A zero-carbon park product carbon footprint traceability method

Abstract

The application discloses a zero-carbon park product carbon footprint traceability method, and belongs to the field of carbon footprint traceability, which comprises the following steps: obtaining energy topology node state data uploaded by a micro-grid monitoring device and energy storage charging and discharging parameters; collecting equipment operation time windows of a production line energy consumption terminal under a target production batch; updating energy storage equivalent carbon emission factors of a target period based on the energy storage charging and discharging parameters and the energy topology node state data; combining the equipment operation time windows, the energy storage equivalent carbon emission factors, the energy topology node state data and initial carbon emission factors of different sources to generate a production line process carbon sorting list; constructing an implied carbon traceability tree corresponding to the target production batch; adding the implied carbon traceability tree and the production line process carbon sorting list to calculate a total product carbon footprint.

Description

Technical Field

[0001] This invention relates to the field of carbon footprint traceability, and in particular to a method for tracing the carbon footprint of products in a zero-carbon industrial park. Background Technology

[0002] With the advancement of global carbon neutrality goals, the construction of zero-carbon industrial parks has become an important direction for the green transformation of the manufacturing industry. Zero-carbon industrial parks are typically equipped with renewable energy power generation facilities such as photovoltaics and wind power, as well as energy storage systems, forming complex microgrids to reduce reliance on traditional grid power. Within zero-carbon industrial parks, accurately calculating and tracing the carbon footprint of each product is a prerequisite for enterprises to engage in carbon trading, cope with international carbon tariffs, and enhance the green competitiveness of their products.

[0003] In related technologies, the calculation of product carbon footprint typically employs a periodic total allocation logic. At the end of each month or quarter, the system reads the total incoming electricity meter, photovoltaic power generation meter, and total electricity consumption of each production line in the park. Based on the overall energy structure of the park, a macro-level average carbon emission factor is calculated. This average carbon emission factor is then multiplied by the total electricity consumption of a particular production line to obtain the total carbon emissions of that production line. This carbon emission is then evenly allocated according to product output to obtain the carbon footprint of an individual product.

[0004] The inventors discovered the following technical defects in the relevant technology: In microgrids that include energy storage systems and multi-source hybrid power supply, the macro-allocation method leads to a significant deviation between the product-level carbon footprint traceability results and actual carbon emissions. Because the energy sources charging the energy storage system vary at different times—for example, low-carbon photovoltaic power is charged at midday and high-carbon grid power is charged during off-peak hours at night—the actual carbon emission factor during discharge is an unknown quantity that varies over time. When the operating time windows of different processes on the production line are misaligned with the energy storage discharge time windows or the time windows for direct green electricity supply, the actual proportions of green and gray electricity consumed by products produced in the same batch differ significantly. The fixed-period total allocation logic cannot capture this carbon emission difference caused by time deviation and hybrid power supply, resulting in a coarse temporal granularity of the carbon footprint traceability results and a weak correlation with actual carbon emissions. Summary of the Invention

[0005] This invention provides a method for tracing the carbon footprint of products in a zero-carbon industrial park, which can solve the problems existing in the prior art.

[0006] A method for tracing the carbon footprint of products in a zero-carbon industrial park includes: Acquire energy topology node status data and energy storage charging and discharging parameters uploaded by microgrid monitoring equipment; Collect data from production line energy consumption terminals within the equipment operating time window for the target production batch; Based on the energy storage charging and discharging parameters and the energy topology node status data, update the energy storage equivalent carbon emission factor for the target time period; By combining the equipment operating time window, energy storage equivalent carbon emission factor, energy topology node status data, and initial carbon emission factors from different sources, a carbon emission sequence for the production line process is generated. Construct the implicit carbon traceability tree corresponding to the target production batch; add the implicit carbon traceability tree to the carbon emission sequence of the production line process to calculate the total carbon footprint of the product.

[0007] Based on the above scheme, the problem that the source tracing results are weakly correlated with actual carbon emissions due to the neglect of the deviation of energy storage charging and discharging time and the mixing of power sources in the allocation method is solved.

[0008] Optionally, acquire energy topology node status data and energy storage charging and discharging parameters uploaded by microgrid monitoring equipment, including: Extract the incoming power and power type identifier of each grid-connected node; It acquires the real-time state of charge and charging / discharging power of the energy storage battery cluster.

[0009] Optionally, the equipment operating time window of the energy consumption collection terminal for the production line under the target production batch includes: Obtain production batch work order instructions issued by the Manufacturing Execution System; Extract the start / stop timestamps and instantaneous power sequences of the equipment corresponding to each process according to the production batch work order instructions; The device operating time window is defined based on the start and stop timestamps.

