A collaborative optimization method and system for low-carbon industrial parks based on dynamic carbon dioxide index assessment

Abstract

This invention discloses a collaborative optimization method and system for low-carbon industrial parks based on dynamic electricity-carbon index assessment. The method includes constructing a comprehensive energy system model for the low-carbon industrial park, encompassing electricity, heat, gas subsystems, and an oxy-fuel combustion carbon capture system; collecting multi-source electricity / carbon data from the park; constructing a dynamic carbon emission factor model based on hourly power generation structure tracing; subsequently calculating three-dimensional indicators for the park's low-carbon assessment: the ratio of direct to indirect carbon emission energy consumption (γ), the ratio of direct to indirect carbon emission amount (δ), and the carbon emission offset ratio (ε); classifying the park into Level 1 (from Level V to Level I) according to a preset grading benchmark threshold, and matching it with an energy collaborative management strategy. This invention solves the problems of lag and accuracy in carbon emission accounting through dynamic monitoring and precise grading.

Description

Technical Field

[0001] This invention relates to the fields of integrated energy system management, carbon emission accounting and assessment, and in particular to a collaborative optimization method and system for low-carbon industrial parks based on dynamic electricity carbon index assessment. Background Technology

[0002] As key units for industrial agglomeration and energy consumption, the low-carbon transformation of industrial parks is crucial for achieving energy conservation and emission reduction across society. Currently, the collaborative optimization operation technology of integrated energy systems (IES) in low-carbon industrial parks has become a research hotspot.

[0003] Regarding the low-carbon assessment system for industrial parks, current systems primarily focus on post-event certification and statistics, lacking a quantitative grading and decision-making model that can directly guide the planning and operation of the park's energy system. Existing optimization technologies mostly focus on how to operate equipment, failing to provide dynamic monitoring and accurate grading, and even less able to address the issues of lag and accuracy in carbon emission accounting, or how to further scientifically optimize carbon emissions.

[0004] In summary, there is an urgent need in this field to build a comprehensive energy system operation technology system for low-carbon industrial parks that integrates refined monitoring, multi-dimensional classification, and collaborative optimization. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a collaborative optimization method and system for low-carbon industrial parks based on dynamic electrocarbon index assessment.

[0006] The objective of this invention is achieved through the following technical solution: A collaborative optimization method for low-carbon industrial parks based on dynamic carbon dioxide index assessment includes: S1. Construct a comprehensive energy system model for a low-carbon industrial park. The comprehensive energy system model includes an electricity subsystem, a heat subsystem, a gas subsystem, and an oxygen-enriched combustion carbon capture system, and collects multi-source electricity / carbon data for the park. S2. Based on hourly power generation structure tracing, construct a dynamic carbon emission factor model for power supply and calculate the real-time carbon emission factor of purchased power in the park. S3. Based on the integrated energy system model and the real-time carbon emission factor of the purchased electricity in the park, calculate the three-dimensional indicators for the low-carbon assessment of the park. The three-dimensional indicators include the ratio of direct carbon emissions to indirect carbon emissions in total energy consumption γ, the ratio of direct carbon emissions to indirect carbon emissions δ, and the carbon emission offset ratio ε. S4. Preset the grading benchmark thresholds for each dimension, and determine the green and low-carbon level of the park based on the comparison results between the calculated three-dimensional indicators and the grading benchmark thresholds. S5. Based on the determined green and low-carbon level, automatically match the corresponding energy collaborative management strategy and adjust the equipment operating parameters or planning configuration in the integrated energy system model.

[0007] Furthermore, in step S2, the dynamic carbon emission factor model for power supply includes the provincial power grid average carbon emission factor and the remaining carbon emission factor. The provincial power grid average carbon emission factor is calculated by weighting emissions from fossil fuel combustion within the province, net imported electricity emissions, and regional power grid interaction. The remaining carbon emission factor is the grid average emission factor after excluding renewable energy electricity, used to characterize the carbon intensity of electricity after deducting environmental benefits; Real-time carbon emission factors of the electricity purchased from outside the park The calculation formula is as follows: ; in, Let be the remaining carbon emission factor at time t. Let be the average carbon emission factor of the provincial power grid at time t; The proportion of purchased regular electricity at time t. The proportion of purchased green electricity with environmental rights at time t. Let be the proportion of purchased hybrid electricity at time t, and satisfy . .

[0008] Furthermore, the calculation method for the three-dimensional index in step S3 is as follows: The ratio of total energy consumption to direct carbon emissions γ = C1 / C2; the ratio of direct carbon emissions to indirect carbon emissions δ = E scope1 / (E scope2 +E scope3 Carbon emission offset ratio ε=O sinks / (E scope1 +E scope2 +E scope3 ); Where C1 represents the total energy consumption of fossil fuels and raw materials, and C2 represents the total energy consumption of purchased electricity and heat; E scope1 For direct carbon emissions, E scope2 E represents indirect carbon emissions from purchased electricity and heat. scope3 For other indirect carbon emissions, O sinks The carbon offset amount; the indirect carbon emissions E from the purchased electricity and heat. scope2 By purchasing electricity from outside With real-time carbon emission factors The result is obtained by multiplying the results hour by hour and summing them up.

[0009] Furthermore, the process of determining the green and low-carbon level of the park in step S4 is as follows: setting a benchmark value γ. th The reference value of δ th And the baseline value of ε th , where ε th The default value is 100%, which means that the carbon offset equals the total emissions, achieving net zero emissions. If γ>γ th It was rated as Level V; If γ≤γ th And δ>δ th It was rated as Level IV; If γ≤γ th And δ≤δ th And ε≤ε th It was rated as Level III; If ε th If ε < 200%, it is rated as Level II; If ε≥200%, it is rated as Level I.

[0010] Furthermore, the energy collaborative management strategy mentioned in step S5 includes one or more combinations of electricity substitution, process improvement, green electricity trading ratio adjustment, and carbon offsetting configuration; the automatically matched corresponding energy collaborative management strategy includes: When rated as Level V, an energy substitution strategy to increase the proportion of electricity is generated; When rated as Level IV, energy-saving strategies for improving processes and reducing overall energy consumption are developed. When the rating is Level III, a scheduling strategy is generated to increase the proportion of green electricity and the volume of green electricity transactions. When assessed as Level II, an allocation strategy is generated to increase investment in carbon offset projects and carbon asset reserves.

[0011] Furthermore, the integrated energy system model adopts a multi-objective bi-level programming model, and the adjustment of equipment operating parameters or planning configuration in the integrated energy system model in step S5 is achieved by solving the multi-objective bi-level programming model; The upper planning layer aims to optimize the economic efficiency of the integrated energy system throughout its entire life cycle. The optimization variables include the installed capacity of new energy equipment, the energy storage configuration capacity, and the configuration capacity of the oxygen-enriched combustion carbon capture system. The lower-level operation layer aims to minimize operating costs and carbon emissions. Based on the real-time carbon emission factor calculated in step S2, it simulates the park's energy production, conversion, and consumption strategies at different times. The bi-level programming model transforms the energy collaborative management strategy generated in step S5 into a constraint input model for solution.

[0012] Furthermore, in the lower-level operation layer, an operation permit coefficient is introduced for the operation control of the oxygen-enriched combustion carbon capture system. The operating license coefficient With real-time carbon emission factors and real-time electricity price Relatedly, it is used to increase carbon capture during periods of low grid carbon intensity and low electricity prices; the oxygen-enriched combustion carbon capture system includes an air separation unit, an oxygen-enriched combustion unit, and a carbon dioxide compression and purification unit.

[0013] Preferably, the present invention also provides a low-carbon industrial park collaborative optimization system based on dynamic electrocarbon index assessment, comprising: The data acquisition and modeling module is used to construct a comprehensive energy system model for a low-carbon industrial park. The comprehensive energy system model includes an electricity subsystem, a heat subsystem, a gas subsystem, and an oxygen-enriched combustion carbon capture system, and collects multi-source electricity / carbon data for the park. The dynamic factor calculation module is used to construct a dynamic carbon emission factor model for power supply based on hourly power generation structure tracing, and to calculate the real-time carbon emission factor of purchased power in the park. The evaluation index calculation module is used to calculate the three-dimensional index of the park's low-carbon assessment based on the integrated energy system model and the real-time carbon emission factor of the park's purchased electricity. The three-dimensional index includes the ratio of direct carbon emissions to indirect carbon emissions in total energy consumption γ, the ratio of direct carbon emissions to indirect carbon emissions δ, and the carbon emission offset ratio ε. The rating determination module is used to preset the rating benchmark thresholds for each dimension, and determine the green and low-carbon rating of the park based on the comparison results between the calculated three-dimensional indicators and the rating benchmark thresholds. The decision optimization module is used to automatically match the corresponding energy collaborative management strategy based on the determined green and low-carbon level, and adjust the equipment operating parameters or planning configuration in the integrated energy system model.

[0014] Preferably, 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, wherein the processor executes the computer program to implement the low-carbon park collaborative optimization method based on dynamic electrocarbon index assessment.

[0015] Preferably, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the low-carbon park collaborative optimization method based on dynamic electrocarbon index assessment.

