A Lean Evaluation Method and System for Power Grid Carbon Indicators to Support the Operation of Low-Carbon Industrial Parks

Abstract

This invention discloses a method and system for refined assessment of power grid carbon indicators to support the operation of low-carbon industrial parks. The method includes: acquiring hourly power generation data of various generating units in the target power grid within a preset time period; calculating hourly carbon indicators based on the power generation data, including the power grid's average carbon emission factor and residual carbon emission factor, wherein the residual carbon emission factor is calculated by deducting the amount of electricity for which green electricity rights have been declared from the total electricity consumption; and comprehensively assessing the green and low-carbon level of the power grid based on the hourly carbon indicators, constructing an assessment indicator system encompassing three dimensions: economic efficiency, green and low-carbon, and flexibility. This method solves the problems of poor timeliness, coarse granularity, and redundant calculation of green electricity environmental value in existing power grid carbon emission factor assessments, achieving dynamic and high-precision assessment of power grid carbon emission levels, and providing a scientific basis for carbon emission accounting and dynamic energy use strategy formulation in low-carbon industrial parks.

Description

Technical Field

[0001] This invention belongs to the field of power system and carbon emission accounting technology, and in particular relates to a method and system for lean evaluation of power grid carbon indicators to support the operation of integrated energy systems in low-carbon parks. Background Technology

[0002] The development of integrated energy systems (IES) for low-carbon industrial parks has become a research hotspot. Existing technologies aim to achieve a multi-objective balance of economy, environmental protection, and reliability by constructing complex optimization models to uniformly schedule various energy sources such as electricity, heat, and gas within the park.

[0003] Existing technologies still face the following pressing technical challenges in practical applications: The lack of a unified and refined indicator system to comprehensively and dynamically assess the green and low-carbon level of new power systems. Existing technologies often focus on single optimization objectives (such as lowest cost or lowest carbon emissions), failing to systematically evaluate the electrical carbon characteristics of the power grid from multiple dimensions such as economic efficiency, greenness, and flexibility. Therefore, they cannot provide comprehensive and quantitative decision-making basis for users such as industrial parks to select optimal energy consumption periods and participate in advanced low-carbon behaviors such as demand-side response.

[0004] Therefore, how to conduct a refined assessment of the power grid's carbon emission indicators to provide scientific data support for the low-carbon transformation of industrial parks is a technical challenge that urgently needs to be addressed in this field. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for lean evaluation of power grid carbon indicators to support the operation of low-carbon industrial parks.

[0006] The objective of this invention is achieved through the following technical solution: A lean assessment method for power grid carbon indicators to support the operation of low-carbon industrial parks includes: S1. Obtain power generation data of various generator sets in the target power grid within a preset time period, in hours; S2. Based on the power generation data, calculate a set of hourly-level carbon emission indicators, including at least the target grid average carbon emission factor and the residual carbon emission factor; wherein, the residual carbon emission factor is calculated by: removing the electricity with declared green environmental rights from the total electricity consumption of the target grid, and then calculating the carbon emission per unit of electricity; S3. Based on the hourly-level carbon emission index, assess the green and low-carbon level of the target power grid and generate assessment results.

[0007] Furthermore, the set of hourly-level carbon electricity indicators also includes the proportion of green electricity and the proportion of zero-carbon electricity; The green electricity ratio is the proportion of the total green electricity generated and the sum of green electricity input in the region to the total electricity volume. The zero-carbon electricity ratio is the proportion of the total zero-carbon electricity generated and the sum of zero-carbon electricity input in the region to the total electricity output.

[0008] Furthermore, in step S2, Green environmental rights include proof of green carbon rights transfer; The calculation of the remaining carbon emission factor satisfies the following condition: the total carbon emissions of the target power grid are divided by a corrected electricity consumption value; the corrected electricity consumption value is the value obtained by deducting the electricity with green carbon rights transfer certificates from the total electricity consumption of the target power grid.

[0009] Furthermore, step S3 includes: Based on the hourly-level carbon emission index, an evaluation index system is constructed from three dimensions: economic efficiency, green and low-carbon, and flexible and adaptable. The evaluation index values ​​for each dimension are calculated separately, and the evaluation results are generated by combining them.

[0010] Furthermore, the evaluation index system specifically includes: The evaluation indicators for the economic efficiency dimension include the rate of change in indirect carbon emission costs from electricity, which is used to characterize the trend of carbon cost fluctuations per unit time. The assessment indicators for the green and low-carbon dimension include the average carbon emission factor of power grid supply and the total amount of green electricity trading, which are used to characterize the degree of cleanliness of the power grid. The flexible assessment indicators include the rate of change in the green electricity environmental premium, which is used to characterize the degree of market volatility in the value of green electricity.

[0011] Furthermore, the assessment results are used to support one or a combination of the following operational strategies for low-carbon industrial parks: Scope 2: Carbon emission accounting, using hourly residual carbon emission factors to calculate the indirect emissions from purchased electricity; Dynamic energy consumption strategies are formulated by adjusting the park's high-energy-consuming production periods or energy storage charging and discharging plans based on the rate of change in indirect carbon emission costs of electricity or the rate of change in green electricity environmental premium.

