Enterprise power carbon emission accounting method based on power transaction flow

Abstract

The invention discloses an enterprise power carbon emission accounting method based on a power transaction flow, and relates to the field of carbon emission accounting. Comprising the following steps: establishing a power transaction classification index, performing classification and curve decomposition on enterprise power transactions to obtain power transaction activity data of an enterprise at different time scales, and correcting the transaction electric quantity; calculating a regional dynamic carbon emission factor based on regional source side power generation and inter-regional power transaction behaviors; and after considering the carbon offset effect of the environmental benefit product, calculating the carbon emission of the enterprise power consumption under different time scales. The enterprise electric power carbon emission accounting method based on the electric power transaction flow has scientificity, practicability and operability, electric power carbon emission of a user side can be more accurately accounting, the temporal-spatial resolution of carbon measurement is improved, and organic combination of an electric power market and a carbon market is promoted.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission accounting, and in particular to a method for calculating carbon emission of enterprise electricity based on electricity trading flow. Background Art

[0002] Currently, about three-quarters of greenhouse gas emissions come from the energy sector. The power industry is China's largest carbon-emitting industry, accounting for more than 40% of the country's total carbon emissions. As the fundamental premise for accurately grasping the trend of corporate carbon emissions, effectively implementing various carbon reduction efforts, and promoting corporate low-carbon transformation, carbon emission accounting in the power industry plays a vital role in achieving carbon emission reduction in the power industry. Establishing a unified and standardized carbon emission accounting system can, on the one hand, protect the environmental rights and interests of green power users and mobilize the enthusiasm of enterprises to use green electricity; on the other hand, it can systematically understand the total carbon emissions of electricity consumption in the country, which is conducive to scientifically analyzing the carbon emission pattern, accelerating the development of the carbon market, and promoting the low-carbon transformation of the whole society.

[0003] However, the current carbon emissions accounting methods in the power industry have problems with low temporal and spatial resolution and low accuracy. At the same time, they also lack the ability to trace enterprises' carbon emissions from electricity consumption at different times and nodes, distinguish the electricity consumption characteristics of different industries and enterprises, and promote the linkage between the carbon market and the electricity market, which is not conducive to guiding enterprises to participate in green electricity trading.

[0004] Therefore, a new carbon emissions accounting method for the power industry is needed to more accurately reflect the carbon emissions at different times and nodes, help various regions to formulate phased carbon peak plans more scientifically and reasonably, improve the accuracy of carbon emission data calculation and verification of key emission-controlled enterprises, and promote the organic combination and coordinated development of the power market and the carbon market.

[0005] This research was supported by the National Key R&D Program of China (No.2022YFB2703500), the National Natural Science Foundation of China (No.52277104), the Key R&D Program of Yunnan Province (No.202303AC100003), and the Applied Basic Research Program of Yunnan Province (No.202301AT070455). Summary of the invention

[0006] The purpose of the present invention is to provide a method for calculating carbon emissions from corporate electricity based on electricity trading flows, which can accurately calculate the carbon emissions from electricity consumption at different time scales by decomposing corporate electricity activity data and calculating dynamic carbon emission factors.

[0007] In order to achieve one or part or all of the above purposes or other purposes, the present invention provides a method for calculating carbon emissions of enterprise electricity based on electricity trading flow, comprising the following steps:

[0008] Establish power transaction classification indicators, classify and decompose corporate power transactions, obtain enterprise power transaction activity data at different time scales, and correct the transaction volume;

[0009] Calculate regional dynamic carbon emission factors based on regional source-side power generation and inter-regional power trading behavior;

[0010] After considering the carbon offset effect of environmental benefit products, the carbon emissions from corporate electricity consumption at different time scales are calculated.

[0011] Optionally, classifying the enterprise power transactions refers to classifying the power contracts according to three classification indicators: transaction mode, power source, and transaction cycle:

[0012] def ET(i)=(Trade,Prov,C)

[0013] Among them, ET(i) is the type of electricity trading contract; Trade is the type of trading method; Prov is the type of electricity source; and C is the type of trading cycle.