[0010] Optionally, updating the energy storage equivalent carbon emission factor for the target time period based on the energy storage charging and discharging parameters and the energy topology node status data includes: The initial carbon emission factor for different sources is obtained based on the power type identifier; Establish the charge-discharge balance equation for the energy storage battery cluster; The energy storage equivalent carbon emission factor is calculated based on the charge / discharge power, the initial carbon emission factor, and the charge / discharge balance equation.

[0011] Optionally, establishing the charge-discharge balance equation for the energy storage battery cluster includes: Obtain the inherent self-discharge rate and charge-discharge conversion efficiency of the energy storage battery cluster; The remaining charge in the previous sampling period is calculated based on the inherent self-discharge rate and the natural charge loss value in the current sampling period. In the charging state, the charging / discharging power is multiplied by the charging / discharging conversion efficiency to calculate the amount of electricity charged; in the discharging state, the charging / discharging power is divided by the charging / discharging conversion efficiency to calculate the amount of electricity discharged. The charge-discharge balance equation is established by subtracting the natural power loss value from the remaining power in the previous sampling period, and then adding the charged power or subtracting the discharged power.

[0012] Optionally, the step of generating a production line process carbon emission sequence by combining the equipment operating time window, the energy storage equivalent carbon emission factor, the energy topology node status data, and the initial carbon emission factors from different sources includes: Calculate the power sharing rate of each microgrid node within the operating time window of the device; Determine the power source type of each microgrid node; if it is a conventional source node, multiply its power sharing rate by the initial carbon emission factor of the corresponding source; if it is an energy storage node, multiply its power sharing rate by the energy storage equivalent carbon emission factor. Summing the products of each term generates the comprehensive carbon emission factor for the microgrid node. The instantaneous carbon emission rate is obtained by multiplying the comprehensive carbon emission factor of the microgrid node by the instantaneous power sequence. The instantaneous carbon emission rate is integrated over the equipment operating time window to obtain the process-level carbon footprint. The carbon footprints at the process level are combined according to the work order flow sequence to generate the carbon emission sequence of the production line process.

[0013] Optionally, constructing the implicit carbon traceability tree corresponding to the target production batch includes: Obtain carbon emission equivalent data when materials enter the warehouse; Extract the handling trajectory and energy consumption data of the logistics equipment within the plant; The implicit carbon source tree is constructed by associating the carbon emission equivalent data with the energy consumption data corresponding to the transportation trajectory according to the product bill of materials hierarchy.

[0014] Optionally, calculating the energy storage equivalent carbon emission factor based on the charge / discharge power, the initial carbon emission factor, and the charge / discharge balance equation includes: Obtain the remaining carbon emissions of the energy storage battery cluster mentioned in the previous time period; The incremental carbon emissions newly added during this period are calculated based on the product of the charging and discharging power when the device is in a charging state and the initial carbon emission factor of the corresponding source. Calculate the carbon emission reduction during this period based on the charging and discharging power; The remaining carbon emission base is added to the carbon emission increment and the carbon emission reduction is subtracted, then divided by the total electricity corresponding to the real-time state of charge, and the energy storage equivalent carbon emission factor is updated.

[0015] Optionally, integrating the instantaneous carbon emission rate over the equipment operating time window to obtain the process-level carbon footprint includes: The first state switching time node for extracting the comprehensive carbon emission factor of the microgrid node based on the power type switching signal; The second state switching time node of the instantaneous power sequence is extracted based on the device start-stop timestamp; Based on the first state switching time node and the second state switching time node, the device operation time window is divided into multiple time sub-intervals; Calculate the segmented carbon emissions for each of the aforementioned time sub-intervals; The carbon emissions of each time sub-interval are summed to obtain the process-level carbon footprint. Attached Figure Description

[0016] Figure 1 The present invention provides a flowchart of a method for tracing the carbon footprint of products in a zero-carbon industrial park. Detailed Implementation

[0017] The specific embodiments of the present invention will be described in detail below, but it should be understood that the scope of protection of the present invention is not limited to the specific embodiments.

[0018] A method for tracing the carbon footprint of products in a zero-carbon industrial park includes: Step S101: Obtain the energy topology node status data and energy storage charging / discharging parameters uploaded by the microgrid monitoring equipment. Specifically, the microgrid monitoring equipment is deployed at each grid-connected node and energy storage battery cluster in the park. It collects and uploads the incoming power and power type identifier of each grid-connected node, as well as the state of charge and charging / discharging power of the energy storage battery cluster at preset time intervals. The aforementioned energy topology node status data refers to the set of parameters describing the electrical connection relationship and operating status of each grid-connected node in the park's microgrid. The energy storage charging / discharging parameters refer to the state of charge and power data of the energy storage battery cluster during operation.

