A method, equipment, and medium for measuring and allocating carbon emissions in a power distribution network.

Abstract

This application discloses a method, device, and medium for carbon emission metering and responsibility allocation in a power distribution network, relating to the field of smart grid technology. The method includes: real-time acquisition of multi-source carbon emission data, preprocessing the multi-source carbon emission data to obtain standard carbon emission data; inputting the standard carbon emission data into a preset dynamic carbon factor model to obtain the dynamic carbon factor of thermal power units, and combining the dynamic carbon factor of thermal power units with the carbon factor of photovoltaic power generation systems to obtain a dynamic carbon intensity vector; performing power flow calculations on the multi-source carbon emission data to obtain a node active power flux matrix and a branch power flow distribution matrix, and combining the dynamic carbon intensity vector, the node active power flux matrix, and the branch power flow distribution matrix to obtain the total carbon potential of each node on the load side; and calculating the carbon flow rate allocated to each node on the load side based on the total carbon potential and the real-time load distribution matrix to obtain the carbon emission responsibility of each node on the load side.

Description

Technical Field

[0001] This application relates to the field of smart grid technology, and in particular to a method, equipment and medium for measuring and allocating carbon emissions in a distribution network. Background Technology

[0002] In the field of carbon emission metering for power distribution networks, the traditional approach is to use a fixed carbon emission factor based on regional or national averages, dividing total generation-side carbon emissions by total grid-connected electricity to obtain a uniform carbon emission coefficient per unit of electricity (e.g.: / kWh This method, while simple and easy to implement, cannot reflect the real-time dynamic changes in the generation side structure. Especially with the large-scale integration of intermittent renewable energy sources such as distributed photovoltaics, the carbon intensity of the power grid fluctuates dramatically over time, and the fixed-factor method cannot characterize these minute- or hour-level changes. Secondly, this method cannot distinguish the true carbon intensity of electricity used by users at different locations in the power grid, assuming all users bear the same unit carbon cost and ignoring the impact of power flow distribution, leading to unfair allocation of responsibility. Traditional life cycle assessment (LCA) methods are mostly static models, making it difficult to integrate with real-time power grid operation data. Although digital twin technology has been applied in the industrial field, it still lacks mature solutions for deep integration with dynamic carbon modeling, real-time IoT data streams, and artificial intelligence prediction models to support high-frequency carbon emission tracking and optimization at the distribution network level.

[0003] Therefore, in the field of carbon emissions, how to measure the carbon emissions of generator sets in real time by constructing a dynamic carbon factor model, and how to fairly allocate carbon emission responsibilities based on power flow calculations, have become urgent problems to be solved. Summary of the Invention

[0004] This application provides a method, device, and medium for measuring and allocating carbon emissions in a power distribution network, in order to solve the following technical problems: in the field of carbon emissions, how to measure the carbon emissions of generator sets in real time by constructing a dynamic carbon factor model, and how to fairly allocate carbon emission responsibilities based on power flow calculations.

[0005] In a first aspect, embodiments of this application provide a method, device, and medium for measuring and allocating carbon emissions in a power distribution network. The method includes: real-time acquisition of multi-source carbon emission data and preprocessing the multi-source carbon emission data to obtain standard carbon emission data; inputting the standard carbon emission data into a preset dynamic carbon factor model to obtain dynamic carbon factors for thermal power units, and combining the dynamic carbon factors of thermal power units with the carbon factors of photovoltaic power generation systems to obtain a dynamic carbon intensity vector; performing power flow calculations on the multi-source carbon emission data to obtain a node active power flux matrix and a branch power flow distribution matrix, and combining the dynamic carbon intensity vector, the node active power flux matrix, and the branch power flow distribution matrix to obtain the total carbon potential of each node on the load side; and calculating the carbon flow rate allocated to each node on the load side based on the total carbon potential and the real-time load distribution matrix to obtain the carbon emission responsibility of each node on the load side.

[0006] In one implementation of this application, the dynamic carbon factor model is represented by the following formula:

[0007]

[0008] in, This indicates the dynamic carbon factor of a thermal power unit. This represents the fixed carbon emission factor for thermal power units when they are in standby mode. This represents the carbon intensity coefficient of the generator output of a thermal power unit. The carbon intensity coefficient represents the increase in generator power per unit time. This represents the actual power output of the generator in the thermal power unit. This refers to the rated power of the generator in the thermal power unit. This represents the power enhancement rate of the generator unit per unit time in a thermal power plant.

[0009] In one implementation of this application, the dynamic carbon factor of the thermal power unit and the carbon factor of the photovoltaic power generation system are combined to obtain a dynamic carbon intensity vector. Specifically, this includes: normalizing the dynamic carbon factor of the thermal power unit and the carbon factor of the photovoltaic power generation system; and weighting and fusing the normalized dynamic carbon factor of the thermal power unit and the carbon factor of the photovoltaic power generation system according to the power generation ratio of the thermal power unit and the photovoltaic power generation system to obtain a dynamic carbon intensity vector.

