Carbon flow data processing method based on carbon accounting scene, storage medium and equipment

Abstract

The invention provides a carbon flow data processing method based on a carbon accounting scene, a storage medium and equipment. The method comprises the following steps: acquiring a scene sample set of various energy load characteristic curves in a microgrid; determining an optimal clustering center number k based on a CH index, selecting k scenes as initial clustering centers, and determining a source load scene curve corresponding to each type of clustering center in the scene sample set based on each initial clustering center; acquiring consumption data of a corresponding scene based on the source load scene curve, and inputting the consumption data into a pre-constructed carbon flow calculation and accounting model and a micro-grid multi-target planning model; and calculating time-sharing carbon emission and a dynamic power carbon emission factor of a user side node based on a carbon flow calculation and accounting model, and optimizing and minimizing the total planning cost of the micro-grid and minimizing the carbon emission of the micro-grid based on a micro-grid multi-target planning model. According to the invention, the features of the original scenes in the sample set can be embodied with as few scenes as possible, and carbon flow data processing is realized.

Description

Technical Field

[0001] This application belongs to the field of carbon stream processing technology, and in particular relates to the field of carbon stream data processing technology based on carbon accounting scenarios. Background Technology

[0002] With the rapid development of low-carbon energy systems, the proportion of renewable energy in the energy structure continues to increase, driving the large-scale development and efficient utilization of distributed renewable energy, represented by photovoltaics. However, the intermittent nature of distributed renewable energy output and the randomness of user energy consumption behavior increase the complexity and computational difficulty of solving the microgrid operation planning problem, potentially leading to significant differences in the microgrid operation planning results and affecting the achievement of low-carbon goals. Therefore, it is necessary to describe the uncertainties of various energy loads and renewable energy output using as few scenarios as possible to reduce the computational complexity and difficulty of the microgrid operation planning problem. Summary of the Invention

[0003] This application provides a carbon flow data processing method, storage medium, and device based on carbon accounting scenarios, which can represent the characteristics of carbon accounting scenarios in a sample set with as few scenarios as possible.

[0004] In a first aspect, embodiments of this application provide a carbon flow data processing method based on carbon accounting scenarios, comprising: acquiring a scenario sample set of various energy load characteristic curves in a microgrid; determining the optimal number of cluster centers k based on the CH index, selecting k scenarios from the scenario sample set as initial cluster centers, and determining the source-load scenario curve corresponding to each cluster center in the scenario sample set based on each initial cluster center; acquiring the consumption data of the corresponding scenario based on the source-load scenario curve, and inputting the consumption data into a pre-constructed carbon flow calculation and accounting model and a microgrid multi-objective planning model; calculating the time-of-use carbon emissions and the dynamic power carbon emission factor of user-side nodes based on the carbon flow calculation and accounting model, and optimizing and minimizing the total planning cost of the microgrid and the carbon emissions of the microgrid based on the microgrid multi-objective planning model.

[0005] In one implementation of the first aspect, determining the source-load scene curve corresponding to the cluster center of each class in the scene sample set based on each of the initial cluster centers includes: calculating the Euclidean distance between each scene in the scene sample set and each of the initial cluster centers, and determining the scene category to which each scene belongs based on the Euclidean distance of the initial cluster center with the shortest distance; calculating the mean of all scenes in each scene category, and updating the cluster center of the scene category based on the mean; iteratively repeating the above process based on the change of the cluster centers before and after the update until the cluster centers before and after the update no longer change or the preset maximum number of iterations is reached; and obtaining the source-load scene curve corresponding to the cluster center when the cluster centers no longer change or the preset maximum number of iterations is reached.

[0006] In one implementation of the first aspect, the updated cluster center of the scene category is:

[0007]

[0008] in, For the first A collection consisting of all scenes within each category. Indicates the number of scenes in the category. Let i be the i-th scene.

[0009] In one implementation of the first aspect, the calculation of time-of-use carbon emissions based on the carbon flow calculation and accounting model includes: determining the time granularity and determining the decomposition time interval of the consumption data based on the time granularity; calculating and obtaining the time-of-use activity data of the consumption data in the decomposition time interval based on the carbon flow calculation and accounting model; the consumption data includes at least electricity consumption data, fuel consumption data, and material consumption data; calculating the time-of-use carbon emissions of each emission source in the decomposition time interval based on the time-of-use activity data and the input carbon emission factor; and obtaining the total time-of-use carbon emissions based on the time-of-use carbon emissions of each emission source and the carbon capture and offset amount.

[0010] In one implementation of the first aspect, the time-of-use carbon emissions of each emission source within the decomposition time interval include: time-of-use carbon emissions from fossil fuel combustion within the decomposition time interval, time-of-use carbon emissions from industrial production processes within the decomposition time interval, time-of-use carbon emissions from fugitive emissions within the decomposition time interval, time-of-use carbon emissions from purchased electricity within the decomposition time interval, time-of-use carbon emissions from purchased heat within the decomposition time interval, and time-of-use carbon emissions from purchased steam within the decomposition time interval.

[0011] In one implementation of the first aspect, the calculation of the dynamic electricity carbon emission factor of the user-side node based on the carbon flow calculation and accounting model includes: real-time collection of electricity data from each metering point, and obtaining the total net electricity consumption and clean energy consumption of the user-side within a period based on the consumption data and the electricity data; calculating the clean energy consumption ratio of each electricity consumption node based on the total net electricity consumption, clean energy consumption, and total electricity consumption of the period; calculating the carbon emission of each electricity consumption node based on the clean energy consumption ratio, total electricity consumption, and carbon emission factor of the mains electricity itself during the period; and calculating the short-cycle dynamic carbon emission factor of each electricity consumption node based on the carbon emission of each electricity consumption node and total electricity consumption of the period.

