances, and comparing the two emission variances with a preset threshold value. When the variance is smaller than the preset threshold value, measuring the carbon emission amount of the enterprise, and averaging the carbon emission amount by the tax and calculating the average value of the carbon emission amount by the electric carbon index; and when the variance is larger than the preset threshold value, screening the emission with the minimum absolute error with the check emission as the carbon emission check value of the enterprise.
And 4, feeding back the energy consumption in real time based on the dynamic monitoring data of the carbon emission of the enterprise, establishing an energy consumption optimization model considering the carbon emission constraint of the enterprise, solving, and obtaining the annual energy consumption behavior of the enterprise according to the solving result.
As a preferable technical means: the step 1 comprises the following steps:
step 101, acquiring enterprise tax registration information and electric power registration information;
step 102, extracting a registration address in the enterprise tax registration information, a production operation address and an ammeter installation address and an actual electricity utilization address in the electric power registration information;
103, performing deep analysis calculation on four registration addresses of two departments by using an intelligent algorithm, and converting to generate a national standard four-level address of a city-county/district-street-doorplate with unified standard;
104, converting the address information into digital longitude and latitude coordinates by using the national standard four-level address through an electronic map public positioning service interface;
and 105, carrying out statistics and induction collection on the tax and electric big data with the same longitude and latitude addresses, and constructing a data matching mode of the electric big data-GIS address information-tax big data to form a data set with the same standard.
As a preferable technical means: the step 2 comprises the following steps:
step 201, acquiring enterprise electricity consumption data, tax data and carbon emission data;
step 202, based on the obtained data of the power consumption and the carbon emission of the enterprise, calculating an electrical carbon index as follows:
Figure BDA0003540991290000031
wherein ECI represents an electrical carbon index,
Figure BDA0003540991290000032
which represents the amount of carbon emissions of the enterprise,
Figure BDA0003540991290000033
representing the power consumption of the enterprise;
step 203, calculating a carbon average tax index and a tax average carbon emission index based on the obtained enterprise tax data and carbon emission data;
A. the direct carbon emissions of the enterprise in the index model are calculated as follows:
Figure BDA0003540991290000034
wherein E isDirect_emission(t) represents the direct carbon emission of the enterprise on the t day, which is determined by the actual Fuel used by the enterprise, i represents the type of Fuel used by the enterprise, Fueli(t) represents the consumption of the ith fuel on the tth day, ξ, by the businessiRepresents the carbon emission factor of the i-th fuel used by the enterprise, Eprocess(t) representsThe process carbon emission of the actual fuel used by enterprises on the tth day;
B. the indirect carbon emissions of the enterprise in the exponential model are calculated as follows:
Figure BDA0003540991290000041
wherein E isIndirect_emission(t) represents the indirect carbon emission amount of the enterprise on the t th day, namely the emission of the enterprise on the t th day caused by using outsourced electric power and heat, m represents the power type of the outsourced electric power of the enterprise, and NeTotal number of power sources, ζ, representing power purchased outside the enterprisemA carbon emission factor representing the m-th class of power purchased by the enterprise,
Figure BDA0003540991290000042
the total quantity of the m-th type of electric power purchased by the enterprise on the t-th day is represented, N represents the type of the heat energy power source purchased by the enterprise, and NhRepresents the total amount of power supply of the heat energy purchased outside the enterprise,
Figure BDA0003540991290000043
a carbon emission factor representing the n-th class of heat purchased by the enterprise,
Figure BDA0003540991290000044
representing the total quantity of the nth type heat energy purchased by the enterprise on the tth day;
C. the carbon emission of the enterprise in the index model is calculated as follows:
ECO2(t)＝EDirect_emission(t)+EIndirect_emission(t)+EOthers(t)
wherein E isCO2(t) represents the carbon emission on the t-th day of the enterprise, EDirect_emission(t) indicates the direct carbon emissions of the enterprise on the t-th day, determined by the actual fuel used by the enterprise, EIndirect_emission(t) denotes indirect carbon emissions on day t of the Enterprise, EOthers(t) represents the t-th day additional carbon emissions of the enterprise;
D. based on the obtained enterprise tax data and carbon emission data, the carbon average tax ranking index is calculated as follows:
Figure BDA0003540991290000045
wherein xicarbonThe carbon-average tax index is shown,
Figure BDA0003540991290000046
represents the tax contribution of the enterprise,
Figure BDA0003540991290000047
represents the carbon emission of the enterprise;
E. based on the obtained enterprise tax data and carbon emission data, calculating the tax-average carbon emission index as follows:
Figure BDA0003540991290000051
therein, ΨrevenueThe carbon-average tax index is expressed,
Figure BDA0003540991290000052
represents the tax contribution of the enterprise,
Figure BDA0003540991290000053
representing the carbon emissions of the enterprise.