[0019] Step S102: Collect the equipment operating time window of the production line energy consumption terminal under the target production batch. Specifically, through the production line energy consumption terminal deployed at the equipment entry point of each process, the duration from start to stop of the corresponding equipment in this production process is collected as the equipment operating time window, using the target production batch as the index; the target production batch refers to the specific product production batch that needs to be traced for carbon footprint, and the equipment operating time window refers to the time interval during which a certain piece of equipment continuously runs in a certain process of that batch.

[0020] Step S103: Update the energy storage equivalent carbon emission factor for the target time period based on energy storage charging and discharging parameters and energy topology node status data. Specifically, different power sources are distinguished according to the power type identifier of each grid-connected node, and the corresponding carbon emission factor is determined according to the carbon emission attributes of each source. The energy storage equivalent carbon emission factor is updated and calculated according to the charging and discharging power and state of charge changes of the energy storage battery cluster according to a preset update cycle. Here, the energy storage equivalent carbon emission factor refers to the carbon emission per unit of electricity corresponding to the energy storage battery cluster when discharging. The value of this factor is updated as the source composition of the electricity charged into the energy storage battery cluster changes.

[0021] Step S104: Generate a production line process carbon emission sequence based on the equipment operating time window, energy storage equivalent carbon emission factor, energy topology node status data, and initial carbon emission factors from different sources. Specifically, within the equipment operating time window, the corresponding carbon emission factor and node power supply are weighted and summed according to the power supply type of each microgrid node to obtain the comprehensive carbon emission rate; the comprehensive carbon emission rate and the instantaneous power of the equipment are accumulated and calculated along the time window to obtain the carbon emission of each process; according to the process flow sequence specified in the work order instruction of the target production batch, the carbon emission of each process is arranged sequentially to obtain the production line process carbon emission sequence; the production line process carbon emission sequence refers to the ordered set of carbon emissions of each stage from the first process to the last process of the target production batch.

[0022] Step S105: Construct the implicit carbon traceability tree corresponding to the target production batch. Specifically, obtain the carbon emission equivalent data of materials entering the warehouse from the warehouse management records, read the travel path and unit distance energy consumption data of the handling equipment from the in-plant logistics scheduling records, and according to the hierarchical structure of the product bill of materials, attach the carbon emission equivalent of each level of materials and the carbon emission amount converted from logistics handling energy consumption to the corresponding material nodes layer by layer to form an implicit carbon traceability tree; the implicit carbon traceability tree referred to here is a tree-shaped data structure with the product bill of materials hierarchy as the skeleton and the carbon emission equivalent of each level of materials and the carbon emission amount of the logistics link as leaf nodes.

[0023] Step S106: Add the implicit carbon traceability tree to the carbon emission sequence of the production line process to calculate the total carbon footprint of the product. Specifically, traverse the carbon emission equivalent value and logistics energy consumption conversion value of each level of material node in the implicit carbon traceability tree and sum them to obtain the total implicit carbon emissions; add the total implicit carbon emissions to the carbon emissions of each process in the carbon emission sequence of the production line process to obtain the total carbon footprint of the product; in this paper, the total carbon footprint of the product refers to the total carbon emissions of a single product from raw material entry into the factory, internal circulation, processing and manufacturing to finished product exit.

[0024] Based on the above scheme, the time-varying characteristics of the equivalent carbon emission factor of energy storage are incorporated into the process-level carbon emission calculation, and an implicit carbon traceability tree is constructed to incorporate the indirect carbon emissions of materials and logistics links. This makes the traceability results of the product carbon footprint reflect the differences in the proportion of green electricity and gray electricity actually consumed in each process under the multi-source hybrid power supply of microgrids. This solves the problem that the traceability results have a weak correspondence with actual carbon emissions due to the neglect of the deviation of energy storage charging and discharging time and the mixing of power sources in the allocation method.

[0025] In some embodiments, acquiring energy topology node status data and energy storage charging and discharging parameters uploaded by microgrid monitoring equipment includes: Obtain the incoming power and power type identifier of each grid-connected node. By using power sensors and phase angle measurement units deployed at each grid-connected node in the park, the instantaneous voltage and current values ​​at the incoming end are collected at a preset sampling period to calculate the incoming power; the power type identifier registered by the grid-connected node in the park's energy management configuration information is read as the power type identifier to distinguish different power sources such as photovoltaic power generation, wind power generation, and grid power.