[0012] In one implementation of the first aspect, the calculation of the dynamic power carbon emission factor of the user-side node based on the carbon flow calculation and accounting model further includes: real-time acquisition of instantaneous power data of each metering point, and calculation of the user-side instantaneous total load power and user-side instantaneous clean energy generation power based on the consumption data and the instantaneous power data; calculation of the instantaneous carbon emission rate of each power consumption node based on the user-side instantaneous total load power, the user-side instantaneous clean energy generation power, the total power of the cycle, and the carbon emission factor of the mains power itself during the time period; and calculation of the instantaneous dynamic emission factor of each power consumption node based on the instantaneous carbon emission rate of each power consumption node and the total power of the cycle.

[0013] In one implementation of the first aspect, the microgrid multi-objective planning model includes a first optimization objective function that minimizes the total planning cost of the microgrid and a second optimization objective function that minimizes the carbon emissions of the microgrid; the optimization of minimizing the total planning cost and carbon emissions of the microgrid based on the microgrid multi-objective planning model includes: modeling various types of equipment in the microgrid based on the consumption data; establishing constraints for the first and second optimization objective functions based on the modeling of various types of equipment; the constraints include equipment investment constraints, equipment operation constraints, power balance constraints, and upper-level network interaction constraints. Rate constraints are applied; the ranges of the first and second optimization objective functions are solved, and anchor points are defined in the solution space using these as coordinates; the solution space is normalized to obtain normalized anchor points and anchor point connections; a constraint formula is constructed based on the normalized anchor points and anchor point connections, and the first optimization objective function is used as the main objective function. The constraint formula is used to replace the second optimization objective function to construct a single-objective optimization function with the total planning cost over the entire life cycle as the optimization objective, and the optimal solution of the single-objective optimization function is obtained, thereby minimizing the total planning cost of the microgrid and minimizing the carbon emissions of the microgrid.

[0014] In a second aspect, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the carbon flow data processing method based on a carbon accounting scenario as described in any one of the first aspects of this application.

[0015] Thirdly, embodiments of this application provide an electronic device, which includes a processor and a memory; the memory stores program instructions; the processor is used to run the program instructions to execute the carbon flow data processing method based on a carbon accounting scenario as described in any one of the first aspects of this application.

[0016] The carbon flow data processing method based on carbon accounting scenarios provided in this application has the following beneficial effects:

[0017] This application can represent the characteristics of the original scenarios in the sample set with as few scenarios as possible, and realize the calculation of time-of-use carbon emissions and dynamic power carbon emission factors of user-side nodes based on the clustered source-load scenario data, as well as the minimization of the total planning cost and carbon emissions of the microgrid. Attached Figure Description

[0018] Figure 1 The diagram shown is an overall flowchart of a carbon flow data processing method based on a carbon accounting scenario, according to an embodiment of this application.

[0019] Figure 2 The flowchart shown is a process for determining the source-load scenario curve corresponding to the cluster center of each class in the scenario sample set in a carbon flow data processing method based on carbon accounting scenarios according to an embodiment of this application.

[0020] Figure 3 The figure shown is an example of a curve representing the CH index relative to the number of clusters k in a carbon flow data processing method based on a carbon accounting scenario according to an embodiment of this application.

[0021] Figures 4 to 6 The graphs shown are curves representing different cluster centers in a carbon flow data processing method based on carbon accounting scenarios according to an embodiment of this application.

[0022] Figure 7 The flowchart shown is a process for obtaining time-sharing total carbon emissions in a carbon flow data processing method based on a carbon accounting scenario, according to an embodiment of this application.

[0023] Figure 8 The flowchart shown is a process for obtaining short-cycle dynamic carbon emission factors in a carbon flow data processing method based on a carbon accounting scenario, according to an embodiment of this application.

[0024] Figure 9 The flowchart shown is a process for obtaining instantaneous dynamic emission factors in a carbon flow data processing method based on a carbon accounting scenario according to an embodiment of this application.

[0025] Figure 10 The flowchart shown is a process for minimizing the total planning cost and carbon emissions of a microgrid in a carbon flow data processing method based on a carbon accounting scenario, according to an embodiment of this application.

[0026] Figure 11 The diagram shown illustrates the normalization space in a carbon flow data processing method based on a carbon accounting scenario according to an embodiment of this application.

[0027] Figure 12 The diagram shown is a structural schematic of an electronic device according to an embodiment of this application.

[0028] Component designation explanation

[0029] 100 electronic devices 101 memory 102 processor 103 monitor S100~S400 step S210~S240 step S411~414 step S421~S424 step S431~433 step S441~S445 step Detailed Implementation

[0030] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0031] The following will refer to the appendices in the embodiments of this application. Figure 1 To be continued Figure 12 This application provides a detailed description of the technical solutions in its embodiments. This allows those skilled in the art to understand and implement the carbon flow data processing method based on a carbon accounting scenario without inventive effort.

[0032] Figure 1 This is a flowchart illustrating a carbon flow data processing method based on a carbon accounting scenario, as shown in an embodiment of this application. Figure 1 As shown, the carbon flow data processing method based on carbon accounting scenarios provided in this application embodiment includes the following steps S100 to S400.

[0033] Step S100: Obtain a scenario sample set of various energy load characteristic curves in the microgrid;

[0034] Step S200: Determine the optimal number of cluster centers k based on the CH index, select k scenes from the scene sample set as initial cluster centers, and determine the source load scene curve corresponding to the cluster center of each class in the scene sample set based on each initial cluster center.

[0035] Step S300: Based on the source-load scenario curve, obtain the consumption data of the corresponding scenario, and input the consumption data into the pre-constructed carbon flow calculation and accounting model and the microgrid multi-objective planning model;

[0036] Step S400: Calculate time-of-use carbon emissions and dynamic power carbon emission factors of user-side nodes based on the carbon flow calculation and accounting model, and optimize and minimize the total planning cost of the microgrid and the carbon emissions of the microgrid based on the microgrid multi-objective planning model.

[0037] Considering that energy sources and loads are crucial factors related to carbon emissions in industrial parks, this embodiment proposes a method for constructing a characteristic scenario set for carbon accounting of power supply and consumption in industrial parks, taking into account typical energy source and load scenarios. For large-scale multi-energy load and renewable energy scenarios, the K-means clustering algorithm is used for scenario clustering. The optimal number of clusters is selected using the CH index (Calinski-Harabasz Index), which can characterize the distance between clusters and within clusters, to obtain the typical energy source and load scenario curves for typical days. This approach aims to represent the characteristics of the original scenarios in the sample set with as few scenarios as possible, providing a data foundation for subsequent low-carbon economic planning in industrial parks.

