Park fine carbon flow dynamic tracking method based on whole life cycle

Abstract

The application discloses a kind of park fine carbon flow dynamic tracking methods based on whole life cycle, it is related to industrial park carbon management technical field, can solve how to realize the accurate time period binding and dynamic control of park power carbon emission responsibility, to support the problem of fine carbon reduction decision-making, including: determining the carbon emission intensity signal of target regional power grid;According to carbon emission intensity signal, the power purchase data of target park at target time and the photovoltaic power generation data of target park, determine the carbon responsibility allocation value of target park at target time;Cluster analysis is carried out to the electricity consumption behavior of target park in historical period, and multiple typical electricity consumption modes are identified;Quantify the carbon reduction potential corresponding to each typical electricity consumption mode, and dynamically adjust the carbon quota allocation quota of target park in future period based on carbon reduction potential.

Description

Technical Field

[0001] This invention relates to the field of carbon management technology for industrial parks, specifically to a method for dynamic tracking of refined carbon flows in industrial parks based on the entire life cycle. Background Technology

[0002] As concentrated areas of energy consumption and carbon emissions, industrial parks require precise carbon accounting and dynamic management to achieve emission reduction targets. Currently, the common method for calculating indirect carbon emissions from electricity at the park level, both domestically and internationally, is to calculate the total carbon emissions by multiplying the park's total electricity consumption by a regional power grid baseline emission factor published by an authoritative institution. This factor is usually an annual average.

[0003] However, this method based on static average factors fails to consider the real-time dynamic characteristics of power generation scheduling within the power grid and the spatiotemporal differences in carbon emission intensity, resulting in relatively coarse accounting results. It is difficult to accurately link carbon emission responsibility to specific electricity consumption behaviors and time periods, thus causing a disconnect between carbon accounting results and actual emission reduction decisions and management optimization, and failing to provide efficient and targeted carbon reduction guidance for the park. Summary of the Invention

[0004] To address the current technical challenge of accurately binding and dynamically controlling carbon emission responsibility for electricity generation in industrial parks to support refined carbon reduction decision-making, this invention aims to provide a refined dynamic carbon flow tracking method for industrial parks based on the entire lifecycle. The specific technical solution adopted is as follows: In a first aspect, the present invention provides a refined dynamic tracking of carbon flow in industrial parks based on the entire life cycle, comprising: determining the carbon emission intensity signal of the power grid in the target area; wherein the carbon emission intensity signal is used to characterize the carbon emission intensity of marginal power generation units in the power grid in the target area that are called upon to supply electricity in response to the incremental electricity demand at the target time; determining the carbon responsibility allocation value of the target park at the target time based on the carbon emission intensity signal, the power purchase data of the target park at the target time, and the photovoltaic power generation data of the target park; wherein the carbon responsibility allocation value is used to characterize the carbon emission responsibility quota allocated to the target park at the target time; performing cluster analysis on the electricity consumption behavior of the target park in historical periods to identify multiple typical electricity consumption patterns; quantifying the carbon reduction potential corresponding to each typical electricity consumption pattern, and dynamically adjusting the carbon quota allocation of the target park in future periods based on the carbon reduction potential; wherein the carbon reduction potential is used to characterize the carbon responsibility allocation value that can be reduced by shifting electricity consumption behavior to a low-carbon responsible electricity consumption pattern per unit time.

[0005] Secondly, this invention provides a refined carbon flow dynamic system for industrial parks based on the entire life cycle, comprising: a carbon emission intensity determination module, used to determine the carbon emission intensity signal of the target area's power grid. The carbon emission intensity signal characterizes the carbon emission intensity of marginal power generation units in the target area's power grid that are called upon to supply electricity in response to the incremental electricity demand at a target time. A carbon responsibility allocation module, used to determine the carbon responsibility allocation value of the target park at the target time based on the carbon emission intensity signal, the target park's electricity purchase power data at the target time, and the target park's photovoltaic power generation data. The carbon responsibility allocation value characterizes the carbon emission responsibility quota allocated to the target park at the target time. An electricity consumption pattern identification module, used to perform cluster analysis on the target park's electricity consumption behavior over historical periods to identify various typical electricity consumption patterns. A carbon reduction potential management module, used to quantify the carbon reduction potential corresponding to each typical electricity consumption pattern and dynamically adjust the target park's carbon quota allocation in future periods based on the carbon reduction potential. The carbon reduction potential characterizes the carbon responsibility allocation value that can be reduced by shifting electricity consumption behavior to a low-carbon responsible electricity consumption pattern per unit time.

[0006] Thirdly, the present invention provides an electronic device, comprising: a processor and a memory; wherein the memory is used to store one or more programs, the one or more programs including computer-executable instructions, and when the electronic device is running, the processor executes the computer-executable instructions stored in the memory to cause the electronic device to perform the campus-based refined carbon flow dynamic tracking method based on the entire life cycle as described in the first aspect and any possible implementation thereof.

[0007] This invention offers the following advantages: By constructing a dual-layer, penetrating tracking architecture of "marginal carbon intensity of power supply + carbon flow responsibility tracing on the industrial park side," it first achieves real-time, dynamic calculation of the carbon emission intensity of marginal units in the power grid, accurately reflecting the real carbon emissions caused by incremental electricity consumption. Then, by combining the dynamic carbon intensity signal with the electricity consumption scenarios within the industrial park, it achieves precise binding of carbon responsibility at every moment. Next, it uses cluster analysis to identify typical electricity consumption patterns with different carbon emission characteristics from historical data. Finally, by quantifying the transfer potential between these patterns and dynamically adjusting carbon quotas based on this potential, a complete management closed loop is formed, from accurate accounting and pattern recognition to potential assessment and quota control. This method overcomes the shortcomings of traditional industrial park carbon accounting methods that rely on static annual average emission factors and treat the power grid as a black box. It enables carbon responsibility to be accurately traced to specific electricity consumption behaviors and time periods, and provides the industrial park with operable and targeted guidance for carbon reduction optimization, achieving deep linkage and closed-loop optimization of carbon accounting, responsibility allocation, and emission reduction management. Attached Figure Description

[0008] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating a method for dynamic tracking of refined carbon flow in industrial parks based on the entire life cycle, provided in one embodiment of the present invention. Figure 2 This is a schematic diagram of the architecture of a refined carbon flow dynamic tracking system for industrial parks based on the entire life cycle, provided as an embodiment of the present invention. Detailed Implementation

[0010] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0011] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0012] The following description, in conjunction with the accompanying drawings, details a specific scheme for a refined dynamic carbon flow tracking method for industrial parks based on the entire life cycle, provided by this invention.