As a preferable technical means: the step 3 comprises the following steps:
step 301, acquiring real-time power consumption data and real-time tax data of an enterprise;
step 302, calculating the t-th day real-time predicted carbon emission theta of the enterprise according to the tax average carbon emission indexCO2(t), calculating the t-th day real-time predicted carbon emission amount Lambda of the enterprise according to the electrical carbon indexCO2(t) and calculating the variance S of the two carbon emissions on the t-th day2(t)：
Figure BDA0003540991290000054
Step 303, when the variance is smaller than the preset threshold, measuring the average carbon emission of the tax and the average carbon emission calculated by the electric carbon index of the enterprise:
Figure BDA0003540991290000055
wherein the content of the first and second substances,
Figure BDA0003540991290000056
representing the dynamic detection value of the carbon emission of the enterprise on the t day;
step 304, when the variance is judged to be larger than the preset threshold value, screening out the emission with the minimum absolute error with the checking emission as the carbon emission check value of the enterprise:
Figure BDA0003540991290000057
as a preferable technical means: the step 4 comprises the following steps:
step 401, acquiring annual energy consumption quota and carbon emission quota data of an enterprise;
step 402, acquiring an address clustering enterprise set based on GIS information;
step 403, based on address-clusterable enterprise set information, establishing an energy-consumption joint optimization enterprise cluster by combining enterprise properties and enterprise historical tax association relations;
A. obtaining an enterprise fit coefficient according to enterprise properties:
Figure BDA0003540991290000061
therein, IndexcoRepresenting the fit index of the enterprises, wherein two enterprises with the same property are considered competitive enterprises, and the fit index is 0; when the two enterprises are different in nature, the enterprise product conflict is not large, and the conformity index is 1.
B. Obtaining an enterprise profit association coefficient according to the historical tax association relation:
Figure BDA0003540991290000062
wherein the content of the first and second substances,
Figure BDA0003540991290000063
representing the return of the enterprise p in the last year,
Figure BDA0003540991290000064
representing the income of the enterprise q in the last year,
Figure BDA0003540991290000065
representing the tax revenue of the enterprise p in the last year,
Figure BDA0003540991290000066
and q tax revenue of enterprises in the last year.
C. Determining an energy consumption joint optimization enterprise cluster according to the enterprise conformity coefficient and the enterprise profit correlation coefficient:
Figure BDA0003540991290000067
wherein omegapqAnd representing the energy-use joint optimization judgment parameter, wherein when the value of the energy-use joint optimization judgment parameter is larger than 0, the enterprise can be incorporated into the energy-use joint optimization enterprise cluster, otherwise, the enterprise is not incorporated.
Step 404, establishing an objective function of an energy consumption optimization model of the enterprise considering the carbon emission constraint, wherein the objective function is to maximize the annual production total income of the enterprise, and the objective function is as follows:
Figure BDA0003540991290000071
wherein alpha istRepresents the business income parameter, beta, which can be created by unit fuel after deducting the purchase cost of fueltRepresenting the enterprise revenue parameter, χ, that can be created by the unit outsourcing power after deducting the cost of the outsourcing powertRepresenting the enterprise income parameters which can be created by the unit outsourcing heat energy after deducting the cost of the outsourcing heat energy,PriceCO2represents the average sale price of annual surplus carbon emission of enterprises,
Figure BDA0003540991290000072
representing annual carbon emission quota of the enterprise, ShareCO2A contract transaction fee representing an inter-enterprise share of the carbon emission allowance.
Step 403, establishing constraint conditions of an energy consumption optimization model considering carbon emission constraints of an enterprise, wherein the constraint conditions comprise enterprise fuel available quantity constraints, outsourcing electric power maximum capacity constraints and outsourcing heat energy maximum capacity constraints;
A. annual fuel availability constraints for enterprises
For each time period t, the enterprise fuel availability constraint is described as:
Fueli(t)≤FuelYear-Fuelhistory of+FuelSharing(t)
Wherein, FuelYearRepresenting the annual maximum Fuel available capacity, Fuel, of the enterpriseHistory ofRepresenting the annual consumed Fuel quantity of the enterprise and representing the Fuel of the enterpriseSharing(t) day t shared fuel capacity, which satisfies the following condition:
Figure BDA0003540991290000073
B. maximum capacity constraint of enterprise outsourcing power
For each time period t, the maximum capacity constraint of the enterprise outsourcing power is described as follows:
Figure BDA0003540991290000081
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003540991290000082
representing the maximum capacity of the outsourcing power of the enterprise;
C. maximum capacity constraint of enterprise outsourcing heat energy
For each time period t, the maximum capacity constraint of the outsourcing heat energy of the enterprise is described as follows:
Figure BDA0003540991290000083
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003540991290000084
representing the maximum capacity of outsourcing heat energy for the enterprise.
