A general equipment cluster linkage regulation method for carbon emission optimization

Abstract

The application discloses a general equipment cluster linkage regulation and control method for carbon emission optimization, and relates to the technical field of industrial load optimization scheduling, wherein the method comprises the following steps: collecting equipment operation condition parameters, plant green power output parameters, power grid dynamic carbon emission factors and production line capacity constraint parameters in real time; constructing a three-dimensional coupling model corresponding to capacity constraints, equipment energy consumption and power grid dynamic carbon emission factors; calling the power grid dynamic carbon emission factor in the three-dimensional coupling model, and dynamically dividing the power consumption period into multiple carbon intensity regulation gears; performing gear-by-gear collaborative regulation and control on the equipment cluster according to each carbon intensity regulation gear obtained through division, and performing rotation operation and dynamic load distribution on the same type of parallel equipment. The scheme is beneficial to realizing dynamic carbon reduction at the equipment level under the premise of ensuring stable production capacity, and is suitable for adapting to the business needs of fine industrial carbon data control.

Description

Technical Field

[0001] This application relates to the field of industrial load optimization and scheduling technology, and in particular to a general equipment cluster linkage control method for carbon emission optimization. Background Technology

[0002] Fans, water pumps, air compressors, and other general auxiliary equipment are the main electrical equipment in industrial plants, and their indirect carbon emissions are a significant component of enterprise carbon emissions. Currently, factory load regulation generally uses electricity consumption and peak-valley electricity costs as optimization targets, and the regulation basis only includes equipment power, electricity consumption, and time-of-use electricity price data. The power grid's generation structure changes in real time with the output of new energy sources, causing the carbon emission intensity per unit of electricity to fluctuate continuously.

[0003] The existing dispatching model only optimizes electricity costs and energy consumption, without dynamically adjusting production load according to the carbon emission level of the power grid. This often results in situations where electricity costs decrease but overall carbon emissions remain high. At the same time, many similar auxiliary machines operate in a fixed start-stop mode. Over the long term, this leads to a polarization of equipment load, with some units experiencing continuous high-load wear and tear while others are idle. This makes it difficult to balance the operating conditions of the equipment, preventing the overall cluster from reaching an optimal operating state and hindering the company's ability to achieve refined carbon emission reduction management. Summary of the Invention

[0004] The purpose of this application is to provide a general equipment cluster linkage control method for carbon emission optimization, which aims to solve the technical problems that existing load dispatching cannot adapt to the dynamic changes in grid carbon emissions and has poor carbon emission reduction effect.

[0005] To achieve the above objectives, this application provides a general-purpose equipment cluster linkage control method for carbon emission optimization, comprising: Real-time acquisition of equipment operating parameters, plant green electricity output parameters, grid dynamic carbon emission factors, and production line capacity constraint parameters; Based on the collected equipment operating condition parameters, plant green power output parameters, grid dynamic carbon emission factors and production line capacity constraint parameters, a three-dimensional coupled model corresponding to capacity constraints, equipment energy consumption and grid dynamic carbon emission factors is constructed. By calling the power grid dynamic carbon emission factor in the three-dimensional coupling model, the electricity consumption period is dynamically divided into multiple carbon intensity control levels; Based on the various carbon intensity control levels obtained, the equipment cluster is subject to coordinated control at different levels, and parallel equipment of the same type is subject to rotation and dynamic load allocation.

[0006] In one embodiment, based on the collected equipment operating condition parameters, plant green power output parameters, grid dynamic carbon emission factors, and production line capacity constraint parameters, a three-dimensional coupled model corresponding to capacity constraints, equipment energy consumption, and grid dynamic carbon emission factors is constructed. This includes: using the upper limit of production line capacity and the minimum production load as boundary constraints, using the energy consumption conditions of each general-purpose equipment as intermediate variables, and using grid dynamic carbon emission factors as target variables to establish a coupling relationship between the three, forming a three-dimensional coupled model for control calculation.

