A carbon emission accounting method, device and storage medium for a newly built zero-carbon park

Abstract

The application discloses a carbon emission accounting method and device for a newly-built zero-carbon park, and a storage medium, comprising the following steps: S1. acquiring real-time scheduling data of a power system, calculating a time-varying marginal emission factor, and outputting a time-varying emission factor sequence; S2. calculating carbon flow of each directed edge according to the time-varying emission factor sequence; S3. calculating comprehensive standard uncertainty of each carbon source of the park according to the carbon flow of each directed edge, outputting entropy weight corrected carbon and a confidence interval; S4. calculating the amount of abandoned electricity according to the entropy weight corrected carbon and feeding back to S1 to update the time-varying marginal emission factor, and outputting a control instruction; and S5. taking the confidence interval and the control instruction as constraints, constructing a carbon balance planning problem in three time scales of an annual layer, a monthly layer and a daily layer, and outputting a carbon shadow price. By using the time-varying marginal emission factor to replace the annual average factor, systematic deviation caused by the difference between peak and valley emission intensities is eliminated, and the carbon emission accounting error is reduced.

Description

Technical Field

[0001] This invention relates to the field of carbon emission accounting technology, specifically to a carbon emission accounting method, equipment, and storage medium for newly built zero-carbon industrial parks. Background Technology

[0002] With the deepening of the "dual carbon" goals, carbon emission accounting for newly built zero-carbon industrial parks has become a key foundational task supporting policy decisions and engineering implementation; however, existing technical solutions have the following shortcomings: Static emission factor distortion: Current methods generally use the annual average grid emission factor, ignoring the physical fact that marginal units in the power system switch in real time with the load curve; under the background of large-scale grid connection of renewable energy, the deviation between the real-time marginal emission rate and the annual average can reach 30% to 60%, resulting in a systematic overestimation or underestimation of renewable energy emission reduction.

[0003] Lack of carbon flow directionality: Traditional methods treat carbon sources and carbon sinks as independent scalars and perform algebraic addition and subtraction, failing to establish a directional topological relationship for energy flow within the park, and thus failing to identify structural inefficiencies such as "carbon flow short circuits" and "carbon flow blockages".

[0004] Uncertainty propagation is overlooked: multiple sources of uncertainty, such as energy metering instrument errors, emission factor statistical biases, and measured fluctuations in carbon sinks, are superimposed during the aggregation process. Existing methods lack a systematic error propagation tracking mechanism, and the calculation results cannot provide confidence intervals.

[0005] Time-series decoupling leads to path failure: annual total accounting masks the dynamic imbalance of monthly and daily carbon balance, and the lack of rolling early warning and intervention mechanisms across time scales results in deviations from the carbon neutrality target only being discovered at the end of the year, with extremely high correction costs. Summary of the Invention

[0006] This invention proposes a carbon emission accounting method, equipment, and storage medium for newly built zero-carbon industrial parks to solve the problems mentioned in the background.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A carbon emission accounting method for newly built zero-carbon industrial parks according to the present invention includes the following steps: S1. Obtain real-time dispatch data of the power system, calculate the time-varying marginal emission factor, and output the time-varying emission factor sequence; S2. Based on the time-varying emission factor sequence, the park's energy system is abstracted into a directed weighted graph, and the carbon flow of each directed edge is calculated; S3. Based on the carbon flow of each directed edge, calculate the comprehensive standard uncertainty of the carbon source in the park, and output the entropy-weighted corrected carbon amount and confidence interval. S4. Correct the carbon content according to the entropy weight, calculate the amount of electricity to be wasted and feed it back to S1 to update the time-varying marginal emission factor, and output the control command; S5. Using the confidence interval and the control command as constraints, construct a carbon balance planning problem in layers at three time scales: annual, monthly, and daily, and output the carbon shadow price; S6. Feed back the carbon shadow price to S2 to guide the redistribution of carbon flows towards low-carbon pathways.

[0008] Preferably, S1 includes: Obtain real-time power system dispatch data and arrange the M generating units in the power grid into a set of generating units in ascending order of marginal cost. The rated emission intensity of each unit is At time t, the minimum set of generating units that satisfies the total grid load L(t) is determined according to the economic dispatch principle; marginal units are... satisfy: And k(t) is the smallest integer that satisfies the above conditions; the actual output of the marginal unit ; Calculate the time-varying marginal emission factor:

[0009] in, For the marginal crossing correction factor, The interval is decomposed into N piecewise linear intervals within 24 hours, with segment nodes. Automatically identified by the inflection point of the power grid load forecast curve, each interval satisfies: Approximation error ; Output time-varying emission factor sequence .