The invention has the beneficial effects that:
traditional high energy consumption enterprise carbon emission control mainly focuses on total energy consumption, total carbon emission, energy consumption intensity and carbon emission intensity, wherein GDP data in the carbon emission intensity and energy consumption intensity indexes are published according to the year, and hysteresis is provided. In this context, it is difficult to consider annual carbon emission constraints when real-time energy usage by an enterprise. The invention provides a dynamic carbon emission monitoring method based on an electric carbon tax index data model, which comprises the steps of establishing an enterprise electric tax data collection model based on GIS information, extracting registration addresses in tax registration information, converting the registration addresses into longitude and latitude coordinates, and collecting tax and electric power big data with the same longitude and latitude addresses; establishing an index model based on a data set, and quantitatively evaluating the relationship between the enterprise electricity consumption data and tax data and the enterprise carbon emission; establishing an index model-based enterprise carbon emission real-time monitoring model, solving, and obtaining enterprise carbon emission real-time data according to a solving result; and establishing an energy consumption optimization model considering carbon emission constraint of the enterprise, solving, and obtaining annual energy consumption behaviors of the enterprise according to a solving result.
The index provided by the invention is based on the corresponding relation between the electric power and the tax jurisdictional data, the calculation method uses the electric power data to calculate, combines the tax data, and has the characteristics of instantaneity, universality and integrity, and is convenient to calculate, high in accuracy and strong in timeliness. The method is applicable to different time scales such as year, month, day, real time and the like, and can carry out special analysis according to the dimensions of areas, industries and enterprises. The system has the advantages of strong index real-time performance and rich data dimensions, and can be used for assisting the dynamic monitoring of carbon emission of enterprises and the optimization of annual energy utilization behaviors of the enterprises, providing decision basis for more accurate adjustment of industrial structures and energy structure layout by governments, and assisting the realization of a double-carbon target.
The technical scheme establishes the energy-consumption combined optimization enterprise cluster screening method based on GIS information and enterprise tax data, is different from the traditional method for establishing the enterprise combined optimization cluster based on geographic positions, and can exclude competitive enterprises in the energy-consumption combined optimization enterprise cluster.
The influence of enterprise tax data on carbon emission is comprehensively considered, and an energy utilization optimization model of the enterprise considering carbon emission constraint is established on the basis. Compared with the traditional method, the method considers the dynamic correction of the tax data on the carbon emission of the enterprise, and the carbon emission data prediction precision is high.
Comprehensively considering the energy utilization combined optimization enterprise cluster concept, enterprises in the cluster can share the carbon emission through annual contracts. Compared with the traditional method, the method can overcome the risk of fluctuation of the real-time carbon emission purchasing price.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
As shown in fig. 1, the method for dynamically monitoring carbon emission and optimizing energy consumption based on electric tax data combination comprises the following steps:
step 1, establishing an enterprise electric tax data collection model based on GIS information, extracting registration addresses in tax registration information, converting the registration addresses into longitude and latitude coordinates, and collecting tax and electric power big data with the same longitude and latitude addresses; extracting a registration address in the tax registration information, a production management address and an ammeter installation address and an actual electricity utilization address in the electric power registration information; performing deep analysis calculation on four registration addresses of two departments by using an intelligent algorithm, and converting to generate a national standard four-level address of a city-county/district-street-house board with unified standard; converting the address information into digital longitude and latitude coordinates by using the national standard four-level address and an electronic map public positioning service interface; and carrying out statistics and induction collection on the tax and electric big data with the same longitude and latitude addresses, and constructing a data matching mode of the electric big data-GIS address information-tax big data to form a data set with the same standard.