[0013] For example, such as Figure 1 The diagram shown is a flowchart illustrating a method for dynamic tracking of refined carbon flows in industrial parks based on the entire lifecycle, according to an embodiment of the present invention. The method includes the following steps: S101. Determine the carbon emission intensity signal of the target area power grid. The carbon emission intensity signal characterizes the carbon emission intensity of marginal power generation units in the target area power grid that are called upon to supply electricity in response to the incremental electricity demand at the target time.

[0014] In this embodiment, the carbon emission intensity signal specifically refers to the marginal carbon emission intensity at the target time. The target area is the area where the subsequent target park is located. Optionally, the specific process for determining the carbon emission intensity signal is as follows: (1) Based on the power generation structure time-series data and load time-series data of the target area power grid within the first time period including the target time, identify the power supply marginal generation unit type at the target time. Among them, the power supply marginal generation unit type includes new energy type and thermal power type.

[0015] First, the system obtains the rate of change sequence of new energy power generation output and the rate of change sequence of total grid load within the first time period.

[0016] Specifically, the system obtains total renewable energy output data, including wind and solar power output, and total grid load data from the power grid dispatch center. The first time period can be the past 15 minutes ending at the target time. The system calculates the change in renewable energy output per minute within this time period (current minute output minus previous minute output) to form a rate of change sequence, and also calculates the change in total load per minute to form a rate of change sequence.

[0017] Following this, the rate of change sequence of new energy power generation output was analyzed. With the total load of the power grid Correlation analysis was performed on the rate of change sequences to obtain the first correlation coefficient. Specifically, the Pearson correlation coefficient between the two sequences was calculated and used as the first correlation coefficient R.

[0018] Furthermore, based on the relationship between the first correlation coefficient and the first and second correlation thresholds, the power generation type of the marginal power generation unit is determined. Specifically, this is divided into the following three cases: This section clarifies the first correlation threshold. The second correlation threshold is used to define the clear or ambiguous state of power grid regulation behavior. For example, the first correlation threshold can be 0.6, and the second correlation threshold... A value of 0.3 is acceptable. The selection rule can be determined based on statistical analysis of historical data over a long period (such as one year). For example, the correlation coefficient between the rate of change in new energy output and the rate of change in load at all historical moments can be calculated, and the 80th percentile of its distribution can be used as the quantile. 20th percentile as .

[0019] Case 1: The first correlation coefficient is greater than the first correlation threshold.

[0020] In this case, the marginal power generation unit is classified as a new energy source. This is because the output of new energy sources is highly positively correlated with the load changes in the power grid, indicating that new energy power generation can effectively track load fluctuations and is the main regulating power source to meet the incremental electricity demand at that moment. Therefore, it is classified as a new energy source.

[0021] Case 2: The first correlation coefficient is less than the first correlation threshold.

[0022] In this case, the marginal power generation unit is determined to be a thermal power unit. This is because the output change of new energy sources is weakly correlated with the load change, indicating that new energy sources do not have the ability to regulate or change in the opposite direction. To meet the load increment, thermal power units need to be called upon, therefore, its type is thermal power.

[0023] Case 3: The first correlation coefficient is greater than or equal to the second correlation threshold, and less than or equal to the first correlation threshold.

[0024] In this case, the higher-order difference eigenvalues ​​of the rate of change sequence of new energy power generation output are first calculated. Specifically, the system calculates the variance of the second-order difference sequence of the rate of change sequence of new energy power generation output as a higher-order difference eigenvalue to quantify the volatility of the changes in new energy power generation output.

[0025] The aforementioned preset threshold for variation characteristics is used to distinguish whether fluctuations in renewable energy output are stable or drastic. This threshold can be selected by analyzing the distribution of the second-order difference variance of historical renewable energy output, choosing a value that can distinguish between stable and drastic fluctuations (such as the median of the distribution or a certain empirical percentile). For example, the threshold for variation characteristics can be 0.01.

[0026] Subsequently, if the higher-order differential eigenvalue is less than the preset change characteristic threshold, the marginal power generation unit type is determined to be thermal power. This is because although the output change of new energy sources is somewhat correlated with load change, its own change process is gradual (small variance) and lacks rapid adjustment capability, thus thermal power is still needed as a marginal unit. For example, the change characteristic threshold can be taken as 0.01.

[0027] It should be noted that if the higher-order difference eigenvalue is greater than or equal to the preset change characteristic threshold, the system will determine the marginal contribution weights of new energy types and thermal power types based on the first correlation coefficient. In this case, the output of new energy changes drastically and is still related to load changes, indicating that new energy and thermal power may jointly undertake the marginal regulation role. Therefore, instead of specifically determining a single type, a mixed weight is determined.

[0028] Specifically, the marginal contribution weight is determined by setting a contribution weight for new energy sources. The contribution weight of thermal power The marginal contribution weight is used in the subsequent calculation of the mixed marginal carbon intensity.

[0029] (2) Determine the carbon emission intensity signal at the target time based on the power supply marginal power generation unit type and power generation structure time series data.

[0030] Corresponding to the identification of the power supply marginal generation unit type in the aforementioned steps, this step is also executed in the following two ways: Case a: The power generation unit at the power supply edge is identified as a thermal power type.

[0031] In this scenario, the system executes a carbon intensity calculation process for thermal power units. This process includes: calculating the overall load factor based on the total output and installed capacity of the thermal power plants; constructing a scheduling sequence model sorted by coal consumption based on unit archive data to locate marginal units; correcting the coal consumption of marginal units according to their real-time load factor; and finally converting the corrected coal consumption into a carbon emission intensity signal. It should be noted that the specific implementation details of this process are described in sections S201-S205 below and will not be repeated here.

[0032] Case b: The power generation unit at the power supply edge is identified as a new energy type.

[0033] In this case, the system specifically adjusts the preset wind power life-cycle carbon emission coefficient based on the specific output ratio of wind power and photovoltaic power at the target time in the power generation structure time-series data. (like ) and the carbon emission coefficient of photovoltaics throughout its entire life cycle (like ) Perform a weighted average. If the wind power output is Photovoltaic power output is Then the carbon emission intensity signal The calculation formula is: .

[0034] In the above formula, It is a very small positive number, for example, it can be 10 to the power of negative 5, and the unit is the same as the wind power output. Photovoltaic power output The same principle applies to prevent the denominator from being zero in extreme cases. This formula uses the real-time output of wind power and photovoltaic power as weights to perform a weighted average of their respective life-cycle carbon emission coefficients. The result is used as the carbon emission intensity of grid power supply at the current moment when renewable energy is the marginal unit.

[0035] Case c: Based on the first correlation coefficient, the marginal contribution weights of new energy types and thermal power types were determined.

[0036] In this case, the system first calculates the carbon emission intensity of the new energy power generation unit in case b. And the carbon emission intensity of the thermal power generation unit calculated through process a. Then, the system determines the marginal contribution weights based on the steps described above. and Then perform a weighted sum on the two:

[0037] Weighted results This serves as a signal of carbon emission intensity at that moment.