[0007] In one embodiment, the power grid dynamic carbon emission factor in the three-dimensional coupled model is invoked to dynamically divide electricity consumption periods into multiple carbon intensity control levels, including: The dynamic carbon emission factor of the power grid is continuously updated in real time from the three-dimensional coupled model; The obtained dynamic carbon emission factor of the power grid is compared with the pre-set two-level threshold. Different carbon intensity control levels are formed based on the numerical comparison results.

[0008] In one embodiment, the read dynamic carbon emission factor of the power grid is compared with a pre-set two-level threshold, including: pre-setting two judgment boundaries, a high carbon threshold and a low carbon threshold, and comparing the dynamic carbon emission factor of the power grid read at each time moment with the high carbon threshold and the low carbon threshold respectively.

[0009] In one embodiment, different carbon intensity control levels are formed based on numerical comparison results, including: When the dynamic carbon emission factor of the power grid is higher than the high carbon threshold, it is classified as a high carbon period. When the dynamic carbon emission factor of the power grid is between the high carbon threshold and the low carbon threshold, it is classified as a medium carbon period. When the dynamic carbon emission factor of the power grid is lower than the low-carbon threshold and there is surplus green electricity output in the plant area, it is classified as the low-carbon green electricity peak period.

[0010] In one embodiment, based on the defined carbon intensity control levels, the equipment cluster is subjected to level-based coordinated control, and parallel equipment of the same type is subject to rotational commissioning and dynamic load allocation, including: Develop corresponding load control strategies according to different carbon intensity control levels; Generate a rotation and commissioning plan for parallel generating units based on the equipment's operating status; The load control strategy and the rotation operation plan are combined to achieve dynamic allocation of the load of each unit.

[0011] In one embodiment, a corresponding load control strategy is formulated according to different carbon intensity control levels, including: Identify redundant power consumption conditions within the cluster during high-carbon periods and formulate control strategies to reduce ineffective loads; Develop a load shifting strategy that can be implemented during peak periods of low-carbon green electricity generation; Develop local load optimization strategies for steady-state operation during the medium carbon period.

[0012] In one embodiment, a parallel unit rotation and commissioning plan is generated based on the equipment operating status, including: Collect data on the cumulative runtime, single-unit carbon emission efficiency, and equipment wear of similar parallel equipment; The start-up and shutdown sequence of the units is arranged alternately based on the collected cumulative running time, single unit carbon emission efficiency, and equipment wear data.

[0013] In one embodiment, the method further includes: For the control schemes formed by coordinated control at different levels, carbon emission optimization verification and energy consumption upper limit verification shall be performed respectively. Based on the verified control scheme, equipment control commands are issued, carbon reduction ledgers are generated, and the entire control process is iterated periodically.

[0014] In one embodiment, the control scheme formed by tiered coordinated control is subjected to carbon emission optimization verification and energy consumption upper limit verification, including: The carbon emission optimization verification of the control plan was performed, and the operating conditions of the equipment cluster were calibrated. The energy consumption limit of the control scheme is verified, the total energy consumption parameters of the equipment cluster are checked, and the total energy consumption of the cluster is limited to be less than or equal to the preset energy consumption quota.

[0015] The above-mentioned technical solution of this application has at least the following beneficial technical effects: The technical solution of this application constructs a three-dimensional coupled model by collecting multiple types of operating parameters, divides the control period according to the carbon emission factor of the power grid, and combines it with the load control of different levels and the rotation allocation mechanism of parallel units. With the help of multi-constraint coupling calculation, it can dynamically adjust the equipment operation strategy according to the power generation structure of the power grid, reduce the ineffective power consumption during high carbon periods, and make full use of the output window of clean energy. The rotation operation of units can balance the operating conditions of each piece of equipment, compress the overall ineffective carbon emissions of the cluster, and help to achieve dynamic carbon reduction at the equipment level while ensuring stable production capacity, which is in line with the business needs of refined management and control of industrial carbon data. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating one implementation of the general equipment cluster linkage control method for carbon emission optimization provided in this application; Figure 2 This is a schematic diagram of the control process of a specific embodiment provided in this application; Figure 3This is an architecture diagram of a specific embodiment provided in this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of this application. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0018] The embodiments described in this application are only some, not all, of the embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments described herein without inventive effort are within the scope of protection of this application.