[0026] The system acquires the state of charge (SOC) and charge / discharge power of the energy storage battery cluster. It reads the terminal voltage, charge / discharge current, and estimated SOC values ​​of the battery cluster from the energy storage battery management system. The charge / discharge power is then calculated by multiplying the terminal voltage by the charge / discharge current, with positive values ​​for power in the charging direction and negative values ​​for power in the discharging direction, thus distinguishing between the charging and discharging periods of the energy storage battery cluster.

[0027] By adopting the above scheme, the incoming power and power type of each grid-connected node are clearly distinguished, and the state of charge and charging and discharging power of the energy storage battery cluster are continuously tracked, so that the calculation of carbon emission factor corresponds to the specific power source and energy storage operation status, thereby obtaining the dual-end input data of the power supply side and energy storage side required for updating the energy storage equivalent carbon emission factor.

[0028] In one implementation, the data collection of production line energy consumption terminals within a target production batch includes the following: Obtain production batch work order instructions issued by the Manufacturing Execution System (MES). When initiating a target production batch, the MES issues work order instructions containing batch number, product model, planned output, and process flow path to the controllers of each process on the production line. Production batch work order instructions are structured control instructions issued by the MES to schedule the production line to complete the production of that batch.

[0029] Based on the production batch work order instructions, obtain the start and stop timestamps and instantaneous power data of the equipment corresponding to each process. According to the process flow path recorded in the work order instructions, retrieve the start and stop timestamps of the equipment corresponding to each process from the historical records of the production line energy consumption terminal, and read the instantaneous power readings within the interval from the start timestamp to the stop timestamp, and sort them in ascending order of time to obtain the instantaneous power data.

[0030] The equipment operation time window is defined based on the start and stop timestamps. The time span from the start timetamp to the stop timetamp is taken as the equipment operation time window for this process. When the same equipment has multiple start and stop times within the target production batch, the time windows corresponding to each start and stop are extracted and spliced ​​together in chronological order.

[0031] Based on the above scheme, the work order instructions issued by the manufacturing execution system are used as the driving force to obtain the start and stop timestamps and instantaneous power data of the equipment in each process. This ensures that the definition of the equipment running time window strictly corresponds to the actual production rhythm of the target production batch, avoiding deviations in the definition of the running time window caused by delays in manual order entry or overlapping operations across batches.

[0032] In one implementation, the energy storage equivalent carbon emission factor for a target time period is updated based on energy storage charge and discharge parameters and energy topology node state data, including: The initial carbon emission factors for different sources are obtained based on the power source type identifier. The carbon emission factor lookup table pre-installed in the park's carbon management parameter area is read. This lookup table is indexed by power source type identifier and records the reference value of carbon emissions per unit of electricity from different power sources such as photovoltaic power generation, wind power generation, and grid power as the initial carbon emission factors.

[0033] Establish a charge-discharge balance equation for the energy storage battery cluster. Based on the electrical relationship between the change in state of charge of the energy storage battery cluster and the charge-discharge power, establish a mathematical equation describing the balance between the inflow and outflow of energy during the charge-discharge process as the charge-discharge balance equation. This equation uses charge-discharge power and state of charge as variables to express the recursive relationship between the remaining energy of the energy storage battery cluster in the current sampling period and the remaining energy in the previous sampling period.

[0034] The equivalent carbon emission factor for energy storage is calculated based on the charge / discharge power, the initial carbon emission factor, and the charge / discharge balance equation. The charge / discharge power is substituted into the charge / discharge balance equation to determine the proportion of electricity from each power source in the energy storage battery cluster during the current period. The electricity share corresponding to each power source type in this proportion is then weighted and summed with its respective initial carbon emission factor to obtain the equivalent carbon emission factor for energy storage. When the energy storage battery cluster is in a discharging state, this equivalent carbon emission factor is used as the basis for calculating the carbon emissions from its power supply to the production line.

[0035] Based on the above scheme, a charge-discharge balance equation for the energy storage battery cluster is established to describe the recursive relationship of electricity inflow and outflow. This allows the energy storage equivalent carbon emission factor to track the changes in the composition of electricity sources within the energy storage battery cluster, avoiding the bias of simply regarding energy storage discharge as a single source of carbon emission factor while ignoring its internal electricity mixing effect.

[0036] In some embodiments, establishing the charge-discharge balance equation for the energy storage battery cluster includes: Obtain the inherent self-discharge rate and charge-discharge conversion efficiency of the energy storage battery cluster. Read the inherent self-discharge rate and charge-discharge conversion efficiency from the factory parameters of the energy storage battery cluster; the inherent self-discharge rate refers to the proportion of energy naturally lost by the energy storage battery cluster per unit time due to internal electrochemical side reactions when no external load is connected, and the charge-discharge conversion efficiency refers to the ratio of the actual amount of energy stored in the battery during the charging process to the amount of energy input.