[0010] In one implementation of this application, power flow calculations are performed on multi-source carbon emission data to obtain a node active power flux matrix and a branch power flow distribution matrix. Specifically, this includes: performing power flow calculations on multi-source carbon emission data to obtain voltage amplitude, phase angle, and branch active power data for each node on the load side; calculating the net active power injection power for each node on the load side based on the voltage amplitude, phase angle, and branch active power data, and combining the net active power injection power in sequence to obtain a node active power flux matrix; and constructing a branch power flow distribution matrix based on the node active power flux matrix, branch active power data, and the grid topology correlation.

[0011] In one implementation of this application, the dynamic carbon intensity vector is combined with the nodal active flux matrix and the branch power flow distribution matrix to obtain the total carbon potential of each node on the load side, expressed by the following formula:

[0012]

[0013] in, Indicates the number of load-side nodes. This represents the carbon potential of all nodes on the load side. Represents the active flux matrix of the nodes. This represents the transpose of the branch power flow distribution matrix. This represents the transpose of the thermal power unit injection distribution matrix, which consists of the connection relationship between the thermal power unit and the power system, and the active power injected by the unit into the power system. This represents the dynamic carbon intensity vector.

[0014] In one implementation of this application, multi-source carbon emission data is collected in real time, and the multi-source carbon emission data is preprocessed to obtain standard carbon emission data. Specifically, this includes: real-time monitoring and collection of operating parameters of generator sets and active power loss data of lines on the distribution network side based on sensors deployed on the distribution network side; real-time collection of energy consumption and environmental parameter data of each node based on sensors deployed on the load side; and time alignment and standardization processing of the operating parameters of generators on the distribution network side and the energy consumption data of each node on the load side to generate standard carbon emission data.

[0015] In one implementation of this application, the carbon flow rate allocated to each node on the load side is calculated based on the total carbon potential and the real-time load distribution matrix to obtain the carbon emission responsibility of each node on the load side. Specifically, this includes: performing a dot product operation between the total carbon potential and the real-time load distribution matrix to obtain the carbon flow rate vector of each node on the load side; and outputting the carbon emission responsibility of each node on the load side based on the carbon flow rate vector.

[0016] In one implementation of this application, after obtaining the carbon emission responsibility of each node on the load side, the method further includes: tracing the carbon flow source path of high-carbon load nodes based on carbon emission responsibility; establishing a carbon potential peak period early warning mechanism based on the carbon flow source path; and optimizing the dispatch strategy of generators on the distribution network side and the demand of each node on the load side to generate load adjustment suggestions.

[0017] Secondly, embodiments of this application also provide a power distribution network carbon emission metering and responsibility allocation device, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: collect multi-source carbon emission data in real time, and preprocess the multi-source carbon emission data to obtain standard carbon emission data; input the standard carbon emission data into a preset dynamic carbon factor model to obtain the dynamic carbon factor of thermal power units, and combine the dynamic carbon factor of thermal power units with the carbon factor of photovoltaic power generation systems to obtain a dynamic carbon intensity vector; perform power flow calculation on the multi-source carbon emission data to obtain a node active power flux matrix and a branch power flow distribution matrix, and combine the dynamic carbon intensity vector, the node active power flux matrix, and the branch power flow distribution matrix to obtain the total carbon potential of each node on the load side; and calculate the carbon flow rate allocated to each node on the load side based on the total carbon potential and the real-time load distribution matrix to obtain the carbon emission responsibility of each node on the load side.

[0018] Thirdly, embodiments of this application also provide a non-volatile computer storage medium for carbon emission metering and responsibility allocation in a power distribution network, storing computer-executable instructions. These instructions are configured to: collect multi-source carbon emission data in real time and preprocess the multi-source carbon emission data to obtain standard carbon emission data; input the standard carbon emission data into a preset dynamic carbon factor model to obtain the dynamic carbon factor of thermal power units, and combine the dynamic carbon factor of thermal power units with the carbon factor of photovoltaic power generation systems to obtain a dynamic carbon intensity vector; perform power flow calculations on the multi-source carbon emission data to obtain a node active power flux matrix and a branch power flow distribution matrix, and combine the dynamic carbon intensity vector, the node active power flux matrix, and the branch power flow distribution matrix to obtain the total carbon potential of each node on the load side; and calculate the carbon flow rate allocated to each node on the load side based on the total carbon potential and the real-time load distribution matrix to obtain the carbon emission responsibility of each node on the load side.

[0019] The present application provides a method, device, and medium for measuring and allocating carbon emissions in a power distribution network, which has the following advantages: By deploying sensors, real-time data from multiple sources, such as power of thermal power units, load energy consumption, and environmental data, are collected, and a dynamic carbon factor model of thermal power units is constructed and integrated with a fixed photovoltaic carbon factor to form a dynamic carbon intensity vector, providing a dynamic and multi-dimensional carbon intensity benchmark for carbon flow tracking; by calculating the active power flux matrix of nodes and the power flow distribution of branches through a power flow algorithm, and combining the dynamic carbon intensity vector, the node carbon potential is generated, thereby realizing high-precision real-time tracking and fair allocation of responsibility for carbon emissions in the power distribution network. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0021] Figure 1 A flowchart illustrating a method for measuring and allocating carbon emissions in a power distribution network, as provided in this application embodiment;

[0022] Figure 2 This is a schematic diagram of the internal structure of a power distribution network carbon emission metering and responsibility allocation device provided in an embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] This application provides a method, device, and medium for measuring and allocating carbon emissions in a power distribution network, in order to solve the following technical problems: in the field of carbon emissions, how to measure the carbon emissions of generator sets in real time by constructing a dynamic carbon factor model, and how to fairly allocate carbon emission responsibilities based on power flow calculations.