[0014] Optionally, the transaction methods are divided into five categories, including direct transaction Trade1(n), export transaction Trade2, purchasing electricity on behalf of others Trade3, contract transaction Trade4 and retail transaction Trade5(n):

[0015] def Trade1(n)=Direct trading

[0016] def Trade2=Outbound trading

[0017] def Trade3=Agency trading

[0018] def Trade4=Contract power trading

[0019] def Trade5(n)=Electricity retail trading

[0020] Among them, n indicates whether it is clearly marked as green electricity trading, 1 means yes, and 0 means no.

[0021] Optionally, the electricity sources are shown divided into 34 categories according to administrative regions:

[0022]

[0023] Where j is the province code, and its value is 1-34, corresponding to each province.

[0024] Optionally, the transaction cycle is divided into six categories, including cross-year transaction YY, annual transaction Y, cross-month transaction MM, monthly transaction M, weekly transaction W and daily transaction D:

[0025]

[0026] Optionally, the decomposition methods of the power trading curve include: decomposition by annual time scale, decomposition by monthly time scale, decomposition by weekly time scale and decomposition by daily time scale.

[0027] Optionally, the enterprise's electricity trading activity data at different time scales is calculated as follows:

[0028] M(ET(i),T)=f(M(ET(i))-M dev )

[0029] Wherein, M(ET(i),T) is the transaction volume of the enterprise under different transaction modes at different time scales; T is the accounting period, the annual scale is T-year, the monthly scale is T-month, the weekly scale is T-week, and the daily scale is T-day; f is the contract decomposition curve; M(ET(i)) is the volume of different power transaction contracts; M dev is the deviation electricity, which is the difference between the total electricity of the pre-signed power purchase and sales contract and the actual total electricity settled. If there is no electricity settlement during the accounting period, M dev is 0.

[0030] Optionally, the regional dynamic carbon emission factor is calculated as follows:

[0031] Step 1: Calculate the carbon emissions from electricity sources across the province and obtain the unadjusted carbon emission factor for the province;

[0032] Step 2: Construct the power trading matrix between different provinces and the green power trading matrix between different provinces;

[0033] Step 3: By calculating the inter-provincial green electricity and green certificate transactions at different time scales, adjusting the total electricity consumption and the proportion of thermal power and green electricity within the province, the regional dynamic carbon emission factor is obtained:

[0034] EF=(E g +E b -E o ) / E total

[0035] Among them, EF is the provincial dynamic electricity carbon emission factor; E g is the carbon emissions of regional power generation; E b E is the carbon emission of electricity purchase; o Carbon offset for green certificates; E total is the total regional electricity.

[0036] Optionally, the method for calculating carbon emissions after considering the carbon offset effect of environmental benefit products is as follows:

[0037] GHG offset (T) = GHG (T) - C offset

[0038] Among them, GHG offset (T) is the carbon emission after carbon offset of environmental benefit products; GHG(T) is the regional carbon emission; C offset Carbon emissions offset.

[0039] Optionally, the environmental benefit products include China certified emission reductions, carbon-containing products, forestry carbon sinks, ocean blue carbon sinks, and recognized carbon offset projects.

[0040] The present invention also provides an enterprise electricity carbon emission accounting system based on electricity trading flow, including: an enterprise electricity activity data analysis module, which is used to classify and curve decompose enterprise electricity transactions, obtain enterprise electricity trading activity data at different time scales, and correct the trading volume; a regional carbon emission factor dynamic adjustment module, which is used to calculate the regional dynamic carbon emission factor; an enterprise electricity purchase carbon accounting module, which is used to calculate the carbon emissions of the enterprise's external electricity purchases based on the regional dynamic carbon emission factor and the enterprise's electricity trading activity data; an environmental benefit product carbon offset module, which is used to calculate the environmental benefit product carbon emission offset to obtain the actual carbon emissions of the enterprise.

[0041] Implementing the embodiments of the present invention will have the following beneficial effects:

[0042] The present invention obtains the activity data of enterprises by decomposing the electricity trading contracts, and then calculates the regional dynamic carbon emission factors based on the regional source-side power generation and inter-regional power trading behavior. After considering the carbon offset effect of environmental benefit products, the carbon emission results of the enterprise's electricity consumption at different time scales are calculated. Through the present invention, the user-side electricity carbon emissions can be calculated more accurately, the spatiotemporal resolution of carbon metering can be improved, users can be guided to actively participate in low-carbon transformation and green electricity consumption, and the organic integration of the electricity market and the carbon market can be promoted, which will help China achieve its dual carbon goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] in:

[0045] Figure 1 It is a flow chart of a method for calculating enterprise electricity carbon emissions based on electricity trading flow provided in an embodiment of the present application.