[0016] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: 1. This invention proposes a three-dimensional evaluation index system and a five-level classification logic that includes energy consumption structure, emission structure and offsetting capacity. This system makes the degree of low carbonization of different parks comparable and can accurately locate the specific shortcomings of parks in terms of energy consumption structure, emission structure or offsetting capacity, providing precise guidance for subsequent optimization. It solves the problems of incomparable low carbon levels of parks and difficulty in locating shortcomings.

[0017] 2. This invention automatically matches differentiated energy collaborative management strategies (such as electricity substitution, green electricity trading, and carbon offsetting) based on the assessed green and low-carbon levels (Level V to Level I), and transforms these strategies into constraints for solving a bi-level programming model. This avoids blind investment and achieves closed-loop decision-making from macro-level rating to micro-level equipment parameter adjustment. For example, electricity substitution is prioritized for Level V parks, while carbon offsetting is emphasized for Level II parks, achieving optimal resource allocation. This achieves precise closed-loop optimization of "assessment-diagnosis-improvement," effectively reducing the cost of low-carbon transition.

[0018] 3. This invention calculates the dynamic carbon emission factor of electricity supply based on hourly data and introduces the concept of residual carbon emission factor, excluding renewable energy electricity and its environmental rights in the calculation. It can accurately capture intraday fluctuations in grid cleanliness and decouple physical electricity flow from environmental rights flow. This ensures that users who purchase green electricity (green certificate holders) truly obtain zero-carbon rights, while those who do not purchase it bear the remaining high carbon emission factor after deducting green electricity, thereby incentivizing users to proactively adjust their energy consumption behavior or purchase green electricity, significantly improving the accuracy and fairness of carbon emission accounting.

[0019] 4. This invention integrates OCCS into the model and utilizes dynamic electric carbon factor and real-time electricity price to jointly drive the OCCS operation strategy (e.g., operation during periods of low electricity price and low carbon emissions). Through bi-level programming model optimization, the OCCS can avoid operation during periods of high price and high carbon emissions, significantly reducing the unit carbon capture cost (data shows a reduction of up to 38%), providing a net-zero emission path that balances economic efficiency and low carbon emissions for industrial parks facing high emission reduction challenges. It achieves deep coupling and economical operation of the oxy-fuel combustion carbon capture system (OCCS) with the integrated energy system. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the method of the present invention; Figure 2 This is a schematic diagram illustrating the process of assessing the green and low-carbon level of the park; Figure 3 This is a schematic diagram illustrating the calculation of the matching degree between the hourly green electricity ratio and the hourly electricity consumption patch of the local power grid after per-unit normalization in the embodiment; Figure 4 This is a graph showing the electricity consumption of a certain university. Figure 5This is a map showing the matching degree between electricity consumption and the proportion of green electricity at a certain university. Detailed Implementation

[0021] To enable those skilled in the art to understand the technical solutions disclosed in this invention, the technical solutions of various embodiments will be described below in conjunction with the embodiments and related drawings. The described embodiments are only some, not all, of the embodiments of this invention. The reference to "embodiment" herein means that a specific feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will understand that the embodiments described herein can be combined with other embodiments.

[0022] Example 1: This embodiment provides a collaborative optimization method for low-carbon industrial parks based on dynamic carbon dioxide index assessment. (See...) Figure 1 This includes the following steps: S1. Construct a comprehensive energy system model for the low-carbon industrial park, which includes electricity, heat, gas subsystems and an oxygen-enriched combustion carbon capture system, and collect multi-source electricity / carbon data for the park; S2. Based on hourly power generation structure tracing, construct a dynamic carbon emission factor model for power supply and calculate the real-time carbon emission factor of purchased power in the park; S3. Based on the integrated energy system model and the real-time carbon emission factor of the park's purchased electricity, calculate the three-dimensional indicators for the park's low-carbon assessment. The three-dimensional indicators include the ratio of direct carbon emissions to indirect carbon emissions in total energy consumption γ, the ratio of direct carbon emissions to indirect carbon emissions δ, and the carbon emission offset ratio ε. The calculation formulas for the three-dimensional indicators are as follows: the ratio of direct carbon emissions to indirect carbon emissions combined energy consumption γ=C1 / C2; the ratio of direct carbon emissions to indirect carbon emissions δ=E scope1 / (E scope2 +E scope3 Carbon emission offset ratio ε=O sinks / (E scope1 +E scope2 +E scope3 ); where C1 is the total energy consumption of fossil fuels and raw materials, and C2 is the total energy consumption of purchased electricity and heat; E scope1 For direct carbon emissions, E scope2 E represents indirect carbon emissions from purchased electricity and heat. scope3 For other indirect carbon emissions, O sinks This is the amount of carbon offset; S4. Preset the grading benchmark thresholds for each dimension, and determine the green and low-carbon level of the park based on the comparison results between the calculated three-dimensional indicators and the benchmark thresholds. S5. Based on the determined green and low-carbon level, automatically match the corresponding energy collaborative management strategy and adjust the equipment operation parameters or planning configuration in the integrated energy system model. The energy collaborative management strategy includes one or more combinations of electricity substitution, process improvement, green electricity trading ratio adjustment and carbon offset configuration.

[0023] This embodiment provides a standardized and quantifiable evaluation standard for the low-carbon level of industrial parks by constructing a three-dimensional evaluation index system including γ, δ, and ε, and a five-level grading logic. This makes the low-carbon transformation levels of different parks comparable and can accurately identify the specific shortcomings of parks in terms of energy consumption structure, emission structure, or offsetting capacity. Furthermore, this embodiment also demonstrates the closed-loop decision-making mechanism based on the evaluation level, which can automatically match differentiated management strategies from electricity substitution to carbon offsetting based on the evaluation results, avoiding blind investment and achieving precise optimization of evaluation-diagnosis-improvement to effectively reduce the cost of low-carbon transformation of industrial parks.

[0024] Preferably, the integrated energy system model includes the following modules: an energy supply and conversion module, an oxygen-enriched combustion carbon capture system, and a park activity data interface module, wherein, (101) Energy supply and conversion module, which includes: Purchased power modules: (Unit: kWh), representing the amount of electricity purchased from the grid during time period t; Gas supply module: (Unit: Nm) 3 This represents the volume of natural gas consumed during time period t, which can be further subdivided according to its application, such as boiler gas. Gas used in cogeneration ; Local renewable energy module: , (Unit: kWh), representing the local photovoltaic and wind power generation respectively; this portion of electricity is zero-carbon, and its environmental rights belong to the park, without generating any carbon emissions. ; Cogeneration (CHP) module: consumes natural gas Electricity production and calories ; Gas boiler module: consumes gas Heat production ; Energy storage module: Records and stores electrical energy. and thermal storage The charge and discharge states; (102) Oxy-fuel combustion carbon capture system (OCCS), which includes: Carbon capture input module: tracks the flue gas flow rate of specified gas-fired equipment (such as boilers, CHP). (Unit: Nm) 3 )and concentration (Unit: kg / Nm) 3 ); Carbon capture process module: Based on the operating status (capture rate) of the OCCS (Optical Carbon Collection System) of the oxygen-enriched combustion system. ), calculate the capture time t Physical quantity: .

[0025] Energy consumption coupling module: Electrical energy consumed during the operation of the OCCS (Oxygen-Enriched Combustion Carbon Capture System). and thermal energy This is fed back to the energy supply module as new load.

[0026] (103) Park activity data interface module, which includes: a model reserved interface for data related to the park management system, such as: official travel mileage. (Unit: km), etc., for calculation .

[0027] This embodiment deeply couples and models oxy-fuel combustion carbon capture technology with an integrated energy system, aiming to provide a more practical negative carbon technology path for the park by utilizing the high-efficiency capture characteristics of oxy-fuel combustion and enhancing the low-carbon and economical nature of the system through heat recovery.

[0028] Preferably, in step S2, the calculation formula of the dynamic carbon emission factor model for power supply covers the provincial power grid average carbon emission factor and the residual carbon emission factor; wherein, the calculation of the provincial power grid average carbon emission factor is based on the weighted calculation of emissions from fossil fuel combustion within the province, net imported electricity emissions, and regional power grid interaction; the calculation of the residual carbon emission factor is the power grid average emission factor after excluding renewable energy electricity, which is used to characterize the carbon intensity of electricity after deducting environmental rights.

[0029] For example, the average carbon emission factor of a provincial power grid Calculate using the following empirical formula: ; Where t represents the current hour (time-level timestamp); Let represent the average carbon emission factor of the provincial power grid at time t-1; thus, it can be seen that the average carbon emission factor of the provincial power grid is calculated through iteration. This represents the power generation (MWh) of the i-th type of power source (such as coal power, gas power, hydropower, wind power, photovoltaic power, etc.) in the province at time t. The carbon emission factor of the i-th type of power source (e.g., in units of tCO2 / MWh) is represented, where the carbon emission factor of renewable energy sources such as hydropower, wind power, and photovoltaic power is set to 0; This represents the net amount of electricity transferred in at time t (e.g., in MWh). The carbon emission factor representing the imported power source can be taken from the average factor or a predetermined value of the power grid of the province from which the power is imported. This represents the net amount of electricity transferred out at time t (e.g., in MWh). Let represent the total power supply of the provincial power grid at time t (e.g., in MWh), and we have: .

