A dynamic electric carbon factor accounting method based on time domain trend and regional heterogeneity correction

Abstract

The application discloses a kind of based on time domain trend and regional heterogeneity correction dynamic electric carbon factor accounting method, including with composite area as research object, construct the time domain trend correction model under current statistical period, to be used to obtain time domain trend correction coefficient;For each power supply subarea in composite area, construct regional heterogeneity correction model, to be used to obtain thermal power deviation, green electricity deviation, cross-region input adjustment, second industry proportion;According to thermal power deviation, green electricity deviation, cross-region input adjustment, second industry proportion, synthesis regional heterogeneity correction coefficient;According to electric carbon factor baseline value, time domain trend correction coefficient and regional heterogeneity correction coefficient, obtain dynamic electric carbon factor.The application can be effectively used for the electric carbon factor accounting of new power system.

Description

Technical Field

[0001] This invention relates to a dynamic electric carbon factor calculation method based on time-domain trend and regional heterogeneity correction, belonging to the field of novel power system carbon metering. Background Technology

[0002] The power system is currently undergoing a profound transformation from a traditional binary balance of "safety-economy" to a ternary synergy of "safety-economy-low carbon". As a fundamental measure of carbon emission responsibility on the electricity consumption side, the accuracy of the electricity carbon dioxide emission factor directly affects the verification of product carbon footprint and the implementation of the dual carbon control system.

[0003] However, existing electricity carbon factor accounting systems still face significant technical bottlenecks in practical applications. Firstly, the spatiotemporal characteristics are ambiguous: current mainstream accounting methods mostly use annual or regional grid average factors, which are "static retrospective" calculations. Under the new power system, renewable energy output fluctuates dramatically, and annual average factors cannot reflect the strong time-varying characteristics of electricity production with seasonal and temporal variations, leading to severe distortion in the allocation of carbon emission responsibility at the electricity consumption end in the time domain. Secondly, regional heterogeneity is ignored: existing administrative division accounting often ignores the heterogeneity of regional resource endowments, economic structures (such as the proportion of secondary industry), and social statistical parameters (such as population and output). Since direct emission sources and indirect responsibility points are geographically separated, and the above methods fail to effectively decouple the responsibility relationship between them, there is a lack of a universal "electricity-carbon coupling" evaluation model to decouple direct emission sources and indirect responsibility points, resulting in unequal carbon reduction incentives between resource-rich areas and load centers. Furthermore, with the popularization of green electricity and green certificate trading, the impact of market trading behavior on carbon emission accounting is becoming increasingly prominent. If the accounting method does not take market transactions into account, the problem of "double accounting for the environmental attributes" of electricity is very likely to occur. The existing "top-down" method is difficult to accurately separate trading trends from natural trends, and cannot form a closed-loop carbon traceability logic.

[0004] Therefore, developing a dynamic carbon factor accounting method that can capture time-domain fluctuation trends in real time and perform multiple corrections based on regional heterogeneity characteristics (such as output value, population, and industrial structure) has important engineering value for building an accurate and transparent carbon metering system. Summary of the Invention

[0005] This invention provides a dynamic electric carbon factor calculation method based on time-domain trend and regional heterogeneity correction, which can be used to realize the electric carbon factor calculation of a new type of power system.

[0006] The technical solution of this invention is:

[0007] According to a first aspect of the present invention, a method for calculating dynamic electrocarbon factor based on time-domain trend and regional heterogeneity correction is provided, comprising:

[0008] S1. Taking the composite region as the research object, construct a time-domain trend correction model under the current statistical period to obtain the time-domain trend correction coefficient; wherein, the composite region includes multiple power supply zones;

[0009] S2. For each power supply zone in the composite area, construct a regional heterogeneity correction model to obtain thermal power deviation, green power deviation, inter-regional input regulation, and the proportion of secondary industry.

[0010] S3. Based on the deviation of thermal power, the deviation of green power, the inter-regional input adjustment, and the proportion of secondary industry, synthesize the regional heterogeneity correction coefficient; based on the baseline value of the electric carbon factor, the time-domain trend correction coefficient, and the regional heterogeneity correction coefficient, obtain the dynamic electric carbon factor.