[0019] Fans, water pumps, air compressors, and other general auxiliary equipment are the main electrical equipment in industrial plants, and their indirect carbon emissions are a significant component of enterprise carbon emissions. Currently, factory load regulation generally uses electricity consumption and peak-valley electricity costs as optimization targets, and the regulation basis only includes equipment power, electricity consumption, and time-of-use electricity price data. The power grid's generation structure changes in real time with the output of new energy sources, causing the carbon emission intensity per unit of electricity to fluctuate continuously.

[0020] The existing dispatching model only optimizes electricity costs and energy consumption, without dynamically adjusting production load according to the carbon emission level of the power grid. This often results in situations where electricity costs decrease but overall carbon emissions remain high. At the same time, many similar auxiliary machines operate in a fixed start-stop mode. Over the long term, this leads to a polarization of equipment load, with some units experiencing continuous high-load wear and tear while others are idle. This makes it difficult to balance the operating conditions of the equipment, preventing the overall cluster from reaching an optimal operating state and hindering the company's ability to achieve refined carbon emission reduction management.

[0021] To address the aforementioned technical challenges, this application provides a general-purpose equipment cluster linkage control method for carbon emission optimization. The technical solution involves constructing a three-dimensional coupled model by collecting multiple operating parameters, dividing control periods according to grid carbon emission factors, and then combining this with tiered load control and parallel unit rotation allocation mechanisms. Through multi-constraint coupled calculations, the method can dynamically adjust equipment operating strategies in accordance with the grid's power generation structure, reducing ineffective electricity consumption during high-carbon periods and fully utilizing clean energy output windows. Unit rotation can balance the operating conditions of each piece of equipment, compressing overall ineffective carbon emissions from the cluster. This facilitates dynamic carbon reduction at the equipment level while ensuring stable production capacity, meeting the business needs of refined industrial carbon data management.

[0022] In one implementation, please refer to Figure 1 The general-purpose equipment cluster linkage control method for carbon emission optimization includes the following steps: Step S1: Real-time collection of equipment operating parameters, plant green electricity output parameters, grid dynamic carbon emission factors, and production line capacity constraint parameters. The specific implementation process of Step S1 is as follows: Real-time collection of four types of parameters across multiple dimensions: First, on the equipment side, real-time power, load rate, start / stop status, continuous operating time, and rated operating parameters of the plant's general auxiliary equipment clusters, such as fans, water pumps, air compressors, cooling towers, central air conditioning, and conveyor motors; Second, on the plant's energy side, real-time output values ​​of distributed photovoltaic and wind power and the proportion of green electricity consumed locally; Third, on the grid side, connecting to the regional energy dispatch platform to obtain grid dynamic carbon emission factors in real time within seconds; Fourth, on the production side, collecting real-time production capacity limits and minimum production load constraint parameters of the production line. By synchronously collecting multi-source terminal data on all dimensions of operating constraints, the system fully covers the four core influencing factors: equipment operation, green electricity supply, grid carbon emissions, and production capacity. This avoids the distortion of subsequent control model calculations caused by missing data in a single dimension, providing complete, real-time, and high-precision underlying data support for subsequent coupled modeling.

[0023] Step S2: Based on the collected equipment operating condition parameters, plant green power output parameters, grid dynamic carbon emission factor, and production line capacity constraint parameters, construct a three-dimensional coupled model corresponding to capacity constraint, equipment energy consumption, and grid dynamic carbon emission factor; in one embodiment, step S2 includes the following steps: taking the production line capacity limit and minimum production load as boundary constraints, taking the energy consumption conditions of each general equipment as intermediate variables, and taking the grid dynamic carbon emission factor as the target variable, establishing a coupling relationship between the three to form a three-dimensional coupled model for control calculation.