[0012] This invention also provides a lean assessment system for power grid carbon index to support the operation of low-carbon industrial parks, comprising: The data acquisition module is used to acquire power generation data of various generator sets in the target power grid in hours within a preset time period; The indicator calculation module is used to calculate a set of hourly carbon emission indicators based on the power generation data, including at least the target grid average carbon emission factor and the residual carbon emission factor; wherein, the residual carbon emission factor is calculated by removing the amount of electricity with declared green environmental rights from the total electricity consumption of the target grid, and then calculating the carbon emission per unit of electricity. The assessment module is used to assess the green and low-carbon level of the target power grid based on the hourly-level carbon emission index and generate assessment results.

[0013] Preferably, the indicator calculation module is also used to calculate the proportion of green electricity and the proportion of zero-carbon electricity; the evaluation module is also used to construct an evaluation indicator system based on the hourly-level carbon electricity indicator from three dimensions: economic efficiency, green and low-carbon, and flexible, and to calculate the evaluation indicator values ​​of the evaluation indicators under each dimension, and output dynamic strategy suggestions to guide the operation of low-carbon parks.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for lean evaluation of power grid carbon indicators supporting the operation of low-carbon industrial parks.

[0015] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for lean evaluation of power grid carbon indicators supporting the operation of low-carbon industrial parks.

[0016] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: 1. This invention traces hourly power generation data to obtain hourly power generation data for various generator sets and calculates hourly carbon emission indicators. This allows for a true and dynamic reflection of real-time changes in the intensity of grid carbon emissions (such as a decrease in carbon factors during peak solar power generation at midday). This provides a precise and dynamic hourly data foundation for low-carbon industrial parks, helping to identify carbon emission peaks and troughs, thereby discovering precise targets for energy conservation and carbon reduction; and improving the accuracy and timeliness of carbon emission accounting (from year to hour).

[0017] 2. The introduction of a residual carbon emission factor, calculated by removing electricity with declared green environmental rights from the total electricity consumption of the power grid, ensures the accuracy and fairness of carbon accounting through decoupling the calculation of physical electricity consumption from environmental rights. Using the residual carbon emission factor in accounting means that consuming green electricity during periods of high residual carbon factor will yield greater emission reduction benefits, thus incentivizing industrial parks to participate in green electricity trading. This solves the industry problem of double-counting the environmental value of green electricity, ensuring the fairness of the accounting.

[0018] 3. An evaluation index system is constructed from three dimensions: economic efficiency, green and low-carbon, and flexibility. This system incorporates the rate of change in indirect carbon emission costs from electricity consumption and the rate of change in the green electricity environmental premium. This achieves synergy between economic and environmental protection. Park managers can use the cost change rate to warn of risks and schedule production or charging during low-carbon and low-cost periods (such as the afternoon when solar power generation is at its peak). Furthermore, by monitoring the rate of change in the green electricity environmental premium, the system identifies green electricity price troughs, automatically triggers trading suggestions, locks in long-term environmental value, and optimizes operating costs. Therefore, this invention can more comprehensively and systematically quantify and evaluate the green and low-carbon level of the new power system. The evaluation results can be used to support parks in formulating dynamic energy consumption strategies that match the real-time low-carbon level of the power grid, thereby guiding deeper emission reductions in actual operation.

[0019] 4. Use the hourly residual carbon factor and assessment results for scope two accounting and dynamic energy use strategy, providing a feasible input for dynamic energy use strategy and scope two accounting. The hourly factor can directly participate in the time-of-use accounting of scope two, enabling the park to identify high carbon periods and reduce carbon emissions through scheduling / electricity purchase; at the same time, the assessment results can trigger automatic or decision-making trading suggestions to achieve cost and emission reduction synergistic optimization.

[0020] 5. The data includes electricity exchanged via interconnecting lines between regional power grids. The calculation of the grid average factor / residual carbon factor considers the weighting of transmitted / imported electricity and the corresponding regional factor. Cross-regional weighting can more accurately reflect the carbon emission contribution of actual electricity sources, improving the traceability and reliability of the calculation.

[0021] 6. Propose a green electricity environmental premium change rate indicator and provide a calculation formula to identify the electricity purchase window; by monitoring premium changes, the park can purchase green electricity with environmental rights when the premium is low to lock in low-cost emission reduction. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the method of the present invention.

[0023] Figure 2 This is a schematic diagram of the average carbon emission factor of the power grid in 2022; Figure 3 This is a diagram illustrating the percentage of green electricity generated by the power grid over the three years from 2022 to 2024. Figure 4 This is a schematic diagram showing the percentage of zero-carbon electricity generated by this power grid over the past three years, from 2022 to 2024. Figure 5 This is a schematic diagram showing the remaining carbon emission factors of the power grid over the three years from 2022 to 2024. Figure 6 This is a dynamic time series diagram of multidimensional carbon emission indicators for this power grid at continuous time points throughout 2024. Figure 7This is a map showing the percentage of green electricity in this power grid. Figure 8 This is a patch diagram of the average carbon emission factor for this power grid; Figure 9 This is a map showing the remaining carbon emission factor of the power grid. Detailed Implementation

[0024] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0025] Example 1 This embodiment provides a refined assessment method for power grid carbon indicators to support the operation of low-carbon industrial parks. This method aims to address the problems of poor timeliness, coarse granularity, and double counting in existing power grid carbon emission factor assessments due to their failure to accurately reflect the environmental value of green electricity.