[0038] The carbon flow data processing method based on carbon accounting scenarios in this embodiment can represent the characteristics of the original scenarios in the sample set with as few scenarios as possible, and realize the calculation of time-of-use carbon emissions and dynamic power carbon emission factors of user-side nodes, as well as the minimization of the total planning cost and carbon emissions of microgrids based on the clustered source-load scenario data.

[0039] The carbon flow data processing method based on carbon accounting in this embodiment is applied to a microgrid in an industrial park system. The microgrid, for example, is a microgrid park, which uses photovoltaic (PV) power generation equipment and electrical storage (ES) equipment for energy storage. These devices work together to optimize and meet the electricity load demands of various end-users within the microgrid park. Alternatively, the microgrid can be a multi-energy microgrid, using PV power generation equipment and incorporating heat pumps (HP), combined heat and power (CHP), and gas boilers (GB).

[0040] As an energy conversion device, it uses energy storage devices (ES) and heat storage devices (HS) to meet the electricity, heat and gas load requirements of end users in the park. Its structure is flexible and can realize the complementarity and synergistic optimization of multiple energy sources. It is an effective way for parks with multi-energy needs to achieve economic and low-carbon development.

[0041] The large dataset of sample sets consisting of multi-energy load and renewable energy output characteristic curves increases the computational complexity of low-carbon economic planning in the industrial park. Therefore, it is necessary to use as few samples as possible to characterize the features of multi-energy load and renewable energy output characteristic curves in all sample sets, in order to reduce the computational complexity of microgrid planning and operation and achieve carbon emission accounting based on source-load characteristics. The K-means scenario clustering method is used to classify the multi-energy load demand curves and renewable energy output curves in the sample set, with the CH index used to select the optimal cluster, resulting in typical source-load scenario curves for typical days.

[0042] The following is in conjunction with the appendix Figure 2 To be continued Figure 10 The above steps S100 to S400 of the carbon flow data processing method based on the carbon accounting scenario in this embodiment will be described in detail.

[0043] Step S100: Obtain a scenario sample set of various energy load characteristic curves in the microgrid.

[0044] To reduce the computational complexity of low-carbon planning for the microgrid economy in the industrial park and to quantitatively characterize the carbon emission attributes of sources and loads, this project employs a K-means-based scenario clustering method to reduce the number of scenarios involving multiple energy loads and renewable energy output. The K-means clustering algorithm, which uses Euclidean distance as a metric to determine the similarity of data samples within a dataset, is widely used in the analysis of various typical energy consumption and supply behaviors.

[0045] In this embodiment, all original scenarios to be processed are selected as samples from the original microgrid multi-energy load and renewable energy (such as photovoltaic) output time series dataset. The data is then standardized to avoid the impact of the order of magnitude difference between data dimensions on the clustering effect.

[0046] Step S200: Determine the optimal number of cluster centers k based on the CH index, select k scenes from the scene sample set as initial cluster centers, and determine the source load scene curve corresponding to the cluster center of each class in the scene sample set based on each initial cluster center.

[0047] In K-means clustering, the selection of the k value is a crucial step in determining the clustering effect. The evaluation of clustering results typically employs internal evaluation methods, relying solely on the clustering results and the inherent attributes of the samples themselves without any external information. The CH index is a method that determines the clustering result by the ratio of inter-cluster distance to intra-cluster distance; a higher CH index indicates better clustering performance. Figure 2This is an example graph showing the CH index versus the cluster number k value in a carbon flow data processing method based on a carbon accounting scenario, according to an embodiment of this application. Figure 2 As shown, the CH index initially increases with the value of k, reaching its maximum value around k=3.15, and then decreases with the increase of the value of k. Therefore, the optimal number of clusters should be k=3.

[0048] In this embodiment, the optimal k value of the K-means clustering method is first determined based on the CH index method. Then, based on the optimal k value, the daily curve shape of multi-energy load demand and photovoltaic power generation data is clustered to obtain the typical daily scenarios of energy load demand and photovoltaic power generation.

[0049] Figure 3 This is a flowchart illustrating the process of determining the source-load scenario curve corresponding to the cluster center of each class in a scenario sample set in a carbon flow data processing method based on carbon accounting scenarios, as shown in an embodiment of this application. Figure 3 As shown, in one implementation of this embodiment, determining the source load scene curve corresponding to the cluster center of each class in the scene sample set based on each of the initial cluster centers includes the following steps S210 to S240.

[0050] Step S210: Calculate the Euclidean distance between each scene in the scene sample set and each initial cluster center, and determine the scene category of each scene belonging to the initial cluster center with the shortest distance based on the Euclidean distance;

[0051] Step S220: Calculate the mean of all scenes in each scene category, and update the cluster center of the scene category based on the mean;

[0052] Step S230: Based on the changes in cluster centers before and after the update, iteratively repeat the above process until the cluster centers before and after the update no longer change or the preset maximum number of iterations is reached.

[0053] Step S240: Obtain the source load scene curve corresponding to the cluster center when the cluster center no longer changes or reaches the preset maximum number of iterations.

[0054] In one implementation of this embodiment, the updated cluster center of the scene category is:

[0055]

[0056] in, For the first A collection consisting of all scenes within each category. Indicates the number of scenes in the category. For the first A scenario.

[0057] In this embodiment, the number of clusters k is determined in advance according to the actual problem requirements; that is, all scenarios need to be clustered into k classes.