[0011] Furthermore, the construction of the time-domain trend correction model under the current statistical period is specifically as follows:

[0012] For composite regions, the total carbon emissions from the power generation side of the previous statistical period are calculated in the current statistical period, using the following formula:

[0013] ;

[0014] in, Indicates the composite region in the statistical period Total carbon emissions from the power generation side For composite regions in the statistical period The baseline value of the electric carbon factor, Indicates the composite region in the statistical period Inner Total electricity generation over a time scale Indicates the total number of time scales for each statistical period;

[0015] Based on the total carbon emissions from power generation in the previous statistical period, the average carbon emission intensity of thermal power in the previous statistical period is obtained, calculated as follows:

[0016] ;

[0017] in, Indicates the composite region in the statistical period The average carbon emission intensity of thermal power, Indicates the composite region in the statistical period Inner Total thermal power generation at a time scale;

[0018] Based on the average carbon emission intensity of thermal power plants in the previous statistical period, the baseline value of the composite regional carbon emission factor for the previous statistical period is obtained, expressed as follows:

[0019] ;

[0020] in, Indicates the period of statistical analysis Baseline values ​​of carbon emission factor in composite regions over time scales;

[0021] By sequentially algebraically coupling the baseline value of the composite regional carbon emission factor timescale from the previous statistical period, the average carbon emission intensity of thermal power, and the total carbon emissions from the power generation side, a time-domain trend correction model is constructed as follows:

[0022] ;

[0023] in, Indicates the composite region in the current statistical period The time-domain trend correction coefficient.

[0024] Furthermore, the construction of the regional heterogeneity correction model is specifically as follows:

[0025] Based on the geographical area, GDP, total resident population, total floating population, and total thermal power generation of the sub-regions of the composite region, the thermal power intensity is obtained; based on the thermal power intensity, the thermal power deviation is obtained by further combining the geographical area, GDP, total resident population, total floating population, total thermal power generation, and total social electricity consumption of the sub-regions of the composite region.

[0026] Based on the consideration of the amount of green electricity transmitted across regions, the green electricity intensity is obtained by taking into account the geographical area, GDP, total resident population, total floating population, and total green electricity generation of each region in the composite area. Based on the green electricity intensity, and further combined with the geographical area, GDP, total resident population, total floating population, total green electricity generation, and total electricity consumption of each region in the composite area, the green electricity deviation is obtained.

[0027] Based on the thermal power transmitted across regions, the inter-regional input regulation amount is obtained;

[0028] The proportion of the secondary industry is obtained by adjusting the impact of industrialization level on load-side emission bias based on the total electricity consumption of the secondary industry in the different zones of the complex area.

[0029] Furthermore, the expressions for the thermal power generation average intensity and thermal power deviation are as follows:

[0030] ;

[0031] ;

[0032] in, To represent partitions in a composite region In the statistical period Inner Thermal power generation intensity at various time scales Indicates partitioning within a composite area In the statistical period Inner Total thermal power generation at a time scale; Partitioning a composite area The geographical area; Indicates partitioning within a composite area In the statistical period Inner Regional GDP at a time scale These represent the partitions within the composite area. In the statistical period Inner Total resident population and total floating population at each time scale; Indicates partitioning within a composite area In the statistical period Inner The deviation of thermal power generation over a time scale; Indicates partitioning within a composite area In the statistical period Inner Electricity consumption of the whole society at a certain time scale.

[0033] Furthermore, the expressions for the green electricity average intensity and the green electricity deviation are as follows:

[0034] ;

[0035] ;

[0036] in, To represent partitions in a composite region In the statistical period Inner Green energy intensity at various time scales Indicates partitioning within a composite area In the statistical period Inner Total green electricity generation at each time scale; Indicates partitioning within a composite area In the statistical period Inner Green electricity transmitted across regions at various time scales; Partitioning a composite area The geographical area; Indicates partitioning within a composite area In the statistical period Inner Regional GDP at a time scale These represent the partitions within the composite area. In the statistical period Inner Total resident population and total floating population at each time scale; Indicates partitioning within a composite area In the statistical period Inner Green electricity deviation over a time scale; Indicates partitioning within a composite area In the statistical period Inner Electricity consumption of the whole society at a certain time scale.

