Park zero-carbon multi-dimensional comprehensive evaluation method based on electric carbon coupling

Abstract

The application provides a park zero-carbon multi-dimensional comprehensive evaluation method based on electric-carbon coupling, relates to the technical field of carbon emission and comprehensive evaluation of power systems, and is characterized in that: based on real-time power operation data and real-time carbon emission monitoring data, key support layer coefficients, optimization and promotion layer coefficients, necessary supply layer coefficients and core rigid layer coefficients are obtained through calculation; based on the core rigid layer coefficients, the necessary supply layer coefficients, the key support layer coefficients and the optimization and promotion layer coefficients, zero-carbon rigid compliance indexes, carbon supply support indexes, carbon lean optimization indexes, carbon balance indexes and carbon comprehensive scalar are obtained through calculation; and the preset hierarchical evaluation mechanism is used for judgment and diagnosis to obtain the park zero-carbon multi-dimensional comprehensive evaluation result. The application solves the problems in the prior art that the park zero-carbon evaluation data is not objective enough, the evaluation dimension is single, the electric-carbon coupling cannot be deeply realized, the evaluation result is inaccurate, and fine physical state diagnosis cannot be provided.

Description

Technical Field

[0001] This invention relates to the field of carbon emission and comprehensive evaluation technology for power systems, and in particular to a multi-dimensional comprehensive evaluation method for zero-carbon industrial parks based on electro-carbon coupling. Background Technology

[0002] Most existing methods for assessing zero-carbon or low-carbon industrial parks have the following shortcomings: First, the existing evaluation system relies heavily on statistical data or empirical estimates based on the patterns of commercial activities. It lacks actual measurements of physical quantities for all electrical links in the park, including generation, transmission, distribution, and consumption. This results in the basic data being outdated, distorted, and easily tampered with, failing to accurately reflect the real-time physical operating status of the park.

[0003] Secondly, in terms of evaluation dimensions, existing methods are often limited to static assessments based on a single dimension. For example, when assessing the consumption of green electricity, daily or annual average values ​​are often used to calculate the static penetration rate, which cannot reflect the fluctuations in real-time zero-carbon levels; when assessing the capacity for renewable energy consumption, only the single logic of actual output / predicted output is used, which fails to fully quantify the physical support role of energy storage equipment in consumption.

[0004] Furthermore, in terms of electricity substitution and energy efficiency assessment, existing evaluation indicators emphasize scale over effectiveness and often employ algorithms such as arithmetic averages that easily mask shortcomings. This results in an inability to accurately assess the temporal operational stability of electricity substitution and the physical energy efficiency losses of the distribution network.

[0005] Finally, traditional evaluation methods typically separate electricity calculation from carbon emission accounting, failing to achieve deep coupling between the physical flow of electricity and the flow of carbon emissions at the underlying level. They also lack a closed-loop mechanism that covers data acquisition, indicator calculation, bottleneck constraints, and comprehensive diagnosis, making it difficult to provide an objective technical status assessment of the power-carbon system in the industrial park. Summary of the Invention

[0006] To address the aforementioned shortcomings in existing technologies, this invention provides a multi-dimensional comprehensive evaluation method for zero-carbon industrial parks based on electro-carbon coupling. This method solves the problems in existing technologies, such as insufficient objectivity of zero-carbon evaluation data, single evaluation dimensions, failure to deeply implement electro-carbon coupling leading to inaccurate evaluation results, and inability to provide refined physical state diagnosis.

[0007] To achieve the aforementioned objectives, the technical solution adopted by this invention is: a multi-dimensional comprehensive evaluation method for zero-carbon industrial parks based on electro-carbon coupling, comprising: S1: Based on real-time power operation data and real-time carbon emission monitoring data, green electricity substitution index data, electricity substitution index data, power efficiency utilization index data, and electricity-carbon synergistic emission reduction index data are obtained through calculation. S2: Based on the electricity substitution index data, the geometric mean synergistic algorithm is used to calculate the key support layer coefficient; based on the power efficiency utilization index data, the dynamic deviation correction algorithm is used to calculate the optimization and improvement layer coefficient; based on the green electricity substitution index data and the electricity-carbon synergistic emission reduction index data, the necessary supply layer coefficient and the core rigid layer coefficient are calculated respectively. S3: Based on the core rigid layer coefficient, necessary supply layer coefficient, key support layer coefficient, and optimization and improvement layer coefficient, the zero-carbon rigid compliance index, carbon supply support index, carbon lean optimization index, carbon balance index, and carbon comprehensive scalar are obtained by calculation. S4: Based on the zero-carbon rigid compliance index, carbon supply support index, carbon lean optimization index, carbon balance index and carbon comprehensive scalar, a pre-set graded evaluation mechanism is used to make judgments and diagnoses, and the results of the park's zero-carbon multidimensional comprehensive evaluation are obtained, thus completing the park's zero-carbon multidimensional comprehensive evaluation.

[0008] Furthermore, based on real-time power operation data and real-time carbon emission monitoring data, green electricity substitution index data is calculated, including: The real-time green electricity penetration rate data is obtained by calculating based on real-time distributed renewable energy output data, purchased green electricity power data and total electricity load data. Based on the predicted output data of new energy sources, the actual output data, and the operation data of energy storage, and combined with the preset energy storage consumption correction logic, the new energy consumption guarantee rate data is obtained. Based on green electricity output data and real-time flue gas monitoring data, the green electricity emission reduction and total carbon emissions are calculated to obtain green electricity emission reduction contribution rate data. Among them, real-time distributed renewable energy output data, purchased green electricity power data, total electricity load data, renewable energy predicted output data, actual output data, energy storage operation data, and green electricity output data belong to real-time power operation data; real-time flue gas monitoring data belong to real-time carbon emission monitoring data; and real-time green electricity penetration rate data, renewable energy consumption guarantee rate data, and green electricity emission reduction contribution rate data belong to green electricity substitution indicator data.

[0009] Furthermore, the electricity substitution index data, calculated based on real-time power operation data and real-time carbon emission monitoring data, includes: Based on the power data of the electricity substitution load and the total load power data, and combined with the preset scenario classification correction logic, the terminal electrification depth data is obtained. Based on the load power data of electricity substitution and the emission data of fossil energy baseline, the emission reduction intensity data of electricity substitution is obtained; Based on the time-series data of the load power substituted by electricity, the load fluctuation characteristics are calculated to obtain the stability data of electricity substitution. Among them, the time-series data of electricity substitution load power, total load power, and electricity substitution load power belong to real-time power operation data; the fossil energy benchmark emission data belong to real-time carbon emission monitoring data; and the terminal electrification depth data, electricity substitution emission reduction intensity data, and electricity substitution stability data belong to electricity substitution indicator data.

[0010] Furthermore, based on real-time power operation data and real-time carbon emission monitoring data, the power efficiency utilization index data is calculated, including: Based on total load power data, combined with load factor and peak-valley difference characteristics, the power load lean rate data is obtained. Based on measured data of the power distribution network and dynamic carbon emission factors, the emission reduction benefits of line loss are calculated, and the power line loss lean rate data of the park is obtained. Based on load data before and after demand response and dynamic carbon emission factors during peak and valley periods, the amount of emission reduction is calculated to obtain the emission reduction contribution rate data of demand response. Among them, total load power data, measured data of distribution network and load data before and after demand response belong to real-time power operation data; dynamic carbon emission factor and peak-valley period dynamic carbon emission factor belong to real-time carbon emission monitoring data; and power load lean rate data, park power line loss lean rate data and demand response emission reduction contribution rate data belong to power efficiency utilization indicator data.