[0030] It should be noted that the dimensions should be unified on the same scale. For example, the units of all terms in the numerator and denominator should be consistent, so that the unit of the carbon emission factor is kgCO2 / kWh or tCO2 / kWh. All the dimensions involved in the formulas in this invention are to be understood in a similar way. The use of different dimensions only indicates whether a conversion coefficient is involved, which is a common practice in the industry.

[0031] The core of the above formula is to calculate the average carbon emissions generated by each kilowatt-hour supplied by a provincial power grid at a specific hour t. The different factors in the formula reflect the characteristics of weighted calculation. (Provincial power grid average carbon emission factor) This reflects the overall greenness of the power grid at time t. The above formula, using hours (t) as the unit, captures the intraday fluctuations in grid cleanliness through real-time or near-real-time power generation and net inflow / outflow data. It is reasonable and scientific, following the perspective of the provincial power grid as a whole system and incorporating net inflow and net outflow electricity, thus conforming to natural scientific laws. It is important to note that the average carbon emission factor of the provincial power grid is dynamic. For example, during midday photovoltaic power generation, the carbon emission factor corresponding to photovoltaic power is 0, leading to a decrease in the average carbon emission factor value of the provincial power grid; at night, when relying on coal power, the average carbon emission factor value of the provincial power grid increases, thus solving the problem that static factors in existing technologies cannot reflect the real-time greenness level of the power grid. In summary, the average carbon emission factor of the provincial power grid is suitable for assessing the carbon emissions of purchased electricity when the park has not declared the use of green electricity or has not conducted green electricity source tracing, and is used to reflect the average carbon intensity background of electricity consumption in the park.

[0032] For example, residual carbon emission factors According to the following empirical formula: ; Where j represents the j-th type of non-renewable power source (such as coal power or gas power); This represents the electricity generated by the j-th type of non-renewable power source (such as coal power, gas power, etc.) within the province at time t (MWh). The carbon emission factor of the j-th type of non-renewable energy source is represented (e.g., in units of tCO2 / MWh). This represents the remaining carbon emission factor at time t-1; thus, it can be seen that the remaining carbon emission factor is calculated through iteration. This represents the total amount of renewable energy (wind power, photovoltaic power, hydropower, etc.) generated in the province at time t (MWh). It refers to the grid's dispatchable electricity after deducting renewable energy sources, i.e., the net supply of non-renewable electricity.

[0033] It should be noted that this formula calculates the carbon intensity of each kilowatt-hour of electricity generated primarily from fossil fuels remaining in the power grid after deducting renewable energy electricity and its environmental rights. This is used to characterize the carbon emission level of the remaining electricity in the grid, dominated by non-renewable energy sources, after green electricity consumption. Deducting, or stripping away, environmental rights is one of the key innovations of this invention. This means that when a park purchases green electricity (e.g., obtains the corresponding green electricity certificate), this portion of electricity and its environmental value should be removed from the total grid output.

[0034] Further examination of the formula reveals that its numerator only calculates carbon emissions from non-renewable energy (coal, gas, etc.) power generation and net imported electricity. This means that green electricity generated within the province is ignored in the calculation, contributing neither to carbon emissions nor electricity (because it has been deducted from the denominator). The denominator is the total power supply from the grid minus renewable energy generation, i.e., the net supply of non-renewable electricity. This fully illustrates the residual carbon emission factor. The calculation formula is objective and accurate, precisely calculating the carbon benefits of green electricity consumption and avoiding double calculation. This prevents the environmental benefits of green electricity from being diluted by all users in the grid average factor, while purchasers fail to reflect their specific emission reduction contributions. Therefore, the remaining carbon emission factor... This ensures the fairness and accuracy of the calculation of zero-carbon benefits for those who purchase green certificates, thus proving that this factor is one of the key innovations of this invention.

[0035] For example, the dynamic carbon emission factor model for electricity supply follows an empirical formula: ; in, This indicates the real-time carbon emission factor of the electricity purchased from outside the industrial park. This represents the proportion of conventional electricity purchased externally at time t; This represents the proportion of purchased green electricity (with environmental rights) at time t; This represents the proportion of purchased hybrid power (without divestiture of equity) at time t; and satisfies the following constraints: .

[0036] It can be observed that this formula is a weighted formula. For the dynamic carbon emission factor model of electricity supply reflected by this formula, the model essentially reflects the average carbon emission factor of the provincial power grid. and remaining carbon emission factors The weighted factor follows a pattern that changes with time t, enabling the model to calculate the overall, personalized, real-time carbon emission factor of the purchased electricity based on the actual electricity purchase composition of the industrial park at a specific hour t. And the macroscopic power grid factor ( and ) and the park's own micro-procurement behavior (electricity purchase ratio) This means that the present invention fully reflects that even under the same power grid, different industrial parks have drastically different carbon intensity in their electricity consumption due to their different procurement strategies, among which: The carbon intensity of the conventional electricity (without environmental rights) purchased on behalf of the park uses the residual carbon emission factor because the environmental rights of green electricity have been purchased by other buyers, and what the park is buying is just gray electricity. The green electricity purchased by the park with environmental rights (such as direct purchase of green electricity or holding green certificates) has a carbon emission of 0. It should be noted that this item must be present in the formula because it involves the following constraints: This constraint indicates It belongs to the dynamic weight function, which replaces the conventional weight, further echoing the dynamic carbon index evaluation of this invention; For mixed electricity purchased without clearly defined rights (such as ordinary market-traded electricity), the carbon intensity is calculated using the provincial grid average carbon emission factor because environmental rights are not separately stripped and attributed.

[0037] This reveals that the weighting formula of the aforementioned dynamic carbon emission factor model for electricity supply achieves a precise mapping from dynamic carbon intensity on the grid side to personalized carbon footprints on the user side, and demonstrates how to calculate real-time carbon emission factors more fairly and accurately through the provincial grid average carbon emission factor and residual carbon emission factor. For example: Based on hourly power generation structure and inter-provincial transaction data provided by power grid operators, the average carbon emission factor of the provincial power grid is calculated. and remaining carbon emission factors The park determines the composition of its hourly purchased electricity based on its own green electricity trading and green certificate holdings. The real-time carbon emission factor is calculated using the weighted formula described above. This represents the park's most accurate and real-time carbon intensity based on electricity consumption. This will inevitably facilitate a fairer and more accurate calculation of carbon emission indicators, grading, and ultimately guiding operational optimization and strategy adjustments, for example, in... Reduce regular electricity consumption during peak periods, or adjust the proportion of green electricity purchases, etc.

[0038] In summary, the technological contribution of the aforementioned dynamic carbon emission factor model for power supply lies in its decoupling and precise tracking of the physical flow, carbon emission flow, and environmental rights flow of the power system. This provides the industrial park with a closed-loop quantitative tool from macro-level perception to micro-level decision-making, facilitating dynamic assessment and precise optimization. The model not only introduces hourly-level dynamic carbon emission factor calculations for power supply but also specifically distinguishes between the provincial power grid's average carbon emission factor and the remaining carbon emission factor. Combined with real-time data, it solves the problem that traditional static factors cannot accurately reflect the real-time green level and environmental value of green electricity in the industrial park, thus improving the accuracy of carbon emission accounting.

[0039] Preferably, in step S3, in the calculation formula of the three-dimensional index, C1 is the comprehensive energy consumption of fossil fuels and raw materials, and C2 is the comprehensive energy consumption of purchased electricity and heat; E scope1 For direct carbon emissions, E scope2 E represents indirect carbon emissions from purchased electricity and heat. scope3 For other indirect carbon emissions, O sinks The specific calculation method for carbon offset includes the following steps: Based on the integrated energy system model, the total gas consumption is first calculated: ; Further calculations : ; in, This refers to the coefficient for converting natural gas to standard coal equivalent. Further calculations : ; in, Carbon emission factor of natural gas; Further calculations : ; in, This is the standard coal equivalent coefficient for electricity. Further calculations based on the integrated energy system model and real-time carbon emission factors of electricity purchased from outside the industrial park. : ; It should be noted that this is a core step, because this step integrates the physical quantities output by the energy system model. Real-time carbon emission factors of electricity purchased from outside the park By multiplying and summing the data hourly, an accurate indirect emission figure can be obtained, which is conducive to achieving collaborative optimization of low-carbon industrial parks based on dynamic electrical carbon index assessment.

[0040] Further calculations : ; This directly produces carbon to offset physical quantities.

[0041] Further calculations : ; in, This is the carbon emission factor for travel; for example, the carbon emission factor for travel is converted to 0.1147 kgCO2 / km.

[0042] Based on the above exemplary calculation formula, we can further calculate: the ratio of direct carbon emissions to indirect carbon emissions combined energy consumption γ = C1 / C2; the ratio of direct carbon emissions to indirect carbon emissions δ = E scope1 / (E scope2 +E scope3 Carbon emission offset ratio ε=O sinks / (E scope1 +E scope2 +E scope3 ).

[0043] Preferably, in step S4, the specific logic for determining the green and low-carbon level of the park is as follows: setting a baseline value γ. th The reference value of δ th And the baseline value of ε th , where ε th The default value is 100%, meaning that the carbon offset equals the total emissions, achieving net-zero emissions; if γ > γth It is rated as level V; if γ≤γ th And δ>δ th It is rated as Level IV; if γ≤γ th And δ≤δ th And ε≤ε th It is rated as Level III; if ε th If ε < 200%, it is rated as Level II; if ε ≥ 200%, it is rated as Level I.