[0010] Preferably, S2 includes: S1 output As boundary conditions, the park's energy system is abstracted as a directed weighted graph. , where the node set Includes energy production nodes, conversion nodes, consumption nodes, and carbon sink nodes; directed edge set. Represents the energy transfer path; edge weight function This represents the power transferred per unit time; for each conversion node and aggregation node... Establish the carbon flow conservation equation:

[0011] By simultaneously solving the conservation equations for all N nodes in the network, we obtain a system of linear equations. Where A is the N×N carbon flux admittance matrix, Let B be the carbon potential vector and B be the boundary condition vector; the carbon potential matrix is ​​obtained by solving. Then, calculate the carbon flux of each directed edge (i→j). Define the carbon flow short-circuit index (CSI) and the carbon flow blockage index (CBI) when... or Timely triggering of structural inefficiency warnings; The element in the i-th row and j-th column of the carbon flux admittance matrix A Defined as: when hour, When i=j, The remaining elements are zero.

[0012] Preferably, S3 includes: The carbon flow vector output by S2 As input, calculate the comprehensive standard uncertainty for each of the K carbon sources in the park:

[0013] Comprehensive standard uncertainty of each carbon source It is composed of three types of components: metering instrument error (Category A assessment, obtained through repeated measurements), statistical bias of emission factors. (Class B assessment, based on confidence intervals from the official factor database), activity data fluctuations. (Category B rating, based on historical data standard deviation); Calculate the relative uncertainty of each carbon source and information entropy Normalization yields entropy weight The total differential propagation formula is used to track the propagation path of uncertainty in the carbon flow network and calculate the total carbon emission uncertainty of the entire network. Output entropy weighted carbon content correction:

[0014] and 95% confidence interval ; Information entropy Calculated using the binary entropy function: ,in The relative uncertainty is given by the entropy weight normalization formula: , ; Uncertainty propagation uses the total differential formula: Total uncertainty of the entire network ;when When the default value is 5%, send an uncertainty warning signal to S5.

[0015] Preferably, S4 includes: The corrected carbon content output in step S3 And the carbon potential matrix of step S2 Define the system state vector as the state variable. ,in For the cumulative carbon deficit, For the growth rate of carbon sink, The energy storage is in its state of charge (SOC). Construct positive definite Lyapunov functions:

[0016] in The coefficients are positive weights; the derivation satisfies... Nonlinear control law ,in For the convergence rate parameter; when the control law When the control command exceeds the grid absorption limit, the amount of abandoned power ΔE is calculated and fed back to S1 for updating. .

[0017] Preferably, S5 includes: Using the confidence interval of S3 and the regulatory directive of S4 as constraints, a carbon balance planning problem is constructed in layers at three time scales: annual, monthly, and daily. The annual level is used to solve for strategic carbon quota planning.

[0018] Output annual carbon quota allocation plan and carbon shadow prices (Lagrange dual variables); the monthly and daily layers use the upper-level output as constraints for rolling solution, with dual iteration convergence conditions: ; The objective function for the monthly layer is: The constraints are: ,in The conservative planning coefficient for S3 feedback; The objective function for the daily layer is: The optimal energy storage charging and discharging strategy, renewable energy dispatch volume, and demand response activation period are solved in a rolling manner over 96 15-minute time intervals.

[0019] Preferably, S6 includes: The carbon shadow price output by S5 Feedback is sent to S2, via the penalty factor. Adjust carbon potential weights to guide carbon flows toward a redistribution of low-carbon pathways.

[0020] Preferably, the abandoned power ΔE(t) received from S4 in S1 is updated at the next time t+1 according to the following rule: at that time, the actual output of the marginal unit decreases, and it is adjusted down proportionally, with the reduction coefficient determined by the ratio of ΔE(t) to it; The carbon shadow price received in step S5 is mentioned in S2. For high carbon potential nodes , The outbound weights of the average carbon potential across the entire network multiplied by a penalty factor. ,in The price elasticity coefficient enables dynamic updates to the carbon flow admittance matrix A, guiding the redistribution of carbon flows toward low-carbon pathways.

[0021] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0022] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0023] As can be seen from the above technical solution, the present invention provides a method for carbon emission accounting of newly built zero-carbon industrial parks. Compared with the prior art, the present invention has the following advantages: 1. This invention eliminates the systematic bias caused by the difference in peak and valley emission intensity by using a time-varying marginal emission factor instead of an annual average factor, thereby reducing the error in carbon emission accounting.