[0024] The specific implementation process of step S2 is as follows: Retrieve the equipment operating parameters, plant green electricity output parameters, grid dynamic carbon emission factors, and production line capacity constraint parameters collected in step S1 to perform modeling calculations. Use the production line capacity ceiling and minimum production load as hard boundary constraints of the model, the real-time energy consumption of each general auxiliary machine as intermediate calculation variables, and the real-time grid dynamic carbon emission factor as the model optimization target variable. Establish a linkage and coupling relationship between capacity constraints, equipment energy consumption, and grid dynamic carbon emission factors to generate a three-dimensional coupled model that can be directly used for real-time control calculations. Through the coupling and binding calculation of these three core indicators, the traditional single-dimensional calculation logic of energy consumption and electricity price is broken. It can simultaneously consider the triple constraints of production baseline, equipment energy consumption, and grid carbon emission, achieving multi-objective synchronous quantitative calculation and providing a unified calculation benchmark for subsequent dynamic classification of carbon intensity levels.

[0025] Step S3: Using the dynamic carbon emission factor of the power grid in the three-dimensional coupled model, the electricity consumption period is dynamically divided into multiple carbon intensity control levels; in one embodiment, step S3 includes the following steps: Step S31: Continuously read the real-time updated dynamic carbon emission factor of the power grid from the three-dimensional coupled model; Step S32: Compare the read grid dynamic carbon emission factor with the pre-set two-level threshold; in one embodiment, step S32 further includes the following specific steps: pre-set two judgment boundaries, a high carbon threshold and a low carbon threshold, and compare the grid dynamic carbon emission factor read at each time moment with the high carbon threshold and the low carbon threshold respectively.

[0026] Step S33: Divide the data into different carbon intensity control levels according to the numerical comparison results. In one embodiment, step S33 further includes the following specific steps: when the grid dynamic carbon emission factor is higher than the high carbon threshold, it is divided into a high carbon period; when the grid dynamic carbon emission factor is between the high carbon threshold and the low carbon threshold, it is divided into a medium carbon period; when the grid dynamic carbon emission factor is lower than the low carbon threshold and there is surplus green electricity output in the plant area, it is divided into a low carbon green electricity peak period.

[0027] The specific implementation process of step S3 is as follows: The dynamic carbon emission factor values ​​of the power grid, stored and updated in real-time within the three-dimensional coupled model constructed in step S2, are invoked. The traditional fixed peak-valley electricity price segmentation logic is completely abandoned. First, the dynamic carbon emission factors of the power grid, updated in real-time by the model, are continuously read. Two sets of judgment boundaries, a fixed high-carbon threshold and a low-carbon threshold, are pre-defined. The real-time carbon factor values ​​read at each moment are compared sequentially with the high-carbon threshold and the low-carbon threshold. Based on the comparison results, three categories of carbon intensity control levels are defined: carbon factor values ​​higher than the high-carbon threshold are defined as high-carbon periods; carbon factor values ​​between the high-carbon and low-carbon thresholds are defined as medium-carbon periods; and carbon factor values ​​lower than the low-carbon threshold, coupled with surplus green electricity output from photovoltaic and wind power plants, are defined as low-carbon green electricity peak periods. By relying on real-time dynamic carbon factor value classification to replace fixed time period divisions, the changes in the real-time grid connection structure of thermal power and new energy can be accurately matched, and high-carbon power consumption windows and green electricity consumption windows can be accurately identified, thus avoiding the problems of high emissions and idle green electricity caused by traditional fixed-time period scheduling.

[0028] Step S4: Based on the obtained carbon intensity control levels, perform level-based coordinated control on the equipment cluster, and perform rotational commissioning and dynamic load allocation for parallel equipment of the same type. In one embodiment, step S4 includes the following steps: Step S41: Formulate corresponding load control strategies according to different carbon intensity control levels; in one embodiment, step S41 further includes the following specific steps: identify redundant power consumption conditions in the cluster during high carbon periods and formulate control strategies to reduce ineffective loads; formulate off-peak load shifting strategies during low carbon green power peak periods; and formulate local load optimization strategies under steady-state operation during medium carbon periods.

[0029] Step S42: Generate a rotation operation plan for parallel units based on the equipment operating status; in one embodiment, step S42 further includes the following specific steps: collect the cumulative running time, single-unit carbon emission efficiency and equipment loss data of parallel equipment of the same type; and arrange the start-up and shutdown sequence of units alternately based on the collected cumulative running time, single-unit carbon emission efficiency and equipment loss data.