[0011] Furthermore, the expression for the real-time green electricity penetration rate data is as follows: ; in, express Real-time green electricity penetration data. This indicates the data collected by power sensors deployed at the photovoltaic and wind power grid connection points in the industrial park. Real-time distributed renewable energy actual output data This indicates the data collected by electricity meters configured at the green electricity trading gateway in the park. Real-time power data of purchased green electricity. This indicates the data collected by bus power transmitters and load-side intelligent data acquisition terminals deployed on the park's distribution bus and major load circuits. Real-time total power consumption data for the park; The expression for the new energy consumption guarantee rate data is: ; in, This indicates the data on the guarantee rate of new energy consumption. This represents the generation of meteorological parameters based on a meteorological-electrical integrated sensor array and combined with a physical mechanism model. Real-time forecasts of solar and wind power output. express Real-time distributed renewable energy actual output data Indicates the sampling time interval. This represents the energy storage absorption correction coefficient calculated based on the adjustable power and remaining capacity of the energy storage obtained from the charge and discharge power sensors and SOC monitoring modules deployed on the PCS side of the energy storage system. The expression for the terminal electrification depth data is: ; in, This represents the terminal's electrification depth data. This represents the data collected by current transformers, voltage transformers, and active power transmitters deployed in the power supply circuits of electricity substitution equipment. Real-time electrical energy substitution load power data, express Real-time total load power data for the park This represents the scenario correction coefficient obtained statistically based on the physical characteristics of a preset energy consumption scenario; The expression for the stability data of the electricity substitution is: ; in, This indicates data on the stability of electricity substitution. This represents the maximum value of the power of electrical energy replacing the load within the sampling period. This represents the minimum value of electrical energy replacing load power within the sampling period. This represents the average power of electrical energy replacing the load within the sampling period; The expression for the power load lean rate data is: ; in, This represents the power load lean rate data. express Real-time total power consumption data for the park Indicates the sampling time interval. This represents the maximum load data extracted based on continuous measured load time-series data within the evaluation period. Indicates the duration of the evaluation period. This represents the peak-valley difference rate data calculated based on the extreme load values ​​of the industrial park. The expression for the power line loss lean rate data of the park is: ; in, This represents the lean rate data for power line losses in the industrial park. This indicates that the data is obtained from smart meters and line loss monitoring terminals at the gateways of the park's power distribution network. Real-time power loss data for the park's power lines. This indicates data obtained from actual measurements at the electricity purchase point. Real-time external power consumption data This represents the derivation of physical parameters based on real-time data collected by the generator set combustion condition sensor array and the flue gas online monitoring system. Real-time dynamic data on power grid carbon emission factors.

[0012] Furthermore, based on real-time power operation data and real-time carbon emission monitoring data, the electricity-carbon synergistic emission reduction index data are calculated, including: Based on the comparison and calculation of multi-source emission reduction accounting data and purchased electricity carbon emission data, the electricity-carbon coupling emission reduction rate data is obtained. The zero-carbon power support rate data is obtained by calculating based on multi-source zero-carbon power supply data and total power consumption data. Consistency calculations are performed based on the forward accounting data and reverse verification data of carbon dioxide to obtain the carbon dioxide balance deviation rate data. Among them, multi-source emission reduction accounting data, multi-source zero-carbon power supply data, and total power consumption data belong to real-time power operation data; purchased electricity carbon emission data, electricity carbon forward accounting data, and reverse verification data belong to real-time carbon emission monitoring data; and electricity carbon coupling emission reduction rate data, zero-carbon power support rate data, and electricity carbon balance deviation rate data belong to electricity carbon coordinated emission reduction indicator data.

[0013] Further, in S2, based on the electricity substitution index data, the geometric mean collaborative algorithm is used to calculate the key support layer coefficient; based on the power efficiency utilization index data, the dynamic deviation correction algorithm is used to calculate the optimization and improvement layer coefficient, including: The terminal electrification depth data, the emission reduction intensity data, and the stability data of the electricity substitution index data are multiplied together to obtain the first product result. The key support layer coefficient is obtained by performing a cube root operation on the first product result. By using the power load lean rate data, park power line loss lean rate data, and demand response emission reduction contribution rate data from the power efficiency utilization index data, the mean data and the maximum value data are obtained through calculation. The time-series fluctuation deviation coefficient of the indicator is obtained by calculating based on the mean and maximum value data. The arithmetic mean of the power load lean rate data, the park power line loss lean rate data, and the demand response emission reduction contribution rate data is calculated, and the arithmetic mean is deducted and corrected based on the time series fluctuation deviation coefficient of the indicators to obtain the optimization and improvement layer coefficient.

[0014] Furthermore, the expression for the key support layer coefficient is as follows: ; in, Indicates the coefficient of the key support layer. This represents the terminal's electrification depth data. This represents the intensity of emission reduction through electricity substitution. This indicates data on the stability of electricity substitution; The expression for the optimization boosting layer coefficient is: ; ; in, This indicates the optimization and lifting layer coefficient. This represents the power load lean rate data. This represents the lean rate data for power line losses in the industrial park. This represents the contribution rate of demand response to emissions reduction. This represents the time-series fluctuation deviation coefficient of the indicator. This represents the average data of power load lean rate, park power line loss lean rate, and demand response emission reduction contribution rate. This represents the maximum value of the power load lean rate data, the park power line loss lean rate data, and the demand response emission reduction contribution rate data.

[0015] Further, S3 includes: The core rigid layer coefficient is used as the zero-carbon rigidity compliance index, and the optimized and improved layer coefficient is used as the carbon lean optimization index. The carbon supply support index is obtained by multiplying the necessary supply layer coefficient and the key support layer coefficient, and then taking the square root of the product. Calculate the absolute difference between the coefficient of the necessary supply layer and the coefficient of the key support layer, the absolute difference between the coefficient of the key support layer and the coefficient of the optimization and improvement layer, and the absolute difference between the coefficient of the necessary supply layer and the coefficient of the optimization and improvement layer; calculate the mean of the three absolute differences, and subtract the mean from the preset base value to obtain the carbon balance index. The carbon scalar value is obtained by multiplying the zero-carbon rigid compliance index, the necessary supply layer coefficient, the key support layer coefficient, the optimization and improvement layer coefficient, and the carbon balance index.

[0016] Further, S4 includes: The baseline verification was performed based on the zero-carbon rigid compliance index, and the first verification result was obtained. When the first verification result meets the preset conditions, physical margin, operational level and coupling balance diagnosis are performed based on carbon supply support index, carbon lean optimization index and carbon balance index to obtain multi-dimensional profile results; Based on the first verification result, the multi-dimensional profile result, and the carbon comprehensive scalar, the physical operation status of the park's power and carbon system is determined, and the zero-carbon multi-dimensional comprehensive evaluation result of the park is obtained, thus completing the zero-carbon multi-dimensional comprehensive evaluation of the park.

[0017] The beneficial effects of this invention are as follows: This invention provides a multi-dimensional comprehensive evaluation method for zero-carbon industrial parks based on electro-carbon coupling. By acquiring real-time power operation data and real-time carbon emission monitoring data of the industrial park, the method calculates the indicator data of each dimension, abandons the traditional commercial estimation, and ensures the physical authenticity, real-time performance and immutability of the underlying data, thus providing accurate underlying data support for comprehensive evaluation.

[0018] By introducing a geometric mean collaborative algorithm to calculate the coefficients of the key support layer, it possesses strong weakness constraint characteristics, avoiding the problem of low compliance rates of individual indicators being masked. By introducing a dynamic deviation correction algorithm to calculate the coefficients of the optimization and improvement layer, it effectively quantifies the park's long-term and refined operational capabilities, and improves the rigor and comprehensiveness of the evaluation dimension coefficient calculation.