[0046] Figure 2 It is the enterprise electricity consumption and environmental benefit product procurement situation (monthly time scale) provided in the embodiment of the present application.

[0047] Figure 3 It is the dynamic carbon emission factor (daily time scale) of Guangdong Province provided in the embodiments of the present application.

[0048] Figure 4 It is the enterprise carbon emission accounting result (daily time scale) provided in the embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to facilitate the understanding of the present invention, the present invention will be described more comprehensively below, and preferred embodiments of the present invention are provided. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive.

[0050] Embodiment 1

[0051] like Figure 1 As shown, the embodiment of the present application provides a method for calculating carbon emissions of enterprise electricity based on electricity trading flow, including the following steps:

[0052] S110. Establish electricity trading classification indicators, classify and curve decompose enterprise electricity trading, obtain enterprise electricity trading activity data at different time scales, and correct the trading volume.

[0053] 1.1. Establish classification indicators for electricity trading.

[0054] According to the contract type and actual situation, there are 36 types of corporate electricity trading contracts, as shown in Table 1.

[0055] Table 1 Types of power trading contracts

[0056]

[0057] These 36 power trading contracts are divided into three categories according to trading methods, power sources and trading cycles, as shown below:

[0058] def ET(i)=(Trade,Prov,C)

[0059] Among them, ET(i) is the type of electricity trading contract; Trade is the type of trading method; Prov is the type of electricity source; and C is the type of trading cycle.

[0060] There are five types of trading methods: direct trading (Trade 1(n), export trading (Trade 2), purchasing electricity on behalf of others (Trade 3), contract trading (Trade 4) and retail trading (Trade 5(n). These indicators are determined as follows:

[0061] def Trade1(n)=Direct trading

[0062] def Trade2=Outbound trading

[0063] def Trade3=Agency trading

[0064] def Trade4=Contract power trading

[0065] def Trade5(n)=Electricity retail trading

[0066] Among them, n indicates whether it is clearly marked as green electricity trading, 1 means yes, and 0 means no.

[0067] Among these five types of transactions, only direct transactions can trace the source of electricity, that is, it can be confirmed that the electricity comes from coal, natural gas or green electricity, etc. Contract transactions themselves do not produce carbon emissions, and export transactions and power purchase transactions cannot trace the source of electricity and the proportion of green electricity. Retail transactions cannot trace the source, but can trace the proportion of green electricity.

[0068] According to China's administrative divisions, electricity sources are divided into 34 categories. The indicator setting method is as follows:

[0069]

[0070] Where j is the province code, and its value is 1-34, corresponding to each province.

[0071] The trading cycles are divided into six categories: multi-year trading YY, annual trading Y, multi-month trading MM, monthly trading M, weekly trading W, and daily trading D. The indicator setting method is as follows:

[0072]

[0073] 1.2. Decomposition of electricity trading curve.

[0074] With the above three indicators, the symbols of the 36 power contracts in Table 1 can be represented. For the contract transaction electricity, it is decomposed into four time scales: year, month, week and day to obtain the enterprise power transaction activity data at different time scales. The deviation electricity is calculated based on the contract electricity and the actual settlement electricity. The calculation method of the enterprise's power activity data under different transaction methods at different time scales is as follows:

[0075] M(ET(i),T)=f(M(ET(i))-M dev )

[0076] Among them, M(ET(i),T) is the transaction volume of the enterprise under different transaction modes at different time scales; T is the accounting period, the annual scale is T-year, the monthly scale is T-month, the weekly scale is T-week, and the daily scale is T-day; f is the contract decomposition curve; M(ET(i)) is the volume of different power transaction contracts; M dev The deviation electricity is the difference between the total electricity of the pre-signed power purchase and sales contract and the actual total electricity settled. If there is no electricity settlement during the accounting period, it is set to 0.

[0077] Take the daily scale as an example:

[0078] M(ET(i),T-day)=f(M(ET(i))-M dev )

[0079] S120. Calculate the regional dynamic carbon emission factor based on regional source-side power generation and inter-regional power trading behavior.

[0080] 2.1. Calculate the unadjusted carbon emission factor.