[0024] 2. This invention constructs a directed carbon flow network by introducing carbon potential and carbon flow admittance matrix, and determines the carbon emissions of each node by solving the node carbon potential equation. This enables quantitative calculation of the flow path and flow rate of carbon emissions within the park between each process node, and can identify carbon flow convergence nodes and carbon flow bottleneck nodes, providing data support for the precise and targeted implementation of carbon emission reduction measures in the park.

[0025] 3. This invention weights and synthesizes measurement uncertainties based on entropy weights from various data sources, and calculates the comprehensive uncertainty through the error propagation equation, thereby upgrading the carbon accounting output from a single-point estimate to an interval estimate containing confidence intervals.

[0026] 4. This invention employs a multi-timescale rolling planning algorithm to construct a three-layer nested optimization model consisting of annual strategic planning, monthly tactical allocation, and daily real-time scheduling. It also introduces carbon shadow prices as a cross-layer transmission signal, thereby achieving automatic decomposition and dynamic correction of macro-level annual carbon quota targets into micro-level daily scheduling plans, ensuring the consistency of carbon constraints across timescales.

[0027] 5. This invention sets up three cross-layer feedback paths, including a carbon shadow price feedback path, a power abandonment trigger rearrangement path, and an uncertainty early warning path, so that the key parameters of each algorithm can be continuously iteratively corrected based on actual operating data without manual recalibration. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating a carbon emission accounting method for a newly built zero-carbon industrial park according to the present invention.

[0029] Figure 2 For the year in this invention Monthly Daily stratified carbon balance planning framework diagram. Detailed Implementation

[0030] 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. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0031] like Figure 1 As shown in this embodiment, a carbon emission accounting method for a newly built zero-carbon industrial park includes the following steps: S1. Obtain real-time dispatch data of the power system, calculate the time-varying marginal emission factor, and output the time-varying emission factor sequence; S2. Based on the time-varying emission factor sequence, the park's energy system is abstracted into a directed weighted graph, and the carbon flow of each directed edge is calculated; S3. Based on the carbon flow of each directed edge, calculate the comprehensive standard uncertainty of the carbon source in the park, and output the entropy-weighted corrected carbon amount and confidence interval. S4. Correct the carbon content according to the entropy weight, calculate the amount of electricity to be wasted and feed it back to S1 to update the time-varying marginal emission factor, and output the control command; S5. Using the confidence interval and the control command as constraints, construct a carbon balance planning problem in layers at three time scales: annual, monthly, and daily, and output the carbon shadow price; S6. Feed back the carbon shadow price to S2 to guide the redistribution of carbon flows towards low-carbon pathways.

[0032] Taking a newly built hybrid zero-carbon industrial park as an example, the park includes three functional zones: a textile manufacturing zone, an electronics assembly zone, and a comprehensive service zone, with a total building area of ​​approximately 800,000 square meters and a designed annual electricity consumption. It is equipped with 30MWp photovoltaic capacity, 20MWh / 10MW energy storage system, and 150,000 square meters of green carbon sink area.

[0033] Input data: Access real-time dispatch data from the East China Power Grid to obtain the total load curve L(t) of the power grid for 96 15-minute time periods on the same day, as well as parameters of various generating units. Unit composition: Coal-fired units ( 5000MW installed capacity), gas turbine units ( 2000MW installed capacity), nuclear power ( 1200MW installed capacity), wind and solar power ( (3000MW installed capacity).

[0034] Execution process: During peak electricity consumption (8:00-10:00), L(t) = 9800MW, all coal-fired units operate at full capacity, and the marginal units are gas-fired units. During off-peak electricity demand (2:00-4:00), L(t) = 4200MW, nuclear power and wind and solar power meet the load, and marginal units are coal-fired peak-shaving units (partial output). .

[0035] Output: 24-hour time-varying emission factor sequence The mean was 0.583 kg CO₂ / kWh, the maximum was 0.875 kg CO₂ / kWh (midnight peak shaving), and the minimum was 0.020 kg CO₂ / kWh (midday full photovoltaic power generation). Compared with the annual average factor of 0.581 kg CO₂ / kWh, the peak-to-valley difference was 43 times, verifying the necessity of time-varying modeling.

[0036] Graph Modeling: The park's energy network is abstracted as an 8-node directed graph. Node Definition: (Mainland power connection, carbon potential) ), (110kV substation in the industrial park) (Photovoltaic power generation, ), (Energy storage system) (Power distribution in the textile zone) (Electronic Zone Power Distribution) (Power distribution in service areas) (Green carbon sink, (A negative value indicates carbon fixation).