[0019] Based on the five core indices calculated, a pre-set hierarchical evaluation mechanism is used for judgment and diagnosis. This achieves a complete closed-loop accounting and evaluation of carbon emissions, from physical underlying data to comprehensive dimensional coefficients and overall operating status. It not only provides quantitative conclusions on zero-carbon compliance but also outputs a diagnosis of the park's underlying physical status, effectively guiding subsequent technical optimization. Attached Figure Description

[0020] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein: Figure 1 This is an exemplary flowchart of a multi-dimensional comprehensive evaluation method for zero-carbon industrial parks based on electro-carbon coupling, as shown in some embodiments of this specification. Detailed Implementation

[0021] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0022] Example Figure 1 This is an exemplary flowchart illustrating a multi-dimensional comprehensive evaluation method for zero-carbon industrial parks based on electro-carbon coupling, according to some embodiments of this specification. Figure 1As shown, the process includes the following steps. In some embodiments, the process may be executed by a processor.

[0023] S1: Based on real-time power operation data and real-time carbon emission monitoring data, green electricity substitution index data, electricity substitution index data, power efficiency utilization index data, and electricity-carbon synergistic emission reduction index data are obtained through calculation.

[0024] Real-time power operation data refers to the time-series set of objective electrical physical quantities generated by various links of generation, transmission, distribution, and consumption during the actual physical operation of the park's power system. Specifically, it includes: real-time distributed renewable energy output data reflecting the grid connection status of photovoltaic and wind power; purchased green energy power data reflecting external green energy input; total electricity load data reflecting the overall energy consumption level of the park; renewable energy predicted output data generated based on meteorological models; actual output data reflecting the physical output of the generation side; energy storage operation data reflecting the charging, discharging, and capacity status of energy storage devices; green energy output data reflecting the overall output of zero-carbon power sources; electricity substitution load power data reflecting the electricity consumption level of electrified equipment; total load power data and time-series data of electricity substitution load power reflecting the status of the distribution network; measured data of the distribution network reflecting distribution line losses and power flow; load data before and after demand response reflecting the level of flexible regulation; and multi-source emission reduction calculation data based on energy conservation and conversion, multi-source zero-carbon power supply data, and total electricity consumption data.

[0025] In some embodiments, the processor can continuously collect electrical physical quantities at a preset sampling frequency of milliseconds or seconds through the underlying electrical sensor network deployed at the distributed power grid connection point, transaction gateway, energy storage converter side and each load circuit in the park, including high-precision power transmitters, current and voltage transformers, smart meters and high-speed acquisition terminals, and obtain real-time power operation data after standardization processing.

[0026] Real-time carbon emission monitoring data refers to the set of physical quantities related to carbon emissions that are directly measured or derived from combustion physics mechanisms during the production operation and external power input processes of the industrial park. Specifically, it includes: real-time flue gas monitoring data reflecting the actual emission status of chimney outlets; fossil energy baseline emission data reflecting the fossil fuel consumption level before substitution; dynamic carbon emission factors and peak-valley period dynamic carbon emission factors reflecting the intensity of carbon emissions on the grid side; purchased electricity carbon emission data reflecting the implicit carbon emissions of input electricity; and forward and reverse verification data of electricity carbon for closed-loop verification.

[0027] In some embodiments, the processor can directly collect real-time flue gas monitoring data through the online flue gas monitoring system (CEMS, such as infrared spectrometer, flue gas flow sensor, etc.) deployed in the park's self-owned power plant or combustion equipment; and combine the real-time data obtained with the generator set combustion condition sensor array including temperature, pressure, flow sensors, etc., to dynamically derive and calculate real-time carbon emission monitoring data such as dynamic carbon emission factor on a time-by-time basis according to the carbon conservation law and the stoichiometric relationship of combustion.

[0028] In some embodiments, the acquisition of the dynamic carbon emission factor and the peak-valley period dynamic carbon emission factor includes: relying on the generator set combustion condition sensor array and flue gas online monitoring system to collect physical parameters such as fuel consumption, furnace conditions, flue gas CO2 concentration, and flue gas velocity in real time. Based on the carbon conservation law and the combustion stoichiometry relationship, the physical value of the power grid carbon emission factor is dynamically derived and calculated on a time-by-time basis, and this factor changes in real time with the operating conditions.

[0029] Green electricity substitution index data refers to a set of indicators used to quantify the real-time level of green electricity consumption and its contribution to physical emission reduction in industrial parks. Specifically, it includes: real-time green electricity penetration rate data reflecting the instantaneous proportion of green electricity, new energy consumption guarantee rate data reflecting the capacity of new energy to accept new energy supported by energy storage, and green electricity emission reduction contribution rate data that is directly related to carbon emission reduction results.

[0030] In some embodiments, the processor can obtain the actual output of distributed new energy, the power of purchased green electricity, and the total power consumption from real-time power operation data, and use the instantaneous power balance model to calculate the ratio to obtain the real-time green electricity penetration rate data; combine the predicted output and energy storage operation data to perform corrective calculations to obtain the new energy consumption guarantee rate data; and combine the dynamic carbon emission factor from real-time carbon emission monitoring data to perform coupled accounting to obtain the green electricity emission reduction contribution rate data.

[0031] In some specific embodiments, the expression for the green electricity emission reduction contribution rate data is as follows: ; in, This indicates the contribution rate of green electricity to emission reduction. This represents the green energy emission reduction data calculated based on green energy output data and real-time flue gas monitoring data. This indicates the total carbon emissions data obtained based on real-time carbon emission monitoring data.

[0032] In some embodiments, the processor can calculate the real-time green electricity penetration rate based on real-time distributed renewable energy output data, purchased green electricity power data, and total electricity load data; calculate the renewable energy consumption guarantee rate based on renewable energy predicted output data, actual output data, and energy storage operation data, combined with preset energy storage consumption correction logic; and calculate the green electricity emission reduction and total carbon emissions based on green electricity output data and real-time flue gas monitoring data to obtain the green electricity emission reduction contribution rate data.

[0033] Electricity substitution index data refers to a set of indicators used to quantify the scale of electrification transformation of end-use energy in industrial parks and its zero-carbon operation effectiveness. Specifically, it includes: end-use electrification depth data reflecting the degree of electrification coverage, electricity substitution emission reduction intensity data reflecting the emission reduction efficiency per unit of substitution load, and electricity substitution stability data reflecting the controllability of equipment operation.

[0034] In some embodiments, the processor can obtain terminal electrification depth data by acquiring power data of electricity substitution load and total load power, and introducing a preset scenario correction coefficient; combine fossil energy baseline emission data and dynamic carbon emission factor to calculate the carbon emission difference before and after substitution to obtain power substitution emission reduction intensity data; and extract statistical fluctuation characteristics from the time series data of power substitution load to calculate power substitution stability data.

[0035] In some specific embodiments, the expression for the emission reduction intensity data of electricity substitution is: ; in, This represents the intensity of emission reduction through electricity substitution. This represents the emission reduction data for electricity substitution calculated based on electricity substitution load power data and fossil fuel baseline emission data. express Real-time electrical energy substitution load power data, Indicates the sampling time interval.

[0036] In some embodiments, the processor can calculate the terminal electrification depth data based on the power data of the electricity substitution load and the total load power data, combined with a preset scenario classification correction logic; calculate the power substitution emission reduction intensity data based on the power data of the electricity substitution load and the fossil energy benchmark emission data; and calculate the load fluctuation characteristics based on the time series data of the power substitution load to obtain the power substitution stability data.

[0037] Electricity efficiency utilization index data refers to a set of indicators used to quantify the energy efficiency level and refined operation capabilities of the power consumption side in industrial parks. Specifically, it includes: power load lean rate data reflecting the overall load operation efficiency and fluctuations; power line loss lean rate data quantifying the physical losses of the distribution network and their emission reduction effectiveness; and demand response emission reduction contribution rate data reflecting the contribution of load-side flexible regulation to emission reduction.

[0038] In some embodiments, the processor can obtain power load lean rate data by extracting extreme values ​​and calculating peak-valley difference rate from total load power time-series data; combine measured data of distribution network to deduce line loss power and multiply it by the corresponding dynamic carbon emission factor to obtain park power line loss lean rate data; and obtain demand response emission reduction contribution rate data by obtaining load difference power before and after demand response and calculating emission reduction amount according to the dynamic carbon emission factor during peak and valley periods.