[0081] The carbon emissions from thermal power generation, renewable energy generation and other power sources throughout the province are calculated to obtain the unadjusted carbon emission factor for the province. The specific calculation process is shown in Table 2.

[0082] Table 2 Calculation of carbon emissions from power generation in the province where the enterprise is located (taking Province A as an example)

[0083] Electricity generation per coal-fired unit A11 Unit gas unit power generation A12 Average coal consumption of coal-fired units A13 Average coal consumption of gas-fired units A14 Number of coal-fired units A15 Number of gas units A16 Average carbon emission factor of coal-fired units A17 Average carbon emission factor of gas units A18 Total thermal power generation A19=A11*A15+A12*A16 Total carbon emissions from thermal power generation A20=A11*A13*A15*A17+A12*A14*A16*A18 Carbon emission factor of thermal power generation A21=A20 / A19 Total renewable energy generation A22 Carbon emission factors for renewable energy 0 Provincial electricity carbon emission factors (unadjusted) A23=A20 / (A19+A22)

[0084] Similarly, if we take Province B as an example, the specific calculation process is shown in Table 3.

[0085] Table 3 Calculation of carbon emissions from power generation in the province where the enterprise is located (taking Province B as an example)

[0086]

[0087]

[0088] The calculation method for other provinces is the same.

[0089] 2.2. Construct a transaction matrix.

[0090] The electricity trading matrix between different provinces (Table 4), the green electricity trading matrix between different provinces (Table 5) and the provincial green certificate situation (Table 6) are shown below.

[0091] In the table, EA+ indicates that province A purchased electricity, EA- indicates that province A sold electricity, Ba indicates that province B transmitted electricity to province A, and Ab indicates that province A transmitted electricity to province B. The other variables are the same as above. The variable suffix 1 indicates that the traded electricity is non-green electricity, and the variable suffix 2 indicates that the traded electricity is green electricity.

[0092] Table 4 Electricity trading matrix between different provinces

[0093]

[0094] Table 5 Green power trading matrix between different provinces

[0095]

[0096] Table 6 Corporate Green Certificate Transactions (Assuming the Company is in Province A)

[0097]

[0098]

[0099] 2.3 Calculation of regional dynamic carbon emission factors

[0100] By calculating the inter-provincial green electricity and green certificate transactions at different time scales, adjusting the total electricity consumption and the proportion of thermal power and green electricity in the province, the dynamic adjustment of the regional carbon emission factor is achieved, and the carbon emission factor EF(A) is obtained. The specific calculation process is shown in Table 7.

[0101] Table 7 Calculation of dynamic carbon emission factors (Province A)

[0102] Carbon emissions (from regional power generation) Em(A,1)=A20 Carbon emissions (from purchased electricity) Em(A,2)=[Ba1-Ba2]*B21+[Ca1-Ca2]*C21+… Green Certificate Carbon Offset Em(A,3)=A34*A23 Total power consumption in the region Egrid(A)=A19+A22+[EA1+]+[EA2+]-[EA1-]-[EA2-] Provincial electricity carbon emission factor (dynamic adjustment) EF(A)=[Em(A,1)+Em(A,2)-Em(A,3)] / Egrid(A)

[0103] Similarly, if we take Province B as an example, the specific calculation process of EF(B) is shown in Table 8.

[0104] Table 8 Calculation of dynamic carbon emission factors (Province B)

[0105] Carbon emissions (from regional power generation) Em(B,1)=B20 Carbon emissions (from purchased electricity) Em(B,2)=[Ab1-Ab2]*A21+[Cb1-Cb2]*C21+… Green Certificate Carbon Offset Em(B,3)=B34*B23 Total power consumption in the region Egrid(B)=B19+B22+[EB1+]+[EB2+]-[EB1-]-[EB2-] Provincial electricity carbon emission factor (dynamic adjustment) EF(B)=[Em(B,1)+Em(B,2)-Em(B,3)] / Egrid(B)

[0106] The carbon emission factor adjustment method for other provinces is the same.

[0107] S130. After considering the carbon offset effect of environmental benefit products, calculate the carbon emissions of corporate electricity consumption at different time scales.