[0044] Preferably, in step S5, the automatic matching of the corresponding energy collaborative management strategy includes: when rated as Level V, generating an electricity substitution strategy to increase the proportion of electricity; when rated as Level IV, generating an energy-saving strategy to improve processes and reduce overall energy consumption; when rated as Level III, generating a scheduling strategy to increase the proportion of green electricity and the volume of green electricity trading; and when rated as Level II, generating an allocation strategy to increase investment in carbon offset projects and carbon asset reserves.

[0045] Therefore, it can be seen that the core of the integrated energy system model in this embodiment lies in quantifying and outputting the physical flow state of the park's energy system. Through the specific examples described above, the integrated energy system model provides... , , Key physical quantity data, and real-time carbon emission factors of electricity purchased from outside the park. In step S3, the precise combination completes the mapping from physical flow to carbon flow, thereby supporting the closed loop of precise evaluation-level determination-improvement or optimization embodied in the inventive concept of this invention.

[0046] See Figure 2 In another embodiment, The park's green and low-carbon level is assessed based on the calculated values ​​of five monitoring and assessment variables and the benchmark values ​​of three indicators, specifically including: Determine whether the ratio γ of direct carbon emissions to indirect carbon emissions in total energy consumption is higher than the benchmark value γ. th If γ > γ th When γ ≤ γ, it is rated as level V. th Then, it is further determined whether the ratio δ of direct carbon emissions to indirect carbon emissions is higher than the benchmark value δ. th If δ>δ th Then it is rated as Level IV, if δ≤δ th Then, it is further determined whether the carbon emission offset ratio ε is higher than the benchmark value ε. th , where ε th The default value is 100%, meaning that the carbon offset equals the total emissions, achieving net-zero emissions; if ε≤ε th Then it is rated as Level III, if ε thIf ε < 200%, it is rated as Level II; if ε ≥ 200%, it is rated as Level I.

[0047] Considering the carbon emission offset ratio ε=O sinks / (E scope1 +E scope2 +E scope3 ), When ε equals 200%, it means that the park not only completely neutralizes its own carbon emissions across all areas, but also generates an additional carbon reduction contribution of the same amount, thus achieving net negative carbon emissions. This is precisely why the above rating involves the 200% threshold.

[0048] In the above rating, the lower the level, the higher the green and low-carbon level of the park. Level II means that the park has achieved net zero emissions, and Level I means that it has achieved full-range carbon emission neutrality. Level I represents the highest level of green and low-carbon development.

[0049] It should be noted that the reference value γ th This information can be obtained based on statistical data, benchmarking against leading industrial parks, or planning. For example: Collect statistical data on the comprehensive energy consumption (C1) of fossil fuels and raw materials, and the comprehensive energy consumption (C2) of purchased electricity and heat in similar industrial parks nationwide or within the province (such as chemical industrial parks, manufacturing industrial parks, science and technology parks, university parks, etc.). Based on the distribution of the ratio (γ) of direct carbon emissions to indirect carbon emissions in the above-mentioned industrial parks, take the median, mean, or 75th percentile as γ. th Alternatively, select a domestically or internationally recognized low-carbon or zero-carbon demonstration park (such as a green park), and use its ratio γ of direct carbon emissions to indirect carbon emissions as a benchmark. Then, use a suitable multiple of 1.1 to 1.5 times this benchmark as the final γ. th This reflects accessibility and operability; or, if a plan requires that the proportion of electricity consumption to total energy consumption in a certain industry park be ≥ 40% (or the proportion of fossil energy ≤ 60%), then (1-40%) / 40% equals 1.5, or alternatively, 60% / (1-60%) also equals 1.5. Therefore, regardless of whether the proportion of electricity consumption to total energy consumption is ≥ 40% or the proportion of fossil energy is ≤ 60%, γ is chosen. th It is 1.5; Reference value δ th The phased targets in the plan are determined by the following: For example, a plan requires that by 2030, the proportion of direct emissions should be reduced to below 60%, then δ... th We can take 60% / 40%, which is 1.5; ε th The baseline value is less than 200%; ε th The default value is 100%, meaning that the carbon offset equals the total emissions, achieving net-zero emissions; εth A value between 100% and 200% indicates negative carbon emissions. It should be noted that for industrial parks in sectors like cement and steel, where emissions are difficult to reduce, the ε value can be appropriately lowered. th To be realistic, for example, reduce it to 80%; similarly, for industrial parks where emissions are easy to reduce, ε can be... th Increase it to between 100% and 200%.

[0050] It is understandable that the above benchmark values ​​are essentially changing with technological advancements and the cleanliness of the power grid.

[0051] In another embodiment, Based on the park's green and low-carbon rating, corresponding upgrade measures are recommended. The following are also exemplary recommendations: Level V: Electricity substitution, increasing the proportion of electricity consumption; Level IV: Energy saving and consumption reduction, improved processes, and reduced overall energy consumption; Level III: Increase the proportion of green electricity and reduce the carbon intensity of electricity consumption; Level II: Increase investment in carbon offset projects and increase purchases of carbon offset commodities.

[0052] In the specific implementation process, the park can adopt multiple measures. For example, it can promote electricity substitution to increase the proportion of electricity, reduce the average carbon emission factor of grid power supply, purchase green electricity to further reduce the carbon emission intensity of electricity consumption, and promote carbon synergy between grid, load, and electricity to bring carbon emissions in areas one and two of the park closer to zero. On this basis, carbon offset commodities can be stockpiled to offset the remaining carbon emissions, thereby achieving carbon emission neutralization in areas one and two.

[0053] In another embodiment, the integrated energy system model adopts a multi-objective bi-level programming model; the upper planning layer aims to optimize the economic efficiency of the system throughout its entire life cycle, and the optimization variables include the installed capacity of new energy equipment and / or energy storage configuration and / or oxygen-enriched combustion carbon capture system; the lower operation layer aims to minimize operating costs and carbon emissions, and simulates the energy production, conversion and consumption strategies of the park at different time periods based on the dynamic carbon emission factor calculated in step S2; the bi-level programming model transforms the energy collaborative management strategy generated in step S5 into a constraint input model for solution.

[0054] It should be noted that the multi-objective, two-layer programming model in this embodiment aims to solve the collaborative optimization problem of the park at two levels: equipment investment planning (hereinafter referred to as the upper planning layer) and daily operation scheduling (hereinafter referred to as the lower operation layer). For example, the upper planning layer takes the optimal economic efficiency throughout the system's entire life cycle as its primary objective, deciding on the optimal installation capacity of various new energy and energy storage devices within the planning period (e.g., the next 5-10 years). The decision result is passed to the lower operation model as a hard constraint. Meanwhile, the lower layer takes minimizing short-term operating costs and carbon emissions as multiple objectives, optimizing typical days (e.g., representative days of the four seasons) or the entire year under a given equipment capacity. The 24-hour (8760-hour) operation strategy, with its optimization results (operating costs, carbon emissions), is fed back to the upper level to evaluate the low-carbon nature of the planning scheme. The energy collaborative management strategy generated in step S5 (such as increasing the proportion of electricity) is quantified into specific constraints or target weights and injected into the two-level planning model for solution, thereby driving the park to evolve towards a higher low-carbon level.

[0055] For example, in the upper-level planning layer model, Decision variables of the upper-level planning model for: ; in, This indicates the installed capacity of the photovoltaic system (kW). This indicates the installed capacity of the wind turbine (kW). This indicates the battery's energy storage capacity (kWh). This indicates the rated power of the battery (kW). Indicates the maximum hourly output of the oxygen-enriched combustion carbon capture system. Capacity (kg) / h); The objective function of the upper-level planning model is to minimize the annualized total cost over the entire life cycle. ,have: ; In the formula: This represents the annualized cost of equipment investment. For example, the calculation formula is as follows: ; in, , The unit capacity investment cost of equipment i; Capital recovery factor The calculation formula is: Where r is the discount rate, For equipment lifespan; The annual maintenance cost can be calculated using a formula such as: ;in, Let i be the annual maintenance cost per unit capacity of device i; This represents the optimal daily operating cost for day d; for example, this cost is determined by the lower operating layer at a given capacity. The results were optimized based on typical daily data. To represent the weight of day d in a year (e.g., the weight of a seasonal representative day is 90 days). For representative day sets (e.g.) ); The constraints of the upper-level planning model include: Capacity boundary constraints: ; Technical correlation constraints: For example, the constraint of battery power-to-capacity ratio: , This is the proportioning coefficient; And, the transformation constraints from the strategy in step S5: 1) If the rating is Level V, the strategy requirement is: electricity substitution, then a lower limit for renewable energy penetration rate is imposed: ; in, For the peak electrical load of the park, This is a preset ratio; 2) If the rating is Level III, the strategy requires: increasing green electricity trading, and imposing a lower limit on energy storage capacity to smooth out green electricity fluctuations and ensure that: ; in, This indicates the minimum energy capacity required for the energy storage system to ensure the stable operation of the park's power grid and effectively absorb green electricity under this strategy.