[0039] In some embodiments, the processor can calculate the power load lean rate data based on the total load power data and the characteristics of load rate and peak-valley difference rate; calculate the line loss emission reduction benefit based on the measured data of the distribution network and the dynamic carbon emission factor to obtain the power line loss lean rate data of the park; and calculate the adjustment emission reduction amount based on the load data before and after demand response and the dynamic carbon emission factor during peak and valley periods to obtain the demand response emission reduction contribution rate data.

[0040] In some specific embodiments, the expression for the demand response emission reduction contribution rate data is: ; in, This represents the contribution rate of demand response to emissions reduction. This indicates that the load data was calculated based on the load data before and after the demand response. In real time, the demand for reducing or shifting power data is required. This represents dynamic carbon emission factor data during peak periods. This represents dynamic carbon emission factor data during off-peak periods. Indicates the sampling time interval. This represents the total carbon emissions data.

[0041] In some embodiments, the expression for real-time green electricity penetration rate data is as follows: ; in, express Real-time green electricity penetration data. This indicates the data collected by power sensors deployed at the photovoltaic and wind power grid connection points in the industrial park. Real-time distributed renewable energy actual output data This indicates the data collected by electricity meters configured at the green electricity trading gateway in the park. Real-time power data of purchased green electricity. This indicates the data collected by bus power transmitters and load-side intelligent data acquisition terminals deployed on the park's distribution bus and major load circuits. Real-time total power consumption data for the park; The expression for the new energy consumption guarantee rate data is: ; in, This indicates the data on the guarantee rate of new energy consumption. This represents the generation of meteorological parameters based on a meteorological-electrical integrated sensor array and combined with a physical mechanism model. Real-time forecasts of solar and wind power output. express Real-time distributed renewable energy actual output data Indicates the sampling time interval. This represents the energy storage absorption correction coefficient calculated based on the adjustable power and remaining capacity of the energy storage obtained from the charge and discharge power sensors and SOC monitoring modules deployed on the PCS side of the energy storage system. The expression for the terminal electrification depth data is: ; in, This represents the terminal's electrification depth data. This represents the data collected by current transformers, voltage transformers, and active power transmitters deployed in the power supply circuits of electricity substitution equipment. Real-time electrical energy substitution load power data, express Real-time total load power data for the park This represents the scenario correction coefficient obtained statistically based on the physical characteristics of a preset energy consumption scenario; The expression for the stability data of the electricity substitution is: ; in, This indicates data on the stability of electricity substitution. This represents the maximum value of the power of electrical energy replacing the load within the sampling period. This represents the minimum value of electrical energy replacing load power within the sampling period. This represents the average power of electrical energy replacing the load within the sampling period; The expression for the power load lean rate data is: ; in, This represents the power load lean rate data. express Real-time total power consumption data for the park Indicates the sampling time interval. This represents the maximum load data extracted based on continuous measured load time-series data within the evaluation period. Indicates the duration of the evaluation period. This represents the peak-valley difference rate data calculated based on the extreme load values ​​of the industrial park. The expression for the power line loss lean rate data of the park is: ; in, This represents the lean rate data for power line losses in the industrial park. This indicates that the data is obtained from smart meters and line loss monitoring terminals at the gateways of the park's power distribution network. Real-time power loss data for the park's power lines. This indicates data obtained from actual measurements at the electricity purchase point. Real-time external power consumption data This represents the derivation of physical parameters based on real-time data collected by the generator set combustion condition sensor array and the flue gas online monitoring system. Real-time dynamic data on power grid carbon emission factors.

[0042] In some embodiments, the new energy consumption guarantee rate data is obtained by calculating based on the predicted output data, actual output data, and energy storage operation data of new energy sources, combined with a preset energy storage consumption correction logic. The energy storage consumption correction coefficient is calculated based on the energy storage state penalty function. Specifically, capacity margin is defined as the real-time energy storage SOC, charging and discharging loss is the efficiency parameter, and physical response delay is the time constant. An energy storage state penalty function is constructed and combined with a nonlinear decay condition: when the real-time energy storage SOC is below a preset lower limit or above a preset upper limit, the penalty function exhibits nonlinear decay. A decay coefficient specific to park-level energy storage is introduced for correction, thereby characterizing the dynamic boundary model limited by specific energy storage hardware characteristics.

[0043] In some embodiments, the calculation based on predicted new energy output data, actual output data, and energy storage operation data, combined with a preset energy storage absorption correction logic, yields the new energy absorption guarantee rate data. The preset energy storage absorption correction logic includes an energy storage absorption correction coefficient, the expression of which is: ; in, This represents the energy storage absorption correction factor. This indicates data collected by monitoring equipment of the energy storage system. Real-time energy storage capacity margin data This represents the energy storage state penalty function corresponding to the energy storage capacity margin data. This represents the physical response delay time constant of the energy storage system. This indicates the preset penalty time adjustment coefficient. This represents the charging and discharging loss efficiency parameter of the energy storage system. Further, the energy storage state penalty function... The logical condition is: when When the value is below 20% or above 80%, the The output value decays non-linearly.

[0044] Electricity-carbon synergistic emission reduction index data refers to a set of core indicators used to directly determine the physical coupling balance level between electricity flow and carbon flow in a park. Specifically, it includes: electricity-carbon coupling emission reduction rate data reflecting the overall emission reduction effectiveness, zero-carbon power support rate data quantifying the overall zero-carbon power supply capacity, and electricity-carbon balance deviation rate data verifying the consistency of accounting.

[0045] In some embodiments, the processor can calculate the proportion of carbon emission reduction data from multiple sources such as green electricity, energy storage, and electricity substitution with carbon emission data from purchased electricity to obtain carbon emission reduction rate data; calculate the time-series ratio of multiple sources of zero-carbon electricity supply data and total electricity consumption data to obtain zero-carbon electricity support rate data; and calculate the relative deviation by comparing forward carbon emission accounting data and reverse verification data to obtain carbon emission balance deviation rate data.

[0046] In some specific embodiments, the expression for the electro-carbon coupling emission reduction rate data is as follows: ; in, This represents the emission reduction rate data from the electro-carbon coupling process. This represents data on emissions reduction from green electricity. This represents the data for calculating energy storage emission reductions. This represents the data on emission reductions from electricity substitution. This indicates carbon emission data for purchased electricity.

[0047] In some embodiments, the processor can perform comparative calculations based on multi-source emission reduction accounting data and purchased electricity carbon emission data to obtain electricity-carbon coupling emission reduction rate data; perform calculations based on multi-source zero-carbon power supply data and total power consumption data to obtain zero-carbon power support rate data; and perform consistency calculations based on electricity-carbon forward accounting data and reverse verification data to obtain electricity-carbon balance deviation rate data.

[0048] In some specific embodiments, the expression for the zero-carbon electricity support rate data is as follows: ; in, This represents the zero-carbon electricity support rate data. express Real-time distributed renewable energy actual output data This indicates data acquired based on energy storage operation data. Real-time energy storage and discharge power data. express Real-time power data of purchased green electricity. express Real-time total power consumption data for the park Indicates the sampling time interval.

[0049] In some specific embodiments, the expression for the electrical carbon balance deviation rate data is as follows: ; in, This represents the data on the deviation rate of the electricity-carbon balance. This represents total carbon emissions data. This indicates carbon emission data for purchased electricity. This represents data on emissions reduction from green electricity. This represents the data for calculating energy storage emission reductions. This represents the data on emission reductions from electricity substitution.

[0050] S2: Based on the electricity substitution index data, the geometric mean synergistic algorithm is used to calculate the key support layer coefficient; based on the power efficiency utilization index data, the dynamic deviation correction algorithm is used to calculate the optimization and improvement layer coefficient; based on the green electricity substitution index data and the electricity-carbon synergistic emission reduction index data, the necessary supply layer coefficient and the core rigid layer coefficient are calculated respectively.