[0058] Select k scenes from the processed dataset as initial cluster centers; for each scene in the dataset other than the cluster centers, calculate its Euclidean distance to each cluster center. Let each scene be denoted as: Cluster center is denoted as Then the scene With cluster center The Euclidean distance between them is Based on the calculated distances, each scene is determined to belong to the category of the cluster center with the shortest distance. If scene With cluster center If the distance is closest, then the scene will be... Assign the scene to class p; after all scenes have been classified, recalculate the mean of all scenes in each class to update the cluster center of that class. The updated cluster center is... Calculate the changes in cluster centers before and after the update, and determine whether the termination condition is met. If the cluster centers no longer change or the algorithm reaches the preset maximum number of iterations, the termination condition is met; otherwise, return and continue to the next round of iteration. Output the final clustering result, which is the cluster center scenario for each class and the scenario set within each class.

[0059] Figures 4 to 6 The graphs shown represent different clustering centers in a carbon flow data processing method based on carbon accounting scenarios according to an embodiment of this application. Figures 4 to 6 As shown, the scenario probabilities corresponding to cluster centers 1, 2, and 3 are 0.260, 0.498, and 0.246, respectively. Analysis of the clustering patterns and the original source-load historical data reveals that the clustering results exhibit significant seasonal characteristics: cluster scenario 1 corresponds to a typical summer day, cluster scenario 2 corresponds to a typical day in the transitional season, and cluster scenario 3 corresponds to a typical winter day.

[0060] Therefore, in this embodiment, the CH index method and K-means clustering algorithm are used to construct a characteristic scenario set for carbon accounting of power supply and consumption in industrial parks, considering typical source-load scenarios. First, the CH index is used to determine the optimal k value for the K-means clustering algorithm. Based on the selected k value, typical daily curve scenarios of multi-energy load and photovoltaic output are clustered. Finally, the scenario selection method described in this section is used to select scenarios from the historical multi-energy load data and historical photovoltaic output data of a microgrid in an industrial park in Zhejiang Province. Typical source-load scenario curves for typical days are obtained, so as to reflect the characteristics of the original scenarios in the sample set with as few scenarios as possible, providing a data foundation for subsequent low-carbon economic planning of microgrid industrial parks that considers carbon flow characteristic scenarios.

[0061] Step S300: Obtain the consumption data of the corresponding scenario based on the source-load scenario curve, and input the consumption data into the pre-constructed carbon flow calculation and accounting model and the microgrid multi-objective planning model.

[0062] Step S400: Calculate time-of-use carbon emissions and dynamic power carbon emission factors of user-side nodes based on the carbon flow calculation and accounting model, and optimize and minimize the total planning cost of the microgrid and the carbon emissions of the microgrid based on the microgrid multi-objective planning model.

[0063] In this embodiment, consumption data for the corresponding scenario can be obtained through a direct monitoring unit and an indirect monitoring / estimation unit. The direct monitoring unit can acquire consumption data for the corresponding scenario through intelligent metering devices. At key energy consumption nodes, such as main production line entrances, large equipment, boilers, compressed air stations, heating / cooling systems, and main entrances of office buildings, intelligent metering devices meeting accuracy requirements are deployed, such as 0.5S-level electricity meters, water meters of corresponding accuracy, gas flow meters, and heat meters.

[0064] In this embodiment, wired or wireless communication technologies such as industrial Ethernet, RS485 bus (supporting protocols such as Modbus RTU), LoRaWAN, NB-IoT, and 4G / 5G are used to transmit the collected data (usually at a frequency of minutes or higher) to the edge monitoring device or central data platform in real time or near real time, while ensuring the reliability and security of data transmission.

[0065] In this embodiment, the indirect monitoring / estimation unit obtains consumption data for the corresponding scenario through methods such as estimation based on mechanism / statistical models, energy consumption decomposition / estimation based on machine learning, and status / reading acquisition based on visual recognition.

[0066] For auxiliary equipment or distributed loads that are difficult to measure directly (such as small fans, pumps, lighting, and office equipment), time-of-use energy consumption can be estimated based on information such as the equipment's rated power, operating status (determined by associated equipment current and switching signals), and operating time (inferred from production scheduling and personnel activity sensors), and using equipment efficiency curves or historical data statistical models. When the total energy consumption of a region is measurable, historical energy consumption data, operating characteristics (such as temperature, pressure, and speed), and environmental parameters of each unit within that region can be used to train machine learning models (e.g., factor analysis, neural networks (NN), and support vector regression (SVR) commonly used in non-intrusive load monitoring (NILM)) to achieve real-time decomposition or estimation of energy consumption in unmonitored units. Industrial cameras can be deployed in specific scenarios (such as instrument panel readings and equipment indicator light status), and image recognition algorithms (such as CNN-based object detection and recognition) can be applied to automatically read instrument values ​​or determine equipment start-up and shutdown status as input information for energy consumption estimation.

[0067] In this embodiment, the data acquisition process is modeled as a decision-making problem. Reinforcement learning algorithms (such as DQN and PPO) are applied to train the agent. The sampling frequency of the sensors and the data upload strategy are dynamically adjusted based on factors such as real-time network conditions, data value assessment, and acquisition costs, aiming to optimize the balance between resource consumption and data quality. Furthermore, relevant production, energy consumption, or equipment status data can be obtained from existing automation and information systems such as Manufacturing Execution Systems (MES), Supervisory Control and Data Acquisition (SCADA), Building Management Systems (BMS), and Energy Management Systems (EMS) through interface development (such as OPC UA, API) or protocol conversion technologies.

[0068] Figure 7 The flowchart shown is a process for obtaining time-of-use total carbon emissions in a carbon flow data processing method based on a carbon accounting scenario, according to an embodiment of this application. Figure 7 As shown, in one implementation of this embodiment, the calculation of time-of-use carbon emissions based on the carbon flow calculation and accounting model includes the following steps S411 to S414.

[0069] Step S411: Determine the time granularity and determine the decomposition time interval of the consumed data based on the time granularity;

[0070] Step S412: Calculate and obtain the time-sharing activity data of the consumption data in the decomposition time interval based on the carbon flow calculation and accounting model; the consumption data includes at least electricity consumption data, fuel consumption data, and material consumption data;

[0071] Step S413: Calculate the time-sharing carbon emissions of each emission source within the decomposition time interval based on the time-sharing activity data and the input carbon emission factor;

[0072] Step S414: Obtain the total time-of-use carbon emissions based on the time-of-use carbon emissions and carbon capture deductions for each emission source.