[0056] For example, in the lower-level runtime model, the lower-level runtime model has a given device capacity in the upper level. and the dynamic carbon emission factor provided in step S2 The following is an hourly optimization and scheduling process for each representative day, in which... Lower-level operational model decision variables have: For each time period t (t=1,2,...,T,T=24), for example, the decision variables include: ; in, The meaning has been described above; This represents the electrical power output of the gas-fired combined heat and power (CHP) system during time period t. This represents the charging power of the battery in the energy storage module during time period t, which is subject to the rated power of the battery. and the current remaining capacity SOC(t) constraint; This represents the battery's discharge power during time period t; This represents the electrical power consumed by the oxygen-enriched combustion carbon capture system during time period t, and its value is related to the physical quantity of CO2 captured during time period t. Proportional (proportionality coefficient) ); This represents the volumetric flow rate of natural gas consumed by the gas-fired combined heat and power system during time period t. This represents the thermal power consumed by the oxygen-enriched combustion carbon capture system during time period t, and its value is related to the physical quantity of CO2 captured during time period t. Proportional (proportionality coefficient) ); SOC(t) represents the state of charge of the battery at the end of time period t; Furthermore, the objective function of the lower-level operational model, based on minimizing daily operating costs and carbon emission penalties, is as follows: ; in, The real-time electricity price for time period t; For natural gas prices; Carbon price is used to convert carbon emission targets into costs. This represents the total carbon emissions for a representative day; for example, its calculation formula is: ; In addition, the constraints of the lower-level runtime model include: Energy balance constraints include: Electrical balance: ; Thermal equilibrium: ; Equipment operating upper and lower limits constraints (determined by the capacity of the upper planning layer): ; in, , These are the normalized output coefficients for photovoltaic and wind power, respectively; And the following energy storage state transition constraints: ; And the following are the OCCS operating constraints for the oxygen-enriched combustion carbon capture system: ; And the transformation constraints from the strategy in step S5: 1) If the level is IV, the strategy requirement is: energy conservation and consumption reduction, then a daily total gas consumption cap is imposed: ; 2) If the level is II, the strategy requires: increasing carbon offsetting, then imposing a minimum daily carbon capture requirement: ; It should be noted that, for example, the two-layer interaction and solution between the upper planning layer and the lower execution layer includes the following steps: S11: Set the upper-level capacity variable The initial value; S12: For each representative day , will be given by the upper level and Input the lower-level operating model and solve for the optimal daily operating cost. and corresponding operating strategies ; S13: Represent each day Weighted summation, substituted into the upper objective function ; S14: Based on the current situation Adjusting capacity decision variables based on optimization algorithms (such as genetic algorithms and particle swarm optimization in existing technologies). ; S15: Repeat steps S12 to S14 until the upper-level objective function is reached. When the change is less than the threshold, or when the maximum number of iterations is reached, the result is... and the corresponding set of lower-level execution strategies This is the optimal planning and scheduling scheme; S16: Implement or simulate the optimal planning and scheduling scheme. After at least one operating cycle, collect new data and repeat steps S1 to S15 to conduct a new round of evaluation, determine the level, and optimize to form a closed loop.

[0057] It can be observed that this invention, through specific examples, realizes a computable and iterative optimization model for a bi-level programming model. Its technical contribution to the prior art lies in: decoupling and co-optimizing long-term capital decisions and short-term operational decisions in a hierarchical manner, and basing the optimization on the real-time carbon emission factor of the electricity purchased from outside the industrial park. As a key parameter, carbon cost calculation directly drives the lower-level operation, guiding the operation strategy to achieve a balance between electricity price and carbon price while realizing the coordinated optimization of low-carbon parks. The qualitative strategy in step S5 is precisely quantified into constraints (such as capacity lower limit, energy consumption upper limit, carbon offset requirements, etc.) or target weights (such as adjustment) in the two-level programming model. This allows the optimizations of the present invention to directly serve the goal of improving the low-carbon level.

[0058] In another embodiment, the oxygen-enriched combustion carbon capture system includes an air separation unit, an oxygen-enriched combustion unit, and a carbon dioxide compression and purification unit. It should be noted that the model considers the energy consumption cost of the carbon capture process, the carbon capture efficiency, and the thermal coupling relationship with the park's thermal system.

[0059] For example, Air separation unit, used to separate high-purity oxygen from the air based on electrical energy consumption; Oxygen-enriched combustion units (such as boilers, combined heat and power plants, and CHP systems) are used to mix and burn oxygen produced by the air separation unit with fuel (such as natural gas) to produce high-concentration oxygen. smoke ; A carbon dioxide compression and purification unit is used to process carbon dioxide from flue gas based on the consumption of electrical and thermal energy. The process involves capture, compression, and purification to ultimately produce a liquid or supercritical fluid that can be sequestered or utilized. .

[0060] In another embodiment, the present invention provides a collaborative optimization method for low-carbon industrial parks based on dynamic electricity carbon index assessment. This method first constructs a comprehensive energy system model for the low-carbon industrial park, encompassing electricity, heat, gas subsystems, and an oxy-fuel combustion carbon capture system. Specifically, it includes an air separation unit, an oxy-fuel combustion unit, and a carbon dioxide compression and purification unit, considering the energy consumption cost, carbon capture efficiency, and thermal coupling relationship with the heat system during the carbon capture process. Simultaneously, multi-source electricity / carbon data for the industrial park are collected. Next, based on hourly power generation structure tracing, a dynamic carbon emission factor model for power supply is constructed. This model calculates the average carbon emission factor and residual carbon emission factor of the provincial power grid; the provincial factor is calculated based on a weighted average of fossil fuel combustion, net imported electricity, and regional interactive electricity, while the residual factor excludes renewable energy electricity, representing the electricity carbon intensity after deducting environmental rights. Based on the model and real-time factors, three-dimensional indicators for the low-carbon assessment of the industrial park are calculated: the ratio of direct carbon emissions to indirect carbon emissions combined with energy consumption γ (C1 / C2), and the ratio of direct carbon emissions to indirect carbon emissions δ (E...). scope1 / (E scope2 +E scope3 )) and carbon emission offset ratio ε (O sinks / E totalSubsequently, a preset grading baseline threshold (γ) was established. th ,δ th ,ε th Based on the comparison results, the park is classified as Level 1, ranging from Level V to Level I. Finally, an automatic feedback mechanism is established to match energy collaborative management strategies (such as electricity substitution, process improvement, green electricity trading, and carbon offsetting) according to the level. The strategies are then transformed into constraints or parameters of a two-level planning model. The upper level plans equipment capacity, while the lower level optimizes operating strategies and adjusts system equipment operating parameters to achieve closed-loop optimization and decision-making for the park's energy system.

[0061] Example 2: Based on the same inventive concept, this application also provides a low-carbon park collaborative optimization system based on dynamic electrocarbon index assessment, which can be used to implement the optimization method described in the above embodiments, specifically including the following: The data acquisition and modeling module is used to construct a comprehensive energy system model including oxygen-enriched combustion carbon capture and to collect data. The dynamic factor calculation module is used to calculate the hourly dynamic carbon emission factor of power supply based on power grid dispatch data; The evaluation index calculation module is used to calculate the ratio of total energy consumption for direct carbon emissions to indirect carbon emissions (γ), the ratio of direct carbon emissions to indirect carbon emissions (δ), and the carbon emission offset ratio (ε). The rating determination module is used to determine the green and low-carbon rating of the park by comparing the calculation results with preset thresholds. The decision optimization module is used to generate corresponding energy management strategies based on the determined levels and adjust the operation or planning parameters of the integrated energy system.

[0062] Preferably, embodiments of this application also provide a specific implementation of an electronic device capable of implementing all steps in the low-carbon park collaborative optimization method based on dynamic electrocarbon index assessment 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.

[0063] 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 low-carbon park collaborative optimization method based on dynamic electrocarbon index evaluation in the above embodiments.

[0064] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the low-carbon park collaborative optimization method based on dynamic electrocarbon index assessment in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the low-carbon park collaborative optimization method based on dynamic electrocarbon index assessment in the above embodiments.

[0065] The following will provide a more detailed explanation through more specific implementation examples.

[0066] Example 3: This embodiment provides supplementary explanations of the above-mentioned collaborative optimization method for low-carbon industrial parks, based on specific applications and data, as follows: This embodiment uses a typical educational park (such as a university) located in a certain province as an example to demonstrate the dynamic assessment and rating process based on current data. This park suffers from an imbalanced energy flow structure and a high dependence on natural gas. First, a comprehensive energy system model is constructed, including gas-fired boilers, distributed photovoltaic power, and purchased electricity. Second, by connecting to local power grid dispatch data, hourly power generation structure for 2024 is obtained, and dynamic carbon emission factors are calculated. Subsequently, the park's annual energy consumption data for 2024 is statistically analyzed, three-dimensional assessment indicators are calculated, and a preset threshold (γ) is applied. th =0.25,δ th =0.30,ε th The system assigns a rating based on a percentage of 105%. Finally, it automatically generates targeted improvement strategies.