[0026] This embodiment discloses a lean assessment method for power grid carbon indicators to support the operation of low-carbon industrial parks. (See...) Figure 1 It includes the following steps: Step S1. Obtain the power generation data of various generator sets of the target power grid in hours within a preset time period; Step S2. Based on power generation data, calculate a set of hourly electricity carbon indicators, including at least the grid average carbon emission factor and the residual carbon emission factor. The calculation of the residual carbon emission factor includes removing the electricity with declared green environmental rights from the total electricity consumption of the grid. The set of hourly electricity carbon indicators also includes the proportion of green electricity and the proportion of zero-carbon electricity.

[0027] Specifically, green environmental rights include proof of green carbon rights transfer; The residual carbon emission factor is calculated by dividing the total carbon emissions of the power grid by an electricity value obtained by deducting the amount of electricity with green carbon equity transfer certificates (such as green electricity certificates) from the total electricity consumption.

[0028] Step S3. Based on hourly-level carbon emission indicators, assess the green and low-carbon level of the target power grid and generate assessment results.

[0029] Specifically, an evaluation index system is constructed from three dimensions: economic efficiency, green and low-carbon development, and flexibility. The evaluation index values ​​for each dimension are calculated. The evaluation indexes for the economic efficiency dimension include the rate of change in indirect carbon emission costs related to electricity consumption; the evaluation indexes for the green and low-carbon dimension include the average carbon emission factor of grid power supply and the total volume of green electricity transactions; and the evaluation indexes for the flexibility dimension include the rate of change in the green electricity environmental premium. The evaluation results are used to support the scope-based carbon emission accounting or the formulation of dynamic energy use strategies for low-carbon industrial parks.

[0030] Example 2 In this embodiment, the method for refined evaluation of the power grid carbon index supporting the operation of low-carbon parks first obtains the power generation data of various generator units of the target power grid in hours within a preset time period. The data covers the output of different types of generator units such as hydropower, thermal power, wind power, and solar power, as well as the power exchange data of cross-regional power grid interconnection lines.

[0031] Next, based on the acquired hourly power generation data, a set of hourly carbon emission indicators is calculated. This set of indicators includes at least the grid average carbon emission factor and the residual carbon emission factor. The grid average carbon emission factor is calculated based on the ratio of total carbon emissions to total power generation within the grid operating boundary, which can dynamically reflect the real-time carbon emission intensity of the grid.

[0032] More importantly, this embodiment introduces the calculation of the residual carbon emission factor, the core of which is to remove the electricity that has been declared to have green environmental rights from the total electricity consumption of the power grid, such as the electricity whose environmental attributes have been separately accounted for through green carbon rights transfer certificates.

[0033] Specifically, the residual carbon emission factor is determined by dividing the total carbon emissions of the power grid by an electricity value obtained by deducting the electricity volume with the aforementioned green carbon equity transfer certificate from the total electricity consumption. This effectively avoids the problem of double-counting the environmental value of green electricity in the grid average factor and the user-side green electricity consumption declaration, ensuring the accuracy and fairness of carbon accounting.

[0034] Preferably, a set of hourly carbon electricity indicators may further include the proportion of green electricity and the proportion of zero-carbon electricity to more comprehensively depict the cleanliness of the power grid.

[0035] After obtaining the hourly-level carbon emission index, this embodiment further conducts a comprehensive assessment of the target power grid's green and low-carbon level. This assessment step preferably includes constructing a systematic assessment index system from three dimensions: economic efficiency, green and low-carbon, and flexibility. Specifically, assessment indicators under the economic efficiency dimension may include the rate of change in indirect carbon emission costs of electricity consumption; assessment indicators under the green and low-carbon dimension may include the average carbon emission factor of grid power supply and the total amount of green electricity transactions; and assessment indicators under the flexibility dimension may include the rate of change in the green electricity environmental premium. By calculating the specific assessment index values ​​for each dimension, a comprehensive and multi-dimensional assessment result is generated.

[0036] Finally, the assessment results generated in this embodiment, particularly the hourly average grid carbon emission factor and residual carbon emission factor, are directly used to support the accurate and dynamic accounting of carbon emissions in Scope 2 (indirect emissions from purchased electricity, steam, heating, and cooling) of low-carbon parks, or as key input parameters to guide parks in formulating dynamic energy use strategies. For example, high-energy-consuming production can be scheduled during periods of lower grid carbon emission factors, thereby achieving synergistic optimization of economic and environmental benefits. It should be noted that, according to the internationally accepted greenhouse gas accounting system, Scope 1 refers to direct carbon emissions (such as carbon emissions from natural gas consumption, which originate from direct emissions from owned or controlled emission sources), Scope 2 refers to indirect emissions from purchased energy, and Scope 3 refers to all other indirect emissions (such as indirect emissions from upstream and downstream activities).