[0073] In one implementation of this embodiment, the time-of-use carbon emissions of each emission source within the decomposition time interval include: time-of-use carbon emissions from fossil fuel combustion, time-of-use carbon emissions from industrial production processes, time-of-use carbon emissions from fugitive emissions, time-of-use carbon emissions from purchased electricity, time-of-use carbon emissions from purchased heat, and time-of-use carbon emissions from purchased steam.

[0074] In this embodiment, the total emissions are broken down into shorter time intervals (defined as the base time granularity). Calculations are performed for each time interval (e.g., 15 minutes). Extract the total amount of activity data within the interval from the preprocessed high-frequency data.

[0075] Specifically, in this embodiment, the power consumption data is obtained by analyzing real-time power... exist Integrating over the interval yields: unit: ).

[0076] Alternatively, you can directly read the incremental amount of electricity consumed by the smart meter during that period.

[0077] In this embodiment, the fuel consumption data is obtained by reading the cumulative consumption increment of relevant flow meters or metering devices during the time period, such as natural gas. (unit: ),coal (unit: ).

[0078] In this embodiment, the material consumption data is obtained from the MES or related production record system to obtain the material input or product output corresponding to the time period: .

[0079] In this embodiment, based on activity data and corresponding emission factors, the emission sources (ES) are calculated over a time period. The calculation process requires distinguishing between different greenhouse gases (GHG) and ultimately converting them into carbon dioxide equivalents (CO2). The summary is presented.

[0080] Among them, fossil fuel combustion:

[0081]

[0082] in, The lower heating value of fuel, The specific output of fuel combustion per unit calorific value Emission factors; This represents the global warming potential.

[0083] Emissions from industrial production processes are as follows:

[0084]

[0085] in, Data at the process activity level, such as product output and raw material consumption; Specific for unit activity level The emission factors.

[0086] Fugitive emissions are estimated based on parameters such as equipment type, quantity, operating time, and leakage rate factor, for example, estimating refrigerant leakage.

[0087] Specific time period The total carbon emissions within the country are the greenhouse gas emissions from each emission source (converted to...). The sum of ( ) and minus carbon capture: .

[0088] By summing up time-of-use emission data as needed, total emissions for different periods such as days, months, and years can be obtained. Time-of-use data provides a data foundation for analyzing emission peak and valley characteristics, identifying key emission periods and processes, and evaluating the real-time effectiveness of energy conservation and emission reduction measures.

[0089] Purchased electricity is a significant indirect source of emissions for energy-intensive enterprises. The carbon emission intensity of the power grid is dynamically affected by various factors, including real-time power generation structure (such as a combination of thermal power, hydropower, wind power, and photovoltaic power), inter-regional power exchange, and line losses. Using a static annual average emission factor cannot accurately reflect this real-time nature. Therefore, this embodiment calculates the dynamic electricity carbon emission factor of user-side nodes to more accurately account for indirect emissions related to purchased electricity.

[0090] Figure 8 The flowchart shown is a process for obtaining short-cycle dynamic carbon emission factors in a carbon flow data processing method based on a carbon accounting scenario, according to an embodiment of this application. Figure 8 As shown, in one implementation of this embodiment, the calculation of the dynamic electricity carbon emission factor of the user-side node based on the carbon flow calculation and accounting model includes:

[0091] Step S421: Collect power data from each metering point in real time, and obtain the total net electricity consumption on the user side and the amount of clean energy consumed on the user side within the period based on the consumption data and the power data.

[0092] Step S422: Calculate the clean energy consumption ratio of each power consumption node based on the total net electricity consumption on the user side during the cycle, the clean energy consumption on the user side during the cycle, and the total electricity consumption during the cycle.

[0093] Step S423: Calculate the carbon emissions of each electricity consumption node based on the clean energy consumption ratio of each electricity consumption node, the total electricity consumption of the cycle, and the carbon emission factor of the mains power itself during the time period.

[0094] Step S424: Calculate the short-cycle dynamic carbon emission factor for each electricity consumption node based on the carbon emissions of each electricity consumption node and the total electricity consumption for that cycle.

[0095] Specifically, for example, real-time collection of bidirectional power metering data from mains power inlet metering points, distributed power source (such as photovoltaic and wind power) grid-connected metering points, energy storage system interface metering points, and major load nodes (it is necessary to distinguish between input power and output power).

[0096] Total net electricity consumption on the user side during the calculation period ( Taking into account the input power from the mains. Distributed generation Distributed generation grid-connected electricity Discharge of energy storage system Energy storage system charging capacity The total net power consumption actually consumed by the user side equals the sum of the power consumption of all loads, and also equals the total system input power minus the total output power. .

[0097] Here This represents the net electricity consumption of the user side as a whole during this time period. The calculation needs to be based on the reading differences of each metering point and take into account metering configurations such as transformer ratios.

[0098] User-side clean energy consumption during the calculation period ( Assuming distributed generation prioritizes meeting local load (local consumption): The actual clean energy consumed by the user. (That is, local power generation cannot exceed local electricity demand (excluding energy storage charging and discharging). A more precise method requires tracking the actual flow of clean energy. A simplified approach is: (This method assumes that internal circuit losses are negligible).

[0099] compute nodes Clean energy consumption ratio: Assuming that the clean energy generated within the user's premises is distributed according to each electricity consumption node. The load is allocated proportionally. If the node The total electricity consumption in this cycle is The estimated amount of clean energy absorbed by this node is: .

[0100] compute nodes carbon emissions ( ): Node The electricity used can be broken down into two parts: the portion from the mains electricity grid (which accounts for the carbon emissions of the mains electricity) and the portion from clean energy sources within the user's premises (which has zero carbon emissions). Its carbon emissions are generated solely from the portion of mains electricity consumed. ,in, The carbon emission factor of the mains electricity itself during this period (unit: ). The value can be provided by the grid company (e.g., based on day-ahead generation plans and real-time generation structure forecasts), or calculated based on generation structure data released / estimated in real time by the regional grid. The emission factor for clean energy (solar, wind, etc.) generated within the user side. It is usually considered to be 0.