[0030] Step S43: Combine load control strategies and rotation commissioning plans to complete the dynamic allocation of load for each unit.

[0031] The specific implementation process of step S4 is as follows: Based on the three carbon intensity control levels of high-carbon, medium-carbon, and low-carbon green electricity peak periods as defined in step S3, the equipment cluster is coordinated and controlled at different levels, and the rotation and commissioning of parallel equipment of the same type and the dynamic allocation of load are completed simultaneously. First, according to the different carbon intensity levels, the corresponding load control strategies are matched. During the high-carbon period, redundant power consumption conditions such as low-load idling within the equipment cluster are identified, and load reduction strategies such as shutting down inefficient auxiliary machines and reducing the frequency of frequency converters are formulated. During the low-carbon green electricity peak period, a production-permissible off-peak load shifting strategy is formulated, and the equipment cluster is scheduled to operate at full load. During the medium-carbon period, the equipment is maintained in a baseline steady state and local load fine-tuning and optimization are carried out. Second, the cumulative running time, single-machine carbon emission efficiency, and equipment loss data of parallel equipment of the same type are collected. Based on the operating data of the three types of equipment, the start-up and shutdown sequence of the units are alternately arranged to generate a rotation and commissioning scheme for parallel units that meets the current load demand. Finally, the load control strategy matched by the level is combined with the unit rotation and commissioning scheme to complete the dynamic allocation of load for all parallel units. Differentiated load regulation based on carbon emission levels can specifically reduce ineffective electricity consumption during high-carbon periods and maximize the use of low-carbon green electricity windows; parallel units are put into operation and dynamically allocated load based on losses, carbon efficiency, and operating time, avoiding long-term overload and aging of some equipment and idling of some equipment under fixed operation mode, balancing the operating losses and service life of all units, and reducing the overall carbon emission redundancy of the cluster while ensuring constant production capacity.

[0032] In one embodiment, the following steps are included after step S4: Step S5: For the control scheme formed by the graded coordinated control, perform carbon emission optimization verification and energy consumption limit verification respectively; in one embodiment, step S5 also includes the following specific steps: perform carbon emission optimization verification on the control scheme to calibrate the operating conditions of the equipment cluster; perform energy consumption limit verification on the control scheme to check the total energy consumption parameters of the equipment cluster and limit the total energy consumption of the cluster to be less than or equal to the preset energy consumption quota. The specific implementation process of step S5 is as follows: perform two types of verifications in parallel on the complete equipment cluster control scheme output by step S4. The first type is carbon emission optimization verification, which recalculates the carbon emission corresponding to the control scheme based on the three-dimensional coupling model and simultaneously calibrates the operating conditions of each general equipment to make the cluster carbon emission reach the optimal level for the current period; the second type is energy consumption limit verification, which calculates the overall total energy consumption parameters of the equipment cluster under the control scheme to make the total energy consumption of the cluster match the preset energy consumption quota of the plant. By simultaneously verifying the dual constraints of carbon emissions and energy consumption, we can ensure that the control plan takes carbon emission reduction as the core optimization goal, while avoiding blindly increasing total energy consumption in order to reduce carbon emissions. This achieves a two-way balance between carbon emission reduction and energy consumption control, and eliminates unreasonable control plans that do not meet the plant's energy consumption quotas and carbon emission optimization goals.

[0033] Step S6: Based on the verified control scheme, issue equipment control commands, generate a carbon reduction ledger, and periodically iterate the entire control process. The specific implementation process of Step S6 is as follows: If all double verifications in Step S5 are successful, issue equipment start / stop commands, frequency converter adjustment commands, and load adjustment commands to all general auxiliary machines in the plant area; simultaneously summarize the equipment load, green electricity consumption, carbon emission reduction, and energy consumption data of the entire control process, generate and output a standardized carbon reduction ledger to meet the enterprise's carbon verification and carbon emission reduction data traceability and archiving requirements; after the end of a single control process, the next control cycle is automatically entered, and all processes from Step S1 to Step S6 are periodically executed to achieve all-weather rolling dynamic optimization of the equipment cluster. After verification, execution commands are issued immediately to ensure the implementation of the control strategy. The carbon reduction ledger retains complete data of the entire process to meet compliance management requirements. Periodic closed-loop iteration can continuously follow the real-time changes in grid carbon factors, plant green electricity, and production capacity to dynamically update the control scheme and continuously maintain the optimal carbon emission operating state of the equipment cluster.