[0037] Example 3 Based on the same inventive concept, this application also provides a lean evaluation system for power grid carbon indicators to support the operation of low-carbon industrial parks. This system can be used to implement the methods described in the above embodiments, and specifically includes the following: The data acquisition module is used to acquire hourly power generation data; The indicator calculation module has the above algorithm logic built in, which is used to calculate the hourly electricity carbon index, especially the remaining carbon emission factor. It also includes an assessment module, which is used to construct an assessment system based on the calculated indicators and assess the green and low-carbon level of the power grid, ultimately generating assessment results.

[0038] Preferably, embodiments of this application also provide a specific implementation of an electronic device capable of implementing all steps in the lean assessment method for power grid carbon index supporting low-carbon park operation described in the above embodiments. The electronic device specifically includes the following: Processor, memory, communications interface, and bus; The processor, memory, and communication interface communicate with each other via a bus; the communication interface is used to realize information transmission between server-side devices, metering devices, and user-side devices.

[0039] The processor is used to call the computer program in the memory. When the processor executes the computer program, it implements all the steps in the lean evaluation method of power grid carbon index supporting the operation of low-carbon parks in the above embodiments.

[0040] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the lean evaluation method for power grid carbon index supporting low-carbon park operation described in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the lean evaluation method for power grid carbon index supporting low-carbon park operation described in the above embodiments.

[0041] Example 4 This embodiment provides a lean assessment method for power grid carbon indicators to support the operation of low-carbon industrial parks, including the following steps: First, obtain the power generation data of various generator sets in the target power grid within a preset time period, in hours. Secondly, based on power generation data, a set of hourly carbon emission indicators are calculated, including at least the grid average carbon emission factor and the residual carbon emission factor. The calculation of the residual carbon emission factor includes removing the electricity with declared green environmental rights from the total electricity consumption of the grid. Based on hourly-level carbon emission indicators, the green and low-carbon level of the target power grid is assessed, and assessment results are generated. The average carbon emission factor of the power grid is calculated using the following formula: ; in, for p The average carbon emission factor of the provincial power grid, kgCO2 / kWh; for p Direct CO2 emissions from power generation in the province; To p Saves net power output n Average CO2 emission factor of provincial power grid, kgCO2 / kWh; for n Province p Net power output (MWh); To the regional power grid i Net export electricity k National average CO2 emission factor from power generation, kgCO2 / kWh; for k Guo Xiang p Electricity exported from the province, in MWh; For regional power grid i The average CO2 emission factor, kgCO2 / kWh; For regional power grid i Towards p Net power output (MWh); for p Total annual power generation of the province, MWh; p For a certain province;n To p Other provinces that have reduced net electricity transmission; k To p The main body of the province's net export electricity; i for p The regional power grid where the province is located; it should be noted that the dimensions should be unified on the same scale, for example, the units of the summation terms in the numerator and denominator should all be unified as tCO2; the dimensions involved in all formulas in this invention should be understood in a similar way, and the use of different dimensions only indicates whether a conversion coefficient is involved; p The formula for calculating the direct CO2 emissions from power generation in the province is as follows: ; in, The consumption of fossil fuel m for power generation in province p, in tons or cubic meters. 3 ; The lower heating value of fossil fuel m is the average lower heating value, in GJ / t or GJ / m3; tCO2 / TJ is the CO2 emission factor for fossil fuel m. Regional power grid i Towards p The formula for calculating the net electricity delivered by the power station is: ; in, for p Total annual electricity consumption of the province, MWh; Regional power grid i The formula for calculating the average CO2 emission factor is: ; in, For regional power grid i The direct CO2 emissions from power generation within the covered geographical area, tCO2; To the regional power grid i Regional power grid with net power transmission j The average CO2 emission factor, kgCO2 / kWh or tCO2 / MWh, remains unchanged whether these are scaled up or down proportionally. For regional power grid j To the regional power grid i Net electricity delivered, MWh; To the regional power grid i Net export electricity k National average CO2 emission factor from power generation, kgCO2 / kWh; for k National regional power grid i Net electricity exports, MWh; For regional power grid i Total annual electricity generation within the covered geographic area, in MWh; i For the regional power grid; j To the regional power grid i Other regional power grids that are net power outflows; k To the regional power grid i Other entities with net electricity exports; Regional power grid i The formula for calculating the direct CO2 emissions from power generation within the covered geographical area is as follows: ; in, For power grid i Fossil fuels used for power generation within the covered geographical area m Consumption volume, t or m 3 ; fossil fuel m Average lower heating value, GJ / t or GJ / m 3 ; fossil fuel m The CO2 emission factor, tCO2 / TJ; m The types of fossil fuels consumed for power generation.

[0042] The formula for calculating the CO2 emission factor of fossil fuel m is: ; fossil fuel m Carbon content per unit calorific value, tC / TJ; fossil fuel m carbon oxidation rate, % is the conversion factor for carbon to carbon dioxide.

[0043] Preferably, a set of hourly carbon electricity indicators also includes the proportion of green electricity and the proportion of zero-carbon electricity. The formula for calculating the proportion of green electricity is: ; in: For the region i The proportion of green electricity; For the region i Total green electricity generation within the country; For the region j To the region i The amount of electricity transmitted; For the region j The proportion of green electricity; J To the region i The total number of areas receiving power; For the regioni Power generation of various types of generator sets within the facility.