[0051] The key support layer coefficient refers to the dimension coefficient that comprehensively evaluates the scale, efficiency and stability of electrification transformation in the corresponding park's electricity substitution dimension.

[0052] In some embodiments, the processor can extract electricity substitution index data, and use a preset geometric mean collaborative algorithm to multiply and cube the terminal electrification depth data, electricity substitution emission reduction intensity data, and electricity substitution stability data to obtain the key support layer coefficient.

[0053] The optimization and enhancement coefficient refers to the dimension coefficient that comprehensively evaluates the stability of lean power management and flexible emission reduction in the corresponding dimension of efficient power utilization in the park.

[0054] In some embodiments, the processor can extract power efficiency utilization index data, calculate a basic score using a preset dynamic deviation correction algorithm, and then deduct and correct the score by combining the index time-series fluctuation deviation coefficient to obtain the optimization and improvement layer coefficient.

[0055] In some embodiments, the processor can multiply the terminal electrification depth data, the electricity substitution emission reduction intensity data, and the electricity substitution stability data in the electricity substitution index data to obtain a first product result; perform a cube root operation on the first product result to obtain the key support layer coefficient; use the power load lean rate data, park power line loss lean rate data, and demand response emission reduction contribution rate data in the power efficiency utilization index data to calculate the mean data and the maximum value data; based on the mean data and the maximum value data, calculate the index time series fluctuation deviation coefficient; calculate the arithmetic mean of the power load lean rate data, park power line loss lean rate data, and demand response emission reduction contribution rate data, and subtract and correct the arithmetic mean based on the index time series fluctuation deviation coefficient to obtain the optimization and improvement layer coefficient.

[0056] The first product result refers to the intermediate transitional data in the process of calculating the key support layer coefficient, which characterizes the comprehensive multiplicative effect of the three electricity substitution indicators.

[0057] In some embodiments, the processor can obtain the first product result by directly multiplying the terminal electrification depth data, the energy substitution emission reduction intensity data, and the energy substitution stability data.

[0058] Mean data and maximum data refer to statistical descriptive quantities of a specific evaluation indicator within a set evaluation period. Mean data reflects the average level of the indicator, while maximum data reflects the extreme value level of the indicator.

[0059] In some embodiments, the processor can obtain mean data and maximum value data by performing arithmetic mean and extreme value search operations on the continuously time-series generated power load lean rate data, park power line loss lean rate data, and demand response emission reduction contribution rate data.

[0060] The time-series fluctuation deviation coefficient is a correction parameter used to quantify the stability of refined power management in industrial parks. A larger value indicates more severe operational fluctuations.

[0061] In some embodiments, the processor can calculate the index time-series fluctuation deviation coefficient by calculating the ratio of the mean data to the maximum data and multiplying it by a preset scaling factor.

[0062] In some embodiments, the expression for the key support layer coefficient is: ; in, Indicates the coefficient of the key support layer. This represents the terminal's electrification depth data. This represents the intensity of emission reduction through electricity substitution. This indicates data on the stability of electricity substitution; The expression for the optimization boosting layer coefficient is: ; ; in, This indicates the optimization and lifting layer coefficient. This represents the power load lean rate data. This represents the lean rate data for power line losses in the industrial park. This represents the contribution rate of demand response to emissions reduction. This represents the time-series fluctuation deviation coefficient of the indicator. This represents the average data of power load lean rate, park power line loss lean rate, and demand response emission reduction contribution rate. This represents the maximum value of the power load lean rate data, the park power line loss lean rate data, and the demand response emission reduction contribution rate data.

[0063] The necessary supply layer coefficient refers to the dimension coefficient that comprehensively evaluates the real-time penetration and absorption capacity of green electricity in the corresponding park's green electricity substitution dimension.

[0064] In some embodiments, the processor can extract green electricity substitution index data, assign corresponding weights to each index, and then perform weighted summation to obtain the necessary supply layer coefficients.

[0065] In some specific embodiments, the expression for the necessary supply layer coefficient is: ; in, Indicates the necessary supply layer coefficient. This represents real-time green electricity penetration data. This indicates the data on the guarantee rate of new energy consumption. This represents the contribution rate of green electricity to emission reduction.

[0066] The core rigid layer coefficient refers to the evaluation coefficient that serves as the bottom-line constraint for the corresponding dimension of carbon-electricity co-reduction in the industrial park.

[0067] In some embodiments, the processor can perform logical multiplication on the data of the co-electro-carbon emission reduction indicators. That is, the core rigid layer coefficient is output as 1 only when all sub-indicators meet the set threshold; otherwise, the corresponding penalty value is output.

[0068] In some specific embodiments, the expression for the core rigidity layer coefficient is: ; in, This represents the core rigidity layer coefficient, i.e., the zero-carbon rigidity compliance index.

[0069] S3: Based on the core rigid layer coefficient, necessary supply layer coefficient, key support layer coefficient, and optimization and improvement layer coefficient, the zero-carbon rigid compliance index, carbon supply support index, carbon lean optimization index, carbon balance index, and carbon comprehensive scalar are obtained through calculation.

[0070] The zero-carbon rigid compliance index is a baseline indicator used for veto verification, which directly determines whether a park has the basic qualifications for zero-carbon evaluation.

[0071] In some embodiments, the processor can directly output the core rigidity layer coefficient as the zero-carbon rigidity compliance index.

[0072] The carbon supply support index is an index that characterizes the comprehensive physical support capacity of the two core emission reduction pathways: green electricity supply and electricity substitution.

[0073] In some embodiments, the processor can obtain the carbon supply support index by performing a geometric operation of multiplying and square-taking the necessary supply layer coefficient and the key support layer coefficient.

[0074] The carbon lean optimization index is an index that characterizes the long-term operational level of a park in terms of energy consumption management and flexible regulation.

[0075] In some embodiments, the processor can directly output the optimization boosting layer coefficient as the carbon lean optimization index.

[0076] The carbon balance index is an index that reflects the physical matching and coordination among various dimensions of power supply, end-use substitution, and lean electricity use in the industrial park.

[0077] In some embodiments, the processor can calculate the absolute differences between each pair of the necessary supply layer coefficient, the key support layer coefficient, and the optimization and improvement layer coefficient, and then subtract the average deviation from the base value of 1 to obtain the carbon balance index. When calculating the carbon balance index, the preset base value is set to 1, and the denominator constant is set to 6. Since the calculation involves the absolute differences between each pair of the three layers, the theoretical maximum value of each difference is 1, and the theoretical maximum value of the total deviation is 3. By setting the denominator to 6, the normalized result of the difference is ensured to fluctuate between 0 and 0.5, thereby ensuring that the final generated carbon balance index is smoothly constrained between 0.5 and 1, representing the degree of deviation between the ideal absolute balance state and the actual state.

[0078] In some specific embodiments, the expression for the carbon balance index is: ; in, This represents the carbon balance index. Indicates the necessary supply layer coefficient. Indicates the coefficient of the key support layer. This indicates the optimization and enhancement layer coefficient.

[0079] The carbon composite scalar refers to the final comprehensive quantitative score that integrates the zero-carbon baseline requirement with the evaluation results of various dimensions.

[0080] In some embodiments, the processor can obtain a comprehensive carbon scalar by multiplying the zero-carbon rigid compliance index, the necessary supply layer coefficient, the key support layer coefficient, the optimization and improvement layer coefficient, and the carbon balance index.

[0081] In some specific embodiments, the expression for the carbon composite scalar is: ; in, Represents the overall carbon scalar value. Indicates the zero-carbon rigid compliance index, Indicates the necessary supply layer coefficient. Indicates the coefficient of the key support layer. This indicates the optimization and lifting layer coefficient. This represents the carbon balance index.