[0067] For example, the university's carbon emission structure is as follows: (a) Scope of measurement According to the Greenhouse Gas Agreement standard (ISO-14064-1), campus carbon emissions can be categorized into Scope I, Scope II, and Scope III. An organization must include Scope I and Scope II when calculating its carbon emissions, while Scope III offers some flexibility in its definition due to the complexity of its boundaries and the difficulty in obtaining data. For example, Scope I corresponds to natural gas and gasoline for owned vehicles, Scope II corresponds to electricity, and Scope III corresponds to official travel.

[0068] (II) Calculation Method Based on the defined measurement range, data for each type of emission is collected and converted into tonnes of carbon dioxide equivalent (TCO). or ).

[0069] Because different greenhouse gases produce varying greenhouse effects on Earth, a standard measurement is needed to determine the overall impact. Considering that carbon dioxide is the primary greenhouse gas produced by human activities, the carbon dioxide equivalent is generally used as the basic unit for measuring the greenhouse effect of different greenhouse gas emissions. The carbon dioxide equivalent of a gas is obtained by multiplying its emissions by its global warming potential (GWP). This method standardizes the effects of different greenhouse gases.

[0070] In practical calculations, gas emissions cannot be directly monitored professionally. Instead, they are converted into carbon dioxide equivalents by multiplying the carbon emission factors of various carbon-related production and business activities. An organization's carbon footprint equals the sum of the carbon dioxide equivalents directly or indirectly generated by its various activities. .

[0071] Among them, the carbon emission factor is the average amount of carbon dioxide emitted per unit of activity (such as per kilometer of driving distance, per kilowatt-hour of electricity consumption, per ton of water, etc.).

[0072] Benchmarking against advanced campus standards both domestically and internationally, the university's carbon emissions were assessed into the following categories: Category 1 (natural gas, gasoline for owned vehicles); Category 2 (electricity); and Category 3 (official travel).

[0073] After completing hourly source tracing of local power grid carbon emission factors, the overall carbon emission structure of a certain university was analyzed, as shown in Table 1. The university's current carbon emission structure shows that direct carbon emissions from natural gas (Scope 1) account for a relatively high proportion, while indirect carbon emissions from electricity consumption (Scope 2) account for a relatively small proportion due to the local power grid's carbon emission factor being at a relatively low level nationwide.

[0074] Table 1. Carbon Emission Structure of a Certain University in 2024 Taking this university as an example, we conducted an assessment and certification of its zero-carbon park's green and low-carbon level. The assessment indicators and grading methods for zero-carbon parks include five monitoring and assessment variables, three dimensions of assessment indicators, and their benchmark values. The specific analysis is as follows: The university's own vehicles consumed 5.348 tons of gasoline. Based on the gasoline-to-standard-coal-rate conversion factor (1.4714 kgce / kg), the comprehensive gasoline energy consumption of these vehicles in Zone 1 is 7.87 tce. Based on the gasoline carbon emission factor (2.93 kg / kg), the gasoline carbon emissions of these vehicles in Zone 1 are 15.67 tCO2. The natural gas consumption in Zone 1 is 7,923,765.25 Nm³. 3 According to the natural gas to standard coal equivalent coefficient (1.3300 kgce / m³) 3The comprehensive energy consumption of natural gas in range one is 9799.68 tce, and the carbon emissions of natural gas in range one are 17249.61 tCO2 based on the natural gas emission factor (2.16 kg / kg). Therefore, the university's comprehensive energy consumption for direct carbon emissions is... It is 9807.55 tce, which is the range of direct carbon emissions. The value is 17265.28tCO2.

[0075] The university's electricity consumption in Zone 2 is 20395.63 MWh. Based on the electricity (equivalent value) to standard coal equivalent factor (0.1229 kgce / kWh), the comprehensive energy consumption of purchased electricity in Zone 2 is 2506.62 tce. Based on the local power grid's hourly carbon emission factor, the carbon emissions from purchased electricity in Zone 2 are 2187.09 tCO2. Therefore, the university's comprehensive energy consumption for indirect carbon emissions... The indirect carbon emissions in range two are 2506.62 tce. The value is 2187.09 tCO2.

[0076] The carbon emissions from official trips within the university area, calculated using the average emission factor per kilometer, are 1903.09 tCO2. Therefore, the indirect carbon emissions within the university area are... The value was 1903.09 tCO2.

[0077] The university's combined energy consumption ratio of direct carbon emissions to indirect carbon emissions The university's ratio of direct to indirect carbon emissions The university's carbon emission offset ratio .

[0078] The university is rated as follows: Based on the university's three-dimensional evaluation indicators and their benchmark values, and combined with the park's green and low-carbon rating methodology, the university was rated as Level V. Specifically, the first step was to determine the ratio of direct carbon emissions to indirect carbon emissions in terms of overall energy consumption. Is it higher than the benchmark value? The ratio of the university's total energy consumption for direct carbon emissions to indirect carbon emissions. It is greater than the benchmark value for the ratio of direct carbon emissions to indirect carbon emissions in total energy consumption. If so, the university will be rated as V.

[0079] As a typical educational park in a plateau region, the university's imbalanced energy flow structure and high carbon lock-in effect are the main reasons for its V-level rating. The university's energy consumption data shows a natural gas dependence rate of 76.2%, mainly used for winter heating boilers, steam supply to laboratory buildings, and backup power generation. Furthermore, the university's electricity greening level is insufficient, with renewable energy accounting for only 32% of its purchased grid electricity. This energy structure, dominated by high-carbon fuels, results in abnormally high direct carbon emission intensity, fundamentally conflicting with the "electrification + green electricity" transition path advocated by global zero-carbon parks.

[0080] This case study reveals the systemic risks of traditional fossil fuel-dependent industrial parks in their zero-carbon transition. Even in areas rich in clean energy, the energy infrastructure lock-in effect caused by a lack of planning will still hinder the low-carbon process.

[0081] The university has implemented the following enhancement measures: Based on the university's Level V green and low-carbon rating, a four-stage synergistic path is constructed, which includes energy structure transformation, system energy efficiency improvement, green electricity consumption optimization, and carbon sink capacity enhancement. A phased and quantifiable technical implementation plan and management mechanism innovation are proposed to support the park in achieving Level III compliance by 2030.

[0082] The university currently relies on natural gas for 76.2% of its energy mix, directly resulting in a combined energy consumption ratio of direct to indirect carbon emissions exceeding the benchmark by more than six times. Therefore, it urgently needs to reduce its dependence on natural gas through electricity substitution. During the energy structure transformation phase, the university will implement electrification of its heating system, replacing existing gas-fired boilers with customized high-altitude ground-source heat pump units. Based on the "Energy Consumption Standard for Civil Buildings" GB / T51161, the annual heat demand of the campus buildings is calculated to be 48.3 MW·h. After the renovation, the carbon emission intensity of heating can be reduced by 67%. Simultaneously, a 6.8 MWp photovoltaic carport will be constructed, equipped with a 2.4 MW / 4.8 MWh vanadium redox flow storage system. Combined with the 1.08 MW rooftop photovoltaic system already built in 2021, this will form a distributed energy complementary network.

[0083] At the level of improving system energy efficiency, a campus carbon-electricity collaborative management system is deployed to monitor carbon emission sources on campus in real time. By adjusting and optimizing load scheduling based on grid-side electricity carbon emission factors and campus energy consumption behavior, a source-grid-load-storage synergy is achieved, where "load follows source."

[0084] The optimization of green electricity consumption relies on the local power trading center to directly purchase wind power contracts and lock in low-priced green electricity at 0.22 yuan / kWh, which further reduces the carbon emission intensity of purchased electricity. At the same time, it applies for IRC international green certificates for campus photovoltaic power stations to build a green electricity-carbon offset closed loop.

[0085] Analysis of the university's energy consumption behavior: The matching degree between the standardized local power grid hourly green electricity ratio and the university's hourly electricity consumption map is calculated using the following formula: , , The per-unit value difference for the h-hour period in the m-th month = the per-unit value of electricity consumption in the h-hour period in the m-th month - the per-unit value of green electricity ratio in the h-hour period in the m-th month, where m ranges from 1 to 12 and h ranges from 0 to 23; See Figures 3 to 5 The horizontal axis represents hours, the left vertical axis represents the 12 months of the year (assuming 365 days in a year, then a year has 12 × 365 = 8760 hours), and the right vertical axis represents the corresponding per-unit value or the difference between per-unit values. Figure 3 shows the percentage of green electricity generated by the university over 8760 hours a year. Darker blocks indicate a higher per-unit value for the percentage of green electricity generated, while lighter blocks indicate a lower per-unit value for the percentage of green electricity generated. Figure 4 The data actually shows the university's electricity consumption over 8760 hours a year. Darker colored blocks indicate higher per-unit electricity consumption, while lighter colored blocks indicate lower per-unit electricity consumption. Figure 5 The per-unit value difference actually demonstrates the matching degree between the per-unit values ​​of electricity consumption and the per-unit values ​​of green electricity ratio for each hour of the year at the university; It should be noted that since there are 365 days in a year, there are 12 × 365 = 8760 hours in a year. Figure 3 , Figure 4 , Figure 5 Data visualization was actually performed on 8760 points; based on Figure 5 It allows for a direct understanding of whether periods of higher electricity consumption occur during times of higher green electricity consumption, and vice versa; for Figure 5 Of the 8760 per-unit differences, those within ±10% (i.e., less than or equal to 10%) represent a high degree of similarity between the corresponding electricity consumption per-unit value and the green electricity ratio per-unit value, indicating a high degree of matching between electricity consumption and green electricity ratio, defined as greater than 90%. Furthermore, the ratio of the total number of all time periods corresponding to these per-unit differences to the total number of the 8760 time periods throughout the year is defined as the proportion with a matching degree greater than 90%. Further, the similarity S between the annual green electricity ratio trajectory and the electricity consumption trajectory is given by the following formula: ; Where: G(t) represents the per-unit value of green electricity ratio in hour t of the whole year, L(t) represents the per-unit value of electricity consumption in hour t of the whole year, and t ranges from 1 to 8760; This represents the average per-unit value of the percentage of green electricity throughout the year; The value of S represents the per-unit value of electricity consumption throughout the year; the similarity S ranges from -1 to 1; S=1 indicates a perfect positive correlation, meaning that the proportion of green electricity is also high during periods of high electricity consumption and low during periods of low electricity consumption; S=0 indicates no linear correlation; S=-1 indicates a perfect negative correlation, meaning that the proportion of green electricity is actually low during periods of high electricity consumption and high during periods of low electricity consumption.