[0037] Furthermore, the expression for the cross-regional input adjustment amount is:

[0038] ;

[0039] in, Partitioning a composite area In the statistical period Inner Cross-regional input adjustment at each time scale Indicates the period of statistical analysis Inner At each time scale, input from partition j to partition j The proportion of thermal power generation, J represents the ratio of thermal power generation in the composite area to that in the zone. There exists a set of zones that transmit thermal power across regions. Indicates partitioning within a composite area In the statistical period Inner Cross-regional input to partition at a time scale The amount of thermal power generated; Indicates partitioning within a composite area In the statistical period Inner Electricity consumption of the whole society at a certain time scale.

[0040] Furthermore, the proportion of the secondary industry is expressed as follows:

[0041] ;

[0042] in, Partitioning a composite area In the statistical period Inner The proportion of the secondary industry at different time scales; Partitioning a composite area In the statistical period Inner Total electricity consumption of the secondary industry at a given time scale; Indicates partitioning within a composite area In the statistical period Inner Electricity consumption of the whole society at a certain time scale.

[0043] Furthermore, S3 specifically refers to:

[0044] Based on the hyperbolic tangent function, construct a smooth response function;

[0045] After processing the thermal power deviation, green power deviation, inter-regional input adjustment, and secondary industry share through a smoothing response function, a regional heterogeneity correction coefficient is synthesized according to the weighted superposition rule. The expression is as follows:

[0046] ;

[0047] in, Indicates partitioning within a composite area In the statistical period Inner Regional heterogeneity correction coefficient at each time scale; Indicates partitioning within a composite area In the statistical period Inner The deviation of thermal power generation over a given time scale express The smooth response function; Partitioning a composite area In the statistical period Inner Cross-regional input adjustment at each time scale Indicates to The smooth response function; Partitioning a composite area In the statistical period Inner The proportion of the secondary industry at different time scales; Indicates to The smooth response function; Indicates partitioning within a composite area In the statistical period Inner Green electricity deviation over a time scale; Indicates to The smooth response function;

[0048] Based on the composite region in the statistical period baseline value of electrocarbon factor Time-domain trend correction coefficient and regional heterogeneity correction coefficient To obtain partitions in a composite region In the statistical period Inner Dynamic electrocarbon factor at various time scales The expression is: .

[0049] Furthermore, the expression for the smooth response function is:

[0050] ;

[0051] Where x represents the proportion of thermal power deviation, green power deviation, inter-regional input regulation, or secondary industry. For the first One adjustment coefficient; For the first An offset.

[0052] According to a second aspect of the present invention, a dynamic electric carbon factor calculation system based on time-domain trend and regional heterogeneity correction is provided, comprising a module of the dynamic electric carbon factor calculation method based on time-domain trend and regional heterogeneity correction as described in any one of the above.

[0053] According to a third aspect of the present invention, a terminal device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0054] The beneficial effects of this invention are:

[0055] The dynamic carbon factor calculation method based on time-domain trends and regional heterogeneity correction proposed in this invention, compared with the mainstream static average factor method at home and abroad, takes into account the proportion of thermal power output and can capture power structure changes at the hourly or even minute level. Furthermore, this invention introduces regional heterogeneity characteristics such as GDP and total number of floating population to calculate the expected thermal power deviation and green power deviation, and further uses them to calculate the inter-regional input adjustment and the proportion of secondary industry. It also applies "penalties" to thermal power surplus areas and "rewards" to green power surplus areas, realizing accurate allocation based on regional heterogeneity. In addition, the smooth response function can map any complex deviation to the (0,1) interval, eliminating the excessive interference of extreme inter-regional data on the weights. Attached Figure Description

[0056] Figure 1 This is a flowchart of the present invention.