[0082] In some embodiments, the constant parameters involved in the formulas of this specification have the following value range and physical meaning: Scene correction coefficient : Used to adjust the terminal electrification depth under different energy consumption scenarios. Based on physical energy efficiency conversion losses and load characteristics, the value is 0.8 for industrial scenarios, 0.6 for commercial scenarios, and 0.7 for residential scenarios. Weighting constants: In the necessary supply layer coefficient formula, the weights assigned to the real-time green electricity penetration rate, new energy consumption guarantee rate, and green electricity emission reduction contribution rate are set to 0.2, 0.3, and 0.5 respectively, to reflect the core position of actual emission reduction contribution. Scaling factor 0.2: In the calculation of the index time-series fluctuation deviation coefficient, the theoretical upper limit of this deviation coefficient is limited to 0.2 to prevent the dimension score from returning to zero due to drastic fluctuations in individual periods, and to maintain the robustness of the evaluation system. Base value 1 and denominator constant 6: In the carbon balance index formula, the base value 1 represents the ideal absolute balance state. Since the calculation includes the absolute differences between each pair of three levels (a total of three terms), the theoretical maximum value of each difference is 1, and the theoretical maximum value of the total deviation is 3. The denominator is set to 6 to ensure that the difference normalization result fluctuates in the range of 0 to 0.5, so that the final exponential smoothing constraint is between 0.5 and 1.

[0083] In some embodiments, the processor can use the core rigid layer coefficient as the zero-carbon rigid compliance index and the optimization and improvement layer coefficient as the carbon lean optimization index; multiply the necessary supply layer coefficient and the key support layer coefficient, and take the square root of the product to obtain the carbon supply support index; calculate the absolute difference between the necessary supply layer coefficient and the key support layer coefficient, the absolute difference between the key support layer coefficient and the optimization and improvement layer coefficient, and the absolute difference between the necessary supply layer coefficient and the optimization and improvement layer coefficient, respectively; calculate the mean of the three absolute differences, and subtract the mean from the preset base value to obtain the carbon balance index; multiply the zero-carbon rigid compliance index, the necessary supply layer coefficient, the key support layer coefficient, the optimization and improvement layer coefficient, and the carbon balance index together to obtain the carbon comprehensive scalar.

[0084] S4: Based on the zero-carbon rigid compliance index, carbon supply support index, carbon lean optimization index, carbon balance index and carbon comprehensive scalar, a pre-set graded evaluation mechanism is used to make judgments and diagnoses, and the results of the park's zero-carbon multidimensional comprehensive evaluation are obtained, thus completing the park's zero-carbon multidimensional comprehensive evaluation.

[0085] The zero-carbon multidimensional comprehensive evaluation result of the park refers to the structured output information that includes compliance qualification determination, physical condition diagnosis and comprehensive technical rating.

[0086] In some embodiments, the processor can perform threshold comparison and logical mapping of the above five comprehensive indices by executing a preset three-level evaluation process to generate a zero-carbon multidimensional comprehensive evaluation result for the park.

[0087] In some embodiments, the processor can perform a baseline verification based on the zero-carbon rigid compliance index to obtain a first verification result; when the first verification result meets the preset conditions, it can perform physical margin, operational level and coupling balance diagnosis based on the carbon supply support index, carbon lean optimization index and carbon balance index to obtain a multi-dimensional profile result; based on the first verification result, the multi-dimensional profile result and the carbon comprehensive scalar, it can determine the physical operation status of the park's power and carbon system, obtain the park's zero-carbon multi-dimensional comprehensive evaluation result, and complete the park's zero-carbon multi-dimensional comprehensive evaluation.

[0088] The first verification result refers to the output conclusion of the first-stage bottom-line verification in the three-level evaluation mechanism, which is used to indicate whether the subsequent evaluation process should continue.

[0089] In some embodiments, the processor can generate a first verification result by determining whether the zero-carbon rigidity compliance index is greater than or equal to a preset threshold (e.g., 0.8). If the threshold is met, the result is a pass; otherwise, the verification is terminated due to failure to meet the standard.

[0090] The multi-dimensional profile results refer to the technical status diagnosis description given for parks that have passed the bottom-line verification in terms of physical margin, loss level and coupling coordination.

[0091] In some embodiments, the processor can generate multi-dimensional profile results by mapping and matching the carbon supply support index, carbon lean optimization index, and carbon balance index with a set diagnostic interval including high, medium, and low intervals.

[0092] In some embodiments, the diagnosis of physical margin, operational level, and coupling balance based on the carbon supply support index, carbon lean optimization index, and carbon balance index to obtain multi-dimensional profile results includes: mapping the carbon supply support index to high supply margin ranges, medium supply margin ranges, and insufficient supply margin ranges to diagnose the physical margin on the power supply side; mapping the carbon lean optimization index to low-loss lean operation ranges, conventional operation ranges, and high-loss inefficient operation ranges to diagnose the power energy efficiency loss level; and mapping the carbon balance index to highly balanced unbiased coupling ranges, basically balanced slightly biased coupling ranges, and unbalanced strongly biased coupling ranges to diagnose the coordination of electricity-carbon flow coupling.

[0093] In some embodiments, after determining the physical operating status of the park's power and carbon system based on the first verification result, multi-dimensional profiling results, and carbon comprehensive scalar, the method further includes: locating the underlying electrical weaknesses of the park's power-carbon system based on the multi-dimensional profiling results. When the carbon supply support index is lower than a set threshold, optimization directions are generated to enhance physical channels for green electricity consumption and expand the capacity of electricity substitution load access; when the carbon lean optimization index is lower than a set threshold, optimization directions are generated to reduce distribution line losses and suppress drastic load fluctuations; when the carbon balance index is lower than a set threshold, optimization directions are generated to balance the physical matching degree of each link and strengthen the temporal synchronization coupling.

[0094] In some embodiments, based on the zero-carbon multidimensional evaluation index, a three-level evaluation mechanism is used to obtain the overall physical operating status of the park's power and carbon system, specifically including the following four steps.

[0095] Step 1: Rigid baseline verification. The processor obtains the zero-carbon rigid compliance index R. It determines whether R is greater than or equal to a preset rigid threshold, which is set to 0.8 in this embodiment. If R ≥ 0.8, the park is determined to have the basic zero-carbon qualification and enters the subsequent multi-dimensional evaluation process; if R < 0.8, it is directly determined to be a non-compliant park, the evaluation process is terminated, and a prompt message indicating the core layer shortcomings is output.

[0096] Step 2: Multi-dimensional profile diagnosis. For parks that pass the rigid verification, i.e. R≥0.8, the processor conducts objective physical state diagnosis on the grid side based on the carbon supply support index P, carbon lean optimization index O, and carbon balance index E: (1) Supply support physical margin diagnosis: If P≥0.9, it is judged as a state of high supply margin and strong absorption capacity, indicating that the physical matching degree between green power output and power substitution load is high and the new energy absorption channel is sufficient; if 0.8≤P<0.9, it is judged as a state of medium supply margin and local absorption limitation, indicating that there is a absorption bottleneck in a local period and the grid side voltage fluctuates slightly; if P<0.8, it is judged as a state of insufficient supply margin and prominent absorption bottleneck, indicating that the physical matching degree between green power output or power substitution load is poor and absorption is hindered. (2) Lean optimization physical level diagnosis: If O≥0.9, it is judged as a low loss and high stability lean operation state, indicating that the load timing fluctuation is small, the line loss is extremely low, and the adjustment accuracy is high; if 0.8≤O<0.9, it is judged as a controllable loss and small fluctuation normal operation state, indicating that the load fluctuation is controllable and the line loss is within a reasonable range; if O<0.8, it is judged as a high loss, strong fluctuation and inefficient operation state, indicating that the load fluctuates drastically, the line loss is significantly high, and the oscillation risk increases. (3) Electric carbon coupling balance diagnosis: If E≥0.9, it is judged as a highly balanced electric carbon link and unbiased coupling state, indicating that the physical matching degree of the three links is consistent and the electric carbon flow timing is synchronized; if 0.8≤E<0.9, it is judged as a basically balanced electric carbon link and slightly biased coupling state, indicating that there is local resource mismatch or timing asynchrony; if E<0.8, it is judged as an unbalanced electric carbon link and strongly biased coupling state, indicating that there is obvious resource mismatch, branch overload, and electric carbon coupling imbalance.