[0037] Carbon flow conservation solution: Establish an 8×8 carbon flow admittance matrix A, with... , The nodal carbon potential is used as a boundary condition (assigned by TMEF-A). Solve the system of linear equations. The carbon potential at each node is obtained: .

[0038] Carbon flow anomaly detection: detected → The path contains a carbon flow short circuit (bypassing the energy storage node) (Direct power supply) Exceeding the threshold of 0.02 triggers a "high-carbon direct supply warning," and it is recommended to optimize scheduling to allocate more load through [the relevant authority / system]. (Energy storage and discharge) supply reduces overall carbon potential.

[0039] Uncertainty Quantification: Measurement Error of Photovoltaic Power Generation Photovoltaic emission factor deviation (Seasonal differences), overall uncertainty ; metering error of mains electricity consumption Emission factor statistical bias Comprehensive uncertainty ; Actual fluctuations in carbon sequestration from greening (Seasonal) moon.

[0040] Entropy weight calculation: Normalized entropy weight: , , Carbon sinks have the highest uncertainty weighting and the largest correction range.

[0041] Correction result: Original carbon emissions Month, after revision Monthly (correction amount) (mainly from the overestimation correction of carbon sinks), 95% confidence interval The warning threshold will not trigger the conservative planning this month.

[0042] State initialization: (The cumulative carbon deficit at the beginning of the month comes from the carryover from the previous month); (Current carbon sequestration rate); (Energy storage SOC 62%). Target equilibrium point: (End-of-month carbon neutralization) (Target carbon sink rate) (Target SOC).

[0043] Control law calculation: Construct the Lyapunov function V(x), and take... ,calculate The state-dependent gain matrix K(x) is determined by the partial derivatives of the carbon dynamic equation; the control law is obtained by solving:

[0044] Stability verification: calculation It satisfies the Lyapunov stability condition and has a convergence rate of The carbon deficit is expected to be It converges to near zero within 4.3 hours. Within); Daily abandoned electricity ( (Within the absorption range), there is no need to trigger the A2 emission factor update.

[0045] like Figure 2 As shown, the annual L1 planning: based on the park's design parameters, the annual carbon neutrality target. (Net-zero emissions), carbon allowances are allocated seasonally, with allocation occurring during the summer months (June-August). (Surplus solar power, net negative emissions), winter (December-February) allocation (Heating demand, positive emissions allowed), ±0 allocated for spring and autumn. Carbon shadow price. (Convergence value of dual variables).

[0046] Monthly tier (L2) rolling: based on the current month (June target net negative emissions) is used as a constraint to solve a 30-day carbon balance scheme on a rolling basis. The average daily quota sequence C_d^quota is Heaven, warning threshold (A monthly warning will be triggered if this value is exceeded).

[0047] Daily Layer (L3) Real-time Scheduling: Solve for the scheduling sequence of 96 time periods, and output the optimal energy storage charging and discharging strategy, photovoltaic tracking angle adjustment, and demand response activation period for each time period; convergence occurs after 3 rounds of dual iteration. carbon shadow price Feedback to CTPD-A triggers an update of carbon potential weights, high carbon potential nodes. marginal penalty factor .

[0048] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0049] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0050] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk).

[0051] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0052] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0053] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for carbon emission accounting in newly built zero-carbon industrial parks, characterized in that, Includes the following steps: S1. Obtain real-time dispatch data of the power system, calculate the time-varying marginal emission factor, and output the time-varying emission factor sequence; S2. Based on the time-varying emission factor sequence, the park's energy system is abstracted into a directed weighted graph, and the carbon flow of each directed edge is calculated; S3. Based on the carbon flow of each directed edge, calculate the comprehensive standard uncertainty of the carbon source in the park, and output the entropy-weighted corrected carbon amount and confidence interval. S4. Correct the carbon content according to the entropy weight, calculate the amount of electricity to be wasted and feed it back to S1 to update the time-varying marginal emission factor, and output the control command; S5. Using the confidence interval and the control command as constraints, construct a carbon balance planning problem in layers at three time scales: annual, monthly, and daily, and output the carbon shadow price; S6. Feed back the carbon shadow price to S2 to guide the redistribution of carbon flows towards low-carbon pathways.

2. The carbon emission accounting method for a newly built zero-carbon industrial park according to claim 1, characterized in that: S1 includes: Obtain real-time power system dispatch data and arrange the M generating units in the power grid into a set of generating units in ascending order of marginal cost. The rated emission intensity of each unit is At time t, the minimum set of generating units that satisfies the total grid load L(t) is determined according to the economic dispatch principle; marginal units are... satisfy: And k(t) is the smallest integer that satisfies the above conditions; the actual output of the marginal unit ; Calculate the time-varying marginal emission factor: in, For the marginal crossing correction factor, The interval is decomposed into N piecewise linear intervals within 24 hours, with segment nodes. Automatically identified by the inflection point of the power grid load forecast curve, each interval satisfies: ; Output time-varying emission factor sequence .