[0057] Figure 2The diagram shows simulation results provided based on embodiments of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0059] Example 1: As Figures 1-2 As shown, according to a first aspect of the present invention, a method for calculating dynamic electrocarbon factor based on time-domain trend and regional heterogeneity correction is provided, comprising:

[0060] S1. Taking the composite region as the research object, a time-domain trend correction model is constructed under the current statistical period to obtain the time-domain trend correction coefficient. The composite region includes multiple power supply zones (in a new power system, a composite region refers to a unified accounting unit formed by merging multiple geographically adjacent power supply zones with close power supply relationships). It should be noted that the construction of the time-domain trend correction model is essentially a process of modifying the baseline value. Dynamic adjustments on the time axis. This invention completes the construction of the time-domain trend correction model through the following three progressive algebraic steps to ensure that the calculated time-domain trend correction coefficients not only conform to historical statistical characteristics but also capture the instantaneous evolution of the energy structure.

[0061] Furthermore, the construction of the time-domain trend correction model under the current statistical period is specifically as follows:

[0062] For composite regions, the total carbon emissions from the power generation side of the previous statistical period are calculated in the current statistical period, using the following formula:

[0063] ;

[0064] in, Indicates the composite region in the statistical period Total carbon emissions from the power generation side For composite regions in the statistical period The baseline value of the electric carbon factor, Indicates the composite region in the statistical period Inner Total electricity generation over a time scale This represents the total number of time scales for each statistical period. For example: in years, if the current statistical period... If it is 2026, then it represents the year 2026. Represents 2025; for each statistical period, measured in months, then... The total power generation is determined by the power generation structure of the complex area, and generally may involve hydropower, thermal power, wind power, solar power, energy storage, etc.

[0065] Based on the total carbon emissions from power generation in the previous statistical period, the average carbon emission intensity of thermal power in the previous statistical period is obtained, calculated as follows:

[0066] ;

[0067] in, Indicates the composite region in the statistical period The average carbon emission intensity of thermal power, Indicates the composite region in the statistical period Inner Total thermal power generation at a time scale.

[0068] Based on the average carbon emission intensity of thermal power plants in the previous statistical period, the baseline value of the composite regional carbon emission factor for the previous statistical period is obtained, expressed as follows:

[0069] ;

[0070] in, Indicates the period of statistical analysis Baseline values ​​of carbon emission factor in composite regions over time scales;

[0071] By sequentially algebraically coupling the baseline value of the composite regional carbon emission factor timescale from the previous statistical period, the average carbon emission intensity of thermal power, and the total carbon emissions from the power generation side, a time-domain trend correction model is constructed as follows:

[0072] ;

[0073] in, Indicates the composite region in the current statistical period The time-domain trend correction coefficient. Through the constructed time-domain trend correction model, it can be seen that... This represents the real-time percentage of thermal power output at the current time scale (e.g., month). This is the average proportion over the baseline period. If the proportion of thermal power output decreases relative to the baseline value in the current period (e.g., green power output increases), then... This leads to a decrease, thereby driving the total electrocarbon factor EF. s,t Shift towards low-carbonization.

[0074] S2. For each power supply zone in the composite area, construct a regional heterogeneity correction model to obtain the thermal power deviation, green power deviation, inter-regional input regulation, and the proportion of secondary industry.

[0075] Furthermore, the construction of the regional heterogeneity correction model is specifically as follows:

[0076] Based on the geographical area, GDP, total resident population, total floating population, and total thermal power generation of the sub-regions of the composite region, the thermal power intensity is obtained. Based on the thermal power intensity, and further combined with the geographical area, GDP, total resident population, total floating population, total thermal power generation, and total electricity consumption of the sub-regions of the composite region, the thermal power deviation is obtained; the specific expression is as follows:

[0077] ;

[0078] ;

[0079] in, To represent partitions in a composite region In the statistical period Inner Thermal power generation intensity at various time scales Indicates partitioning within a composite area In the statistical period Inner Total thermal power generation at a time scale; Partitioning a composite area The geographical area; Indicates partitioning within a composite area In the statistical period Inner Regional GDP at a time scale These represent the partitions within the composite area. In the statistical period Inner Total resident population and total floating population at each time scale; Indicates partitioning within a composite area In the statistical period Inner The deviation of thermal power generation over a time scale; Indicates partitioning within a composite area In the statistical period Inner The total electricity consumption of the whole society at a certain time scale. As mentioned above, the thermal power deviation aims to calculate the deviation between the actual thermal power contribution of the corresponding region and the "expected contribution" based on its economic endowment.