The method specifically comprises the following steps:
step 101, acquiring enterprise tax registration information and electric power registration information;
step 102, extracting a registration address in the enterprise tax registration information, a production operation address and an ammeter installation address and an actual electricity utilization address in the electric power registration information;
103, performing deep analysis calculation on four registration addresses of two departments by using an intelligent algorithm, and converting to generate a national standard four-level address of a city-county/district-street-doorplate with unified standard;
104, converting the address information into digital longitude and latitude coordinates by using the national standard four-level address through an electronic map public positioning service interface;
and 105, carrying out statistics and induction collection on the tax and electric big data with the same longitude and latitude addresses, and constructing a data matching mode of the electric big data-GIS address information-tax big data to form a data set with the same standard.
Step 2, establishing an index model based on a data set, and quantitatively evaluating the relationship between the enterprise electricity consumption data and tax data and the enterprise carbon emission; and calculating the electricity-carbon index based on the carbon emission of the enterprise and the electricity consumption of the enterprise, and calculating the carbon average tax and tax average carbon emission index based on the carbon emission of the enterprise and the tax data.
The method specifically comprises the following steps:
step 201, acquiring enterprise electricity consumption data, tax data and carbon emission data;
step 202, based on the obtained data of the power consumption and the carbon emission of the enterprise, calculating an electrical carbon index as follows:
Figure BDA0003540991290000111
wherein ECI represents an electrical carbon index,
Figure BDA0003540991290000112
which represents the amount of carbon emissions of the enterprise,
Figure BDA0003540991290000113
representing the power consumption of the enterprise;
step 203, calculating a carbon average tax index and a tax average carbon emission index based on the obtained enterprise tax data and carbon emission data;
A. the enterprise direct carbon emissions in the exponential model were calculated as follows:
Figure BDA0003540991290000114
wherein E isDirect_emission(t) represents the direct carbon emission of the enterprise on the t day, which is determined by the actual Fuel used by the enterprise, i represents the type of Fuel used by the enterprise, Fueli(t) represents the consumption of the ith fuel on the tth day, ξ, by the businessiRepresents the carbon emission factor of the i-th fuel used by the enterprise, Eprocess(t) represents the process carbon emissions of the actual fuel used by the enterprise on day t;
B. the indirect carbon emission of the enterprise in the index model is calculated as follows:
Figure BDA0003540991290000115
wherein E isIndirect_emission(t) represents the indirect carbon emission of the enterprise on the tth day, namely the emission of the enterprise on the tth day caused by using outsourced electric power and heat, m represents the power type of the outsourced electric power of the enterprise, NeTotal number of power sources, ζ, representing power purchased outside the enterprisemA carbon emission factor representing the m-th class of power purchased by the enterprise,
Figure BDA0003540991290000116
the total quantity of the m-th type of electric power purchased by the enterprise on the t-th day is represented, N represents the type of the heat energy power source purchased by the enterprise, and NhRepresents the total quantity of power supplies for purchasing heat energy outside the enterprise,
Figure BDA0003540991290000121
a carbon emission factor representing the n-th class of heat purchased by the enterprise,
Figure BDA0003540991290000122
representing the total quantity of the nth type heat energy purchased by the enterprise on the tth day;
C. the carbon emission of the enterprise in the index model is calculated as follows:
ECO2(t)＝EDirect_emission(t)+EIndirect_emission(t)+EOthers(t)
wherein E isCO2(t) represents the carbon emission on the t-th day of the enterprise, EDirect_emission(t) indicates the direct carbon emissions of the enterprise on the t-th day, determined by the actual fuel used by the enterprise, EIndirect_emission(t) denotes indirect carbon emissions on day t of the Enterprise, EOthers(t) represents the t-th day additional carbon emissions of the enterprise;
D. based on the obtained enterprise tax data and carbon emission data, the carbon average tax ranking index is calculated as follows:
Figure BDA0003540991290000123
wherein xicarbonThe carbon-average tax index is shown,
Figure BDA0003540991290000124
represents the tax contribution of the enterprise,
Figure BDA0003540991290000125
represents the carbon emission of the enterprise;
E. based on the obtained enterprise tax data and carbon emission data, calculating the tax-average carbon emission index as follows:
Figure BDA0003540991290000126
therein, ΨrevenueIndicating carbon number average tax index，
Figure BDA0003540991290000127
Represents the tax contribution of the enterprise,
Figure BDA0003540991290000128
representing the carbon emissions of the enterprise.