[0086] Given that for Figures 3 to 5 In the illustrated example, the percentage of times the local power grid's green electricity ratio matched the university's hourly electricity consumption by more than 90% throughout the year was 22.65%. The similarity between the annual green electricity ratio trajectory and the electricity consumption trajectory was 35.2%, indicating that there is still significant room for improvement in the university's electricity consumption behavior. Energy consumption behavior should be further adjusted according to the user's electricity consumption curve and the overall grid's green electricity ratio curve, shifting towards periods with higher green electricity ratios. Based on the current electricity consumption behavior, the university's carbon emissions in 2024 are 2187.09 tCO2; if the matching degree is adjusted to ensure a perfect match between the university's electricity consumption behavior and the green electricity ratio, the carbon emissions will be 1596.58 tCO2. Scope two reduces indirect carbon emissions from electricity consumption by 590.51 tCO2.

[0087] Adjusting user-side energy consumption behavior can not only reduce indirect emissions in Scope 2 and increase users' willingness to participate in renewable energy power consumption during specific periods, but also improve the demand-side response of the grid.

[0088] For the carbon emissions in Scope 2, green electricity can be used to replace the existing electricity consumption. Assuming a green electricity premium of 3 cents per kilowatt-hour, it would cost 611,900 yuan to purchase green electricity.

[0089] For carbon emissions in categories one and three, they can be offset by purchasing Certified Emission Reductions (CCERs). According to the publicly available transaction price range, it would cost between RMB 1,217,200 and RMB 1,656,100 to purchase CCERs.

[0090] Regarding the implementation costs of the corresponding measures, the unit carbon emission reduction cost of purchasing green electricity is as high as 279.76 yuan / ton, while the unit carbon emission reduction cost of purchasing CCER is the transaction price.

[0091] The high unit carbon emission cost of purchasing green electricity indicates that for grid regions where the average carbon emission factor is already low enough, the unit cost required to achieve zero emissions by purchasing green electricity would be very high. Therefore, carbon emissions can be reduced through other feasible means.

[0092] Based on the university's energy consumption behavior, it is recommended that users increase their electricity consumption during specific periods to reduce indirect carbon emissions in Scope 2, while also improving the demand-side response of the local power grid.

[0093] It is understood that the above embodiments demonstrate the following contribution to the prior art: Although a certain university is located in a province rich in clean energy, its actual carbon emission level is extremely low (Level V) due to its own energy consumption structure (heavy gas, light electricity). The campus was accurately identified through dynamic assessment, which corrected the cognitive bias of focusing only on the cleanliness of the power grid while ignoring the campus's own structure, and provided a quantitative basis for subsequent transformation.

[0094] Assume the aforementioned university campus has implemented heating electrification upgrades (deploying a 6.8MWp photovoltaic carport + 2.4MW / 4.8MWh vanadium redox flow storage + ground source heat pump), and introduced an oxygen-enriched combustion carbon capture system to treat the remaining necessary exhaust gases from gas-fired equipment. Based on a two-level planning model, the equipment operation is optimized and scheduled, and its green and low-carbon rating is reassessed.

[0095] Therefore, this invention not only significantly optimizes the carbon emission structure of industrial parks by introducing oxygen-enriched combustion and electrification as alternatives, combined with source-load interaction based on dynamic factors (load follows source), but also verifies the effectiveness of guiding industrial parks from Level V to Level III during the transformation or optimization process. Experiments show that after optimizing and adjusting energy consumption behavior (matching the proportion of green electricity), emissions in Scope II decreased to 1596.58 tCO2, a reduction of approximately 27%.

[0096] If the same campus data from the aforementioned universities is used, but indirect emissions are calculated using the annual average carbon emission factor (static value, such as the national average of 0.5703 tCO2 / MWh or a fixed provincial annual average), instead of using the three-dimensional index classification disclosed in this invention, and only the total carbon emissions are used as the single optimization target, due to the lack of structural constraints on γ and δ, using the national average would significantly overestimate range two carbon emissions, and using the static provincial average would fail to reflect the time-period differences in range two carbon emissions. In other words, the calculated range two carbon emissions may deviate significantly from the true value. This naturally tends to lead to simply purchasing carbon sinks without making substantial adjustments to the energy structure (such as ignoring natural gas substitution), which is clearly incomparable to the method and effects disclosed in this invention.

[0097] Example 4: This refers to an industrial low-carbon park, specifically a chemical new materials manufacturing park. Its core production processes (such as ammonia synthesis and olefin polymerization) involve high-temperature reactions, requiring the use of natural gas as both raw material and fuel. This constitutes an unavoidable direct carbon emission source, with annual CO2 emissions estimated at approximately 200,000 tons, primarily from process furnaces and self-owned gas-fired boilers. Direct carbon emissions (i.e., Scope 1) account for as much as 85%, currently classified as Level IV. The park's existing energy system, in addition to power transmission lines, includes two 20t / h gas-fired boilers for supplying process steam and a 10MW gas turbine for simultaneously supplying electricity and heat. Furthermore, the park has built its own 5MW rooftop photovoltaic system for self-generation.

[0098] To achieve the park's zero-carbon transition goal (assuming an upgrade to Level II), this means that a large amount of direct carbon emissions from processes must be addressed. Therefore, this embodiment can automatically match the following strategies based on its Level IV rating: process improvement, energy saving and consumption reduction, and coupling negative carbon technology for deep decarbonization.

[0099] Furthermore, due to technological limitations, natural gas cannot be completely replaced by green electricity. Therefore, this embodiment can further utilize the aforementioned evaluation results to drive the aforementioned two-level programming model for precise configuration of the oxy-fuel combustion carbon capture system. This allows for the direct correlation of macro-level rating indicators with specific equipment operation control logic, achieving a balance between economic efficiency and low carbon emissions. Dynamic factors guide the operation of high-energy-consuming capture equipment, reducing the unit carbon capture cost. For example, configuring the oxy-fuel combustion carbon capture system (OCCS) and optimizing its operation can reduce the unit product carbon intensity by 40% over the next five years. Specifically: The upper level (planning level) adds the following objective: to reduce carbon emission intensity per unit of product by 40% within the planning period (5 years); The lower layer (operation layer) introduces a dynamic carbon factor-driven OCCS operation strategy; Specifically, for the rating result, Level IV, the following constraints are set: ; in, , , These represent the direct emissions, OCCS carbon offset, and product output in year y, respectively.

[0100] It is understandable that the above constraints directly translate the macro-decarbonization target (40%) into requirements for OCCS capacity.

[0101] Then, the upper-level planning layer model iteratively calls the lower-level execution layer model to calculate different... Considering annual operating costs and carbon emissions, the option that best meets decarbonization targets and has the lowest total life-cycle cost will be selected. ,in, This indicates the maximum hourly CO2 capture capacity.

[0102] For example, the solved .

[0103] It's understandable that OCCS has high energy consumption. Since the park can only be optimized through OCCS, its operating costs are high. Directly subject to real-time electricity price from the power grid Real-time carbon emission factors of electricity purchased from outside the industrial park The impact necessitates optimizing configuration and operation to reduce the unit carbon capture cost, taking into account the capture cost during time period t. The physical quantities are as described above. We need to consider the following OCCS runtime constraints: ; in, yes and The relevant operating license coefficient, for example, can be exemplified by the empirical formula: ; The scientific basis of this empirical formula lies in the fact that when the power grid is both clean (i.e., ...) Below the threshold It's cheap (i.e.) Below the threshold When the above two conditions are met, the OCCS can operate at full load (i.e., coefficient is 1); if only one of the above two conditions is met, it will operate at reduced load with a coefficient of 0.5; otherwise, the OCCS will not operate. Obviously, this empirical formula also reflects the inventive concept of using dynamic factors to guide the operation of high-energy-consuming capture equipment, which is also based on dynamic carbon dioxide index.