[0025] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0026] Figure 1 This is a flowchart illustrating a method for measuring and allocating carbon emissions in a power distribution network, as provided in an embodiment of this application. Figure 1 As shown in the figure, the carbon emission metering and responsibility allocation method for a power distribution network provided in this application embodiment specifically includes the following steps:

[0027] Step 10: Collect multi-source carbon emission data in real time and preprocess the multi-source carbon emission data to obtain standard carbon emission data.

[0028] As an optional embodiment, multi-source carbon emission data is collected in real time and preprocessed to obtain standard carbon emission data. Specifically, it may include: Step 101: Based on sensors deployed on the distribution network side, real-time monitoring and collection of the operating parameters of generator sets on the distribution network side and active power loss data of the lines.

[0029] In this step, an Internet of Things (IoT) sensor network is deployed on the distribution network side. This network is responsible for real-time monitoring and data collection of the operating status of generator sets and the power loss of the lines on the distribution network side. The sensors will continuously track key operating parameters such as the actual operating power of the generator sets and the power change rate per unit time. These parameters can directly reflect the current power generation level and power regulation dynamics of the generator sets. At the same time, the sensors will also collect data on the active power loss generated by the distribution network lines during power transmission in real time, providing basic data support for the subsequent accurate calculation of carbon emissions from the distribution network.

[0030] Step 102: Based on the sensors deployed on the load side, collect energy consumption and environmental parameter data of each node in real time.

[0031] In this step, corresponding sensor devices are also deployed on the load side to achieve real-time data collection on the energy consumption of each load node and the surrounding environmental conditions. From the perspective of energy consumption data, smart meters and other sensor devices are used to collect real-time energy consumption information of the entire building and different areas, clearly understanding the distribution and variation patterns of electricity consumption at each load node; from the perspective of environmental parameters, environmental sensors are used to monitor indoor temperature, humidity, and other parameters. In addition to using indicators such as concentration, indoor infrared sensors are also used to capture data on human activities. These environmental parameters and human activity data can not only provide a basis for analyzing the factors affecting load energy consumption changes, but also lay a data foundation for subsequent optimization of building energy equipment operation strategies.

[0032] Step 103: Perform time alignment and standardization on the generator operating parameters on the distribution network side and the energy consumption data of each node on the load side to generate standard carbon emission data.

[0033] This step first addresses the asynchrony between the generator operating parameters on the distribution network side and the energy consumption data of each node on the load side. Linear interpolation is used to uniformly adjust these data into a fixed time interval format, achieving data alignment in the time dimension and ensuring a consistent time base for subsequent calculations and analyses. Then, the time-aligned generator operating parameters and energy consumption data of each node on the load side are converted from their original format to a unified semantic format, and the converted data is timestamped to clearly identify the specific time. Simultaneously, data cleaning is performed to remove invalid and abnormal data, ensuring data accuracy and reliability. Through this time alignment and standardization process, the originally inconsistent and asynchronous generator operating parameters and energy consumption data of each node on the distribution network side are transformed into standardized data with a unified format, consistent time, and reliable quality. This provides high-quality input for subsequent dynamic carbon factor modeling and carbon flow allocation calculations, laying a solid data foundation for real-time carbon emission metering and responsibility allocation in the distribution network.

[0034] Step 20: Input the standard carbon emission data into the preset dynamic carbon factor model to obtain the dynamic carbon factor of the thermal power unit, and combine the dynamic carbon factor of the thermal power unit with the carbon factor of the photovoltaic power generation system to obtain the dynamic carbon intensity vector.

[0035] As an optional embodiment, standard carbon emission data is input into a preset dynamic carbon factor model to obtain the dynamic carbon factor of the thermal power unit, and the dynamic carbon factor of the thermal power unit is combined with the carbon factor of the photovoltaic power generation system to obtain a dynamic carbon intensity vector. Specifically, this includes: Step 201: Dynamic carbon factor model, represented by the following formula:

[0036]

[0037] in, This indicates the dynamic carbon factor of a thermal power unit. This represents the fixed carbon emission factor for thermal power units when they are in standby mode. This represents the carbon intensity coefficient of the generator output of a thermal power unit. The carbon intensity coefficient represents the increase in generator power per unit time. This represents the actual power output of the generator in the thermal power unit. This refers to the rated power of the generator in the thermal power unit. This represents the power enhancement rate of the generator unit per unit time in a thermal power plant.