[0101] compute nodes Dynamic emission factors ( ): Node The equivalent dynamic emission factor is defined as the ratio of its total carbon emissions to its total electricity consumption: .

[0102] The results show that the dynamic emission factor of any electricity consumption node on the user side depends on the overall clean energy consumption ratio of the entire user-side system and the real-time emission factor of the grid power itself during that period.

[0103] Figure 9 The flowchart shown is a process for obtaining instantaneous dynamic emission factors in a carbon flow data processing method based on a carbon accounting scenario, according to an embodiment of this application. Figure 9 As shown, in one implementation of this embodiment, the calculation of the dynamic electricity carbon emission factor of the user-side node based on the carbon flow calculation and accounting model further includes:

[0104] Step S431: Collect instantaneous power data of each metering point in real time, and calculate the instantaneous total load power on the user side and the instantaneous clean energy generation power on the user side based on the consumption data and the instantaneous power data;

[0105] Step S432: Calculate the instantaneous carbon emission rate of each electricity consumption node based on the instantaneous total load power on the user side, the instantaneous clean energy generation power on the user side, the total power of the cycle, and the carbon emission factor of the mains power itself during the time period.

[0106] Step S433: Calculate the instantaneous dynamic emission factor of each power consumption node based on the instantaneous carbon emission rate of each power consumption node and the total power of the cycle.

[0107] Specifically, in this embodiment, the instantaneous dynamic carbon emission factor The calculation process is as follows:

[0108] Collect instantaneous power data from various relevant metering points. .

[0109] Calculate the instantaneous total load power on the user side .

[0110] Calculate the instantaneous clean energy generation power (net output) on the user side. .

[0111] Compute node 𝑁 instantaneous carbon emission rate .

[0112] Compute node 𝑁 instantaneous dynamic emission factor .

[0113] By implementing the above dynamic factor calculation method, the real-time carbon intensity of enterprises' actual electricity consumption can be reflected more accurately, especially in application scenarios with high penetration of distributed renewable energy.

[0114] In addition, this embodiment provides a graphical or text-based configuration interface, allowing authorized users to perform the following operations:

[0115] Selection and Instantiation: Select the appropriate formula template for the specific emission sources identified by the enterprise.

[0116] Variable binding: Explicitly link the active data variables in the formula to the corresponding real-time data measurement point labels or database fields in the data acquisition system.

[0117] Parameter configuration: This refers to the parameters included in the formula (e.g., ...). Specify the source of the value. The source can be: a standard value stored in the carbon flow database or a company-specific parameter value. Enter a fixed value directly. Link to another dynamically calculated variable (e.g., a dynamic grid factor calculated in real time).

[0118] Custom formulas: For special emission sources or calculation logic that cannot be covered by the standard template, users with the corresponding permissions and professional knowledge are allowed to write custom calculation scripts or formulas (a strict verification and approval process must be established).

[0119] Version control: Version management is implemented for formula templates and parameter configuration schemes, recording the historical information, effective date, and operator of each modification. When accounting standards are updated or enterprise production processes change, a new version of the configuration can be created and applied to the relevant emission sources, ensuring the traceability and historical consistency of the accounting process.

[0120] Figure 10 The flowchart shown is a process for minimizing the total planning cost and carbon emissions of a microgrid in a carbon flow data processing method based on a carbon accounting scenario, according to an embodiment of this application. Figure 10 As shown, in one implementation of this embodiment, the microgrid multi-objective planning model includes a first optimization objective function that minimizes the total planning cost of the microgrid and a second optimization objective function that minimizes the carbon emissions of the microgrid; the optimization of minimizing the total planning cost and minimizing the carbon emissions of the microgrid based on the microgrid multi-objective planning model includes:

[0121] Step S441: Model various devices of the microgrid based on the consumption data;

[0122] Step S442: Based on the modeling of various types of equipment, establish constraints for the first optimization objective function and the second optimization objective function; the constraints include equipment investment constraints, equipment operation constraints, power balance constraints, and upper-level network interaction power constraints;

[0123] Step S443: Solve for the range of values ​​of the first optimization objective function and the second optimization objective function, and use these as coordinates to define anchor points in the solution space;

[0124] Step S444: Normalize the solution space to obtain normalized anchor points and anchor point connections;

[0125] Step S445: Based on the normalized anchor points and anchor point connections, a constraint formula is constructed, and the first optimization objective function is used as the main objective function. The constraint formula is used to replace the second optimization objective function to construct a single-objective optimization function with the total planning cost of the entire life cycle as the optimization objective. The optimal solution of the single-objective optimization function is obtained, thereby obtaining the minimized total planning cost of the microgrid and the minimized carbon emissions of the microgrid.

[0126] The various devices in the microgrid are modeled as follows:

[0127] 1) Photovoltaics

[0128] Based on typical daily solar irradiance data, it is assumed that the photovoltaic system operates in Maximum Power Point Tracking (MPPT) mode. exist The maximum output power curve under is considered to be The predicted output power curve is given, and the actual output power of the PV at any time cannot exceed the maximum output power, as shown in equation (6); in addition, the ramping constraint of the PV between adjacent time series also needs to be considered:

[0129]

[0130] In the formula, They represent the first Time period Actual output power and The maximum output power under MPPT is determined by the current... The installed capacity and output coefficient are used to determine this; These represent the upper and lower limits of the PV ramp constraint, respectively.

[0131] 2) Energy storage devices

[0132] The operation of energy storage devices includes upper and lower limits of charging and discharging power, constraints that prevent simultaneous charging and discharging, and the characteristics of the State of Charge (SOC) changing with charging and discharging power, as well as upper and lower limits of SOC.

[0133]

[0134]

[0135] In the formula, These respectively represent the energy storage device in Charging power and discharging power during the period

[0136] Rate and state of charge; These are the charging efficiency, discharging efficiency, and capacity of the energy storage device.