[0080] Based on the consideration of green electricity transmitted across regions, the green electricity intensity is obtained by taking into account the geographical area, GDP, total resident population, total floating population, and total green electricity generation of each sub-region within the composite area. Furthermore, based on the green electricity intensity and considering the cross-regional transmission of green electricity, the green electricity deviation is obtained by combining the geographical area, GDP, total resident population, total floating population, total green electricity generation, and total electricity consumption of each sub-region within the composite area. The specific expression is as follows:

[0081] ;

[0082] ;

[0083] in, To represent partitions in a composite region In the statistical period Inner Green energy intensity at various time scales Indicates partitioning within a composite area In the statistical period Inner Total green electricity generation at each time scale; Indicates partitioning within a composite area In the statistical period Inner Green electricity deviation over a time scale; Indicates partitioning within a composite area In the statistical period Inner The amount of green electricity transmitted across regions at a given time scale (this is deducted in the calculation to reflect the net value of green electricity within the region). Based on the above, the green electricity deviation aims to calculate the contribution of the region to the absorption of clean energy.

[0084] Based on the thermal power transmitted across regions, the inter-regional input regulation is obtained, expressed as:

[0085] ;

[0086] in, Partitioning a composite area In the statistical period Inner Cross-regional input adjustment at each time scale Indicates the period of statistical analysis Inner At each time scale, input from partition j to partition j The proportion of thermal power generation, J represents the ratio of thermal power generation in the composite area to that in the zone. There exists a set of zones that transmit thermal power across regions. Indicates partitioning within a composite area In the statistical period Inner Cross-regional input to partition at a time scale The amount of electricity generated by thermal power plants.

[0087] Based on the total electricity consumption of the secondary industry in different zones of the complex area, the impact of industrialization level on load-side emission bias is adjusted, and the proportion of the secondary industry is obtained, expressed as:

[0088] ;

[0089] in, Partitioning a composite area In the statistical period Inner The proportion of the secondary industry at different time scales; Partitioning a composite area In the statistical period Inner The total electricity consumption of the secondary industry (industry) at a time scale is used as a proxy variable for the level of industrialization to correct for the potential increase in carbon emission weights caused by production load intensity.

[0090] S3. Based on the deviation of thermal power, the deviation of green power, the inter-regional input adjustment, and the proportion of secondary industry, synthesize the regional heterogeneity correction coefficient; based on the baseline value of the electric carbon factor, the time-domain trend correction coefficient, and the regional heterogeneity correction coefficient, obtain the dynamic electric carbon factor.

[0091] Furthermore, S3 specifically refers to:

[0092] Based on the hyperbolic tangent function, construct a smooth response function;

[0093] After processing the thermal power deviation, green power deviation, inter-regional input adjustment, and secondary industry share through a smoothing response function, a regional heterogeneity correction coefficient is synthesized according to the weighted superposition rule. The expression is as follows:

[0094] ;

[0095] in, Indicates partitioning within a composite area In the statistical period Inner Regional heterogeneity correction coefficient at each time scale; Indicates partitioning within a composite area In the statistical period Inner The deviation of thermal power generation over a given time scale express The smooth response function; Partitioning a composite area In the statistical period Inner Cross-regional input adjustment at each time scale Indicates to The smooth response function; Partitioning a composite area In the statistical period Inner The proportion of the secondary industry at different time scales; Indicates to The smooth response function; Indicates partitioning within a composite area In the statistical period Inner Green electricity deviation over a time scale; Indicates to The smooth response function.

[0096] Based on the composite region in the statistical period baseline value of electrocarbon factor Time-domain trend correction coefficient and regional heterogeneity correction coefficient To obtain partitions in a composite region In the statistical period Inner Dynamic electrocarbon factor at various time scales The expression is: .

[0097] Furthermore, in order to ensure the dynamic electrocarbon factor The numerical stationarity of the above deviations is assessed using a smooth response function constructed based on the hyperbolic tangent (tanh). Perform nonlinear fitting:

[0098] ;

[0099] Where x represents the proportion of thermal power deviation, green power deviation, inter-regional input adjustment, or secondary industry (when...). At that time, it represents the deviation of thermal power. ,when At that time, it represents the cross-regional input adjustment amount. ,when At that time, it represented the proportion of the secondary industry. ,when At that time, it represents the deviation of green electricity. ), For the first An adjustment coefficient is used to control the slope of the function graph, i.e. the response sensitivity and the marginal contribution of each deviation to the final factor. For the first An offset is used to control the response activation threshold point for accounting.