[0038] In this step, the dynamic carbon factor model characterizes the real-time carbon emission intensity of thermal power units through mathematical formulas, in order to The dynamic carbon factor represents the thermal power unit. The fixed carbon emission factor required for thermal power units to maintain standby status is used to quantify the basic carbon emissions of the unit under minimum operating conditions. The carbon intensity coefficient for power generation by generators in thermal power units reflects the relationship between the carbon intensity of generators and the proportion of power generation. The carbon intensity coefficient represents the increase in generator power per unit time, reflecting the impact of power regulation rate on carbon emissions; It is the real-time power output of the generator in the thermal power unit during actual operation, and is directly related to the actual power generation level of the current unit; The rated power of the generator of the thermal power unit serves as a benchmark for measuring the relative level of actual power. This represents the power enhancement rate of the thermal power unit generator per unit time, used to capture the carbon emission change trend when the unit's power generation is dynamically adjusted. By substituting the real-time operating parameters of the thermal power unit in the standard carbon emission data into this formula, the dynamic carbon factor of the thermal power unit under the current operating state can be accurately calculated. The dynamic carbon factor model structure comprehensively considers the basic emissions of the unit maintaining standby state, the load level of the actual power generation relative to the rated power, and the impact of the power change rate per unit time on carbon intensity.

[0039] Step 202: Normalize the dynamic carbon factor of the thermal power unit and the carbon factor of the photovoltaic power generation system.

[0040] In this step, after completing the calculation of the dynamic carbon factor of the thermal power unit and the determination of the carbon factor of the photovoltaic power generation system, the two types of carbon factors need to be normalized first. This aims to eliminate the differences in numerical dimensions between the dynamic carbon factor of the thermal power unit and the carbon factor of the photovoltaic power generation system, ensuring that they are within a unified comparison and calculation benchmark range. This lays the foundation for subsequent weighted fusion, avoids interference with the fusion results due to the different magnitudes of the original values, and ensures the rationality and accuracy of subsequent calculations.

[0041] Step 203: Based on the power generation ratio of thermal power units and photovoltaic power generation systems, the dynamic carbon factor of the normalized thermal power units and the carbon factor of the photovoltaic power generation system are weighted and fused to obtain the dynamic carbon intensity vector.

[0042] In this step, after normalization, the two types of carbon factors are weighted and fused based on the actual power generation ratios of thermal power units and photovoltaic power generation systems in the current distribution network. The power generation ratio of thermal power units is used as the weight of their normalized dynamic carbon factors, and the power generation ratio of photovoltaic power generation systems is used as the weight of their normalized carbon factors. Through weighted calculation, the two types of carbon factors are integrated into a comprehensive indicator that reflects the overall carbon emission intensity level of the current distribution network, namely the dynamic carbon intensity vector. This vector will be updated in real time with the changes in the power generation ratios of thermal power units and photovoltaic power generation systems, accurately reflecting the impact of distribution network power generation structure adjustments on overall carbon intensity. This provides core carbon intensity basis for subsequent real-time carbon emission measurement, carbon flow allocation, and responsibility accounting of the distribution network. It is also synchronized to the carbon flow allocation module through the data pipeline of the digital twin platform to support the implementation of subsequent tasks.

[0043] Step 30: Perform power flow calculations on the multi-source carbon emission data to obtain the node active flux matrix and branch power flow distribution matrix, and combine the dynamic carbon intensity vector, node active flux matrix and branch power flow distribution matrix to obtain the total carbon potential of each node on the load side.

[0044] As an optional embodiment, power flow calculation is performed on multi-source carbon emission data to obtain the node active flux matrix and branch power flow distribution matrix. The dynamic carbon intensity vector, node active flux matrix and branch power flow distribution matrix are combined to obtain the total carbon potential of each node on the load side. Specifically, it may include: Step 301: Perform power flow calculation on multi-source carbon emission data to obtain the voltage amplitude, phase angle and branch active power data of each node on the load side.

[0045] In this step, after completing the preliminary work such as collecting, time-aligning and standardizing multi-source carbon emission data, and modeling dynamic carbon factors, power flow calculations are carried out based on these multi-source carbon emission data to obtain the key electrical parameters of each node on the load side. The power flow calculation process fully utilizes multi-source information, including generator operating parameters, line parameters, and energy consumption data of each node on the load side, collected from the distribution network side. Using power flow calculation methods such as the Newton-Raphson method, it accurately solves for the operating status of the distribution network. This power flow calculation clearly reveals the flow patterns of electrical energy in the distribution network, outputting voltage amplitude, phase angle, and branch active power data for each node on the load side. The voltage amplitude and phase angle data for each node on the load side reflect the power quality and operational stability of each node, serving as crucial evidence for assessing the reliability of the distribution network. The branch active power data visually presents the power transmission losses and power distribution along each line of the distribution network. This data not only provides core input for constructing the node active power flux matrix and branch power flow distribution matrix but also lays a critical electrical parameter foundation for further carbon flow tracking calculations, determining node carbon potential, and allocating carbon flow responsibility. This ensures that subsequent carbon flow-related analyses and calculations are closely integrated with the actual operating status of the distribution network, improving the accuracy of carbon emission metering and responsibility allocation.