[0037] The remaining charge in the previous sampling period is calculated based on the inherent self-discharge rate, representing the natural charge loss in the current sampling period. The remaining charge at the end of the previous sampling period is multiplied by the product of the inherent self-discharge rate and the sampling period duration to obtain the natural charge loss due to self-discharge from the previous sampling period to the current sampling period.

[0038] During charging, the charge / discharge power is multiplied by the charge / discharge conversion efficiency to calculate the amount of electricity charged. During discharging, the charge / discharge power is divided by the charge / discharge conversion efficiency to calculate the amount of electricity discharged. When the energy storage battery cluster is charging, the charging power of the current sampling period is multiplied by the charge / discharge conversion efficiency and the sampling period duration to obtain the actual amount of electricity charged into the battery. When the energy storage battery cluster is discharging, the discharging power is divided by the charge / discharge conversion efficiency and then multiplied by the sampling period duration to obtain the actual amount of electricity discharged from the battery.

[0039] The charge-discharge balance equation is established by subtracting the natural energy loss from the remaining energy in the previous sampling period, and then adding the charged energy or subtracting the discharged energy. The equation, which states that the remaining energy in the current sampling period equals the remaining energy in the previous sampling period minus the natural energy loss, plus the charged energy or subtracted the discharged energy, fully describes the recursive evolution of the energy state of the energy storage battery cluster between consecutive sampling periods.

[0040] By adopting the above scheme, the inherent self-discharge rate and charge-discharge conversion efficiency, two intrinsic parameters of the energy storage battery cluster, are incorporated into the charge-discharge balance equation, making the equation more complete in describing the changes in the energy capacity of the energy storage battery cluster and improving the calculation completeness of the proportion of energy capacity in the energy storage equivalent carbon emission factor.

[0041] In one implementation, a carbon emission sequence for production line processes is generated based on equipment operating time windows, energy storage equivalent carbon emission factors, energy topology node status data, and initial carbon emission factors from different sources, including: Calculate the power sharing rate of each microgrid node within the equipment operating time window. Within the equipment operating time window, the power supplied by each microgrid node to the production line equipment is accumulated. The accumulated power of each node is divided by the total power supplied by all nodes to obtain the power sharing rate of each microgrid node. The power sharing rate refers to the proportion of power supplied by a single microgrid node to the total power consumption within the equipment operating time window.

[0042] Determine the power source type of each microgrid node; if it is a conventional source node, multiply its power contribution rate by the corresponding source's initial carbon emission factor; if it is an energy storage node, multiply its power contribution rate by the energy storage equivalent carbon emission factor; sum the products to generate the comprehensive carbon emission factor of the microgrid node. Iterate through each microgrid node, determining its attribute based on its power source type identifier; for conventional source nodes such as photovoltaic, wind power, and grid power, multiply the node's power contribution rate by its corresponding initial carbon emission factor to obtain the node's carbon emission contribution value; for energy storage nodes, multiply the node's power contribution rate by the energy storage equivalent carbon emission factor to obtain the energy storage node's carbon emission contribution value; sum the carbon emission contribution values ​​of all nodes to obtain the comprehensive carbon emission factor of the microgrid node; the comprehensive carbon emission factor of the microgrid node refers to the carbon emission per unit of electricity, which is a weighted average reflecting the comprehensive carbon emission level of the entire microgrid within the equipment operating time window.

[0043] The instantaneous carbon emission rate is obtained by multiplying the comprehensive carbon emission factor of the microgrid node by the instantaneous power data. The instantaneous carbon emission rate data is obtained by multiplying the comprehensive carbon emission factor of the microgrid node by the instantaneous power value at each sampling time in the instantaneous power data; the instantaneous carbon emission rate at each time represents the amount of carbon emissions generated by the device consuming a unit of electricity at that time.

[0044] The instantaneous carbon emission rate is integrated over the equipment operating time window to obtain the process-level carbon footprint. Using the equipment operating time window as the integration interval, the instantaneous carbon emission rate is integrated over time, and the total carbon emissions of the process within that equipment operating time window are accumulated as the process-level carbon footprint. The integration calculation can be approximated using trapezoidal numerical integration or rectangular accumulation.

[0045] The carbon footprints at the process level are combined according to the work order flow sequence to generate a production line process carbon emission sequence. Following the process flow sequence specified in the target production batch work order instructions, the process-level carbon footprints corresponding to each process are arranged sequentially to generate the production line process carbon emission sequence; each element in this sequence records the carbon emissions of a process and its corresponding process and equipment identifiers.