[0038] Therefore, this step, by constructing a "marginal carbon intensity of power supply" analysis layer, dynamically identifies and calculates the actual carbon intensity of the units actually called upon in response to the increase in electricity consumption in the park, and obtains an accurate upstream carbon signal benchmark, overcoming the problem of carbon emission causal misalignment caused by the use of static average emission factors in traditional methods.

[0039] S102. Based on the carbon emission intensity signal, the electricity purchase data of the target park at the target time, and the photovoltaic power generation data of the target park, determine the carbon responsibility allocation value of the target park at the target time. The carbon responsibility allocation value is used to characterize the carbon emission responsibility quota allocated to the target park at the target time.

[0040] Specifically, the carbon liability allocation value is the sum of the carbon emission liabilities incurred by the park due to consuming electricity purchased from the grid and consuming self-generated photovoltaic power. The determination process includes the following steps: (1) Obtain the transmission line loss rate of the power grid in the target area, and calculate the carbon emission intensity of the target park at the gateway side based on the carbon emission intensity signal and the transmission line loss rate.

[0041] Specifically, the system obtains the annual average transmission line loss rate of the target area's power grid from the power grid company's annual operation report. (In practical applications, this is a percentage value greater than 0 and less than 1, such as 5%). Then, the carbon emission intensity signal determined by S101 is... Divide by This yields the carbon emission intensity of electricity purchases, converted to the electricity consumption threshold of the industrial park and including transmission losses. : .

[0042] Since grid-side carbon intensity is an attribute of the power generation side, and the park purchases electricity at the gateway, the carbon emissions corresponding to losses (line losses) during power transmission must also be borne by the power purchaser. The above formula, by dividing the power generation-side carbon intensity by (1 - line loss rate), converts the carbon emission intensity to the park's power purchase gateway, ensuring the integrity of the carbon responsibility accounting boundary.

[0043] (2) Determine the first carbon responsibility allocation value based on the carbon emission intensity and power of electricity purchased at the checkpoint.

[0044] Specifically, the system obtains the power purchased by the target park from the grid at the target time from the park's energy management system. The time interval calculated in the design is... (e.g., 1 hour), then the first carbon responsibility allocation value for: The formula calculates the result in Δ t During the specified time period, the park experienced a surge in consumer spending. The carbon emission responsibility of electricity generated by the grid is the product of carbon emission intensity, power consumption, and electricity consumption time.

[0045] (3) Based on the photovoltaic power generation data, determine the photovoltaic self-generation and self-consumption power of the target park at the target time.

[0046] Specifically, the system obtains the total power generation of the park's distributed photovoltaic system from the park's energy management system. and actual network power Then the photovoltaic power generated and consumed by the park itself (i.e., the photovoltaic power generated and consumed by the park itself) for: This formula calculates the actual photovoltaic power absorbed by the park by subtracting the grid-connected portion from the total photovoltaic power generation. The park is responsible for the carbon emissions corresponding to this portion of the power.

[0047] (4) Determine the second carbon responsibility allocation value based on the photovoltaic self-generation and self-consumption power and the preset distributed power generation life cycle carbon emission coefficient.

[0048] Specifically, the photovoltaic full life cycle carbon emission coefficient is adopted. (e.g., 50gCO2 / kWh), then the second carbon responsibility allocation value for: The formula calculates the result in Δ t During the period, the park was affected by the disposal of waste. P pv_self The carbon emission responsibility for self-generated photovoltaic power is calculated as the product of the photovoltaic carbon emission intensity, the self-generated and self-consumed power, and the duration of electricity consumption.

[0049] (5) Determine the carbon responsibility allocation value of the target park at the target time based on the first carbon responsibility allocation value and the second carbon responsibility allocation value.

[0050] Specifically, the two are added together to obtain the carbon responsibility allocation value of the park at that moment. : The formula sums the carbon responsibility for the two main electricity consumption scenarios in the park (purchased electricity and self-generated and self-consumed renewable energy) to obtain the carbon responsibility allocation value of the park at the target time. This enables comprehensive accounting of carbon responsibility.

[0051] Therefore, this step, through "park-side carbon flow responsibility tracing," precisely combines the upstream dynamic carbon intensity signal with the specific electricity consumption behavior and power structure within the park, realizing the refined allocation of carbon responsibility in two core scenarios: electricity purchase and distributed photovoltaic self-consumption, replacing the traditional fuzzy model based on total average allocation.

[0052] S103. Perform cluster analysis on the electricity consumption behavior of the target park during historical periods to identify a variety of typical electricity consumption patterns.

[0053] The clustering analysis algorithm used in this step can be the K-means clustering algorithm. The identified typical electricity consumption patterns can be categorized as "high load - high carbon responsibility," "high load - low carbon responsibility," "low load - high carbon responsibility," and "low load - low carbon responsibility," etc. Specifically, the clustering analysis steps include: (1) For each historical moment within the historical cycle, construct a multidimensional feature vector. The multidimensional feature vector shall include at least the carbon responsibility allocation value, carbon emission intensity signal, electricity purchase ratio, photovoltaic self-consumption ratio and total load power at the historical moment.

[0054] Specifically, for a complete historical cycle of one year (8760 hours in total), the system constructs a five-dimensional feature vector for each time point t (intervals of hours). :

[0055] in, This represents the carbon responsibility allocation value at time t; This represents the carbon emission intensity signal at time t. For the proportion of purchased electricity, This represents the proportion of photovoltaic power generation and self-consumption.

[0056] (2) Using a pre-defined clustering algorithm, the multidimensional feature vectors corresponding to all historical moments within the historical period are clustered to obtain multiple clusters and identify various typical electricity consumption patterns. Each cluster corresponds to a typical electricity consumption pattern.

[0057] Furthermore, the system uses the K-means algorithm to cluster all 8760 feature vectors, and determines the optimal number of clusters using the elbow rule. ,get The system is divided into several clusters. Within each cluster, electricity consumption and carbon emission characteristics are similar at all times, and the cluster center vector represents a typical electricity consumption pattern. The system calculates the average total carbon responsibility across all times within each cluster. and according to Clusters are sorted and labeled from highest to lowest carbon responsibility, for example, the cluster with the highest average carbon responsibility is labeled as a "high carbon responsibility cluster".

[0058] Specifically, the elbow rule is as follows: calculate the sum of squared distances (SSE) from all samples within a cluster to its cluster center as the number of clusters k increases from 1 to a preset maximum value, plot the k-SSE curve, and select the k value corresponding to the inflection point of the curve (i.e., where the rate of decrease of SSE suddenly slows down) as the optimal number of clusters.