[0046] Step 302: Based on the voltage amplitude, phase angle and branch active power data, calculate the net active power injection power of each node on the load side, and combine the net active power injection power in sequence to obtain the node active flux matrix.

[0047] In this step, after obtaining the voltage amplitude, phase angle, and active power data of each node on the load side through power flow calculation, the net active power injection of each node on the load side is further calculated based on these electrical parameters. Taking a node-by-node approach, the active power exchange between the node and adjacent branches is comprehensively considered, including active power input to the node and active power output from the node. By calculating the difference between input and output active power, the net active power injection of each load-side node is obtained. This power value accurately reflects the actual active power balance of a single node during the power flow process in the distribution network. After completing the calculation of the net active power injection of all load-side nodes, the calculation is performed according to a preset... By sequentially combining the net active power injection of each node according to the node numbering order or the node arrangement logic in the distribution network topology, a node active power flux matrix is ​​formed that can comprehensively characterize the active power injection of all load-side nodes in the distribution network. This matrix not only clearly presents the net active power injection scale of each load-side node, but also reflects the positional relationship and power distribution characteristics of each node in the distribution network through orderly arrangement. This provides structured active power data support for subsequent node carbon potential calculation, carbon flow allocation, and load-side carbon responsibility accounting based on carbon flow theory, ensuring that carbon flow-related calculations can accurately match the actual power flow state of the distribution network and improving the accuracy of carbon emission measurement and responsibility allocation.

[0048] Step 303: Construct a branch power flow distribution matrix based on the node active power flux matrix, branch active power data, and grid topology correlation.

[0049] In this step, after acquiring the node active power flux matrix, branch active power data, and clarifying the grid topology relationships, a branch power flow distribution matrix is ​​constructed based on these. First, the grid topology relationships clearly define the connection logic between each node and branch in the distribution network, clarifying the starting and ending nodes of each branch, providing a structural basis for determining the direction and attribution of branch power flow. Combined with the branch active power data, the actual active power transmitted by each branch under the current operating state can be obtained. The node active power flux matrix helps verify the balance between branch power and the net active power injection of nodes, ensuring data consistency and accuracy. When constructing the branch power flow distribution matrix, the branch numbering rules or grid topology are followed. The logical order of the topology integrates the key information corresponding to each branch in an orderly manner. The rows and columns of the matrix are usually associated with branch identifiers and node information, respectively. The matrix elements clearly record the core contents of each branch, such as the starting node, ending node, active power transmitted, and power flow direction. This structured matrix form can not only intuitively present the overall power distribution of the entire distribution network, but also accurately reflect the power interaction relationship between branches and nodes. It provides key branch-level power distribution data support for subsequent carbon flow tracking calculations based on carbon flow theory, determining the carbon flow transmission intensity of each branch, and ultimately achieving accurate measurement and responsibility allocation of carbon emissions in the distribution network. This ensures that carbon flow analysis is highly consistent with the actual power transmission status of the distribution network.

[0050] Step 304: Combine the dynamic carbon intensity vector with the nodal active flux matrix and the branch power flow distribution matrix to obtain the total carbon potential of each node on the load side, expressed by the following formula:

[0051]

[0052] in, Indicates the number of load-side nodes. This represents the carbon potential of all nodes on the load side. Represents the active flux matrix of the nodes. This represents the transpose of the branch power flow distribution matrix. This represents the transpose of the thermal power unit injection distribution matrix, which consists of the connection relationship between the thermal power unit and the power system, and the active power injected by the unit into the power system. This represents the dynamic carbon intensity vector.

[0053] In this step, after constructing the dynamic carbon intensity vector, the nodal active power flux matrix, and the branch power flow distribution matrix, these three core data types are combined and calculated using a specific formula to obtain the total carbon potential of each node on the load side. The nodal active power flux matrix... The distribution of net active power injection at each node on the load side and the transpose of the branch power flow distribution matrix are clearly presented. This supplements the data from the reverse direction of branch power transmission, revealing the power interaction relationship between nodes and branches. Together, these two dimensions form the fundamental data framework reflecting the power flow structure of the distribution network. Dynamic carbon intensity vector. As a system-level carbon intensity benchmark, it includes dynamic carbon factors of thermal power units and carbon factors of photovoltaic systems, quantifying the carbon emission characteristics of different units; while the transpose of the injection distribution matrix of thermal power units... Based on the connection relationship between thermal power units and the power system, and the actual active power injected by the units, the specific distribution of the power transmitted by each thermal power unit was clarified, providing a power allocation basis for the transmission of carbon flow from the generation side to the load side; the total carbon potential at each node on the load side was calculated. At that time, matrix operations are first performed. A matrix is ​​constructed to characterize the power balance relationship of nodes. This matrix essentially reflects the net power state of load-side nodes after deducting the power interaction effects of branches. Then, the inverse of this matrix is ​​obtained and combined with the transpose of the thermal power unit injection distribution matrix. and dynamic carbon intensity vector Matrix multiplication is performed sequentially. Through this series of operations, the carbon emission intensity on the power generation side is calculated. Based on power injection distribution and power flow structure of distribution network The carbon potential is precisely allocated to each node on the load side, ultimately resulting in the carbon potential of all nodes on the load side. It can dynamically reflect the carbon emission intensity borne by each unit of electricity consumed by different load nodes in the distribution network, providing key node-level carbon emission basis for subsequent load-side carbon responsibility accounting.