[0046] Based on the above scheme, the carbon emission contribution of each microgrid node is allocated proportionally by the power sharing rate, and the carbon emission factor is matched differently according to the power source type. This allows the carbon emission sequence of the production line process to reflect the actual carbon emission composition of different power sources in the microgrid step by step and node by node, realizing the decomposition of carbon emissions from the park level to the process level.

[0047] In some embodiments, constructing an implicit carbon traceability tree corresponding to a target production batch includes: Obtain carbon emission equivalent data when materials enter the warehouse. Read the carbon emission equivalent certification data that comes with each batch of materials when they enter the warehouse from the park's warehouse management records, or read the unit carbon emission equivalent value of each specification of material from the carbon footprint report provided by the upstream supplier; carbon emission equivalent data refers to the converted value of the carbon emissions accumulated by the material from raw material mining to transportation to the plant.

[0048] Acquire the material handling trajectory and energy consumption data of the in-plant logistics equipment. Retrieve the travel path coordinates and unit distance energy consumption data of material handling equipment such as forklifts and automated guided vehicles during the target production batch from the in-plant logistics scheduling records; the material handling trajectory records the movement path of materials from the warehouse to each workstation on the production line, and the energy consumption data records the energy consumption of the material handling equipment on that path.

[0049] Based on the product bill of materials (BOM) hierarchy, carbon emission equivalent data is linked to energy consumption data corresponding to the material handling path to construct an implicit carbon traceability tree. Using the hierarchical structure of the BOM as the framework, carbon emission equivalent data for each level of material is attached to the corresponding material node; energy consumption data corresponding to the material handling path is assigned to logistics node between each level according to the material movement path; nodes are organized layer by layer according to the parent-child relationship of the BOM, forming an implicit carbon traceability tree with finished products as the root node and materials and logistics links at each level as child nodes. The implicit carbon traceability tree refers to a hierarchical carbon emission data set organized in a tree structure, containing both material carbon emission equivalents and in-plant logistics carbon emissions.

[0050] Based on the above scheme, an implicit carbon traceability tree is constructed with the product bill of materials as the framework. The indirect carbon emission equivalent of materials and the direct carbon emission of in-plant logistics are organized hierarchically, so that the carbon footprint of the product can be traced back to the carbon emission contribution of each level of materials and each logistics link, which fills the blind spot in the carbon emission sequence of the production line process that only covers the processing and manufacturing links.

[0051] In some embodiments, the equivalent carbon emission factor for energy storage is calculated based on the charge / discharge power, the initial carbon emission factor, and the charge / discharge balance equation, including: Obtain the remaining carbon emission baseline of the energy storage battery cluster in the previous time period. Read the remaining carbon emission baseline at the end of the previous time period from the carbon emission baseline memory; the remaining carbon emission baseline refers to the total amount of carbon emissions stored inside the energy storage battery cluster at the end of the previous time period, that is, the cumulative carbon emission equivalent value corresponding to the current amount of electricity inside the battery.

[0052] The carbon emission increment newly added during this period is calculated by multiplying the charging / discharging power while in a charging state by the initial carbon emission factor of the corresponding source. When the energy storage battery cluster is in a charging state, the charging / discharging power is multiplied by the initial carbon emission factor of the current charging source corresponding to the power type, and then multiplied by the sampling period duration to obtain the carbon emission increment of the energy storage battery cluster newly added during this period due to charging behavior; the carbon emission increment reflects the source carbon emission attribute of the charged electricity during this period.

[0053] The carbon emission reduction consumed during the discharge period is calculated based on the charging and discharging power. When the energy storage battery cluster is in a discharging state, the charging and discharging power is multiplied by the current energy storage equivalent carbon emission factor and then by the sampling period duration to obtain the carbon emission reduction consumed by the amount of electricity released from the energy storage battery cluster due to the discharge behavior during this period; the carbon emission reduction reflects the deduction of the carbon emissions corresponding to the amount of electricity discharged during this period from the energy storage battery cluster.

[0054] The energy storage equivalent carbon emission factor is updated by adding the carbon emission increment to the remaining carbon emission baseline and subtracting the carbon emission reduction, then dividing by the total amount of electricity corresponding to the current state of charge. The remaining carbon emission baseline of the previous period is added to the carbon emission increment newly added in the current period, and then subtracted from the carbon emission reduction consumed during the current period to obtain the remaining carbon emission baseline of the current period. This remaining carbon emission baseline is then divided by the total amount of electricity corresponding to the current state of charge of the energy storage battery cluster to obtain the updated energy storage equivalent carbon emission factor. This recursive update process realizes the continuous evolution of the energy storage equivalent carbon emission factor with changes in the charging and discharging behavior of the energy storage battery cluster and the source of electricity.