[0034] In one specific embodiment, please refer to Figure 2 and Figure 3 , Figure 2The schematic diagram of the control process in this embodiment illustrates the entire process logic from control initiation, data acquisition and modeling, carbon time period classification and determination, load control by level, parallel unit rotation allocation, to dual constraint verification, command issuance and periodic looping. The system first completes multi-source data acquisition and builds a three-dimensional coupled model. Based on the grid carbon factor, it divides the peak periods into three categories: high carbon, medium carbon, and low carbon green electricity peak periods. Differentiated load strategies are implemented for different periods. Then, the parallel equipment is uniformly balanced and rotated. After dual verification of carbon emissions and energy consumption, control commands are issued and ledgers are generated, and continuous rolling iterative optimization is carried out. Figure 3 The architecture diagram of this embodiment is divided into five layers from top to bottom: data acquisition layer, carbon emission coupling modeling layer, carbon time period dynamic judgment layer, intelligent linkage control layer, and dual-constraint execution module. The data acquisition layer collects four types of data: equipment, grid carbon factor, green electricity in the plant area, and production capacity. It is then sent to the three-dimensional coupling model to complete multi-constraint coupling calculations. Then, three judgment modules divide the carbon intensity level and activate three types of load control modules accordingly. Finally, after carbon emission optimal verification and energy consumption threshold verification, the system outputs equipment control commands and carbon reduction ledgers, realizing a one-to-one correspondence between software and hardware modules and control steps.

[0035] This embodiment applies to a factory equipment cluster consisting of multiple parallel auxiliary units including fans, water pumps, air compressors, cooling towers, and conveyor motors. The production line has fixed upper and lower limits for capacity, and is equipped with distributed photovoltaic and wind power green energy sources within the factory area. The specific implementation process is as follows: The control program is initiated, and the system enters a closed-loop optimization cycle.

[0036] Four types of data acquisition units are activated to simultaneously collect real-time data. The general equipment acquisition unit continuously collects real-time power, load rate, start / stop status, runtime, and rated operating conditions of each auxiliary machine. The dynamic carbon factor acquisition unit connects to the power grid platform to continuously read real-time dynamic carbon emission factors from the power grid. The plant's green power output acquisition unit collects real-time output and local consumption ratios of photovoltaic and wind power. The production line capacity constraint acquisition unit reads the production line's capacity ceiling and minimum production load constraints. All collected data is sent to the carbon emission coupled modeling layer, setting the production line capacity ceiling and minimum production load as boundary constraints, using the energy consumption conditions of each general equipment as intermediate variables, and the power grid dynamic carbon emission factor as the target variable. This establishes a coupled relationship between capacity constraints, equipment energy consumption, and the power grid dynamic carbon emission factor, constructing a real-time calculable three-dimensional coupled model to provide a unified calculation benchmark for subsequent classification and determination.

[0037] The system calls upon the real-time dynamic carbon emission factor of the power grid output from the three-dimensional coupled model, and pre-sets two-level judgment boundaries: high-carbon threshold and low-carbon threshold. It continuously reads the carbon factor values ​​updated in real time by the model and compares the real-time values ​​with the two-level thresholds in turn. The three judgment modules of the carbon period dynamic judgment layer complete the level division: the value is determined to be a high-carbon period when it is higher than the high-carbon threshold, and corresponds to the high-carbon period judgment module; the value is determined to be a medium-carbon period when it is between the two thresholds, and corresponds to the medium-carbon period judgment module; when the value is lower than the low-carbon threshold and there is surplus green electricity output in the plant area, it is determined to be a low-carbon green electricity peak period, and corresponds to the low-carbon green electricity peak judgment module.