[0097] Step 3: Comprehensive physical state determination. Based on the carbon comprehensive scalar M, the processor combines R, P, O, and E to determine the overall physical operating state of the park's power-carbon system: (1) When M≥0.9, and R≥0.9, P≥0.9, O≥0.9, and E≥0.9, it is determined to be "high-reliability coupling state of power grid-new energy". This indicates that the power grid operating voltage is stable and the carbon flow and energy flow are completely synchronously balanced. (2) When 0.8≤M<0.9, and R≥0.8, P≥0.8, O≥0.8, and E≥0.8, it is determined to be "stable coupling state of power grid-new energy". This indicates that the power grid operating voltage is controllable and the carbon flow and energy flow are basically balanced. (3) When 0.7≤M<0.8, and R≥0.8, but any one of P, O, or E is below 0.8, it is determined to be "weak coupling fluctuation state of power grid-new energy". This indicates that the coupling is weak and there is a local imbalance. (4) When M<0.7 and R≥0.8, it is judged as "low-frequency oscillation risk and carbon overflow state". This indicates that the coupling has failed and the carbon flow is severely unbalanced.

[0098] Step 4: Weakness Identification and Optimization Suggestion Generation. Based on the multi-dimensional profiling and diagnostic results above, the processor generates corresponding optimization suggestions: If P is low, suggestions are generated to optimize the grid-connected topology of new energy sources, improve the response speed of energy storage, and expand the capacity of electricity substitution access; if O is low, suggestions are generated to optimize the load time-series curve, manage distribution line losses, and improve the accuracy of demand response regulation; if E is low, suggestions are generated to supplement the configuration of weak electrical equipment and optimize the power supply-load time-series coordinated control.

[0099] In some embodiments, an industrial park is used as the application scenario, and the evaluation period is set to 1 year. The coefficients and index calculation results obtained by the processor are as follows: Zero-carbon rigid compliance index R = 0.88; Green electricity substitution dimension coefficient β2 = 0.86 for the necessary supply layer; Electricity substitution dimension coefficient β3 = 0.82 for the key support layer; Power efficiency utilization dimension coefficient β4 = 0.80 for the optimization and improvement layer; Balance index E = 0.89; Comprehensive scalar M = R × β2 × β3 × β4 × E ≈ 0.54. Based on the preset three-level evaluation process, the processor performs judgment and diagnosis: In the first step of rigid baseline verification, since R = 0.88 ≥ 0.8, the park is judged to have passed the zero-carbon basic qualification verification and enter the multi-dimensional profile. In the second step of multi-dimensional profile diagnosis: The carbon supply support index P = √(β2 × β3) ≈ 0.84 is calculated. Since 0.8 ≤ P < 0.9, the physical margin of the park's supply support is judged to be a medium supply margin with limited local absorption. The carbon lean optimization index O = β4 = 0.80. Since 0.8 ≤ O < 0.9, the lean optimization physical level of the park is determined to be a normal operating state with controllable losses and small fluctuations. The carbon balance index E = 0.89. Since 0.8 ≤ E < 0.9, the electric-carbon coupling balance of the park is determined to be a state of basic balance and slight bias in the electric-carbon link. In the third step of comprehensive physical state determination: since the comprehensive scalar M ≈ 0.54 < 0.7 and R = 0.88 ≥ 0.8, the processor determines the overall physical operating state of the industrial park as a "start-up park, i.e., a state of low-frequency oscillation risk and carbon spillover". In the fourth step of bottleneck identification: based on the results that P, O, and E are all in the range of 0.8 to 0.9, the processor outputs targeted optimization suggestions to improve the power supply-side regulation capability, improve the demand response level, and eliminate local resource mismatches.

Claims

1. A multi-dimensional comprehensive evaluation method for zero-carbon industrial parks based on electro-carbon coupling, characterized in that, include: S1: Based on real-time power operation data and real-time carbon emission monitoring data, green electricity substitution index data, electricity substitution index data, power efficiency utilization index data, and electricity-carbon synergistic emission reduction index data are obtained through calculation. S2: Based on the electricity substitution index data, the coefficient of the key support layer is calculated using the geometric mean collaborative algorithm; Based on the power efficiency utilization index data, the dynamic deviation correction algorithm is used to calculate and obtain the optimization and improvement layer coefficient; based on the green electricity substitution index data and the electricity-carbon synergistic emission reduction index data, the necessary supply layer coefficient and the core rigid layer coefficient are calculated respectively. S3: Based on the core rigid layer coefficient, necessary supply layer coefficient, key support layer coefficient, and optimization and improvement layer coefficient, the zero-carbon rigid compliance index, carbon supply support index, carbon lean optimization index, carbon balance index, and carbon comprehensive scalar are obtained by calculation. S4: Based on the zero-carbon rigid compliance index, carbon supply support index, carbon lean optimization index, carbon balance index and carbon comprehensive scalar, a pre-set graded evaluation mechanism is used to make judgments and diagnoses, and the results of the park's zero-carbon multidimensional comprehensive evaluation are obtained, thus completing the park's zero-carbon multidimensional comprehensive evaluation.

2. The method for comprehensive zero-carbon multidimensional evaluation of industrial parks based on electro-carbon coupling according to claim 1, characterized in that, Based on real-time power operation data and real-time carbon emission monitoring data, green electricity substitution index data is calculated, including: The real-time green electricity penetration rate data is obtained by calculating based on real-time distributed renewable energy output data, purchased green electricity power data and total electricity load data. Based on the predicted output data of new energy sources, the actual output data, and the operation data of energy storage, and combined with the preset energy storage consumption correction logic, the new energy consumption guarantee rate data is obtained. Based on green electricity output data and real-time flue gas monitoring data, the green electricity emission reduction and total carbon emissions are calculated to obtain green electricity emission reduction contribution rate data. Among them, real-time distributed renewable energy output data, purchased green electricity power data, total electricity load data, renewable energy predicted output data, actual output data, energy storage operation data, and green electricity output data belong to real-time power operation data; real-time flue gas monitoring data belong to real-time carbon emission monitoring data; and real-time green electricity penetration rate data, renewable energy consumption guarantee rate data, and green electricity emission reduction contribution rate data belong to green electricity substitution indicator data.

3. The method for comprehensive zero-carbon multidimensional evaluation of industrial parks based on electro-carbon coupling according to claim 1, characterized in that, The electricity substitution index data, calculated based on real-time power operation data and real-time carbon emission monitoring data, includes: Based on the power data of the electricity substitution load and the total load power data, and combined with the preset scenario classification correction logic, the terminal electrification depth data is obtained. Based on the load power data of electricity substitution and the emission data of fossil energy baseline, the emission reduction intensity data of electricity substitution is obtained; Based on the time-series data of the load power substituted by electricity, the load fluctuation characteristics are calculated to obtain the stability data of electricity substitution. Among them, the time-series data of electricity substitution load power, total load power, and electricity substitution load power belong to real-time power operation data; the fossil energy benchmark emission data belong to real-time carbon emission monitoring data; and the terminal electrification depth data, electricity substitution emission reduction intensity data, and electricity substitution stability data belong to electricity substitution indicator data.

4. The method for comprehensive zero-carbon multidimensional evaluation of industrial parks based on electro-carbon coupling according to claim 1, characterized in that, Based on real-time power operation data and real-time carbon emission monitoring data, the power efficiency utilization index data are calculated, including: Based on total load power data, combined with load factor and peak-valley difference characteristics, the power load lean rate data is obtained. Based on measured data of the power distribution network and dynamic carbon emission factors, the emission reduction benefits of line loss are calculated, and the power line loss lean rate data of the park is obtained. Based on load data before and after demand response and dynamic carbon emission factors during peak and valley periods, the amount of emission reduction is calculated to obtain the emission reduction contribution rate data of demand response. Among them, total load power data, measured data of distribution network and load data before and after demand response belong to real-time power operation data, while dynamic carbon emission factor and peak-valley dynamic carbon emission factor belong to real-time carbon emission monitoring data.