[0100] To verify the accuracy of the proposed carbon factor calculation, this invention sets up three typical scenarios, selecting a provincial power grid as the composite region, and conducting a 12-month (monthly) comparative calculation of three typical simulated zones under the jurisdiction of the provincial power grid. The specific parameter settings are shown in Table 1:

[0101] Zone A (Region A): The electricity consumption of the secondary industry accounts for more than 75%, the local thermal power capacity is insufficient, and it is heavily dependent on external power sources.

[0102] Zone B (Region B): The industrial structure is balanced, and the proportion of new energy is on par with the provincial average.

[0103] Zone C (Region C): Rich in wind and solar resources, with a large surplus of green electricity, it undertakes the main peak-shaving and power transmission tasks for the entire province.

[0104] Table 1

[0105]

[0106] The correction formula proposed in this invention The calculations were performed, and the results for partition A are shown in Table 2 below (using the traditional static method). Electric carbon factor considering time-domain correction ):

[0107] Table 2

[0108]

[0109] The simulation results in Table 2 demonstrate that, in terms of high-frequency capture capability, this invention successfully captures fluctuations of over 30% in the overall grid's carbon factor caused by seasonal renewable energy consumption, effectively solving the data lag problem of traditional static factors. Regarding spatial premium, the difference in carbon factor between partition A and partition C at the same time can reach over 100% through regional correction, accurately reflecting the fairness principle of "whoever uses thermal power is responsible, and whoever generates green power benefits." In terms of algorithm stability, after introducing the tanh smoothing response function, even under extreme power conditions in partition A, the calculated factor can still converge stably within the physical upper limit, proving its robustness.

[0110] Furthermore, simulation results for the statistical period 2025 are presented as follows: Figure 2 As shown, through Figure 2It is evident that the accounting method proposed in this invention possesses significant technological advantages: First, this method successfully overcomes the "time lag" defect of traditional accounting methods in the time dimension. Compared to the black dashed line (0.5839) representing the annual static baseline in the figure, the dynamic carbon factor generated by the method of this invention for each region can accurately capture the seasonal fluctuations of the power system's power source composition, truly restoring the emission characteristics during peak summer demand and periods of high renewable energy generation. Second, by introducing regional heterogeneity correction, this method achieves a significant distinction between the accounting results of region A (high-energy-consuming industrial type) and region C (green energy-rich type), accurately quantifying the carbon emission responsibility of different regions and reflecting the fairness and scientific nature of the accounting logic. Furthermore, this method innovatively adopts a hyperbolic tangent (tanh) smoothing response function, ensuring that the curves of each scenario always maintain nonlinear smooth evolution when facing data fluctuations, effectively avoiding step-like abrupt changes caused by extreme data, and the algorithm possesses robustness.

[0111] According to a second aspect of the present invention, a dynamic electric carbon factor calculation system based on time-domain trend and regional heterogeneity correction is provided, comprising modules of the dynamic electric carbon factor calculation method based on time-domain trend and regional heterogeneity correction described in any one of the above embodiments. Specifically, it includes: a first module, configured to perform S1: taking a composite region as the research object, constructing a time-domain trend correction model under the current statistical period to obtain a time-domain trend correction coefficient; wherein the composite region includes multiple power supply zones; a second module, configured to perform S2: for each power supply zone in the composite region, constructing a regional heterogeneity correction model to obtain thermal power deviation, green power deviation, inter-regional input adjustment, and the proportion of secondary industry; a third module, configured to perform S3: synthesizing a regional heterogeneity correction coefficient based on the thermal power deviation, green power deviation, inter-regional input adjustment, and the proportion of secondary industry; and obtaining a dynamic electric carbon factor based on the electric carbon factor baseline value, the time-domain trend correction coefficient, and the regional heterogeneity correction coefficient. For parts of the modules not described in detail above, please refer to the relevant descriptions in this embodiment.