[0038] Entering the intelligent linkage control layer, the following tiered strategies are implemented: 1) If it is determined to be a high-carbon period, the redundant load reduction module is activated to identify redundant power consumption conditions such as no-load and low-load within the equipment cluster, shut down inefficient auxiliary machines, and simultaneously reduce the load of frequency converters to reduce ineffective power consumption during high-carbon periods; 2) If it is determined to be a low-carbon green energy peak period, the green energy consumption control module is activated to shift the production-permitted off-peak load to the current period, control the entire equipment cluster to operate at full load, and fully consume the surplus green energy in the plant area; 3) If it is determined to be a medium-carbon period, the equipment cluster is maintained in a baseline steady-state operating state, with only minor optimization adjustments made to the load of local equipment.

[0039] Regardless of the carbon emission period, the intelligent rotation and load allocation module for multiple devices is activated. The cumulative running time, single-unit carbon emission efficiency, and equipment loss data of parallel devices of the same type are collected. Based on the above three data, the start-up and shutdown sequence of the units are arranged alternately to generate a rotation operation plan. Then, combined with the load control strategy corresponding to the current level, the parallel units are dynamically load-allocated to balance the operating loss of each device and maintain a constant production line capacity throughout the process.

[0040] Enter the dual-constraint execution module to carry out dual verification: First, run the carbon emission optimal verification module to recalculate the cluster carbon emissions under the whole set of control schemes and calibrate the operating conditions of each device; then run the energy consumption threshold constraint verification module to check the total energy consumption parameters of the equipment cluster, so that the total energy consumption of the cluster matches the preset energy consumption quota, and screen out control schemes that do not meet the dual-constraint conditions.

[0041] After the verification is passed, the equipment instruction execution unit issues start / stop, frequency conversion, and load distribution control instructions to each auxiliary machine; at the same time, the emission reduction ledger output unit summarizes the load data, green electricity consumption data, energy consumption and carbon emission data of this round of regulation, and automatically outputs a standardized carbon reduction ledger to meet the carbon verification data traceability requirements.

[0042] Once this round of regulation and control is completed, the system automatically returns to the data collection phase, periodically executing the entire process to achieve continuous, rolling optimization around the clock.

[0043] This application aims to protect a general-purpose equipment cluster linkage control method for carbon emission optimization. Its technical solution constructs a three-dimensional coupled model by collecting multiple types of operating parameters, divides control periods according to the grid's carbon emission factors, and then combines this with a tiered load control and parallel unit rotation allocation mechanism. Through multi-constraint coupled calculations, it can dynamically adjust equipment operation strategies in accordance with the grid's power generation structure, reducing ineffective electricity consumption during high-carbon periods and fully utilizing clean energy output windows. Unit rotation operation can balance the operating conditions of each piece of equipment, compressing overall ineffective carbon emissions from the cluster. This facilitates dynamic carbon reduction at the equipment level while ensuring stable production capacity, adapting to the business needs of refined industrial carbon data management.

[0044] It should be understood that the specific embodiments described above are merely illustrative or explanatory of the principles of this application and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of this application should be included within the protection scope of this application. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

Claims

1. A general-purpose equipment cluster linkage control method for carbon emission optimization, characterized in that, include: Real-time acquisition of equipment operating parameters, plant green electricity output parameters, grid dynamic carbon emission factors, and production line capacity constraint parameters; Based on the collected equipment operating condition parameters, plant green power output parameters, grid dynamic carbon emission factors and production line capacity constraint parameters, a three-dimensional coupled model corresponding to capacity constraints, equipment energy consumption and grid dynamic carbon emission factors is constructed. By calling the power grid dynamic carbon emission factor in the three-dimensional coupling model, the electricity consumption period is dynamically divided into multiple carbon intensity control levels; Based on the various carbon intensity control levels obtained, the equipment cluster is subject to coordinated control at different levels, and parallel equipment of the same type is subject to rotation and dynamic load allocation.