And 3, establishing an index model-based enterprise carbon emission real-time monitoring model, solving, and obtaining enterprise carbon emission real-time data according to a solving result. And calculating the carbon emission of the enterprise according to the tax average carbon emission and the electric carbon index, solving two emission variances, and comparing the two emission variances with a preset threshold value. When the variance is smaller than the preset threshold value, measuring the carbon emission amount of the enterprise, and averaging the carbon emission amount by the tax and calculating the average value of the carbon emission amount by the electric carbon index; and when the variance is larger than the preset threshold value, screening the emission with the minimum absolute error with the checking emission as the carbon emission check value of the enterprise.
The method specifically comprises the following steps:
step 301, acquiring real-time electricity consumption data and real-time tax data of an enterprise;
step 302, calculating the t-th day real-time predicted carbon emission theta of the enterprise according to the tax average carbon emission indexCO2(t), calculating the t-th day real-time predicted carbon emission amount Lambda of the enterprise according to the electrical carbon indexCO2(t) and calculating the variance S of the two carbon emissions on the t-th day2(t)：
Figure BDA0003540991290000131
Step 303, when the variance is smaller than the preset threshold, measuring the average carbon emission of the tax and the average carbon emission calculated by the electric carbon index of the enterprise:
Figure BDA0003540991290000132
wherein the content of the first and second substances,
Figure BDA0003540991290000133
representing the dynamic detection value of the carbon emission of the enterprise on the t day;
step 304, when the variance is judged to be larger than the preset threshold value, screening out the emission with the minimum absolute error with the checking emission as the carbon emission check value of the enterprise:
Figure BDA0003540991290000134
and 4, establishing an energy utilization optimization model considering carbon emission constraints of the enterprise based on the dynamic monitoring data of the carbon emission of the enterprise, solving, and obtaining annual energy utilization behaviors of the enterprise according to a solving result. The method specifically comprises the following steps:
step 401, acquiring annual energy consumption quota and carbon emission quota data of an enterprise;
step 402, acquiring an address clustering enterprise set based on GIS information;
step 403, based on address-clusterable enterprise set information, establishing an energy-consumption joint optimization enterprise cluster by combining enterprise properties and enterprise historical tax association relations;
A. obtaining an enterprise fit coefficient according to enterprise properties:
Figure BDA0003540991290000141
therein, IndexcoThe method comprises the following steps of representing the fit indexes of enterprises, wherein two enterprises with the same property are considered competitive enterprises, and the fit index is 0; when the two enterprises are different in nature, the enterprise product conflict is not large, and the conformity index is 1.
B. Obtaining an enterprise profit association coefficient according to the historical tax association relation:
Figure BDA0003540991290000142
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003540991290000143
representing the return of the enterprise p in the last year,
Figure BDA0003540991290000144
representing the income of the enterprise q in the last year,
Figure BDA0003540991290000145
representing the tax revenue of the enterprise p in the last year,
Figure BDA0003540991290000146
and q tax revenue of enterprises in the last year.
C. Determining an energy consumption joint optimization enterprise cluster according to the enterprise conformity coefficient and the enterprise profit correlation coefficient:
Figure BDA0003540991290000147
wherein omegapqAnd representing the energy-use joint optimization judgment parameter, wherein when the value of the energy-use joint optimization judgment parameter is larger than 0, the enterprise can be incorporated into the energy-use joint optimization enterprise cluster, otherwise, the enterprise is not incorporated.
Step 404, establishing an objective function of an energy consumption optimization model of the enterprise considering the carbon emission constraint, wherein the objective function is to maximize the annual production total income of the enterprise, and the objective function is as follows:
Figure BDA0003540991290000151
wherein alpha istRepresents the business revenue parameter, beta, that a unit of fuel can create after deducting the purchase cost of the fueltRepresenting the enterprise revenue parameter, χ, that can be created by the unit outsourcing power after deducting the cost of the outsourcing powertRepresents the enterprise income parameter, Price, which can be created by unit outsourcing heat energy after deducting the cost of the outsourcing heat energyCO2Represents the average sale price of annual surplus carbon emission of enterprises,
Figure BDA0003540991290000152
representing annual carbon emission quota, Share, of the enterpriseCO2Contract transaction fee representing inter-enterprise share carbon emission quota。
Step 403, establishing constraint conditions of an energy consumption optimization model considering carbon emission constraints of an enterprise, wherein the constraint conditions comprise enterprise fuel available quantity constraints, outsourcing electric power maximum capacity constraints and outsourcing heat energy maximum capacity constraints;
A. annual fuel availability constraints for enterprises
For each time period t, the enterprise fuel availability constraint is described as:
Fueli(t)≤FuelYear-Fuelhistory of+FuelSharing(t)
Wherein, FuelYearRepresenting the annual maximum Fuel available capacity, Fuel, of the enterpriseHistory ofRepresenting the amount of Fuel consumed by the enterprise in the year, and representing the Fuel of the enterpriseSharing(t) day t shared fuel capacity, which satisfies the following condition:
Figure BDA0003540991290000153
B. maximum capacity constraint of enterprise outsourcing power
For each time period t, the maximum capacity constraint of the enterprise outsourcing power is described as follows:
Figure BDA0003540991290000161
wherein the content of the first and second substances,
Figure BDA0003540991290000162
representing the maximum capacity of the outsourcing power of the enterprise;
C. maximum capacity constraint of enterprise outsourcing heat energy
For each time period t, the maximum capacity constraint of the outsourcing heat energy of the enterprise is described as follows:
Figure BDA0003540991290000163
wherein the content of the first and second substances,
Figure BDA0003540991290000164
representing the maximum capacity of outsourcing heat energy for the enterprise.