[0054] Step 40: Based on the total carbon potential and the real-time load distribution matrix, calculate the carbon flow rate allocated to each node on the load side to obtain the carbon emission responsibility of each node on the load side.

[0055] As an optional embodiment, the carbon flow rate allocated to each node on the load side is calculated based on the total carbon potential and the real-time load distribution matrix to obtain the carbon emission responsibility of each node on the load side. Specifically, it may include: Step 401: Perform a dot product operation between the total carbon potential and the real-time load distribution matrix to obtain the carbon flow rate vector of each node on the load side.

[0056] In this step, the total carbon potential serves as the core parameter characterizing carbon emission intensity. Each element in its matrix corresponds to the carbon potential level of a single node on the load side, directly related to the carbon emission responsibility density corresponding to the node's electricity consumption behavior. The real-time load distribution matrix focuses on the actual scale of electricity demand, with matrix elements accurately reflecting the current active power consumption of each node, demonstrating the differences in electricity load among different nodes. During the dot product operation, the elements of the total carbon potential matrix and the corresponding nodes in the real-time load distribution matrix are multiplied one by one, that is, the carbon potential value of each node is multiplied by the real-time active power consumption value of that node. The result is the amount of carbon emissions generated by that node per unit time, which is the carbon flow rate of a single node. The carbon flow rates of all nodes calculated in this way are integrated in node order to form a carbon flow rate vector that comprehensively reflects the carbon emission rate of each node on the load side. This vector not only clearly presents the magnitude of the carbon emission contribution of each load node, but also provides a precise quantitative basis for subsequently clarifying the carbon responsibility attribution on the load side, conducting carbon flow tracking, and formulating targeted emission reduction strategies.

[0057] Step 402: Based on the carbon flow rate vector, output the carbon emission responsibility of each node on the load side.

[0058] In this step, after calculating the carbon flow rate vector for each node on the load side, the carbon emission responsibility of each node is output based on this vector. Each element in the carbon flow rate vector corresponds to the carbon flow rate of a single node on the load side, which is essentially the amount of carbon emissions generated by that node per unit time based on its actual electricity consumption behavior. This directly quantifies the node's contribution to the carbon emission process of the distribution network. Since the carbon flow rate vector is obtained by multiplying the total carbon potential by the real-time load distribution matrix, the result fully integrates the intensity and scale of the node's electricity consumption, accurately reflecting the differences in carbon emission responsibility caused by the differences in electricity consumption characteristics of different nodes. Therefore, by interpreting the value of each element in the carbon flow rate vector, the share of carbon emission responsibility that each node on the load side should bear can be clearly defined. The higher the carbon flow rate value, the heavier the corresponding carbon emission responsibility, and vice versa. These clear carbon emission responsibility division results provide a clear basis for tracing the carbon flow source of high-carbon load nodes and formulating targeted emission reduction strategies. This facilitates distribution network operators and load-side users to clearly understand their own carbon responsibility status and provides data support for low-carbon decision-making.

[0059] Step 403: Based on carbon emission responsibility, trace the carbon flow source paths of high carbon load nodes.

[0060] In this step, after clarifying the carbon emission responsibilities of each node on the load side, for high-carbon load nodes with high carbon flow rate vector values ​​and heavy carbon emission responsibilities, the carbon flow source path tracing work is carried out by combining the topological relationships of the distribution network, the branch power flow distribution matrix, and the carbon flow tracing logic. First, the grid topological relationships clearly define the connection structure between high-carbon load nodes and surrounding branches, adjacent nodes, and generator sets, clarifying the physical path framework for power transmission; the branch power flow distribution matrix provides the direction and magnitude of active power transmission in each branch, reflecting the specific flow of power from the generation side to the load side, providing a quantitative basis for power flow tracing for carbon flow transmission; during the tracing process, high-carbon load nodes are used as the source path for carbon flow tracing. Starting from the load node, the power transmission path is deduced backward based on the branch power flow distribution matrix. This determines which branches the electricity consumed by the node is transmitted from upstream nodes. The source of electricity in upstream nodes is then traced further until the generator unit providing the electricity is located. At the same time, by combining the carbon factor characteristics of each unit in the dynamic carbon intensity vector, the proportion of electricity consumed by the high-carbon load node from different types of generator units can be determined simultaneously. Through this series of tracing steps, a complete carbon flow source path of the high-carbon load node is finally formed, clearly showing the source of the power generation end and the power transmission process corresponding to the node's carbon emission responsibility. This provides a precise path basis for subsequent analysis of the causes of high carbon emissions and the formulation of targeted emission reduction strategies.