[0059] Therefore, this step, by conducting multi-dimensional pattern recognition on electricity consumption behavior throughout the year, summarizes massive amounts of time-series data into limited typical patterns with clear carbon emission characteristics, breaking the limitations of a single carbon responsibility index and laying the foundation for subsequent exploration of transfer patterns and carbon reduction potential between different patterns.

[0060] S104. Quantify the carbon reduction potential corresponding to each typical electricity consumption pattern, and dynamically adjust the carbon quota allocation of the target park in future cycles based on the carbon reduction potential. Among them, the carbon reduction potential is used to characterize the carbon responsibility allocation value that can be reduced by shifting electricity consumption behavior to a low-carbon responsible electricity consumption pattern per unit time.

[0061] Specifically, carbon reduction potential is an indicator used to quantify the optimization space. Its determination process includes: the system statistically analyzes all events that transition from a high-carbon responsibility cluster to a low-carbon responsibility cluster over time; calculates the difference in carbon responsibility before and after each transition as the carbon reduction achieved by that transition; then, it averages the carbon reduction of all transition events belonging to the same starting cluster (i.e., the same typical electricity consumption pattern), and this average is defined as the carbon reduction potential corresponding to that typical electricity consumption pattern. It should be noted that the specific implementation of the aforementioned process is detailed in S301-S303 below and will not be repeated here.

[0062] Furthermore, the system dynamically adjusts the carbon quota allocation for target industrial parks in future cycles based on carbon reduction potential. Specifically, this includes: calculating the weighted average carbon reduction potential for the current window in half-monthly adjustment windows, and predicting the carbon reduction potential for the next window based on historical data from the same period; determining an adjustment coefficient based on the ratio of the predicted carbon reduction potential to the historical maximum carbon reduction potential, and using this coefficient to compressively adjust the baseline carbon quota for the next window, thereby generating the adjusted carbon quota allocation. See S304-S308 below for the specific process, which will not be elaborated here.

[0063] Therefore, this step quantifies the improvement potential of various electricity consumption patterns and feeds this potential prediction back to the carbon quota allocation mechanism, achieving intelligent linkage between carbon responsibility and carbon quotas. By compressing the carbon allocation quota during high-carbon periods, this forces industrial parks to take optimization measures (such as shifting load to low-carbon periods and increasing real-time photovoltaic consumption), providing direct decision support for industrial parks to formulate refined and targeted carbon reduction strategies, and solving the technical blind spot where traditional carbon accounting results cannot effectively support emission reduction decisions.

[0064] Based on the above technical solution, this invention constructs a two-layer penetrating tracking architecture of "marginal carbon intensity of power supply + carbon flow responsibility tracing on the industrial park side." First, it achieves real-time, dynamic calculation of the carbon emission intensity of marginal units in the power grid, accurately reflecting the real carbon emissions caused by increased electricity consumption. Then, it combines the dynamic carbon intensity signal with the electricity consumption scenarios within the industrial park, completing the precise binding of carbon responsibility at every moment. Next, it uses cluster analysis to identify typical electricity consumption patterns with different carbon emission characteristics from historical data. Finally, by quantifying the transfer potential between these patterns and dynamically adjusting carbon quotas based on this potential, a complete management closed loop is formed, from accurate accounting and pattern recognition to potential assessment and quota control. This method overcomes the shortcomings of traditional industrial park carbon accounting methods that rely on static annual average emission factors and treat the power grid as a black box. It enables carbon responsibility to be accurately traced to specific electricity consumption behaviors and time periods, and provides the industrial park with operable and targeted carbon reduction optimization guidance, achieving deep linkage and closed-loop optimization of carbon accounting, responsibility allocation, and emission reduction management.

[0065] For example, in another embodiment of the present invention, a refined carbon flow dynamic tracking method for industrial parks based on the entire life cycle is provided. When the power supply edge generation unit type is determined to be thermal power, the carbon emission intensity signal at the target time is determined according to the power supply edge generation unit type and the time series data of the power generation structure. Specifically, the method includes the following steps: S201. Determine the overall thermal power load rate based on the total thermal power output and total thermal power installed capacity of the target area power grid at the target time.

[0066] The purpose of this step is to assess the current utilization level of the entire thermal power system's resources, providing a macroscopic operational context for the subsequent positioning of marginal units. The overall thermal power load factor reflects the ratio of the total output of all online thermal power units in the region to their theoretical maximum generating capacity (total installed capacity).

[0067] Specifically, the system extracts the target time from the power generation structure time series data. Total output of thermal power And obtain the total installed capacity of thermal power in the regional power grid from the unit archive database. The system calculates the overall load factor of thermal power plants using the following formula. :

[0068] in, Indicates the target time At that time, the total power output of all thermal power units within the regional power grid; This represents the sum of the rated installed capacity of all thermal power units within the regional power grid. The formula calculates a load value between 0 and 1 by dividing the real-time total output of thermal power units by their total installed capacity. This is because, during normal power system dispatching and operation, the total output... It will not exceed the total installed capacity C coal Therefore, this load factor value is usually less than or equal to 1. This value reflects the overall stress level of the thermal power system; a higher load factor indicates that the efficient, low-coal-consumption units in the system may be fully utilized.

[0069] S202. Based on the rated coal consumption and rated capacity of each thermal power unit in the target area power grid, construct a unit scheduling sequence model sorted by coal consumption.

[0070] This step aims to establish a virtual unit dispatch sequence model based on the general principle of efficiency-first power dispatch. This model simulates the behavior of the dispatch system in prioritizing low-coal-consumption, high-efficiency units when meeting load demands, thus providing a theoretical basis for inferring marginal units from total output data.

[0071] Specifically, the system extracts the static technical parameters of all thermal power units from the regional power grid's unit file database, including the rated capacity of each unit. Coal consumption for power generation under rated operating conditions (Unit: grams of standard coal / kilowatt-hour). Next, the system will adjust the coal consumption of all thermal power units according to their rated coal consumption. The units are sorted from low to high to obtain an ordered sequence. Based on this sequence, two key model curves are constructed to form the unit scheduling sequence model: Cumulative capacity curve: Calculates the first... The sum of the installed capacity of the units is expressed as Any point on this curve represents the point at which the generating units are selected from low to high coal consumption. The maximum theoretical output that the unit can provide.

[0072] Weighted average coal consumption curve (optional, for understanding the model): Calculates the top [coal consumption curve] in the sorted sequence. The capacity-weighted average coal consumption of the Taiwanese generating units is expressed as: This curve illustrates the trend of marginal coal consumption of the system as the call capacity increases.

[0073] S203. Based on the total output of thermal power and the unit scheduling sequence model, determine the marginal thermal power units and rated coal consumption at the target time.

[0074] The purpose of this step is to use the model built in S202 to identify the last unit that is called up and whose output is still adjustable under the current load demand from the total thermal power output data, i.e., the marginal unit, and to obtain its coal consumption value under the design operating conditions.