According to the technical scheme, the index association relation of the enterprise electricity consumption data, the tax data and the carbon emission data is comprehensively considered, the dynamic monitoring of the carbon emission intensity of the enterprise is realized, the energy consumption of the enterprise can be fed back in real time, the data support is provided for optimizing the energy consumption structure of the enterprise and promoting the green upgrade of the industry, the decision basis is provided for the government to more accurately make and implement the carbon peak reaching, carbon neutral and related industry development policies, and the realization of differentiation and containment type coordination emission reduction is facilitated.
The foregoing description merely represents preferred embodiments of the present invention, which are described in some detail and detail, and should not be construed as limiting the scope of the present invention. It should be noted that various changes, modifications and substitutions may be made by those skilled in the art without departing from the spirit of the invention, and all are intended to be included within the scope of the invention. Therefore, the protection scope of the present patent should be subject to the appended claims.

Claims (5)

1. The dynamic carbon emission monitoring and energy consumption optimizing method based on electric tax data combination is characterized by comprising the following steps: the method comprises the following steps:
step 1, establishing an enterprise electric tax data collection model based on GIS information, extracting registration addresses in tax registration information, converting the registration addresses into longitude and latitude coordinates, and collecting tax and electric power big data with the same longitude and latitude addresses; extracting a registration address in the tax registration information, a production operation address and an ammeter installation address and an actual electricity utilization address in the electric power registration information; performing deep analysis calculation on four registration addresses of two departments by using an intelligent algorithm, and converting to generate a standard four-level address of a city-county/district-street-house board with unified standard; converting the address information into digital longitude and latitude coordinates by using the standard four-level address through an electronic map public positioning service interface; carrying out statistics and induction collection on the tax and electric big data with the same longitude and latitude addresses, and constructing a data matching mode of the electric big data, GIS address information and tax big data to form a data set with the same standard;
step 2, establishing an index model based on a data set, and quantitatively evaluating the relationship between the enterprise electricity consumption data and tax data and the enterprise carbon emission; calculating an electric carbon index based on the carbon emission of the enterprise and the power consumption of the enterprise, and calculating a carbon average tax and a tax average carbon emission index based on the carbon emission of the enterprise and tax data;
step 3, establishing an index model-based enterprise carbon emission real-time monitoring model, solving, and obtaining enterprise carbon emission real-time data according to a solving result; calculating the carbon emission of the enterprise according to the tax average carbon emission and the electric carbon index, solving two emission variances, and comparing the two emission variances with a preset threshold value; when the variance is smaller than the preset threshold value, measuring the carbon emission amount of the enterprise, and averaging the carbon emission amount by the tax and calculating the average value of the carbon emission amount by the electric carbon index; when the variance is larger than a preset threshold value, screening the emission with the minimum absolute error with the checking emission as an enterprise carbon emission check value;
and 4, feeding back the energy consumption in real time based on the dynamic monitoring data of the carbon emission of the enterprise, establishing an energy consumption optimization model considering the carbon emission constraint of the enterprise, solving, and obtaining annual energy consumption behaviors of the enterprise according to a solving result.
2. The method for dynamically monitoring and optimizing carbon emission and energy consumption based on electric tax data combination as claimed in claim 1, wherein: the step 1 comprises the following steps:
step 101, acquiring enterprise tax registration information and electric power registration information;
step 102, extracting a registration address in the enterprise tax registration information, a production operation address and an ammeter installation address and an actual electricity utilization address in the electric power registration information;
103, performing deep analysis calculation on four registration addresses of two departments by using an intelligent algorithm, and converting to generate a national standard four-level address of a city-county/district-street-doorplate with unified standard;
104, converting the address information into digital longitude and latitude coordinates by using the national standard four-level address through an electronic map public positioning service interface;
and 105, carrying out statistical induction and aggregation on the tax and electric power big data with the same latitude and longitude addresses, and constructing a data matching mode of the electric power big data-GIS address information-tax big data to form a data set with the same standard.