3. The carbon emission accounting method for a newly built zero-carbon industrial park according to claim 2, characterized in that: S2 includes: S1 output As boundary conditions, the park's energy system is abstracted as a directed weighted graph. , where the node set Includes energy production nodes, conversion nodes, consumption nodes, and carbon sink nodes; directed edge set. Represents the energy transfer path; edge weight function This represents the power transferred per unit time; for each conversion node and aggregation node... Establish the carbon flow conservation equation: By simultaneously solving the conservation equations for all N nodes in the network, we obtain a system of linear equations. Where A is the N×N carbon flux admittance matrix, Let B be the carbon potential vector and B be the boundary condition vector; the carbon potential matrix is ​​obtained by solving. Then, calculate the carbon flux of each directed edge (i→j). Define the carbon flow short-circuit index (CSI) and the carbon flow blockage index (CBI) when... or Timely triggering of structural inefficiency warnings; The element in the i-th row and j-th column of the carbon flux admittance matrix A Defined as: when hour, When i=j, The remaining elements are zero.

4. The carbon emission accounting method for a newly built zero-carbon industrial park according to claim 3, characterized in that: S3 includes: The carbon flow vector output by S2 As input, calculate the comprehensive standard uncertainty for each of the K carbon sources in the park: Comprehensive standard uncertainty of each carbon source It is composed of three types of components: metering instrument error Statistical bias of emission factors Activity data fluctuations ; Calculate the relative uncertainty of each carbon source and information entropy Normalization yields entropy weight The total differential propagation formula is used to track the propagation path of uncertainty in the carbon flow network and calculate the total carbon emission uncertainty of the entire network. Output entropy weighted carbon content correction: and 95% confidence interval ; Information entropy Calculated using the binary entropy function: ,in The relative uncertainty is given by the entropy weight normalization formula: , ; Uncertainty propagation uses the total differential formula: Total uncertainty of the entire network ;when At that time, an uncertainty warning signal is sent to S5.

5. The carbon emission accounting method for a newly built zero-carbon industrial park according to claim 4, characterized in that: S4 includes: The corrected carbon content output in step S3 And the carbon potential matrix of step S2 Define the system state vector as the state variable. ,in For the cumulative carbon deficit, For the growth rate of carbon sink, The energy storage is in its state of charge (SOC). Construct positive definite Lyapunov functions: in The coefficients are positive weights; the derivation satisfies... Nonlinear control law ,in For the convergence rate parameter; when the control law When the control command exceeds the grid absorption limit, the amount of abandoned power ΔE is calculated and fed back to S1 for updating. .

6. The carbon emission accounting method for a newly built zero-carbon industrial park according to claim 5, characterized in that: S5 includes: Using the confidence interval of S3 and the regulatory directive of S4 as constraints, a carbon balance planning problem is constructed in layers at three time scales: annual, monthly, and daily. The annual level is used to solve for strategic carbon quota planning. Output annual carbon quota allocation plan and carbon shadow prices The monthly and daily layers use the upper-level output as constraints for rolling solution, with the dual iteration convergence condition as follows: ; The objective function for the monthly layer is: The constraints are: ,in The conservative planning coefficient for S3 feedback; The objective function for the daily layer is: The optimal energy storage charging and discharging strategy, renewable energy dispatch volume, and demand response activation period are solved in a rolling manner over 96 15-minute time intervals.

7. The carbon emission accounting method for a newly built zero-carbon industrial park according to claim 6, characterized in that: S6 includes: The carbon shadow price output by S5 Feedback is sent to S2, via the penalty factor. Adjust carbon potential weights to guide carbon flows toward a redistribution of low-carbon pathways.

8. The carbon emission accounting method for a newly built zero-carbon industrial park according to claim 7, characterized in that: The abandoned power ΔE(t) received from S4 in S1 is updated at the next time t+1 according to the following rule: when the actual output of the marginal unit decreases, it is adjusted down proportionally, and the reduction coefficient is determined by the ratio of ΔE(t) to it. The carbon shadow price received in step S5 is mentioned in S2. The outgoing edge weights of high carbon potential nodes are multiplied by a penalty factor. ,in The price elasticity coefficient enables dynamic updates to the carbon flow admittance matrix A, guiding the redistribution of carbon flows toward low-carbon pathways.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 8.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 8.

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