[0137] quantity; The scheduling time step is determined by considering equipment scheduling on an hourly time scale. , , represent the SOC of the energy storage device at the beginning and end of the daily scheduling cycle, respectively, and T0 represents the scheduling cycle of the energy storage device.

[0138] In this embodiment, a sufficiently large positive real number is introduced. and variable The constraint is linearized to obtain a solvable linear programming model for subsequent solution.

[0139]

[0140] This indicates that the energy storage device is discharging. This indicates that the energy storage device is charging.

[0141] The first optimization objective function minimizes the total planning cost of the microgrid to optimize the economics of microgrid planning and operation; the second optimization objective function minimizes the carbon emissions of the microgrid to improve the low-carbon nature of microgrid planning and operation.

[0142] The first optimization objective function includes annualized investment costs, operating costs, and maintenance costs, aiming to minimize the total planning cost. The calculation is as follows:

[0143]

[0144] In the formula: This represents the annualized construction cost; These represent the annual operating costs and maintenance costs, respectively.

[0145] The first optimization objective function is the carbon emissions of the microgrid. A conversion factor method is used to measure the carbon emissions of the microgrid, and it is assumed that the carbon emissions of the microgrid mainly come from electricity purchased from the upper-level grid. The goal is to minimize the actual carbon emissions of the microgrid. The calculation is as follows:

[0146]

[0147] In the formula, This indicates the carbon emissions corresponding to the purchase of one unit of electricity by a microgrid.

[0148] In this embodiment, the multi-objective programming model can be simplified as follows:

[0149]

[0150] In the formula: It is a set of variables; These are the corresponding constraints. For multi-objective optimization problems, in order to obtain a uniform Pareto front, this paper adopts the Normalized Normal Constraint (NNC) method to solve the multi-objective programming model.

[0151] Consider the objective function The single-objective optimization, keeping the constraints unchanged, yields the total planning cost over the entire lifecycle. The minimum value is Then only consider the objective function. Single-objective optimization with added constraints. The carbon emissions of the park's microgrid were calculated to be... The solution is obtained. Solutions A1 and A2 are the two extreme points of a multi-objective optimization problem, which are also the anchor points in the solution space.

[0152] The solution space of a multi-objective optimization problem is normalized according to the following formula:

[0153]

[0154] Solution space normalization diagram as shown Figure 11 As shown, Figure 11 middle, These are the ordinate and abscissa of the graph, respectively. After normalization, the anchor points become... The line connecting the anchor points is the Utopia Line. .

[0155] Transform a multi-objective optimization problem into a single-objective optimization problem.

[0156] Define the Utopia Line On point Point of view The vector is At the same time, the Utopia line Dividing the sample into 'a' equal parts results in (a+1) equally spaced dividing points. The calculation method is as follows:

[0157]

[0158] In the formula: , At the dividing point Draw a perpendicular line to the Utopia line. The intersection of this perpendicular line and the Pareto front is .definition and The origin points to and The vector.

[0159] Select objective function The primary objective function is to set the objective function as the main objective function. This is transformed into additional constraints. Constraints are added to the original multi-objective optimization problem's existing constraints. Total planning cost over the entire life cycle To optimize the objective, a single-objective optimization problem is constructed as follows:

[0160]

[0161] The optimal solution to this single-objective optimization problem corresponds to a multi-objective optimization problem. A point on the frontier. Repeatedly adjust the constraints, for each split point... By successively substituting the solutions into the single-objective optimization problem, the corresponding solutions in the Pareto front for each segmentation point can be obtained, ultimately leading to a uniform Pareto front. This yields the optimal solution to the single-objective optimization function, thereby minimizing the total planning cost and carbon emissions of the microgrid.

[0162] The scope of protection for the carbon flow data processing method based on carbon accounting scenarios described in this application is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this application is included within the scope of protection of this application.

[0163] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the carbon flow data processing method based on a carbon accounting scenario provided in any embodiment of this application.

[0164] In the embodiments of this application, any combination of one or more storage media can be used. The storage medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, RAM, ROM, erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0165] This application also provides an electronic device. Figure 12 The diagram shown is a structural schematic of the electronic device 100 provided in an embodiment of this application. In some embodiments, the electronic device may be a mobile phone, tablet computer, wearable device, in-vehicle device, augmented reality (AR) / virtual reality (VR) device, laptop computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), or other terminal device. Furthermore, the carbon flow data processing method based on carbon accounting scenarios provided in this application can also be applied to databases, servers, and service response systems based on terminal artificial intelligence. This application does not limit the specific application scenarios of the carbon flow data processing method based on carbon accounting scenarios.

[0166] like Figure 12 As shown, the electronic device 100 provided in this application embodiment includes a memory 101 and a processor 102.

[0167] The memory 101 is used to store computer programs; preferably, the memory 101 includes various media that can store program code, such as ROM, RAM, magnetic disk, USB flash drive, memory card or optical disk.

[0168] Specifically, memory 101 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. Electronic device 100 may further include other removable / non-removable, volatile / non-volatile computer system storage media. Memory 101 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0169] The processor 102 is connected to the memory 101 and is used to execute the computer program stored in the memory 101 so that the electronic device 100 executes the carbon flow data processing method based on carbon accounting scenario provided in any embodiment of this application.

[0170] Optionally, the processor 102 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0171] Optionally, in this embodiment, the electronic device 100 may further include a display 103. The display 103 is communicatively connected to the memory 101 and the processor 102, and is used to display the relevant GUI interactive interface of the carbon flow data processing method based on the carbon accounting scenario.