3. The method for dynamically monitoring and optimizing carbon emission and energy consumption based on electric tax data combination as claimed in claim 2, wherein: the step 2 comprises the following steps:
step 201, acquiring enterprise electricity consumption data, tax data and carbon emission data;
step 202, based on the obtained data of the power consumption and the carbon emission of the enterprise, calculating an electrical carbon index as follows:
Figure FDA0003540991280000021
wherein ECI represents an electrical carbon index,
Figure FDA0003540991280000022
which represents the amount of carbon emissions of the enterprise,
Figure FDA0003540991280000023
representing the power consumption of the enterprise;
step 203, calculating a carbon average tax index and a tax average carbon emission index based on the obtained enterprise tax data and carbon emission data;
A. the enterprise direct carbon emissions in the exponential model were calculated as follows:
Figure FDA0003540991280000031
wherein E isDirect_emission(t) represents the direct carbon emission of the enterprise on the t day, which is determined by the actual Fuel used by the enterprise, i represents the type of Fuel used by the enterprise, Fueli(t) represents the consumption of the ith fuel on the tth day, ξ, by the enterpriseiRepresenting a corporate officeCarbon emission factor with i-th fuel, Eprocess(t) represents the process carbon emissions of the actual fuel used by the enterprise on day t;
B. the indirect carbon emission of the enterprise in the index model is calculated as follows:
Figure FDA0003540991280000032
wherein, EIndirect_emission(t) represents the indirect carbon emission of the enterprise on the tth day, namely the emission of the enterprise on the tth day caused by using outsourced electric power and heat, m represents the power type of the outsourced electric power of the enterprise, NeTotal number of power sources, ζ, representing power purchased outside the enterprisemA carbon emission factor representing the m-th class of power purchased by the enterprise,
Figure FDA0003540991280000033
representing the total quantity of m-type electric power purchased by the enterprise on the t day, N representing the type of the heat energy power source purchased by the enterprise, and NhRepresents the total quantity of power supplies for purchasing heat energy outside the enterprise,
Figure FDA0003540991280000034
a carbon emission factor representing the n-th class of heat purchased by the enterprise,
Figure FDA0003540991280000035
representing the total quantity of the nth type heat energy purchased by the enterprise on the tth day;
C. the carbon emission of the enterprise in the index model is calculated as follows:
ECO2(t)＝EDirect_emission(t)+EIndirect_emission(t)+EOthers(t)
wherein E isCO2(t) represents the carbon emission on the t-th day of the enterprise, EDirect_emission(t) indicates the direct carbon emissions of the enterprise on the t-th day, determined by the actual fuel used by the enterprise, EIndirect_emission(t) denotes the t-day indirect carbon emissions of the enterprise, EOthers(t) represents the t-th day additional carbon emissions of the enterprise;
D. based on the obtained enterprise tax data and carbon emission data, the carbon average tax ranking index is calculated as follows:
Figure FDA0003540991280000041
wherein xicarbonThe carbon-average tax index is shown,
Figure FDA0003540991280000042
represents the tax contribution of the enterprise,
Figure FDA0003540991280000043
represents the carbon emission of the enterprise;
E. based on the obtained enterprise tax data and carbon emission data, calculating the tax average carbon emission index as follows:
Figure FDA0003540991280000044
therein, ΨrevenueThe carbon-average tax index is shown,
Figure FDA0003540991280000045
represents the tax contribution of the enterprise,
Figure FDA0003540991280000046
representing the carbon emissions of the enterprise.