[0108] 3.1. Calculation of carbon emissions from corporate electricity consumption without considering carbon offset effects

[0109] By analyzing the enterprise electricity consumption data through the above steps, we can obtain the total electricity consumption, its source (within or outside the province), and electricity consumption attributes (thermal power or green power, etc.) during the enterprise accounting period. Then, the corresponding carbon emission factors within and outside the province are obtained through the regional dynamic carbon emission factor calculation method, so that the carbon emissions of purchased electricity within and outside the enterprise accounting period can be derived. The carbon emissions of enterprises are calculated using the carbon emission factor method. Different types of electricity contracts use different calculation methods. The specific calculation method is shown in Table 9, where GHG represents carbon emissions.

[0110] Table 9 Calculation of carbon emissions from different electricity contracts

[0111]

[0112]

[0113] The carbon emissions from electricity consumption of an enterprise are calculated based on the time scale of use. The carbon emissions from electricity consumption of an enterprise at any given time scale are as follows:

[0114]

[0115] 3.2. Calculation of carbon emissions from corporate electricity consumption considering carbon offset effect

[0116] Enterprises can use environmental benefit products to offset the carbon emissions generated by their electricity consumption. These products include China Certified Emission Reductions (CCER), carbon-containing products, forestry carbon sinks, ocean blue carbon sinks and recognized carbon offset projects. The audited enterprise voluntarily declares the carbon emissions to be offset and provides the corresponding certification qualification documents of the environmental benefit product issuing unit. After the audit is completed, the enterprise needs to issue a statement and certification documents stating that the corresponding environmental benefit products will not be used a second time. Once verified, the carbon offset results of the enterprise will be released to the audited enterprise. After considering the carbon offset effect of environmental benefit products, the carbon emissions of corporate electricity consumption are as follows:

[0117] GHG offset (T) = GHG (T) - C offset

[0118] Among them, GHG offset (T) is the carbon emission after carbon offset of environmental benefit products; GHG(T) is the regional carbon emission; C offset Carbon emissions offset.

[0119] Embodiment 2

[0120] Take a fictitious enterprise as an example, which is characterized as an electricity-consuming enterprise located in Guangdong, China. The company has electricity demand throughout the year, and its electricity demand and load distribution are designed to be different in different time periods. In addition, the enterprise participates in the purchase of green certificates and CCERs to offset the carbon emissions generated by its purchased electricity.

[0121] Large manufacturing companies, chemical companies or heavy industries often have a large amount of electricity consumption, which may range from hundreds to thousands of megawatt hours (MWh). In contrast, the electricity consumption of small and medium-sized enterprises is usually lower, usually between tens and hundreds of MWh. Assume that the enterprise in this embodiment is a large manufacturing enterprise in Guangdong Province, and its monthly electricity consumption is several hundred MWh.

[0122] The electricity demand of enterprises usually varies between peak and off-peak seasons, which is related to climate and seasonal demand. Guangdong Province is located in southern China and has a subtropical climate with hot and humid summers and mild winters. Generally speaking, the off-peak season is in spring and autumn, that is, from March to May and from September to November. During these periods, the weather is pleasant and the use of air conditioning and refrigeration equipment is relatively low, resulting in reduced electricity demand. The peak season mainly occurs in the summer, starting in June and lasting until August or September. The hot and humid summers in Guangdong lead to high temperatures, which increase the demand for air conditioning and refrigeration equipment, thereby pushing up electricity consumption.

[0123] Design the decomposition ratio of different time scales based on the enterprise's electricity consumption. The annual scale parameter is set to M, the monthly scale parameter is set to f_1, and the daily scale parameter is set to f_2. The specific value of the corresponding time scale is M×f_1×f_2. The monthly time scale decomposition rate is shown in Table 10, and the daily scale decomposition rate is shown in Table 11.

[0124] Table 10 scale decomposition curve settings (f_1)

[0125]

[0126]

[0127] Table 11 Daily scale decomposition curve settings (f_2)

[0128]

[0129] In order to maintain its image, reduce carbon emissions and obtain green product certification, the company purchased green certificates and China Certified Emission Reductions (CCERs). See Table 12 for specific parameter settings.