[0172] In summary, this application can represent the characteristics of the original scenarios in the sample set with as few scenarios as possible, and based on the clustered source-load scenario data, it can calculate the time-of-use carbon emissions and the dynamic power carbon emission factors of user-side nodes, and minimize the total planning cost and carbon emissions of the microgrid. Therefore, this application effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0173] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A carbon flow data processing method based on a carbon accounting scenario, characterized in that, include: Obtain a sample set of scenarios for the characteristic curves of various energy loads in a microgrid; The optimal number of cluster centers k is determined based on the CH index. The optimal number of cluster centers k scenes are selected from the scene sample set as initial cluster centers. Based on each initial cluster center, the source load scene curve corresponding to the cluster center of each class in the scene sample set is determined. Based on the source-load scenario curve, the consumption data of the corresponding scenario is obtained, and the consumption data is input into the pre-constructed carbon flow calculation and accounting model and the microgrid multi-objective planning model. The time-of-use carbon emissions and the dynamic power carbon emission factor of user-side nodes are calculated based on the carbon flow calculation and accounting model. The total planning cost of the microgrid and the carbon emissions of the microgrid are minimized based on the microgrid multi-objective planning model.

2. The carbon flow data processing method based on carbon accounting scenarios according to claim 1, characterized in that, The step of determining the source load scene curve corresponding to the cluster center of each class in the scene sample set based on each of the initial cluster centers includes: Calculate the Euclidean distance between each scene in the scene sample set and each initial cluster center, and determine the scene category of each scene belonging to the initial cluster center with the shortest distance based on the Euclidean distance; Calculate the mean of all scenes in each scene category, and update the cluster center of the scene category based on the mean; Based on the changes in cluster centers before and after the update, the above process is repeated iteratively until the cluster centers no longer change or the preset maximum number of iterations is reached. Obtain the source load scenario curve corresponding to the cluster center when the cluster center no longer changes or reaches the preset maximum number of iterations.

3. The carbon flow data processing method based on carbon accounting scenarios according to claim 2, characterized in that, The updated cluster centers for the aforementioned scene categories are: ； in, For the first A collection consisting of all scenes within each category. Indicates the number of scenes in the category. Let i be the i-th scene.

4. The carbon flow data processing method based on carbon accounting scenarios according to claim 1, characterized in that, The calculation of time-of-use carbon emissions based on the carbon flow calculation and accounting model includes: Determine the time granularity, and determine the decomposition time interval of the consumed data based on the time granularity; The consumption data is calculated and obtained based on the carbon flow calculation and accounting model, specifically the time-sharing activity data within the decomposition time interval; the consumption data includes at least electricity consumption data, fuel consumption data, and material consumption data. The time-sharing carbon emissions of each emission source within the decomposition time interval are calculated based on the time-sharing activity data and the input carbon emission factors. The total time-of-use carbon emissions are obtained based on the time-of-use carbon emissions and carbon capture deductions for each emission source.

5. The carbon flow data processing method based on carbon accounting scenarios according to claim 4, characterized in that, The time-of-use carbon emissions from each emission source within the decomposition time interval include: time-of-use carbon emissions from fossil fuel combustion, time-of-use carbon emissions from industrial production processes, time-of-use carbon emissions from fugitive emissions, time-of-use carbon emissions from purchased electricity, time-of-use carbon emissions from purchased heat, and time-of-use carbon emissions from purchased steam.

6. The carbon flow data processing method based on carbon accounting scenarios according to claim 1, characterized in that, The calculation of the dynamic electricity carbon emission factor of the user-side node based on the carbon flow calculation and accounting model includes: Real-time collection of power data from each metering point, and based on the consumption data and the power data, obtaining the total net electricity consumption on the user side within the period and the amount of clean energy consumed on the user side within the period; The clean energy consumption ratio of each electricity consumption node is calculated based on the total net electricity consumption on the user side during the cycle, the amount of clean energy consumed on the user side during the cycle, and the total electricity consumption during the cycle. The carbon emissions of each electricity consumption node are calculated based on the clean energy consumption ratio of each electricity consumption node, the total electricity consumption of the cycle, and the carbon emission factor of the mains power itself during the period. The short-cycle dynamic carbon emission factor of each electricity consumption node is calculated based on the carbon emissions of each node and the total electricity consumption for that cycle.

7. The carbon flow data processing method based on carbon accounting scenarios according to claim 1 or 6, characterized in that, The calculation of the dynamic electricity carbon emission factor of the user-side node based on the carbon flow calculation and accounting model also includes: Real-time collection of instantaneous power data from each metering point, and calculation of the user-side instantaneous total load power and user-side instantaneous clean energy generation power based on the consumption data and the instantaneous power data; The instantaneous carbon emission rate of each electricity consumption node is calculated based on the instantaneous total load power on the user side, the instantaneous clean energy generation power on the user side, the total power of the cycle, and the carbon emission factor of the mains power itself during the time period. The instantaneous dynamic emission factor of each power consumption node is calculated based on the instantaneous carbon emission rate of each node and the total power of the cycle.

8. The carbon flow data processing method based on carbon accounting scenarios according to claim 1, characterized in that, The microgrid multi-objective programming model includes a first optimization objective function that minimizes the total planning cost of the microgrid and a second optimization objective function that minimizes the carbon emissions of the microgrid. The optimization based on the microgrid multi-objective programming model to minimize the total planning cost and carbon emissions of the microgrid includes: Modeling of various devices in the microgrid is based on the aforementioned consumption data; Based on the modeling of various types of equipment, constraints are established for the first optimization objective function and the second optimization objective function; the constraints include equipment investment constraints, equipment operation constraints, power balance constraints, and upper-level network interaction power constraints; Solve for the range of values ​​of the first and second optimization objective functions, and use these ranges as coordinates to define anchor points in the solution space; The solution space is normalized to obtain normalized anchor points and anchor point connections; Based on the standardized anchor points and anchor point connections, a constraint formula will be constructed. The first optimization objective function will be used as the main objective function, and the constraint formula will replace the second optimization objective function. A single-objective optimization function with the total planning cost over the entire life cycle as the optimization objective will be constructed, and the optimal solution of the single-objective optimization function will be obtained, thereby minimizing the total planning cost of the microgrid and minimizing the carbon emissions of the microgrid.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the carbon flow data processing method based on the carbon accounting scenario as described in any one of claims 1 to 8.

10. An electronic device, characterized in that, The electronic device includes: Processor and memory; The memory stores program instructions; The processor is configured to run the program instructions to execute the carbon flow data processing method based on a carbon accounting scenario as described in any one of claims 1 to 8.

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