4. The method for dynamically monitoring and optimizing carbon emission and energy consumption based on electric tax data combination as claimed in claim 3, wherein: the step 3 comprises the following steps:
step 301, acquiring real-time electricity consumption data and real-time tax data of an enterprise;
step 302, calculating the real-time predicted carbon emission theta of the enterprise on the t day according to the tax average carbon emission indexCO2(t), calculating the t-th day real-time predicted carbon emission amount Lambda of the enterprise according to the electrical carbon indexCO2(t) and calculating the variance S of the two carbon emissions on the t-th day2(t)：
Figure FDA0003540991280000047
Step 303, when the variance is smaller than the preset threshold, measuring the average carbon emission of the tax and the average carbon emission calculated by the electric carbon index of the enterprise:
Figure FDA0003540991280000051
wherein the content of the first and second substances,
Figure FDA0003540991280000052
representing the dynamic detection value of the carbon emission of the enterprise on the t day;
step 304, when the variance is larger than the preset threshold value, screening the emission with the minimum absolute error with the checking emission as the carbon emission check value of the enterprise:
Figure FDA0003540991280000053
5. the method for dynamically monitoring and optimizing carbon emission and energy consumption based on electric tax data combination as claimed in claim 4, wherein: the step 4 comprises the following steps:
step 401, acquiring annual energy consumption quota and carbon emission quota data of an enterprise;
step 402, acquiring an address clustering enterprise set based on GIS information;
step 403, based on address-clusterable enterprise set information, establishing an energy-consumption joint optimization enterprise cluster by combining enterprise properties and enterprise historical tax association relations;
A. obtaining an enterprise fit coefficient according to enterprise properties:
Figure FDA0003540991280000054
therein, IndexcoRepresenting the fit index of the enterprises, wherein two enterprises with the same property are considered competitive enterprises, and the fit index is 0; when the two enterprises are different in property, determining that the enterprise products are not large in conflict and the conformity index is 1;
B. obtaining an enterprise profit association coefficient according to the historical tax association relationship:
Figure FDA0003540991280000061
wherein the content of the first and second substances,
Figure FDA0003540991280000062
representing the return of the enterprise p in the last year,
Figure FDA0003540991280000063
representing the income of the enterprise q in the last year,
Figure FDA0003540991280000064
representing the tax revenue of the enterprise p in the last year,
Figure FDA0003540991280000065
enterprise q tax in the last year;
C. determining an energy consumption joint optimization enterprise cluster according to the enterprise conformity coefficient and the enterprise profit correlation coefficient:
Figure FDA0003540991280000066
wherein omegapqRepresenting an energy consumption joint optimization judgment parameter, wherein when the value of the energy consumption joint optimization judgment parameter is larger than 0, the enterprise can be brought into the energy consumption joint optimization enterprise cluster, otherwise, the enterprise is not brought into the energy consumption joint optimization enterprise cluster;
step 404, establishing an objective function of an energy consumption optimization model of the enterprise considering carbon emission constraint, wherein the objective function is to maximize the annual production total income of the enterprise, and the objective function is as follows:
Figure FDA0003540991280000067
wherein alpha istRepresents the business revenue parameter, beta, that a unit of fuel can create after deducting the purchase cost of the fueltRepresenting the enterprise revenue parameter, χ, that can be created by the unit outsourcing power after deducting the cost of the outsourcing powertRepresents the enterprise income parameter, Price, which can be created by unit outsourcing heat energy after deducting the cost of the outsourcing heat energyCO2Represents the average sale price of annual surplus carbon emission of enterprises,
Figure FDA0003540991280000068
representing annual carbon emission quota, Share, of the enterpriseCO2A contract transaction fee representing an inter-enterprise share carbon emission quota;
step 403, establishing constraint conditions of an energy utilization optimization model for considering carbon emission constraints of an enterprise, wherein the constraint conditions comprise enterprise fuel available quantity constraints, outsourcing electric power maximum capacity constraints and outsourcing heat energy maximum capacity constraints;
A. annual fuel availability constraints for enterprises
For each time period t, the enterprise fuel availability constraint is described as:
Fueli(t)≤FuelYear-Fuelhistory of+FuelSharing(t)
Wherein, FuelYearRepresenting the annual maximum Fuel available capacity, Fuel, of the enterpriseHistory ofRepresenting the amount of Fuel consumed by the enterprise in the year, and representing the Fuel of the enterpriseSharing(t) day t shared fuel capacity, which satisfies the following condition:
Figure FDA0003540991280000071
B. maximum capacity constraint of enterprise outsourcing power
For each time period t, the maximum capacity constraint of the enterprise outsourcing power is described as follows:
Figure FDA0003540991280000072
wherein the content of the first and second substances,
Figure FDA0003540991280000073
representing the maximum capacity of the outsourcing power of the enterprise;
C. maximum capacity constraint of enterprise outsourcing heat energy
For each time period t, the maximum capacity constraint of the outsourcing heat energy of the enterprise is described as follows:
Figure FDA0003540991280000074
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003540991280000075
representing the maximum capacity of outsourcing heat energy for the enterprise.
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