[0075] Optionally, the system will use the total thermal power output obtained in S201 at the current moment. The cumulative capacity curve constructed with S202 Perform a comparison. Find the unit serial number that meets the following conditions. :

[0076] in, This indicates the order after sorting by coal consumption. The cumulative installed capacity of the Taiwanese units; Indicates the preceding The cumulative installed capacity of the Taiwanese units; This accounts for the current total output of thermal power.

[0077] It should be noted that this search condition is based on the "efficiency-first scheduling" assumption. The inequality shows that in order to satisfy the current total output... The lowest coal consumption Taiwanese units (cumulative capacity) The generator is already at full capacity, but its output is still insufficient, so the first generator in the sorted sequence must be called. Taiwanese unit. Due to Not exceeding the previous The maximum capacity of the unit means that the first The unit is operating at partial load and is considered a "marginal unit" with some output adjustment margin. The system then retrieves this marginal unit from the unit's records. Rated coal consumption under operating conditions .

[0078] S204. Based on the average load rate of the marginal thermal power units in the second time period, the rated operating coal consumption is corrected to obtain the corrected marginal coal consumption.

[0079] The purpose of this step is to take into account that in actual operation, the coal consumption of thermal power units will increase as the load rate deviates from the rated operating condition (usually the economic operating condition). Therefore, it is necessary to correct the rated coal consumption obtained in S203 to obtain a coal consumption value that is closer to the current actual operating condition.

[0080] For example, the system obtains the marginal unit Calculate the average load factor within the output data for a second time period (e.g., the past 60 minutes) that includes the target time. The coal consumption-load characteristic of thermal power units typically exhibits a U-shaped curve. This embodiment employs a quadratic function for fitting and correction. The correction formula is as follows:

[0081] in, This indicates the corrected marginal unit coal consumption; Indicates marginal units Rated coal consumption under operating conditions; This represents the coal consumption penalty coefficient, reflecting the sensitivity of the unit's coal consumption to load rate deviations. Indicates marginal units Average load rate during the second time period.

[0082] It should be noted that this formula simulates the typical characteristics of unit coal consumption changing with load rate. This applies when the unit is operating at rated load. When the correction term is 1, the coal consumption is the rated value. When the load factor deviates from 1 (whether too high or too low), A positive value leads to increased coal consumption, and the greater the deviation, the greater the penalty. (Coefficient) This determines the intensity of the penalty; a typical value of 0.15 can be exemplified.

[0083] S205. Determine the carbon emission intensity signal based on the corrected marginal coal consumption, the carbon emission factor of standard coal, and the energy conversion coefficient.

[0084] This step is the final conversion step, and its purpose is to convert the "coal consumption" indicator, which reflects fuel consumption efficiency, into a "carbon intensity" signal, which reflects carbon emissions, so as to provide a directly usable benchmark value for subsequent carbon responsibility accounting on the park side.

[0085] Specifically, the system obtains the carbon emission factor of standard coal. (Example value: 2.6 kg CO2 / kg standard coal) and energy conversion factor (1 kg standard coal = 0.293 kWh). Corrected coal consumption calculated using S204. The marginal carbon emission intensity signal of thermal power is calculated using the following formula. :

[0086] in, This represents the calculated carbon emission intensity signal (unit: gCO2 / kWh); This represents the corrected marginal coal consumption (unit: g standard coal / kWh); This represents the carbon emission factor of standard coal (unit: kgCO2 / kg standard coal). The formula directly multiplies the coal consumption of the generating unit by the corresponding fuel's carbon emission factor to obtain the carbon dioxide emissions per unit of electricity generated, i.e., the carbon emission intensity. This signal accurately characterizes the true carbon emission intensity generated by the grid side for providing a unit of incremental electricity when the specific thermal power unit is used as a marginal unit.

[0087] Based on the above technical solution, this invention constructs a unit scheduling sequence model to infer the location of marginal thermal power units from the total output data, and corrects the rated coal consumption by combining their actual load rate, ultimately calculating a signal that accurately reflects the current marginal carbon emission intensity of the power grid. This method overcomes the difficulty of directly obtaining scheduling details, and utilizes publicly available static unit parameters and macroscopic operating data to achieve effective penetration and quantification of the carbon emission characteristics of marginal units in the power grid "black box," providing a reliable upstream input for refined carbon management downstream.

[0088] For example, in another embodiment of the present invention, a refined carbon flow dynamic tracking method for industrial parks based on the entire life cycle is provided. The method quantifies the carbon reduction potential corresponding to each typical electricity consumption pattern and dynamically adjusts the carbon quota allocation of the target industrial park in future cycles based on the carbon reduction potential. Specifically, the method includes the following steps: S301. Identify state transition events in historical time series, showing a shift in electricity consumption patterns from high-carbon responsible electricity consumption to low-carbon responsible electricity consumption. Specifically, the average carbon responsibility allocation value for high-carbon responsible electricity consumption is higher than that for low-carbon responsible electricity consumption.

[0089] The purpose of this step is to capture specific instances of optimized and improved electricity consumption behavior within the park's time series. State transition events refer to situations where, within adjacent time periods, the park's electricity consumption behavior pattern (defined by labels obtained from S103 clustering) shifts from a typical pattern of higher carbon emission responsibility to a typical pattern of lower carbon emission responsibility. Identifying these events is fundamental to quantifying the effectiveness of the improvements.

[0090] Specifically, the system sets a time window (e.g., 1 hour) to define adjacent moments. This applies to all consecutive time pairs (moments) within a historical period (e.g., the past year). With time +1), the system executes the following judgment process: (1) Obtaining time Typical electricity consumption pattern cluster label ; (2) Obtaining time Typical electricity consumption pattern cluster label ; (3) Determine whether the conditions are met: and .in, This represents a predefined set of high-carbon responsible electricity consumption patterns (e.g., based on the S103 clustering results, the top 50% of the average carbon responsibility allocation values ​​are classified as high-carbon responsible patterns). This represents a predefined set of low-carbon responsible electricity consumption patterns (the clusters that correspond to the bottom 50% of the average carbon responsibility allocation value).

[0091] If the above conditions are met, then it is determined that at time [time]... arrive An event occurred during which the state transitioned from a high-carbon responsibility model to a low-carbon responsibility model, which is recorded as a successful transition case.

[0092] S302. For each state transition event, calculate the difference in carbon responsibility allocation value before and after the transition.

[0093] The purpose of this step is to quantify the specific carbon reduction effect of each successful state transition. This difference directly reflects the immediate carbon reduction achievable through optimization of electricity consumption behavior patterns.