5. The method for comprehensive zero-carbon multidimensional evaluation of industrial parks based on electro-carbon coupling according to any one of claims 1 to 4, characterized in that, The expression for the real-time green electricity penetration rate data is: ； in, express Real-time green electricity penetration data. This indicates the data collected by power sensors deployed at the photovoltaic and wind power grid connection points in the industrial park. Real-time distributed renewable energy actual output data This indicates the data collected by electricity meters configured at the green electricity trading gateway in the park. Real-time power data of purchased green electricity. This indicates the data collected by bus power transmitters and load-side intelligent data acquisition terminals deployed on the park's distribution bus and major load circuits. Real-time total power consumption data for the park; The expression for the new energy consumption guarantee rate data is: ； in, This indicates the data on the guarantee rate of new energy consumption. This represents the generation of meteorological parameters based on a meteorological-electrical integrated sensor array and combined with a physical mechanism model. Real-time forecasts of solar and wind power output. express Real-time distributed renewable energy actual output data Indicates the sampling time interval. This represents the energy storage absorption correction coefficient calculated based on the adjustable power and remaining capacity of the energy storage obtained from the charge and discharge power sensors and SOC monitoring modules deployed on the PCS side of the energy storage system. The expression for the terminal electrification depth data is: ； in, This represents the terminal's electrification depth data. This represents the data collected by current transformers, voltage transformers, and active power transmitters deployed in the power supply circuits of electricity substitution equipment. Real-time electrical energy substitution load power data, express Real-time total load power data for the park This represents the scenario correction coefficient obtained statistically based on the physical characteristics of a preset energy consumption scenario; The expression for the stability data of the electricity substitution is: ； in, This indicates data on the stability of electricity substitution. This represents the maximum value of the power of electrical energy replacing the load within the sampling period. This represents the minimum value of electrical energy replacing load power within the sampling period. This represents the average power of electrical energy replacing the load within the sampling period; The expression for the power load lean rate data is: ； in, This represents the power load lean rate data. express Real-time total power consumption data for the park Indicates the sampling time interval. This represents the maximum load data extracted based on continuous measured load time-series data within the evaluation period. Indicates the duration of the evaluation period. This represents the peak-valley difference rate data calculated based on the extreme load values ​​of the industrial park. The expression for the power line loss lean rate data of the park is: ； in, This represents the lean rate data for power line losses in the industrial park. This indicates that the data is obtained from smart meters and line loss monitoring terminals at the gateways of the park's power distribution network. Real-time power loss data for the park's power lines. This indicates data obtained from actual measurements at the electricity purchase point. Real-time external power consumption data This represents the derivation of physical parameters based on real-time data collected by the generator set combustion condition sensor array and the flue gas online monitoring system. Real-time dynamic data on power grid carbon emission factors.

6. The method for comprehensive zero-carbon multidimensional evaluation of industrial parks based on electro-carbon coupling according to claim 1, characterized in that, Based on real-time power operation data and real-time carbon emission monitoring data, the electricity-carbon synergistic emission reduction index data are calculated, including: Based on the comparison and calculation of multi-source emission reduction accounting data and purchased electricity carbon emission data, the electricity-carbon coupling emission reduction rate data is obtained. The zero-carbon power support rate data is obtained by calculating based on multi-source zero-carbon power supply data and total power consumption data. Consistency calculations are performed based on the forward accounting data and reverse verification data of carbon dioxide to obtain the carbon dioxide balance deviation rate data. Among them, multi-source emission reduction accounting data, multi-source zero-carbon power supply data, and total power consumption data belong to real-time power operation data; purchased electricity carbon emission data, electricity carbon forward accounting data, and reverse verification data belong to real-time carbon emission monitoring data; and electricity carbon coupling emission reduction rate data, zero-carbon power support rate data, and electricity carbon balance deviation rate data belong to electricity carbon coordinated emission reduction indicator data.

7. The method for comprehensive zero-carbon multidimensional evaluation of industrial parks based on electro-carbon coupling according to claim 1, characterized in that, In S2, the coefficient of the key support layer is obtained by using the geometric mean collaborative algorithm based on the electricity substitution index data. Based on power efficiency utilization index data, the optimization and improvement layer coefficients are calculated using a dynamic deviation correction algorithm, including: The terminal electrification depth data, the emission reduction intensity data, and the stability data of the electricity substitution index data are multiplied together to obtain the first product result. The key support layer coefficient is obtained by performing a cube root operation on the first product result. By using the power load lean rate data, park power line loss lean rate data, and demand response emission reduction contribution rate data from the power efficiency utilization index data, the mean data and the maximum value data are obtained through calculation. The time-series fluctuation deviation coefficient of the indicator is obtained by calculating based on the mean and maximum value data. The arithmetic mean of the power load lean rate data, the park power line loss lean rate data, and the demand response emission reduction contribution rate data is calculated, and the arithmetic mean is deducted and corrected based on the time series fluctuation deviation coefficient of the indicators to obtain the optimization and improvement layer coefficient.

8. The method for comprehensive zero-carbon multidimensional evaluation of industrial parks based on electro-carbon coupling according to claim 1, characterized in that, The expression for the key support layer coefficient is as follows: ； in, Indicates the coefficient of the key support layer. This represents the terminal's electrification depth data. This represents the intensity of emission reduction through electricity substitution. This indicates data on the stability of electricity substitution; The expression for the optimization boosting layer coefficient is: ； ； in, This indicates the optimization and lifting layer coefficient. This represents the power load lean rate data. This represents the lean rate data for power line losses in the industrial park. This represents the contribution rate of demand response to emissions reduction. This represents the time-series fluctuation deviation coefficient of the indicator. This represents the average data of power load lean rate, park power line loss lean rate, and demand response emission reduction contribution rate. This represents the maximum value of the power load lean rate data, the park power line loss lean rate data, and the demand response emission reduction contribution rate data.

9. The method for comprehensive zero-carbon multidimensional evaluation of industrial parks based on electro-carbon coupling according to claim 1, characterized in that, S3 includes: The core rigid layer coefficient is used as the zero-carbon rigidity compliance index, and the optimized and improved layer coefficient is used as the carbon lean optimization index. The carbon supply support index is obtained by multiplying the necessary supply layer coefficient and the key support layer coefficient, and then taking the square root of the product. Calculate the absolute difference between the coefficient of the necessary supply layer and the coefficient of the key support layer, the absolute difference between the coefficient of the key support layer and the coefficient of the optimization and improvement layer, and the absolute difference between the coefficient of the necessary supply layer and the coefficient of the optimization and improvement layer; calculate the mean of the three absolute differences, and subtract the mean from the preset base value to obtain the carbon balance index. The carbon scalar value is obtained by multiplying the zero-carbon rigid compliance index, the necessary supply layer coefficient, the key support layer coefficient, the optimization and improvement layer coefficient, and the carbon balance index.

10. The method for comprehensive zero-carbon multidimensional evaluation of industrial parks based on electro-carbon coupling according to claim 1, characterized in that, S4 includes: The baseline verification was performed based on the zero-carbon rigid compliance index, and the first verification result was obtained. When the first verification result meets the preset conditions, physical margin, operational level and coupling balance diagnosis are performed based on carbon supply support index, carbon lean optimization index and carbon balance index to obtain multi-dimensional profile results; Based on the first verification result, the multi-dimensional profile result, and the carbon comprehensive scalar, the physical operation status of the park's power and carbon system is determined, and the zero-carbon multi-dimensional comprehensive evaluation result of the park is obtained, thus completing the zero-carbon multi-dimensional comprehensive evaluation of the park.

Images