[0044] The formula for calculating the proportion of zero-carbon electricity is: ; in: For the region i The proportion of zero-carbon electricity; For the region i Total zero-carbon electricity generation; For the region j To the region i The amount of electricity transmitted; For the region j The proportion of zero-carbon electricity; J To the region i The total number of areas receiving power; For the region i Power generation of various types of generator sets within the facility.

[0045] Preferably, the remaining carbon emission factor is calculated by dividing the total carbon emissions of the power grid by an electricity value obtained by deducting the electricity volume with green carbon rights transfer certificates, such as green certificates, from the total electricity consumption.

[0046] The formula for calculating the residual carbon emission factor is: ; in: For the region i The remaining carbon emission factor of the power grid; For the region i Direct carbon emissions generated by various types of generator sets within the facility; For the region j To the region i The amount of electricity transmitted; For the region j The carbon emission factor of the power grid; J To the region i The total number of areas receiving power; For the region i Power generation of various types of generator sets within the facility; For the region i Includes electricity volume for green carbon rights transfer certificates; For the region j It contains the amount of electricity for green carbon rights transfer certificates.

[0047] Preferably, the steps for assessing the green and low-carbon level of the target power grid include: constructing an assessment index system based on hourly-level electricity carbon indicators from three dimensions: economic efficiency, green and low-carbon, and flexibility; and calculating the assessment index values ​​for each dimension. The assessment indicators for the economic efficiency dimension include the rate of change in indirect carbon emission costs of electricity consumption; the assessment indicators for the green and low-carbon dimension include the average carbon emission factor of power grid supply and the total amount of green electricity transactions; and the assessment indicators for the flexibility dimension include the rate of change in the green electricity environmental premium.

[0048] It should be noted that the specific implementation method for calculating the average carbon emission factor of grid power supply has been described above, while the total amount of green electricity trading only requires summing the amounts of each green electricity transaction. Therefore, this section further illustrates how to obtain the rate of change in indirect carbon emission costs of electricity consumption and the rate of change in the green electricity environmental premium through other exemplary descriptions, specifically: The rate of change in indirect carbon emission costs from electricity use is calculated using the following formula: ; in, This represents the rate of change in indirect carbon emission costs from electricity consumption over a time period t (such as the current hour, day, or month compared to the previous time period). A positive value indicates an increase in costs, while a negative value indicates a decrease in costs. Park managers can use this indicator to warn of rising carbon costs and schedule high-energy-consuming production activities during periods of lower costs. This indicates the applicable carbon trading price (unit: yuan / ton CO2) within the time period t. It can be understood that this price comes from the national or local carbon market. This represents either the average carbon emission factor of the power grid or the remaining carbon emission factor calculated within the time period t; preferably, the remaining carbon emission factor is used because it better reflects the actual carbon cost faced by the user in this invention. , These represent the carbon trading price for the previous comparable time period, and either the grid average carbon emission factor or the remaining carbon emission factor, respectively.

[0049] The rate of change of the green electricity environmental premium is calculated using the following formula: ; in, This represents the rate of change of the green electricity environmental premium over time period t. This variable reflects the degree of volatility in the green electricity environmental value market. A rapid increase in the premium may indicate strong market demand for green electricity or tight supply. A decrease in the premium may indicate sufficient supply of green electricity or moderate market demand. The park can make dynamic decisions based on this indicator: increase green electricity procurement when the premium is low to lock in low-cost environmental value; postpone procurement when the premium is high, rely on the remaining carbon emission factor for accounting, or call upon self-stored green electricity. This represents the transaction price (unit: yuan / kWh) of green electricity (with accompanying green certificates or environmental rights certificates) within a time period t. This represents the market transaction price or benchmark electricity price (excluding environmental rights) of conventional electricity (in yuan / kWh) within a time period t. , These represent the green electricity price and the benchmark electricity price for the previous comparable time period, respectively. This represents the absolute value of the green energy environmental premium within the time period t, which represents the additional cost of purchasing green energy environmental rights.

[0050] Preferably, the assessment results are used to support the scope-based carbon emission accounting or dynamic energy use strategy development for low-carbon industrial parks. For example, an example of dynamic energy use strategy development is as follows: Assuming the time period t is measured in hours, the study monitors the rate of change in indirect carbon emission costs of electricity consumption (assessment indicators under the economic efficiency dimension), the average carbon emission factor of grid power supply and the total volume of green electricity transactions (assessment indicators under the green and low-carbon dimension), and the rate of change in the green electricity environmental premium (assessment indicators under the flexibility dimension). For example, suppose it's from 13:00 to 14:00 on a certain day; The background conditions are: the carbon factor decreases, the indirect carbon cost decreases; the sunlight is good during this period, the photovoltaic output is large, the residual carbon factor is low, and the calculated result of the residual carbon emission factor at this time is: 0.70 kgCO2 / kWh.

[0051] Furthermore, due to the abundant sunshine during this period, photovoltaic power output was high, resulting in an oversupply of green electricity and a significant decrease in the environmental premium compared to the same period yesterday. Assuming the calculated change rate of the green electricity environmental premium is -15%; Furthermore, it is known that the park's energy storage batteries have 80% remaining capacity, and the photovoltaic power generation meets 30% of the park's load. Therefore, the following dynamic energy consumption strategy can be formulated at this time: immediately purchase an additional batch of afternoon photovoltaic power with accompanying green certificates on the power trading platform.