[0094] For each state transition event identified by S301 (from time...) Transfer to time The system obtains the total carbon responsibility allocation values ​​for these two moments from historical data. and The carbon responsibility difference for this transfer is calculated using the following formula. :

[0095] in, Represents the moment before the state transition. The carbon responsibility allocation value; Indicates the time after the state transition The carbon responsibility allocation value. It should be noted that if... If the value is ≤0, it is considered that the transfer has not achieved carbon reduction and will not be included in the set of valid state transfer events for subsequent carbon reduction potential calculation.

[0096] The above formula calculates the reduction in carbon responsibility after the shift relative to before the shift. Since this is based on a shift to a low-carbon responsibility model, theoretically... It should be a positive value, and its magnitude represents the amount of carbon emissions avoided per unit of time (e.g., 1 hour) by this change in behavior pattern.

[0097] S303. For each typical electricity consumption pattern, calculate the carbon reduction potential corresponding to the typical electricity consumption pattern based on the difference in carbon responsibility allocation values ​​of the state transition events corresponding to all historical moments classified as typical electricity consumption patterns.

[0098] The purpose of this step is to aggregate the carbon reduction effects scattered across each successful transfer event to the “source model” from which it originated, thereby assessing the average room for improvement, or “carbon reduction potential,” inherent in each high-carbon responsible electricity use model.

[0099] For each typical model categorized as a high-carbon responsible electricity consumption mode ( (For the pattern index), the system performs the following calculations: (1) Find all those that use the following formula: This is the state transition event that starts from this point. That is, it satisfies... And the moment when the successful transfer occurs A set, denoted by , contains One event.

[0100] (2) Obtain this The carbon liability difference for each event in the event. .

[0101] (3) Calculate the pattern carbon reduction potential That is, the average of all these differences. The higher the value, the more significant the carbon reduction effect that can be achieved by optimizing the model, and the higher its carbon reduction potential.

[0102] S304. Obtain the carbon reduction potential corresponding to each typical electricity consumption mode identified in the target industrial park during the current adjustment cycle, as well as the proportion of each typical electricity consumption mode in the current adjustment cycle.

[0103] The purpose of this step is to prepare data for calculating the overall carbon reduction efficiency for the current period. The "adjustment period" is the basic time unit for dynamic carbon quota management, such as every two weeks. The system needs to summarize the characteristics of all electricity consumption patterns within the current period.

[0104] Specifically, the system first determines the "current adjustment cycle" (e.g., the second half of July). Then, for that cycle: (1) Obtain carbon reduction potential: Using the method of S303, clustering and potential calculation are performed again based on historical data within the cycle (or recent data containing the cycle) to obtain the typical electricity consumption pattern of each high carbon responsibility in the current cycle. Corresponding carbon reduction potential For low-carbon responsible electricity use, the carbon reduction potential is set to 0.

[0105] (2) Percentage of the quantity obtained: Statistics are collected for each typical electricity consumption mode within the current adjustment cycle. Number of moments Calculate the percentage of this pattern in the current period. . This reflects the frequency of this electricity consumption pattern within the current cycle.

[0106] S305. Calculate the weighted average carbon reduction potential value for the current adjustment cycle based on carbon reduction potential and quantity proportion.

[0107] The purpose of this step is to calculate a single indicator to comprehensively characterize the potential for carbon reduction improvements in the overall electricity consumption behavior of the park during the current adjustment cycle. Weighted averaging can simultaneously consider the potential value and frequency of occurrence of each pattern.

[0108] The system uses S304 to obtain each mode carbon reduction potential and the proportion of quantity The weighted average carbon reduction potential value for the current adjustment cycle is calculated using the following formula. :

[0109] in, This represents the weighted average carbon reduction potential value for the current adjustment cycle; Indicates the first The carbon reduction potential calculated for a typical electricity consumption pattern in the current adjustment cycle; M represents the total number of typical electricity consumption patterns. Indicates the first The proportion of each pattern in the current adjustment cycle.

[0110] It should be noted that this formula derives an overall expected value by weighting and summing the carbon reduction potential of all electricity consumption patterns according to their frequency of occurrence. It reflects the average carbon reduction that the park can potentially achieve per unit of time based on the current cycle's electricity consumption pattern distribution. This value incorporates the inertia of recent operations and is a core indicator for assessing short-term carbon reduction trends.

[0111] S306. Obtain the historical weighted average carbon reduction potential value of the target park in at least one historical adjustment cycle.

[0112] The purpose of this step is to incorporate historical data from the same period to capture recurring patterns caused by factors such as seasons, climate, and production cycles. This helps improve the stability of predictions for the potential of the next cycle.

[0113] Specifically, the system retrieves historical data from the target park that is completely contemporaneous with the "current adjustment cycle" in one or more past years. For example, if the current period is the second half of July 2024, then historical data from the second half of July 2023, the second half of July 2022, and other cycles will be retrieved. For each historical contemporaneous cycle... The system calculates the corresponding historical weighted average carbon reduction potential value according to the methods in S304 and S305. .

[0114] In addition, if multiple historical data points from the same period are available, their average value is calculated as the final historical weighted average carbon reduction potential value. .

[0115] S307. Based on the weighted average carbon reduction potential value of the current adjustment cycle and the historical weighted average carbon reduction potential value, predict the carbon reduction potential value of the next adjustment cycle.

[0116] The purpose of this step is to predict the carbon reduction potential for the upcoming adjustment cycle by leveraging recent trends and historical patterns. A weighted moving average method is used to quickly respond to recent changes while smoothing out random fluctuations.

[0117] For example, the system calculates the value obtained in S305. (representing recent trends) and calculated using S306 (Based on historical patterns) a weighted average is used to predict the carbon reduction potential for the next adjustment cycle. The calculation formula is as follows:

[0118] in, This indicates the predicted carbon reduction potential for the next adjustment cycle; This represents the weighted average carbon reduction potential value for the current adjustment cycle; This represents the (average) weighted average carbon reduction potential value for the same historical adjustment cycle. It assigns weights to data in the current period, reflecting the level of trust in recent information.

[0119] It should be noted that this formula makes predictions by linearly combining recent and historical values. Weights The settings embody the prediction strategy and are exemplary. This means that 60% of the forecast is based on the performance of the current cycle (capturing short-term changes), and 40% is based on historical patterns (maintaining seasonal stability).

[0120] S308. Determine the carbon quota adjustment coefficient based on the predicted carbon reduction potential and the historical maximum carbon reduction potential, and calculate the adjusted carbon quota allocation for the next adjustment cycle based on the carbon quota adjustment coefficient and the benchmark carbon quota of the target park.