[0055] By adopting the above scheme, a recursive update relationship for carbon emissions is established with the remaining carbon emission baseline as an intermediate variable. This allows the equivalent carbon emission factor of energy storage to evolve gradually with the charging and discharging behavior of the energy storage battery cluster in each sampling cycle. This achieves cycle-by-cycle updates of the carbon emission factor of energy storage, avoiding carbon emission calculation errors caused by update lag in the fixed-cycle batch update mode.

[0056] In some embodiments, the instantaneous carbon emission rate is integrated over a equipment operating time window to obtain the process-level carbon footprint, including: The first state switching time node of the comprehensive carbon emission factor of microgrid nodes is obtained based on the power type switching signal. The power type switching signal of each grid-connected node in the park microgrid is monitored; when the comprehensive carbon emission factor of any microgrid node jumps due to photovoltaic power output fluctuations, grid power switching, or energy storage charging and discharging state transition, the time of the jump is recorded as the first state switching time node; this first state switching time node marks the moment when the carbon emission factor value changes.

[0057] The second state switching time node is obtained based on the equipment start-stop timestamps to acquire instantaneous power data. The start-up and stop timestamps of the corresponding equipment for each process are read from the energy consumption terminal records of the production line, as well as the power step times caused by process switching or standby during equipment operation. These times are marked as the second state switching time nodes; the second state switching time nodes mark the times when the instantaneous power value of the equipment changes.

[0058] Based on the first and second state switching time nodes, the equipment operating time window is divided into multiple time sub-intervals. The first and second state switching time nodes are merged and arranged in ascending order of time. Using two adjacent merged switching time nodes as boundaries, the equipment operating time window is further divided into multiple time sub-intervals. Within each time sub-interval, the comprehensive carbon emission factor of the microgrid node and the instantaneous power of the equipment remain constant, thus ensuring a constant instantaneous carbon emission rate within that time sub-interval.

[0059] Calculate the segmented carbon emissions for each time sub-interval. For each time sub-interval, multiply the constant instantaneous carbon emission rate within that time sub-interval by the time length of that time sub-interval to obtain the segmented carbon emissions for that time sub-interval; segmented carbon emissions refer to the carbon emission segments within a single time sub-interval that can be directly calculated because both the carbon emission factor and power remain constant.

[0060] The carbon emissions of each time sub-interval are summarized to obtain the process-level carbon footprint. The carbon emissions of all time sub-intervals are summed to obtain the total carbon emissions of the process within the equipment operation time window, which is taken as the process-level carbon footprint. This process-level carbon footprint reflects the complete summation of the carbon emission contributions of each time sub-interval during equipment operation.

[0061] Based on the above scheme, by obtaining the state switching time nodes of carbon emission factors and equipment power, the equipment operation time window is divided into multiple time sub-intervals where both carbon emission factors and power are constant. In each time sub-interval, constant value product is used to replace the whole-process integral approximation, which reduces the complexity of integral calculation and avoids integral approximation errors caused by jumps in carbon emission factors or power in the integral interval, thereby improving the calculation accuracy of process-level carbon footprint.

[0062] The above-disclosed embodiments are merely a few specific examples of the present invention. However, the embodiments of the present invention are not limited thereto, and any variations that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A method for tracing the carbon footprint of products in a zero-carbon industrial park, characterized in that, include: Acquire energy topology node status data and energy storage charging and discharging parameters uploaded by microgrid monitoring equipment; Collect data from production line energy consumption terminals within the equipment operating time window for the target production batch; Based on the energy storage charging and discharging parameters and the energy topology node status data, update the energy storage equivalent carbon emission factor for the target time period; By combining the equipment operating time window, energy storage equivalent carbon emission factor, energy topology node status data, and initial carbon emission factors from different sources, a carbon emission sequence for the production line process is generated. Construct the implicit carbon traceability tree corresponding to the target production batch; The implicit carbon traceability tree is added to the carbon emission sequence of the production line process to calculate the total carbon footprint of the product.

2. The method for tracing the carbon footprint of products in a zero-carbon industrial park as described in claim 1, characterized in that, Acquire energy topology node status data and energy storage charging and discharging parameters uploaded by microgrid monitoring equipment, including: Extract the incoming power and power type identifier of each grid-connected node; It acquires the real-time state of charge and charging / discharging power of the energy storage battery cluster.