[0130] Table 12 Monthly electricity consumption data and purchase of environmentally friendly products by a large enterprise in Guangdong Province

[0131]

[0132]

[0133] In order to achieve dynamic adjustment of carbon emission factors, three sets of operational data were designed. The first set is data on power generation in Guangdong Province. By designing different proportions of thermal power and green power, the unadjusted provincial carbon emission factors can be obtained. The second set is data on power input from other provinces. Power input scenarios and corresponding carbon emission factors for different provinces were designed to complete the first adjustment of provincial carbon emission factors. The third set is the purchase and sale scenarios of environmental benefit products, as well as the purchase and sale scenarios of green certificates, to complete the second adjustment of provincial carbon emission factors.

[0134] The first set of data is the data on power generation in Guangdong Province. The overall power consumption is shown in Table 13, and the data comes from the 2022 Guangdong Power Market Operation Annual Report.

[0135] Table 13 Total electricity consumption in Guangdong Province (annual scale)

[0136] project Electricity (MWh) Total power generation <![CDATA[7.62×10 8 ]]> Inter-provincial power import <![CDATA[1.77×10 8 ]]> Coal-fired power generation <![CDATA[3.16×10 8 ]]> Gas-fired power generation <![CDATA[8.29×10 7 ]]> Renewable energy generation <![CDATA[1.86×10 8 ]]>

[0137] According to China's "Implementation Plan for the Setting and Allocation of Total Carbon Emission Trading Indicators (Power Generation Industry)", the carbon emission factor for thermal power generation is set at 0.877tCO2 / MWh, and the carbon emission factor for gas power generation is set at 0.328tCO2 / MWh.

[0138] The second set of data is the power import data from other provinces. The inter-provincial power import situation is shown in Table 14. It is assumed that the power import comes from Hubei (Central China Power Grid), Jiangsu (East China Power Grid) and Qinghai (Northwest China Power Grid). The carbon emission factor is calculated based on the average value published by the National Climate Center of China in 2012.

[0139] Table 14 Inter-provincial electricity import (annual scale)

[0140]

[0141]

[0142] The third group is the input and output scenarios of environmental benefit products, as well as the settings of sales and purchase scenarios of green certificates. The situation of environmental benefit products is shown in Table 15. Here, only the offsetting of electricity carbon emissions through green certificates is considered.

[0143] Table 15 Green Certificate Transactions in Guangdong Province (Annual Scale)

[0144]

[0145] Based on the above case design and assumptions, the carbon emissions of the company's electricity consumption were calculated using the corporate electricity purchase carbon accounting module. The corporate electricity consumption data analysis module was used to analyze the company's electricity consumption data. The results are as follows: Figure 2 shown. Figure 2 The figure shows the company's electricity consumption and environmental benefit product purchases (monthly), which shows the company's total electricity consumption, green electricity purchases, green certificate purchases and monthly conversions, as well as CCER purchases. It can be observed from the figure that the annual green electricity consumption ratio (including green electricity and green certificates) is 35.6%. In addition, June, July and August are peak electricity consumption periods, which lead to an increase in electricity purchases. In order to offset the increased carbon emissions in these months, the company increased the purchase of green electricity and CCERs.

[0146] The dynamic carbon emission factor adjustment module was used to calculate the dynamic carbon emission factor of Guangdong Province on a daily time scale. The results are as follows: Figure 3 In general, the carbon emission coefficient fluctuates between 0.516tCO2 / MWh and 0.545tCO2 / MWh. Green electricity trading has a significant impact on the carbon emission factor. The increase in green electricity trading will reduce the carbon emission factor in Guangdong.

[0147] Using the enterprise electricity purchase carbon accounting module and the environmental benefit product carbon offset module, the carbon emissions of electricity consumption in the design case were calculated on a daily time scale. The results are as follows: Figure 4 As shown in the figure, the overall trend of carbon emissions is almost the same as that of corporate electricity consumption data. More electricity consumption leads to more carbon emissions. Purchasing more environmentally friendly products during peak electricity consumption periods can help reduce carbon emissions from corporate electricity consumption.

[0148] The above specific implementations are only descriptions of the preferred implementations of the present invention, and do not limit the protection scope of the present invention. Without departing from the design concept and spirit of the present invention, various modifications, substitutions and improvements made by ordinary technicians in this field to the technical solution of the present invention based on the text description and drawings provided by the present invention should all fall within the protection scope of the present invention.