[0052] It is understandable that the scientific basis of this dynamic energy consumption strategy lies in the following: purchasing green electricity can directly reduce the area's emissions; although the current remaining carbon emission factor is low, further purchasing green electricity can further reduce it to near zero; and since the green electricity environmental premium change rate is negative, this means that the green electricity premium is at a low level, which is a better window to lock in long-term environmental value. This means that this embodiment is conducive to identifying the opportunity window brought about by cost fluctuations, automatically triggering green electricity trading suggestions, and is conducive to adjusting the energy storage system plan, coupling charging periods with low-priced green electricity periods, further reducing costs while taking into account environmental protection, thereby reflecting the scientific nature of the decision and the accuracy of the data processing on which the decision is based.

[0053] For example, the carbon emission accounting example for Scope II is as follows: Assuming carbon emissions are calculated at the end of the month, the traditional approach is to multiply the annual average grid factor by the total electricity consumption. However, this fails to reflect the difference between nighttime electricity consumption (high carbon factor) and midday electricity consumption (low carbon factor) in the industrial park, and it also cannot accurately reflect green electricity deductions or avoid double counting. Therefore: Summary of hourly electricity consumption in the park and hourly green electricity purchases ; Carbon emissions in Scope II are calculated using the following formula: ; Because the above formula excludes the hourly green electricity purchase amount Therefore, it ensures that the environmental rights of green electricity are calculated only once across the entire network; Furthermore, the above formula incorporates the residual carbon emission factor over h hours. It was included in the calculation, so the impact of green electricity across the entire network was deducted; In summary, the above formula implies that during periods of high residual carbon emission factors, if the industrial park utilizes green electricity, it will gain greater carbon emission reduction accounting benefits. This will inevitably incentivize the park and the clean power grid to move towards cleaner technologies, contributing to the technological development of environmental protection and green industries. Furthermore, because the formula can be set to hourly granularity, the results for different hourly periods can help identify periods of high carbon emissions and discover their causes, providing precise targets for further energy conservation and carbon reduction.

[0054] Therefore, this embodiment achieves refined and non-duplicated calculation of carbon emissions in Scope 2 by adopting an hourly residual carbon emission factor based on green certificate deduction, which truly reflects the carbon footprint and green electricity contribution of the park's electricity consumption.

[0055] In summary, this invention overcomes the problems of poor timeliness, coarse granularity, and repetitive calculations in traditional systems, which is the most critical technical contribution. Thus, by connecting this invention with the park's energy management system, a feasible, assessable, and optimizable core engine for low-carbon park energy and carbon management can be realized.

[0056] In another embodiment, the present invention also provides a lean assessment system for power grid carbon indexes supporting the operation of low-carbon industrial parks, comprising: The data acquisition module is used to acquire power generation data of various generator sets in the target power grid in hours within a preset time period; The indicator calculation module is used to calculate a set of hourly electricity carbon indicators based on power generation data, including at least the grid average carbon emission factor and the residual carbon emission factor. The calculation of the residual carbon emission factor includes removing the electricity with declared green environmental rights from the total electricity consumption of the grid. The assessment module is used to evaluate the green and low-carbon level of the target power grid based on hourly-level carbon emission indicators and generate assessment results.

[0057] Preferably, the indicator calculation module is also used to calculate the proportion of green electricity and the proportion of zero-carbon electricity.

[0058] Preferably, the evaluation module is specifically used to: construct an evaluation index system based on hourly-level carbon emission indicators from three dimensions: economic efficiency, green and low-carbon, and flexible and adaptable, and calculate the evaluation index values ​​of the evaluation indicators under each dimension.

[0059] The invention is illustrated below with a more specific example, which demonstrates lean evaluation based on hourly data from a power grid throughout a specific year, wherein: Figure 2 This is a schematic diagram of the average carbon emission factor of the power grid in 2022. In this histogram, the horizontal axis represents different regional power grids, and the vertical axis represents the magnitude of the average carbon emission factor of each regional power grid, in units of tCO2 / MWh. The figure visually demonstrates the significant differences in carbon emission levels caused by differences in energy structure across regions. For example, province Q, marked in the figure, has a relatively low average carbon emission factor of 0.1567 due to its higher proportion of clean energy.

[0060] Figure 3 This is a diagram illustrating the percentage of green electricity generated by the power grid over the three years from 2022 to 2024. The horizontal axis of this bar chart represents the year, and the vertical axis represents the percentage of green electricity in the total electricity generation. The data shows that the proportion of green electricity in the target power grid has been steadily increasing year by year, rising from 41.48% in 2022 to 44.41% in 2023 and 44.51% in 2024. This data indicates that the target power grid is continuously transforming towards a green and low-carbon direction.

[0061] Figure 4 This is a schematic diagram showing the percentage of zero-carbon electricity generated by this power grid over the past three years, from 2022 to 2024. The horizontal axis of this bar chart represents the year, and the vertical axis represents the percentage of zero-carbon electricity in the total electricity consumption. The chart reveals the annual macro-level proportion of zero-carbon electricity over the past three years, specifically 84.47% in 2022, 83.70% in 2023, and 88.65% in 2024, providing data support for assessing the overall cleanliness of the power grid.