[0104] Assuming the above implementation method is used, the following specific results are obtained by solving the problem using a bi-level programming model: OCCS's main operating hours are concentrated between 10:00 and 16:00 daily (when photovoltaic power generation is at full capacity). Low) and off-peak electricity demand at night (electricity price) (Low); at this time, the average unit carbon capture cost drops from 450 yuan / tCO2 (traditional constant operation mode) to 280 yuan / tCO2, a decrease of 38%; the annual CO2 capture volume is approximately 56,000 tons (capture rate of approximately 70%), making the park... The percentage will increase from 5% to 128%, and the rating is expected to jump from Level IV to Level II (net zero emissions).

[0105] In summary, although this embodiment increases the investment and operating costs of OCCS, it avoids periods of high electricity prices and high carbon emissions by optimizing operation and can also utilize the waste heat from CHP to power OCCS. Under the condition that the overall energy cost increase is not particularly significant, it achieves the upgrading and transformation of the low-carbon park, taking into account both environmental protection and sustainable development. Combined with carbon trading revenue (calculated at 80 yuan / tCO2) and the expected carbon tax in the future, the payback period for the initial investment cost is estimated to be about 6 years, which is economically feasible.

[0106] This embodiment also demonstrates that for industrial parks with unavoidable direct carbon emissions, due to technological limitations, natural gas cannot be completely replaced by green electricity. This invention uses three-dimensional index assessment and classification (e.g., Level IV in this embodiment) to trigger a macro-level strategy primarily reliant on OCCS (Optimal Carbon Emission Control System). This strategy is quantified into carbon emission intensity reduction constraints and dynamic OCCS operation constraints in a two-layer programming model. The upper-layer model determines the optimal OCCS capacity (e.g., 8 tCO2 / h), and the lower-layer model determines the real-time carbon emission factor for electricity purchased from outside the park. and real-time electricity price The combined constraints ultimately led to a refined control strategy for operating OCCS at opportune times. While meeting the hard target of a 40% reduction in carbon intensity, it significantly reduced the unit carbon capture cost, achieving the best balance between economic efficiency, low carbon emissions, and sustainable development.

[0107] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0108] While this application provides method operation steps as shown in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual device or client product execution, the method can be executed in the order shown in the embodiments or drawings or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0109] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0111] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0112] 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 collaborative optimization method for low-carbon industrial parks based on dynamic carbon dioxide index assessment, characterized in that, include: S1. Construct a comprehensive energy system model for a low-carbon industrial park. The comprehensive energy system model includes an electricity subsystem, a heat subsystem, a gas subsystem, and an oxygen-enriched combustion carbon capture system, and collects multi-source electricity / carbon data for the park. S2. Based on hourly power generation structure tracing, construct a dynamic carbon emission factor model for power supply to calculate the real-time carbon emission factor of purchased power in the park; the dynamic carbon emission factor model for power supply includes the provincial power grid average carbon emission factor and the remaining carbon emission factor; The provincial power grid average carbon emission factor is calculated by weighting emissions from fossil fuel combustion within the province, net imported electricity emissions, and regional power grid interaction. The remaining carbon emission factor is the grid average emission factor after excluding renewable energy electricity, used to characterize the carbon intensity of electricity after deducting environmental benefits; Real-time carbon emission factors of the electricity purchased from outside the park The calculation formula is as follows: ； in, Let be the remaining carbon emission factor at time t. Let be the average carbon emission factor of the provincial power grid at time t; The proportion of purchased regular electricity at time t. The proportion of purchased green electricity with environmental rights at time t. Let be the proportion of purchased hybrid electricity at time t, and satisfy . ; S3. Based on the integrated energy system model and the real-time carbon emission factor of the park's purchased electricity, calculate the three-dimensional indicators for the park's low-carbon assessment. These three-dimensional indicators include the ratio γ of direct to indirect carbon emission energy consumption, the ratio δ of direct to indirect carbon emission amount, and the carbon emission offset ratio ε. The calculation method for the three-dimensional indicators is as follows: The ratio of direct carbon emission to indirect carbon emission comprehensive energy consumption γ = C1 / C2; the ratio of direct carbon emission to indirect carbon emission δ = E scope1 / (E scope2 +E scope3 ); the carbon emission offset ratio ε = O sinks / (E scope1 +E scope2 +E scope3 ) Where C1 represents the total energy consumption of fossil fuels and raw materials, and C2 represents the total energy consumption of purchased electricity and heat; E scope1 For direct carbon emissions, E scope2 E represents indirect carbon emissions from purchased electricity and heat. scope3 For other indirect carbon emissions, O sinks The carbon offset amount; the indirect carbon emissions E from the purchased electricity and heat. scope2 By purchasing electricity from outside With real-time carbon emission factors The result is obtained by multiplying and summing the results hour by hour; S4. Preset the grading benchmark thresholds for each dimension, and determine the green and low-carbon level of the park based on the comparison results between the calculated three-dimensional indicators and the grading benchmark thresholds. S5. Based on the determined green and low-carbon level, automatically match the corresponding energy collaborative management strategy and adjust the equipment operating parameters or planning configuration in the integrated energy system model.

2. The low-carbon industrial park collaborative optimization method based on dynamic electrocarbon index assessment according to claim 1, characterized in that, The process of determining the green and low-carbon level of the park in step S4 is as follows: setting a baseline value γ. th The reference value of δ th And the baseline value of ε th , where ε th The default value is 100%, which means that the carbon offset equals the total emissions, achieving net zero emissions. If γ>γ th It was rated as Level V; If γ≤γ th And δ>δ th It was rated as Level IV; If γ≤γ th And δ≤δ th And ε≤ε th It was rated as Level III; If ε th If ε < 200%, it is rated as Level II; If ε≥200%, it is rated as Level I.

3. The low-carbon industrial park collaborative optimization method based on dynamic electrocarbon index assessment according to claim 2, characterized in that, The energy synergy management strategy described in step S5 includes one or more combinations of electricity substitution, process improvement, green electricity trading ratio adjustment, and carbon offsetting configuration. The energy collaborative management strategies corresponding to the automatic matching include: When rated as Level V, an energy substitution strategy to increase the proportion of electricity is generated; When rated as Level IV, energy-saving strategies for improving processes and reducing overall energy consumption are developed. When the rating is Level III, a scheduling strategy is generated to increase the proportion of green electricity and the volume of green electricity transactions. When assessed as Level II, an allocation strategy is generated to increase investment in carbon offset projects and carbon asset reserves.

4. The method for collaborative optimization of low-carbon industrial parks based on dynamic electrocarbon index assessment according to claim 1, characterized in that, The integrated energy system model adopts a multi-objective bi-level programming model. In step S5, the adjustment of equipment operating parameters or planning configuration in the integrated energy system model is achieved by solving the multi-objective bi-level programming model. The upper planning layer aims to optimize the economic efficiency of the integrated energy system throughout its entire life cycle. The optimization variables include the installed capacity of new energy equipment, the energy storage configuration capacity, and the configuration capacity of the oxygen-enriched combustion carbon capture system. The lower-level operation layer aims to minimize operating costs and carbon emissions. Based on the real-time carbon emission factor calculated in step S2, it simulates the park's energy production, conversion, and consumption strategies at different times. The bi-level programming model transforms the energy collaborative management strategy generated in step S5 into a constraint input model for solution.

5. The low-carbon industrial park collaborative optimization method based on dynamic electrocarbon index assessment according to claim 4, characterized in that, In the lower-level operation layer, an operation permit coefficient is introduced for the operation control of the oxygen-enriched combustion carbon capture system. The operating license coefficient With real-time carbon emission factors and real-time electricity price Relatedly, it is used to increase carbon capture during periods of low grid carbon intensity and low electricity prices; the oxygen-enriched combustion carbon capture system includes an air separation unit, an oxygen-enriched combustion unit, and a carbon dioxide compression and purification unit.

6. A collaborative optimization system for low-carbon industrial parks based on dynamic electrocarbon index assessment, based on the collaborative optimization method for low-carbon industrial parks based on dynamic electrocarbon index assessment as described in any one of claims 1-5, characterized in that, include: The data acquisition and modeling module is used to construct a comprehensive energy system model for a low-carbon industrial park. The comprehensive energy system model includes an electricity subsystem, a heat subsystem, a gas subsystem, and an oxygen-enriched combustion carbon capture system, and collects multi-source electricity / carbon data for the park. The dynamic factor calculation module is used to construct a dynamic carbon emission factor model for power supply based on hourly power generation structure tracing, and to calculate the real-time carbon emission factor of purchased power in the park. The evaluation index calculation module is used to calculate the three-dimensional index of the park's low-carbon assessment based on the integrated energy system model and the real-time carbon emission factor of the park's purchased electricity. The three-dimensional index includes the ratio of direct carbon emissions to indirect carbon emissions in total energy consumption γ, the ratio of direct carbon emissions to indirect carbon emissions δ, and the carbon emission offset ratio ε. The rating determination module is used to preset the rating benchmark thresholds for each dimension, and determine the green and low-carbon rating of the park based on the comparison results between the calculated three-dimensional indicators and the rating benchmark thresholds. The decision optimization module is used to automatically match the corresponding energy collaborative management strategy based on the determined green and low-carbon level, and adjust the equipment operating parameters or planning configuration in the integrated energy system model.

7. 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 low-carbon park collaborative optimization method based on dynamic electrocarbon index assessment as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the low-carbon park collaborative optimization method based on dynamic electrocarbon index assessment as described in any one of claims 1 to 5.

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