[0121] This step is the final step in the closed-loop control process, aiming to translate the predicted carbon reduction potential into concrete carbon quota management actions. By dynamically compressing quotas, the park is incentivized to convert the predicted carbon reduction potential into actual emission reduction actions. Specifically, it includes the following steps: (1) Determine the carbon quota adjustment coefficient : In this step, the system queries the historical database for the weighted average maximum carbon reduction potential of all adjustment cycles recorded. Adjustment coefficient Calculated using the following formula, with constraints imposed to prevent over-adjustment:

[0122] in, This represents the carbon quota adjustment factor; The carbon reduction potential for the next cycle as predicted by S307; This represents the largest potential carbon reduction value in history. This is the upper limit threshold for the adjustment coefficient, used to prevent quota adjustments from being too drastic.

[0123] It should be noted that in the initial stages of system operation, it is possible that no carbon reduction potential will be found in the historical database, leading to... The case where it is 0, therefore, for this extreme case, we set... The minimum value is 0.001 to avoid the denominator of the formula being zero.

[0124] The above formula calculates the proportion of predicted potential to the historical maximum potential, which serves as the benchmark for the adjustment coefficient. A higher proportion indicates a larger predicted carbon reduction space, and therefore a higher allowable reduction ratio should be achieved. (Function) Used to limit the adjustment coefficient to a preset upper limit. Within, exemplarily A value of 0.3 (i.e., 30%) can be used to ensure that the adjustment is within a reasonable range.

[0125] (2) Calculate the adjusted carbon quota allocation. : For example, the system obtains the target park's total annual carbon allowance and distributes it evenly across each adjustment cycle to obtain the baseline carbon allowance for the next adjustment cycle. (For example, annual allowance / 24 half-month cycle). It should be noted that the target park's total annual carbon allowance represents the upper limit of total carbon emissions permitted for the target park within a full year. It is typically determined by a higher-level carbon market or carbon control policy and serves as an externally input management benchmark. After being broken down into benchmark allowances, it is used to determine the dynamic allowances for each cycle.

[0126] Finally, the adjusted quota is calculated based on the adjustment coefficient:

[0127] in, This indicates the adjusted carbon quota allocation for the next adjustment cycle; This indicates the baseline carbon allowance for the next adjustment cycle; This is the calculated carbon quota adjustment coefficient.

[0128] The above formula is obtained by multiplying the base amount by... To obtain the final quota. The larger the threshold, the greater the quota reduction, thus creating stronger pressure on the industrial park to take measures such as load shifting and increasing clean energy consumption in order to achieve actual emission reductions under the high carbon reduction potential forecast, thereby transforming the predicted management signals into actual carbon flow optimization.

[0129] Based on the above technical solution, this invention quantifies the carbon reduction effect of shifting from high-carbon to low-carbon electricity consumption patterns and defines the carbon reduction potential of each typical pattern. Then, based on current and historical potential values, it predicts future carbon reduction potential. Finally, it transforms this predicted value into a dynamic adjustment coefficient for carbon quotas, achieving intelligent and forward-looking control of carbon allocation in the industrial park. This method establishes a closed-loop link of "pattern recognition - potential quantification - prediction - control," directly and dynamically feeding back carbon accounting results to carbon quota management. It solves the technical problem of the disconnect between carbon responsibility accounting and emission reduction decision-making in traditional methods, providing the industrial park with precise, targeted, and incentive-based carbon reduction decision support.

[0130] For example, such as Figure 2 The diagram shown is an architectural schematic of a refined carbon flow dynamic tracking system for industrial parks based on the entire life cycle (hereinafter referred to as the dynamic tracking system) provided in an embodiment of the present invention. The dynamic tracking system 20 includes: a carbon emission intensity determination module 21, a carbon responsibility allocation module 22, an electricity consumption pattern identification module 23, and a carbon reduction potential management module 24. The modules are described in detail below: The carbon emission intensity determination module 21 is used to determine the carbon emission intensity signal of the target area power grid. This carbon emission intensity signal characterizes the carbon emission intensity of marginal power generation units in the target area power grid that are called upon to supply electricity in response to the incremental electricity demand at a target time. For detailed procedures, please refer to S101 above.

[0131] The carbon responsibility allocation module 22 is used to determine the carbon responsibility allocation value of the target park at the target time based on the carbon emission intensity signal, the power purchase data of the target park at the target time, and the photovoltaic power generation data of the target park. The carbon responsibility allocation value represents the carbon emission responsibility quota allocated to the target park at the target time. For detailed procedures, please refer to S102 above.

[0132] The electricity consumption pattern recognition module 23 is used to perform cluster analysis on the electricity consumption behavior of the target park during historical periods to identify various typical electricity consumption patterns. For details of the process, please refer to S103 above.

[0133] The carbon reduction potential management module 24 is used to quantify the carbon reduction potential corresponding to each typical electricity consumption pattern and dynamically adjust the carbon quota allocation of the target park in future cycles based on the carbon reduction potential. The carbon reduction potential characterizes the carbon responsibility allocation value that can be reduced by shifting electricity consumption behavior to a low-carbon responsible electricity consumption pattern per unit time. See section S104 above for detailed procedures.

[0134] The technical effects achieved by the dynamic tracking system 20 can be found in the above-mentioned technical effects of the park-based refined carbon flow dynamic tracking method based on the whole life cycle, and will not be repeated here.

[0135] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0136] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for refined dynamic tracking of carbon flow in industrial parks based on the entire life cycle, characterized in that, The method includes: Determine the carbon emission intensity signal of the power grid in the target area; wherein, the carbon emission intensity signal is used to characterize the carbon emission intensity of the marginal power generation units in the power grid of the target area that are called upon to supply electricity in response to the increase in electricity demand at the target time. Based on the carbon emission intensity signal, the electricity purchase data of the target park at the target time, and the photovoltaic power generation data of the target park, the carbon responsibility allocation value of the target park at the target time is determined; wherein, the carbon responsibility allocation value is used to characterize the carbon emission responsibility quota allocated to the target park at the target time. Cluster analysis was performed on the electricity consumption behavior of the target park during historical periods to identify a variety of typical electricity consumption patterns; The carbon reduction potential corresponding to each of the typical electricity consumption patterns is quantified, and the carbon quota allocation of the target park in the future cycle is dynamically adjusted based on the carbon reduction potential; wherein, the carbon reduction potential is used to characterize the carbon responsibility allocation value that can be reduced by shifting electricity consumption behavior to a low-carbon responsible electricity consumption pattern per unit time.

2. The method for dynamic tracking of refined carbon flow in industrial parks based on the entire life cycle as described in claim 1, characterized in that, Determine the carbon emission intensity signal of the power grid in the target area, specifically including: Based on the power generation structure time-series data and load time-series data of the target area power grid within a first time period including the target time, the power supply marginal power generation unit type at the target time is identified; wherein, the power supply marginal power generation unit type includes new energy type and thermal power type; Based on the type of the marginal power generation unit and the time series data of the power generation structure, the carbon emission intensity signal at the target time is determined.