[0062] Figure 5 This is a schematic diagram showing the remaining carbon emission factors of the power grid over the three years from 2022 to 2024. The horizontal axis of this line graph represents the year, and the vertical axis represents the numerical value of the residual carbon emission factor, in units of tCO2 / MWh. The graph shows the trajectory of the residual carbon emission factor over the past three years: 0.130 in 2022, 0.140 in 2023, and decreasing to 0.097 in 2024. It visually reflects the interannual trend of the actual carbon emission intensity per unit of physical electricity after adopting the calculation method proposed in this invention, which "excludes electricity with declared green environmental rights."

[0063] Figure 6 This is a dynamic time series graph of multidimensional carbon emission indicators for this power grid at continuous time points throughout 2024. The horizontal axis of the graph is a continuous time axis accurate to the day, the left main vertical axis corresponds to the magnitude of the emission factor (tCO2 / MWh), and the right secondary vertical axis corresponds to the percentage (%). The graph reveals the dramatic dynamic fluctuations of various carbon emission indicators during the day and different seasons through the interweaving of three curves: "emission factor," "green electricity percentage," and "zero-carbon electricity percentage." This fully reflects the effectiveness of this invention in capturing high-resolution carbon emission characteristics and identifying carbon emission peaks and troughs. From January 1, 2024 to March 25, 2024, the emission factor and the proportion of green electricity were generally negatively correlated; from March 25, 2024 to September 3, 2024, the emission factor and the proportion of green electricity were generally positively correlated; from September 3, 2024 to December 8, 2024, the emission factor and the proportion of green electricity were generally positively correlated; from December 8, 2024 to December 26, 2024, the emission factor and the proportion of green electricity were generally negatively correlated. From January 1, 2024 to March 25, 2024, the proportion of green electricity and the proportion of zero-carbon electricity were generally positively correlated; from March 25, 2024 to September 3, 2024, the proportion of green electricity and the proportion of zero-carbon electricity were generally negatively correlated; from September 3, 2024 to December 8, 2024, the proportion of green electricity and the proportion of zero-carbon electricity were generally positively correlated; from December 8, 2024 to December 26, 2024, the proportion of green electricity and the proportion of zero-carbon electricity were generally positively correlated.

[0064] Figure 7This is a map showing the proportion of green electricity in the power grid. The vertical axis represents the 1st to 12th months of the year, and the horizontal axis represents the 0th to 23rd hours of the day. In the legend on the right, the shades of green represent the proportion of green electricity, with darker green indicating a higher proportion. This map reveals the spatial and temporal distribution of green electricity output. For example, in the central area of ​​the map (i.e., the midday period from 10:00 to 16:00), a distinct dark green high-value cluster is formed due to the large-scale generation of new energy sources such as photovoltaics.

[0065] Figure 8 This is a patch diagram of the average carbon emission factor for this power grid; the x and y coordinate systems of this diagram are... Figure 7 Consistent with this, the grayscale legend on the right side of the graph represents the average carbon emission factor, ranging from 0.0074 to 0.4941 tCO2 / MWh, with darker colors indicating higher emission factors. Combined with... Figure 7 It is evident that during the midday period when the proportion of green electricity is high, the corresponding area in the graph appears as light white (i.e., the emission factor is at its lowest), which intuitively verifies the negative correlation between the grid carbon emission intensity and the real-time fluctuation of clean energy output.

[0066] Figure 9 This is a map showing the remaining carbon emission factors of the power grid; its coordinate system is also the same as... Figure 7 , Figure 8 To maintain consistency, the grayscale legend on the right side of the graph represents the remaining carbon emission factor after deducting the environmental rights of green electricity, with values ​​ranging from 0.0076 to 0.5343 tCO2 / MWh. Compared to... Figure 8 This patch map further highlights the true spatiotemporal distribution of actual carbon emissions after excluding declared green certificate electricity. By comparing the characteristics of such patches, park managers can accurately identify the peak and trough periods of actual carbon emissions, thereby helping to trigger optimal low-carbon energy use or green electricity trading strategies.

[0067] It can be observed that the core of this embodiment lies in using high-resolution, real-time data to dynamically trace the carbon emission attributes of the power grid. By introducing the residual carbon emission factor, the problem of double counting in carbon accounting for green electricity consumption is solved: that is, while green electricity reduces the average emission factor of the power grid, its environmental value is separately declared by users who purchase green certificates. This invention makes carbon emission calculation more precise and fair. Undoubtedly, the above example is equivalent to treating a province as a park, which fully demonstrates that this invention can achieve a precise assessment of the power grid's carbon indicators to support the operation of low-carbon parks; treating a province as a park is realistic and has guiding significance: if even smaller units of parks do not meet the provincial indicators, then the provincial indicators should be referenced for optimization. Therefore, even according to the above embodiment using a province as an example, this invention can still support a precise assessment of the power grid's carbon indicators for the operation of low-carbon parks.