[0112] According to a third aspect of the present invention, a terminal device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0113] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A dynamic electrocarbon factor calculation method based on time-domain trend and regional heterogeneity correction, characterized in that, include: S1. Taking the composite region as the research object, construct a time-domain trend correction model under the current statistical period to obtain the time-domain trend correction coefficient; wherein, the composite region includes multiple power supply zones; S2. For each power supply zone in the composite area, construct a regional heterogeneity correction model to obtain thermal power deviation, green power deviation, inter-regional input regulation, and the proportion of secondary industry. S3. Based on the deviation of thermal power, the deviation of green power, the inter-regional input adjustment, and the proportion of secondary industry, synthesize the regional heterogeneity correction coefficient; based on the baseline value of the electric carbon factor, the time-domain trend correction coefficient, and the regional heterogeneity correction coefficient, obtain the dynamic electric carbon factor.

2. The dynamic electrocarbon factor calculation method based on time-domain trend and regional heterogeneity correction according to claim 1, characterized in that, The construction of the time-domain trend correction model under the current statistical period is as follows: For composite regions, the total carbon emissions from the power generation side of the previous statistical period are calculated in the current statistical period, using the following formula: ； in, Indicates the composite region in the statistical period Total carbon emissions from the power generation side For composite regions in the statistical period The baseline value of the electric carbon factor, Indicates the composite region in the statistical period Inner Total electricity generation over a time scale Indicates the total number of time scales for each statistical period; Based on the total carbon emissions from power generation in the previous statistical period, the average carbon emission intensity of thermal power in the previous statistical period is obtained, calculated as follows: ； in, Indicates the composite region in the statistical period The average carbon emission intensity of thermal power, Indicates the composite region in the statistical period Inner Total thermal power generation at a time scale; Based on the average carbon emission intensity of thermal power plants in the previous statistical period, the baseline value of the composite regional carbon emission factor for the previous statistical period is obtained, expressed as follows: ； in, Indicates the period of statistical analysis Baseline values ​​of carbon emission factor in composite regions over time scales; By sequentially algebraically coupling the baseline value of the composite regional carbon emission factor timescale from the previous statistical period, the average carbon emission intensity of thermal power, and the total carbon emissions from the power generation side, a time-domain trend correction model is constructed as follows: ； in, Indicates the composite region in the current statistical period The time-domain trend correction coefficient.

3. The dynamic electrocarbon factor calculation method based on time-domain trend and regional heterogeneity correction according to claim 1, characterized in that, The construction of the regional heterogeneity correction model is as follows: Based on the geographical area, GDP, total resident population, total floating population, and total thermal power generation of the sub-regions of the composite region, the thermal power intensity is obtained; based on the thermal power intensity, the thermal power deviation is obtained by further combining the geographical area, GDP, total resident population, total floating population, total thermal power generation, and total social electricity consumption of the sub-regions of the composite region. Based on the consideration of the amount of green electricity transmitted across regions, the green electricity intensity is obtained by taking into account the geographical area, GDP, total resident population, total floating population, and total green electricity generation of each region in the composite area. Based on the green electricity intensity, and further combined with the geographical area, GDP, total resident population, total floating population, total green electricity generation, and total electricity consumption of each region in the composite area, the green electricity deviation is obtained. Based on the thermal power transmitted across regions, the inter-regional input regulation amount is obtained; The proportion of the secondary industry is obtained by adjusting the impact of industrialization level on load-side emission bias based on the total electricity consumption of the secondary industry in the different zones of the complex area.

4. The dynamic electrocarbon factor calculation method based on time-domain trend and regional heterogeneity correction according to claim 3, characterized in that, The expressions for the thermal power generation average intensity and thermal power deviation are: ； ； in, To represent partitions in a composite region In the statistical period Inner Thermal power generation intensity at various time scales Indicates partitioning within a composite area In the statistical period Inner Total thermal power generation at a time scale; Partitioning a composite area The geographical area; Indicates partitioning within a composite area In the statistical period Inner Regional GDP at a time scale These represent the partitions within the composite area. In the statistical period Inner Total resident population and total floating population at each time scale; Indicates partitioning within a composite area In the statistical period Inner The deviation of thermal power generation over a time scale; Indicates partitioning within a composite area In the statistical period Inner Electricity consumption of the whole society at a certain time scale.