3. The method for dynamic tracking of refined carbon flow in industrial parks based on the entire life cycle as described in claim 2, characterized in that, Based on the power generation structure time-series data and load time-series data of the target area power grid within a first time period including the target time, the type of marginal power generation unit at the target time is identified, specifically including: Obtain the rate of change sequence of new energy power generation output and the rate of change sequence of total grid load during the first time period; A correlation analysis was performed on the rate of change sequence of the new energy power generation output and the rate of change sequence of the total grid load to obtain the first correlation coefficient; If the first correlation coefficient is greater than the first correlation threshold, then the power supply marginal power generation unit type is determined to be a new energy type; If the first correlation coefficient is less than the second correlation threshold, then the power supply marginal generation unit type is determined to be thermal power type; wherein, the second correlation threshold is less than the first correlation threshold.

4. The method for dynamic tracking of refined carbon flow in industrial parks based on the entire life cycle as described in claim 3, characterized in that, If the first correlation coefficient is greater than or equal to the second correlation threshold, and less than or equal to the first correlation threshold, the method further includes: Calculate the higher-order difference eigenvalues ​​of the rate of change sequence of the new energy power generation output; If the higher-order differential feature value is less than the preset change feature threshold, then the power supply marginal power generation unit type is determined to be thermal power type; If the higher-order difference feature value is greater than or equal to the preset change feature threshold, then the marginal contribution weight of new energy type and thermal power type is determined according to the first correlation coefficient.

5. The method for dynamic tracking of refined carbon flow in industrial parks based on the entire life cycle as described in claim 4, characterized in that, When the power supply marginal generation unit type is determined to be thermal power, the carbon emission intensity signal at the target time is determined based on the power supply marginal generation unit type and the time series data of the power generation structure, specifically including: The overall thermal power load rate is determined based on the total thermal power output and total thermal power installed capacity of the target area power grid at the target time. Based on the rated coal consumption and rated capacity of each thermal power unit in the target area power grid, a unit scheduling sequence model sorted by coal consumption is constructed. Based on the total thermal power output and the unit scheduling sequence model, determine the marginal thermal power units and rated coal consumption at the target time. The rated operating coal consumption is corrected based on the average load rate of the marginal thermal power unit during the second time period to obtain the corrected marginal coal consumption. The carbon emission intensity signal is determined based on the corrected marginal coal consumption, the carbon emission factor of standard coal, and the energy conversion coefficient.

6. The method for dynamic tracking of refined carbon flow in industrial parks based on the entire life cycle as described in claim 4, characterized in that, After determining the marginal contribution weights of new energy types and thermal power types based on the first correlation coefficient, the carbon emission intensity signal at the target time is determined, specifically including: Based on the time-series data of the power generation structure, the carbon emission intensity of the new energy power generation unit and the carbon emission intensity of the thermal power generation unit are determined respectively. Based on the marginal contribution weight, the carbon emission intensity of the new energy power generation unit and the carbon emission intensity of the thermal power generation unit are weighted and calculated to obtain the carbon emission intensity signal at the target time.

7. The method for dynamic tracking of refined carbon flow in industrial parks based on the entire life cycle as described in claim 1, characterized in that, Based on the carbon emission intensity signal, the electricity purchase data of the target industrial park at the target time, and the photovoltaic power generation data of the target industrial park, the carbon responsibility allocation value of the target industrial park at the target time is determined, specifically including: Obtain the transmission line loss rate of the power grid in the target area, and calculate the carbon emission intensity of the target park at the border based on the carbon emission intensity signal and the transmission line loss rate; The first carbon responsibility allocation value is determined based on the carbon emission intensity of electricity purchase at the checkpoint and the electricity purchase power data. Based on the photovoltaic power generation data, determine the photovoltaic self-generation and self-consumption power of the target park at the target time; The second carbon responsibility allocation value is determined based on the photovoltaic self-generation and self-consumption power and the preset distributed power generation life cycle carbon emission coefficient. Based on the first carbon responsibility allocation value and the second carbon responsibility allocation value, the carbon responsibility allocation value of the target park at the target time is determined.

8. The method for dynamic tracking of refined carbon flow in industrial parks based on the entire life cycle as described in claim 1, characterized in that, Cluster analysis was performed on the electricity consumption behavior of the target park over a historical period to identify several typical electricity consumption patterns, including: For each historical moment within the historical period, a multidimensional feature vector is constructed; wherein, the multidimensional feature vector includes at least the carbon responsibility allocation value, carbon emission intensity signal, electricity purchase ratio, photovoltaic self-consumption ratio, and total load power at the historical moment; A preset clustering algorithm is used to cluster the multidimensional feature vectors corresponding to all historical moments within the historical period to obtain multiple clusters and identify various typical electricity consumption patterns; wherein, each cluster corresponds to one typical electricity consumption pattern.

9. The method for dynamic tracking of refined carbon flow in industrial parks based on the entire life cycle as described in claim 1, characterized in that, Quantifying the carbon reduction potential corresponding to each of the aforementioned typical electricity consumption patterns, specifically including: In the time series of the historical period, identify state transition events in which electricity consumption behavior patterns shift from high-carbon responsible electricity consumption patterns to low-carbon responsible electricity consumption patterns; wherein, the average carbon responsibility allocation value of the high-carbon responsible electricity consumption pattern is higher than that of the low-carbon responsible electricity consumption pattern. For each state transition event, calculate the difference in carbon responsibility allocation before and after the transition; For each of the typical electricity consumption patterns, the carbon reduction potential corresponding to the typical electricity consumption pattern is calculated based on the difference in carbon responsibility allocation values ​​of the state transition events corresponding to all historical moments classified under the typical electricity consumption pattern.

10. The method for dynamic tracking of refined carbon flow in industrial parks based on the entire life cycle as described in claim 1, characterized in that, Based on the aforementioned carbon reduction potential, the carbon allowance allocation for the target industrial park in future cycles will be dynamically adjusted, specifically including: Obtain the carbon reduction potential corresponding to each of the typical electricity consumption patterns identified in the target park during the current adjustment cycle, as well as the proportion of each of the typical electricity consumption patterns in the current adjustment cycle; Based on the carbon reduction potential and the quantity ratio, calculate the weighted average carbon reduction potential value of the current adjustment cycle; Obtain the historical weighted average carbon reduction potential value of the target industrial park during at least one historical adjustment period; Based on the weighted average carbon reduction potential value of the current adjustment cycle and the historical weighted average carbon reduction potential value, predict the carbon reduction potential value of the next adjustment cycle. The carbon quota adjustment coefficient is determined based on the predicted carbon reduction potential and the historical maximum carbon reduction potential. The adjusted carbon quota allocation for the next adjustment cycle is then calculated based on the carbon quota adjustment coefficient and the baseline carbon quota of the target park.

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