[0068] This invention is not limited to the embodiments described above. The above description of specific embodiments is intended to illustrate and explain the technical solutions of this invention. The specific embodiments described above are merely illustrative and not restrictive. Without departing from the spirit and scope of the claims, those skilled in the art can make many specific modifications based on the teachings of this invention, and these modifications all fall within the scope of protection of this invention.

Claims

1. A method for lean evaluation of grid electricity carbon indicators supporting low-carbon park operation, characterized in that, include: S1. Obtain power generation data of various generator sets in the target power grid within a preset time period, in hours; S2. Based on the power generation data, calculate a set of hourly-level carbon emission indicators, including at least the target grid average carbon emission factor and the residual carbon emission factor. The set of hourly-level carbon emission indicators also includes the proportion of green electricity and the proportion of zero-carbon electricity. The residual carbon emission factor is calculated by removing the electricity with declared green environmental rights from the total electricity consumption of the target grid and then calculating the carbon emission per unit of electricity. S3. Based on the hourly-level carbon emission index, assess the green and low-carbon level of the target power grid and generate assessment results; based on the hourly-level carbon emission index, construct an assessment index system from three dimensions: economic efficiency, green and low-carbon, and flexibility; calculate the assessment index values ​​of the assessment indicators under each dimension, and generate the assessment results in a comprehensive manner. The evaluation index system includes: The evaluation indicators for the economic efficiency dimension include the rate of change in indirect carbon emission costs from electricity, which is used to characterize the trend of carbon cost fluctuations per unit time. The assessment indicators for the green and low-carbon dimension include the average carbon emission factor of power grid supply and the total amount of green electricity trading, which are used to characterize the degree of cleanliness of the power grid. The flexible and adaptable evaluation indicators include the rate of change in the green electricity environmental premium, which is used to characterize the degree of market volatility in the environmental value of green electricity. The assessment results are used to support the following operational strategies for low-carbon industrial parks: Scope 2: Carbon emission accounting, using hourly residual carbon emission factors to calculate the indirect emissions from purchased electricity; Dynamic energy consumption strategies are formulated by adjusting the park's high-energy-consuming production periods or energy storage charging and discharging plans based on the rate of change in indirect carbon emission costs of electricity or the rate of change in green electricity environmental premium.

2. The method for refined evaluation of power grid carbon index according to claim 1, characterized in that, The green electricity ratio is the proportion of the total green electricity generated and the sum of green electricity input in the region to the total electricity volume. The zero-carbon electricity ratio is the proportion of the total zero-carbon electricity generated and the sum of zero-carbon electricity input in the region to the total electricity output.

3. The method of claim 1, wherein, In step S2, Green environmental rights include proof of green carbon rights transfer; The calculation of the remaining carbon emission factor satisfies the following condition: the total carbon emissions of the target power grid are divided by a corrected electricity value; The corrected electricity value is obtained by subtracting the electricity with green environmental benefits from the total electricity consumption of the target power grid.

4. A lean evaluation system for power grid carbon indicators supporting the operation of low-carbon industrial parks, characterized in that, include: The data acquisition module is used to acquire power generation data of various generator sets in the target power grid in hours within a preset time period; The indicator calculation module is used to calculate a set of hourly carbon emission indicators based on the power generation data, including at least the target grid average carbon emission factor, residual carbon emission factor, green electricity ratio, and zero-carbon electricity ratio; wherein, the residual carbon emission factor is calculated by removing the electricity with declared green environmental rights from the total electricity consumption of the target grid, and then calculating the carbon emission per unit of electricity. The evaluation module is used to evaluate the green and low-carbon level of the target power grid based on the hourly-level carbon emission index and generate evaluation results; and is used to: construct an evaluation index system from three dimensions—economic efficiency, green and low-carbon, and flexibility—based on the hourly-level carbon emission index; calculate the evaluation index values ​​of the evaluation indexes under each dimension respectively, and generate the evaluation results in a comprehensive manner. The evaluation index system includes: The evaluation indicators for the economic efficiency dimension include the rate of change in indirect carbon emission costs from electricity, which is used to characterize the trend of carbon cost fluctuations per unit time. The assessment indicators for the green and low-carbon dimension include the average carbon emission factor of power grid supply and the total amount of green electricity trading, which are used to characterize the degree of cleanliness of the power grid. The flexible and adaptable evaluation indicators include the rate of change in the green electricity environmental premium, which is used to characterize the degree of market volatility in the environmental value of green electricity. The assessment results are used to support the following operational strategies for low-carbon industrial parks: Scope 2: Carbon emission accounting, using hourly residual carbon emission factors to calculate the indirect emissions from purchased electricity; Dynamic energy consumption strategies are formulated by adjusting the park's high-energy-consuming production periods or energy storage charging and discharging plans based on the rate of change in indirect carbon emission costs of electricity or the rate of change in green electricity environmental premium.

5. The evaluation system according to claim 4, characterized in that, The assessment module is also used to construct an assessment index system based on the hourly-level carbon emission index from three dimensions: economic efficiency, green and low-carbon, and flexible and adaptable. It also calculates the assessment index values ​​of the assessment indexes under each dimension and outputs dynamic strategy suggestions to guide the operation of low-carbon parks.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the lean evaluation method for power grid carbon index as described in any one of claims 1 to 3.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the lean evaluation method for the power grid carbon index as described in any one of claims 1 to 3.

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