Claims

1. A method for calculating enterprise electricity carbon emissions based on electricity trading flow, characterized in that: The steps include: Establish power transaction classification indicators, classify and decompose corporate power transactions, obtain enterprise power transaction activity data at different time scales, and correct the transaction volume; Calculate regional dynamic carbon emission factors based on regional source-side power generation and inter-regional power trading behavior; After considering the carbon offset effect of environmental benefit products, the carbon emissions from corporate electricity consumption at different time scales are calculated.

2. A method for calculating carbon emissions from corporate electricity based on electricity trading flows as claimed in claim 1, characterized in that: Classifying the enterprise power transactions refers to classifying power contracts according to three classification indicators: transaction mode, power source, and transaction cycle: def ET(i)=(Trade,Prov,C) Among them, ET(i) is the type of electricity trading contract; Trade is the type of trading method; Prov is the type of electricity source; and C is the type of trading cycle.

3. The method for calculating carbon emissions from corporate electricity based on electricity trading flow as claimed in claim 2, characterized in that: The trading methods are divided into five categories, including direct trading Trade1(n), export trading Trade2, purchasing electricity Trade3, contract trading Trade4 and retail trading Trade5(n): def Trade1(n)=Direct trading def Trade2=Outbound trading def Trade3=Agency trading def Trade4=Contract power trading def Trade5(n)=Electricity retail trading Among them, n indicates whether it is clearly marked as green electricity trading, 1 means yes, and 0 means no.

4. The method for calculating carbon emissions from corporate electricity based on electricity trading flow according to claim 2, characterized in that: The electricity sources shown are divided into 34 categories based on administrative regions: Where j is the province code, and its value is 1-34, corresponding to each province.

5. The method for calculating carbon emissions from corporate electricity based on electricity trading flow according to claim 2, characterized in that: The transaction cycles are divided into six categories, including cross-year transactions YY, annual transactions Y, cross-month transactions MM, monthly transactions M, weekly transactions W and daily transactions D:

6. The method for calculating carbon emissions from corporate electricity based on electricity trading flow according to claim 1, characterized in that: The decomposition methods of electricity trading curve include: decomposition by annual time scale, decomposition by monthly time scale, decomposition by weekly time scale and decomposition by daily time scale.

7. The method for calculating carbon emissions from corporate electricity based on electricity trading flow according to claim 1, characterized in that: The calculation method of the electricity trading activity data of the enterprises at different time scales is as follows: M(ET(i),T)=f(M(ET(i))-M dev ) Wherein, M(ET(i),T) is the transaction volume of the enterprise under different transaction modes at different time scales; T is the accounting period, the annual scale is T-year, the monthly scale is T-month, the weekly scale is T-week, and the daily scale is T-day; f is the contract decomposition curve; M(ET(i)) is the volume of different power transaction contracts; M dev is the deviation electricity, which is the difference between the total electricity of the pre-signed power purchase and sales contract and the actual total electricity settled. If there is no electricity settlement during the accounting period, M dev is 0.

8. The method for calculating carbon emissions from corporate electricity based on electricity trading flow according to claim 1, characterized in that: The regional dynamic carbon emission factor is calculated as follows: Step 1: Calculate the carbon emissions from electricity sources across the province and obtain the unadjusted carbon emission factor for the province; Step 2: Construct the power trading matrix between different provinces and the green power trading matrix between different provinces; Step 3: By calculating the inter-provincial green electricity and green certificate transactions at different time scales, adjusting the total electricity consumption and the proportion of thermal power and green electricity within the province, the regional dynamic carbon emission factor is obtained: EF=(E g +E b -AND o ) / AND total Among them, EF is the provincial dynamic electricity carbon emission factor; E g is the carbon emissions of regional power generation; E b E is the carbon emission of electricity purchase; o Carbon offset for green certificates; E total is the total regional electricity.

9. The method for calculating carbon emissions from corporate electricity based on electricity trading flow according to claim 1, characterized in that: The calculation method of carbon emissions after considering the carbon offset effect of environmental benefit products is as follows: GHG offset (T)=GHG(T)-C offset Among them, GHG offset (T) is the carbon emission after carbon offset of environmental benefit products; GHG(T) is the regional carbon emission; C offset Carbon emissions offset.

10. A method for calculating enterprise electricity carbon emissions based on electricity trading flow according to claim 1 or 9, characterized in that: The environmental benefit products include China certified emission reductions, carbon-containing products, forestry carbon sinks, ocean blue carbon sinks, and recognized carbon offset projects.

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