5. The dynamic electrocarbon factor calculation method based on time-domain trend and regional heterogeneity correction according to claim 3, characterized in that, The expressions for the green electricity average intensity and green electricity deviation are: ； ； in, To represent partitions in a composite region In the statistical period Inner Green energy intensity at various time scales Indicates partitioning within a composite area In the statistical period Inner Total green electricity generation at each time scale; Indicates partitioning within a composite area In the statistical period Inner Green electricity transmitted across regions at various time scales; Partitioning a composite area The geographical area; Indicates partitioning within a composite area In the statistical period Inner Regional GDP at a time scale These represent the partitions within the composite area. In the statistical period Inner Total resident population and total floating population at each time scale; Indicates partitioning within a composite area In the statistical period Inner Green electricity deviation over a time scale; Indicates partitioning within a composite area In the statistical period Inner Electricity consumption of the whole society at a certain time scale.

6. The dynamic electrocarbon factor calculation method based on time-domain trend and regional heterogeneity correction according to claim 3, characterized in that, The expression for the cross-regional input adjustment amount is: ； in, Partitioning a composite area In the statistical period Inner Cross-regional input adjustment at each time scale Indicates the period of statistical analysis Inner At each time scale, input from partition j to partition j The proportion of thermal power generation, J represents the ratio of thermal power generation in the composite area to that in the zone. There exists a set of zones that transmit thermal power across regions. Indicates partitioning within a composite area In the statistical period Inner Cross-regional input to partition at a time scale The amount of thermal power generated; Indicates partitioning within a composite area In the statistical period Inner Electricity consumption of the whole society at a certain time scale.

7. The dynamic electrocarbon factor calculation method based on time-domain trend and regional heterogeneity correction according to claim 1, characterized in that, The proportion of the secondary industry is expressed as follows: ； in, Partitioning a composite area In the statistical period Inner The proportion of the secondary industry at different time scales; Partitioning a composite area In the statistical period Inner Total electricity consumption of the secondary industry at a given time scale; Indicates partitioning within a composite area In the statistical period Inner Electricity consumption of the whole society at a certain time scale.

8. The dynamic electrocarbon factor calculation method based on time-domain trend and regional heterogeneity correction according to claim 1, characterized in that, Specifically, S3 is: Based on the hyperbolic tangent function, construct a smooth response function; After processing the thermal power deviation, green power deviation, inter-regional input adjustment, and secondary industry share through a smoothing response function, a regional heterogeneity correction coefficient is synthesized according to the weighted superposition rule. The expression is as follows: ； in, Indicates partitioning within a composite area In the statistical period Inner Regional heterogeneity correction coefficient at each time scale; Indicates partitioning within a composite area In the statistical period Inner The deviation of thermal power generation over a given time scale express The smooth response function; Partitioning a composite area In the statistical period Inner Cross-regional input adjustment at each time scale Indicates to The smooth response function; Partitioning a composite area In the statistical period Inner The proportion of the secondary industry at different time scales; Indicates to The smooth response function; Indicates partitioning within a composite area In the statistical period Inner Green electricity deviation over a time scale; Indicates to The smooth response function; Based on the composite region in the statistical period baseline value of electrocarbon factor Time-domain trend correction coefficient and regional heterogeneity correction coefficient To obtain partitions in a composite region In the statistical period Inner Dynamic electrocarbon factor at various time scales The expression is: .

9. The dynamic electrocarbon factor calculation method based on time-domain trend and regional heterogeneity correction according to claim 8, characterized in that, The expression for the smooth response function is: ； Where x represents the proportion of thermal power deviation, green power deviation, inter-regional input regulation, or secondary industry. For the first One adjustment coefficient; For the first An offset.

10. A dynamic electric carbon factor calculation system based on time-domain trend and regional heterogeneity correction, characterized in that, The module includes the dynamic electric carbon factor calculation method based on time-domain trend and regional heterogeneity correction as described in any one of claims 1-9.

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