[0061] Step 404: Based on the carbon flow source path, establish a carbon potential peak period early warning mechanism, and optimize the dispatch strategy of generators on the distribution network side and the demand of each node on the load side to generate load adjustment suggestions.

[0062] In this step, after tracing the carbon flow source path of high-carbon load nodes, a peak carbon potential early warning mechanism is constructed based on this path information, combined with dynamic carbon factor change patterns, historical carbon potential data, and real-time operating parameters. First, by analyzing the carbon factor fluctuation characteristics of each generator unit in the carbon flow source path, the impact weight of different generator unit power generation changes on the overall carbon potential of the distribution network is clarified. Simultaneously, by combining historical operating data from peak carbon potential periods, the correlation between carbon potential increases and generator unit scheduling and load changes is explored. Based on this, a pre-deployed lightweight artificial neural network is input with real-time collected data such as temperature and load power to predict the carbon potential change trend of the distribution network over a future period. When the predicted carbon potential reaches a set threshold, an early warning is automatically triggered, promptly pushing warning information to distribution network operators and load-side users, allowing time for subsequent adjustments. With the support of this early warning mechanism, the distribution network is further optimized. The grid-side generator scheduling strategy prioritizes generators with lower carbon factors based on the dynamic carbon factors of each thermal power unit and the carbon factor differences of the photovoltaic system along the carbon flow source path. During periods of sufficient photovoltaic power generation, the power generation ratio of high-carbon-factor thermal power units is appropriately reduced, and the carbon factors of thermal power units are updated in real time through a dynamic carbon factor model to reduce total carbon emissions. Simultaneously, the power transmission path is adjusted in conjunction with the branch power flow distribution to ensure that low-carbon generators prioritize supplying high-carbon-potential nodes, thereby reducing node carbon potential. For each load-side node, load adjustment suggestions are generated based on the node carbon emission causes reflected in the carbon flow source path and the real-time load distribution. During peak carbon potential periods, it is recommended to reduce non-essential loads such as air conditioning and lighting for high-carbon load nodes to reduce their dependence on high-carbon generators. During peak photovoltaic power generation and periods of low carbon potential, load-side nodes are guided to prioritize the use of electrically driven equipment to improve the absorption rate of low-carbon generators.

[0063] The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide a power distribution network carbon emission metering and responsibility allocation device, the structure of which is as follows: Figure 2 As shown.

[0064] Figure 2 This is a schematic diagram of the internal structure of a power distribution network carbon emission metering and responsibility allocation device provided in an embodiment of this application. Figure 2 As shown, the device includes:

[0065] At least one processor 201;

[0066] And a memory 202 that is communicatively connected to at least one processor;

[0067] The memory 202 stores instructions executable by at least one processor. These instructions are executed by at least one processor 201 to enable the processor 201 to: collect multi-source carbon emission data in real time and preprocess the multi-source carbon emission data to obtain standard carbon emission data; input the standard carbon emission data into a preset dynamic carbon factor model to obtain the dynamic carbon factor of the thermal power unit, and combine the dynamic carbon factor of the thermal power unit with the carbon factor of the photovoltaic power generation system to obtain a dynamic carbon intensity vector; perform power flow calculation on the multi-source carbon emission data to obtain the node active power flux matrix and the branch power flow distribution matrix, and combine the dynamic carbon intensity vector, the node active power flux matrix, and the branch power flow distribution matrix to obtain the total carbon potential of each node on the load side; and calculate the carbon flow rate allocated to each node on the load side based on the total carbon potential and the real-time load distribution matrix to obtain the carbon emission responsibility of each node on the load side.

[0068] Some embodiments of this application provide corresponding to Figure 1 A non-volatile computer storage medium for carbon emission metering and responsibility allocation in a power distribution network stores computer-executable instructions. These instructions are configured to: collect multi-source carbon emission data in real time and preprocess the data to obtain standard carbon emission data; input the standard carbon emission data into a preset dynamic carbon factor model to obtain the dynamic carbon factor of thermal power units, and combine the dynamic carbon factor of thermal power units with the carbon factor of photovoltaic power generation systems to obtain a dynamic carbon intensity vector; perform power flow calculations on the multi-source carbon emission data to obtain a node active power flux matrix and a branch power flow distribution matrix, and combine the dynamic carbon intensity vector, node active power flux matrix, and branch power flow distribution matrix to obtain the total carbon potential of each node on the load side; and calculate the carbon flow rate allocated to each node on the load side based on the total carbon potential and the real-time load distribution matrix to obtain the carbon emission responsibility of each node on the load side.