2. The general equipment cluster linkage control method for carbon emission optimization according to claim 1, characterized in that, Based on the collected equipment operating parameters, plant green power output parameters, grid dynamic carbon emission factors, and production line capacity constraint parameters, a three-dimensional coupled model corresponding to capacity constraints, equipment energy consumption, and grid dynamic carbon emission factors is constructed. This includes: using the production line capacity limit and minimum production load as boundary constraints, using the energy consumption conditions of each general-purpose equipment as intermediate variables, and using the grid dynamic carbon emission factors as target variables to establish a coupling relationship between the three, forming a three-dimensional coupled model for control calculations.

3. The general-purpose equipment cluster linkage control method for carbon emission optimization according to claim 1, characterized in that, By invoking the power grid dynamic carbon emission factor in the aforementioned three-dimensional coupled model, the electricity consumption period is dynamically divided into multiple carbon intensity control levels, including: The dynamic carbon emission factor of the power grid is continuously updated in real time from the three-dimensional coupled model; The obtained dynamic carbon emission factor of the power grid is compared with the pre-set two-level threshold. Different carbon intensity control levels are formed based on the numerical comparison results.

4. The general equipment cluster linkage control method for carbon emission optimization according to claim 3, characterized in that, The dynamic carbon emission factor of the power grid read is compared with the pre-set two-level thresholds. This includes: setting two judgment boundaries in advance, namely a high carbon threshold and a low carbon threshold, and comparing the dynamic carbon emission factor of the power grid read at each time moment with the high carbon threshold and the low carbon threshold respectively.

5. The general equipment cluster linkage control method for carbon emission optimization according to claim 4, characterized in that, Based on the numerical comparison results, different carbon intensity control levels are established, including: When the dynamic carbon emission factor of the power grid is higher than the high carbon threshold, it is classified as a high carbon period. When the dynamic carbon emission factor of the power grid is between the high carbon threshold and the low carbon threshold, it is classified as a medium carbon period. When the dynamic carbon emission factor of the power grid is lower than the low-carbon threshold and there is surplus green electricity output in the plant area, it is classified as the low-carbon green electricity peak period.

6. The general-purpose equipment cluster linkage control method for carbon emission optimization according to claim 1, characterized in that, Based on the various carbon intensity control levels obtained from the classification, the equipment cluster is subject to coordinated control at each level, and parallel equipment of the same type is subject to rotational commissioning and dynamic load allocation, including: Develop corresponding load control strategies according to different carbon intensity control levels; Generate a rotation and commissioning plan for parallel generating units based on the equipment's operating status; The load control strategy and the rotation operation plan are combined to achieve dynamic allocation of the load of each unit.

7. The general equipment cluster linkage control method for carbon emission optimization according to claim 6, characterized in that, Develop corresponding load control strategies according to different carbon intensity control levels, including: Identify redundant power consumption conditions within the cluster during high-carbon periods and formulate control strategies to reduce ineffective loads; Develop a load shifting strategy that can be implemented during peak periods of low-carbon green electricity generation; Develop local load optimization strategies for steady-state operation during the medium carbon period.

8. The general equipment cluster linkage control method for carbon emission optimization according to claim 6, characterized in that, Based on the equipment operating status, a rotation and commissioning plan for parallel generating units is generated, including: Collect data on the cumulative runtime, single-unit carbon emission efficiency, and equipment wear of similar parallel equipment; The start-up and shutdown sequence of the units is arranged alternately based on the collected cumulative running time, single unit carbon emission efficiency, and equipment wear data.

9. The general equipment cluster linkage control method for carbon emission optimization according to claim 1, characterized in that, Also includes: For the control schemes formed by coordinated control at different levels, carbon emission optimization verification and energy consumption upper limit verification shall be performed respectively. Based on the verified control scheme, equipment control commands are issued, carbon reduction ledgers are generated, and the entire control process is iterated periodically.

10. The general-purpose equipment cluster linkage control method for carbon emission optimization according to claim 9, characterized in that, For the control scheme formed by coordinated regulation at different levels, carbon emission optimization verification and energy consumption upper limit verification shall be performed respectively, including: The carbon emission optimization verification of the control plan was performed, and the operating conditions of the equipment cluster were calibrated. The energy consumption limit of the control scheme is verified, the total energy consumption parameters of the equipment cluster are checked, and the total energy consumption of the cluster is limited to be less than or equal to the preset energy consumption quota.

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