3. The method for tracing the carbon footprint of products in a zero-carbon industrial park as described in claim 2, characterized in that, The equipment operating time window of the energy consumption collection terminal for the production line under the target production batch includes: Obtain production batch work order instructions issued by the Manufacturing Execution System; Extract the start / stop timestamps and instantaneous power sequences of the equipment corresponding to each process according to the production batch work order instructions; The device operating time window is defined based on the start and stop timestamps.

4. The method for tracing the carbon footprint of products in a zero-carbon industrial park as described in claim 3, characterized in that, The step of updating the energy storage equivalent carbon emission factor for the target time period based on the energy storage charging and discharging parameters and the energy topology node status data includes: The initial carbon emission factor for different sources is obtained based on the power type identifier; Establish the charge-discharge balance equation for the energy storage battery cluster; The energy storage equivalent carbon emission factor is calculated based on the charge / discharge power, the initial carbon emission factor, and the charge / discharge balance equation.

5. The method for tracing the carbon footprint of products in a zero-carbon industrial park as described in claim 4, characterized in that, The process of establishing the charge-discharge balance equation for the energy storage battery cluster includes: Obtain the inherent self-discharge rate and charge-discharge conversion efficiency of the energy storage battery cluster; The remaining charge in the previous sampling period is calculated based on the inherent self-discharge rate and the natural charge loss value in the current sampling period. In the charging state, the charging / discharging power is multiplied by the charging / discharging conversion efficiency to calculate the amount of electricity charged; in the discharging state, the charging / discharging power is divided by the charging / discharging conversion efficiency to calculate the amount of electricity discharged. The charge-discharge balance equation is established by subtracting the natural power loss value from the remaining power in the previous sampling period, and then adding the charged power or subtracting the discharged power.

6. The method for tracing the carbon footprint of products in a zero-carbon industrial park as described in claim 5, characterized in that, The process of generating a carbon emission sequence for the production line steps by combining the equipment operating time window, the energy storage equivalent carbon emission factor, the energy topology node status data, and the initial carbon emission factors from different sources includes: Calculate the power sharing rate of each microgrid node within the operating time window of the device; Determine the power source type of each microgrid node; if it is a conventional source node, multiply its power sharing rate by the initial carbon emission factor of the corresponding source; if it is an energy storage node, multiply its power sharing rate by the energy storage equivalent carbon emission factor. Summing the products of each term generates the comprehensive carbon emission factor for the microgrid node. The instantaneous carbon emission rate is obtained by multiplying the comprehensive carbon emission factor of the microgrid node by the instantaneous power sequence. The instantaneous carbon emission rate is integrated over the equipment operating time window to obtain the process-level carbon footprint. The carbon footprints at the process level are combined according to the work order flow sequence to generate the carbon emission sequence of the production line process.

7. The method for tracing the carbon footprint of products in a zero-carbon industrial park as described in claim 6, characterized in that, The construction of the implicit carbon traceability tree corresponding to the target production batch includes: Obtain carbon emission equivalent data when materials enter the warehouse; Extract the handling trajectory and energy consumption data of the logistics equipment within the plant; The implicit carbon source tree is constructed by associating the carbon emission equivalent data with the energy consumption data corresponding to the transportation trajectory according to the product bill of materials hierarchy.

8. The method for tracing the carbon footprint of products in a zero-carbon industrial park as described in claim 7, characterized in that, The step of calculating the energy storage equivalent carbon emission factor based on the charge / discharge power, the initial carbon emission factor, and the charge / discharge balance equation includes: Obtain the remaining carbon emissions of the energy storage battery cluster mentioned in the previous time period; The incremental carbon emissions newly added during this period are calculated based on the product of the charging and discharging power when the device is in a charging state and the initial carbon emission factor of the corresponding source. Calculate the carbon emission reduction during this period based on the charging and discharging power; The remaining carbon emission base is added to the carbon emission increment and the carbon emission reduction is subtracted, then divided by the total electricity corresponding to the real-time state of charge, and the energy storage equivalent carbon emission factor is updated.

9. A method for tracing the carbon footprint of products in a zero-carbon industrial park as described in claim 8, characterized in that, The process-level carbon footprint is obtained by integrating the instantaneous carbon emission rate over the equipment operating time window, including: The first state switching time node for extracting the comprehensive carbon emission factor of the microgrid node based on the power type switching signal; The second state switching time node of the instantaneous power sequence is extracted based on the device start-stop timestamp; Based on the first state switching time node and the second state switching time node, the device operation time window is divided into multiple time sub-intervals; Calculate the segmented carbon emissions for each of the aforementioned time sub-intervals; The carbon emissions of each time sub-interval are summed to obtain the process-level carbon footprint.

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