[0069] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0070] The systems, media, and methods provided in this application are one-to-one correspondences. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0071] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0075] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0076] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0077] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0079] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for measuring and allocating responsibility for carbon emissions in a power distribution network, characterized in that, The method includes: Multi-source carbon emission data is collected in real time and preprocessed to obtain standard carbon emission data; wherein, the multi-source carbon emission data includes generator operating parameters on the distribution network side and energy consumption data of each node on the load side; The standard carbon emission data is input into a preset dynamic carbon factor model to obtain the dynamic carbon factor of the thermal power unit, and the dynamic carbon factor of the thermal power unit is combined with the carbon factor of the photovoltaic power generation system to obtain a dynamic carbon intensity vector; wherein, the carbon factor of the photovoltaic power generation system is a fixed value calculated by an internationally recognized life cycle assessment method. Power flow calculations are performed on the multi-source carbon emission data to obtain the node active power flux matrix and branch power flow distribution matrix. The dynamic carbon intensity vector, the node active power flux matrix, and the branch power flow distribution matrix are then combined to obtain the total carbon potential of each node on the load side. The node active power flux matrix includes carbon flow sources and carbon flow sinks. The branch power flow distribution matrix is ​​used to achieve path tracking of carbon flows. Based on the total carbon potential and the real-time load distribution matrix, the carbon flow rate allocated to each node on the load side is calculated to obtain the carbon emission responsibility of each node on the load side. The method further includes: The dynamic carbon factor model is expressed by the following formula: in, This indicates the dynamic carbon factor of a thermal power unit. This represents the fixed carbon emission factor for thermal power units when they are in standby mode. This represents the carbon intensity coefficient of the generator output of a thermal power unit. The carbon intensity coefficient represents the increase in generator power per unit time. This represents the actual power output of the generator in the thermal power unit. This refers to the rated power of the generator in the thermal power unit. This represents the power enhancement rate of the generator unit per unit time in a thermal power unit. The dynamic carbon factor of the thermal power unit is combined with the carbon factor of the photovoltaic power generation system to obtain a dynamic carbon intensity vector, specifically including: The dynamic carbon factor of the thermal power unit and the carbon factor of the photovoltaic power generation system are normalized. Based on the power generation ratio of the thermal power unit and the photovoltaic power generation system, the dynamic carbon factor of the normalized thermal power unit and the carbon factor of the photovoltaic power generation system are weighted and fused to obtain the dynamic carbon intensity vector. Power flow calculations are performed on the multi-source carbon emission data to obtain the node active power flux matrix and branch power flow distribution matrix, specifically including: Power flow calculations are performed on the multi-source carbon emission data to obtain the voltage amplitude, phase angle, and active power data of each node on the load side. Based on the voltage amplitude, the phase angle, and the active power data of the branch, the net active power injection power of each node on the load side is calculated, and the net active power injection power is combined in sequence to obtain the active power flux matrix of the node. Based on the node active power flux matrix, the branch active power data, and the grid topology correlation, the branch power flow distribution matrix is ​​constructed.

2. The method for measuring and allocating carbon emissions in a power distribution network according to claim 1, characterized in that, The dynamic carbon intensity vector, the nodal active flux matrix, and the branch power flow distribution matrix are combined to obtain the total carbon potential of each node on the load side, expressed by the following formula: in, Indicates the number of load-side nodes. This represents the carbon potential of all nodes on the load side. Represents the active flux matrix of the nodes. This represents the transpose of the branch power flow distribution matrix. This represents the transpose of the thermal power unit injection distribution matrix, which consists of the connection relationship between the thermal power unit and the power system, and the active power injected by the unit into the power system. This represents the dynamic carbon intensity vector.

3. The method for measuring and allocating carbon emissions in a power distribution network according to claim 1, characterized in that, Real-time acquisition of multi-source carbon emission data, followed by preprocessing of the multi-source carbon emission data to obtain standard carbon emission data, specifically including: Based on sensors deployed on the distribution network side, the operating parameters of the generator sets on the distribution network side and the active power loss data of the lines are monitored and collected in real time. Based on sensors deployed on the load side, energy consumption and environmental parameter data of each node are collected in real time. The operating parameters of the generators on the distribution network side and the energy consumption data of each node on the load side are time-aligned and standardized to generate the standard carbon emission data.

4. The method for measuring and allocating carbon emissions in a power distribution network according to claim 1, characterized in that, Based on the total carbon potential and the real-time load distribution matrix, the carbon flow rate allocated to each node on the load side is calculated to obtain the carbon emission responsibility of each node on the load side, specifically including: The total carbon potential is multiplied by the real-time load distribution matrix to obtain the carbon flow rate vector of each node on the load side. Based on the carbon flow rate vector, the carbon emission responsibility of each node on the load side is output.

5. The method for measuring and allocating carbon emissions in a power distribution network according to claim 1, characterized in that, After obtaining the carbon emission responsibility of each node on the load side, the method further includes: Based on the aforementioned carbon emission responsibility, trace the carbon flow source paths of high carbon load nodes; Based on the carbon flow source path, an early warning mechanism for peak carbon potential periods is established, and the scheduling strategy of generators on the distribution network side and the demand of each node on the load side are optimized to generate load adjustment suggestions.

6. A device for measuring and assigning responsibility for carbon emissions in a power distribution network, characterized in that, The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method as described in any one of claims 1-5.

7. A non-volatile computer storage medium for carbon emission metering and responsibility allocation in a power distribution network, storing computer-executable instructions, characterized in that, When the computer-executable instructions are executed, they implement the method as described in any one of claims 1-5.

Images