Electric power carbon emission flow metering method and device based on edge-end cooperation

Abstract

The invention discloses an electric power carbon emission flow metering method and device based on end-to-end cooperation, and relates to the technical field of carbon emission, comprising data acquisition and processing, intelligent terminal processing and fine management, end-to-end cooperation and carbon emission allocation, data acquisition and processing through end-to-end equipment based on a carbon emission model, and abnormal data removal. Electric power data of each node of the park are collected in real time through side-end equipment, accurate accounting of carbon emission flow is carried out in combination with power flow analysis of a power system, data collection, carbon emission calculation and responsibility allocation are completed in a terminal system by adopting a side-end cooperation technology, efficient cooperation and accurate calculation of data among different nodes are ensured, and meanwhile, the system has the advantages of being simple in structure and convenient to use. The distributed photovoltaic and energy storage system characteristic fine management optimizes energy scheduling and carbon emission calculation by considering the charging and discharging efficiency and energy storage photovoltaic state characteristics of the distributed photovoltaic power generation and energy storage system in detail, and further improves the accuracy and efficiency of carbon emission accounting.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission technology, and in particular to a method and device for measuring electric power carbon emission flow based on edge collaboration. Background Art

[0002] In the context of global climate change response, the power system, as a key area of carbon emissions, has attracted widespread attention for its carbon emissions measurement and management. With the development of a low-carbon economy and green energy technologies, scientifically measuring and managing carbon emissions has become a key challenge for energy management and environmental protection. In cities, industrial parks, and industrial zones, the need for accurate and real-time monitoring and management of energy consumption and carbon emissions is becoming increasingly urgent.

[0003] my country has published a variety of fixed electricity carbon emission factors, but these factors struggle to reflect the temporal and spatial variations in the large-scale integration of renewable energy, and their effectiveness in guiding carbon reduction is insufficient. Furthermore, my country's vast territory and vast differences in resource endowments across provinces make the national average factor inadequate to reflect the spatial distribution of electricity carbon emissions.

[0004] Current power system carbon emission measurement relies primarily on estimating overall grid-side carbon emission factors or carbon emission flow models based on traditional power flow analysis. These methods typically only consider the carbon emission intensity of the power generation side, without deeply tracking the dynamic carbon emission contributions of each node and branch during the power transmission process. Furthermore, existing technologies lack detailed modeling of the interactive carbon emission characteristics of distributed energy resources (such as distributed photovoltaics, energy storage, and charging stations). This makes it difficult to achieve real-time and accurate carbon emission responsibility allocation, especially in complex campus-level energy systems.

[0005] Through analysis of existing technologies, it is found that there are the following technical problems that need to be solved urgently: 1. Insufficient spatiotemporal resolution. Existing accounting methods based on fixed electricity carbon factors cannot reflect the temporal variability caused by fluctuations in renewable energy output. Research shows that electricity carbon emissions calculated using annual factors can have deviations of up to 35%. Furthermore, a unified national factor fails to capture the spatial distribution of carbon emissions due to differences in energy mix between eastern and western power grids, leading to systematic errors in park-level carbon responsibility allocation.

[0006] 2. Lack of dynamic tracking capabilities. Existing power flow analysis models have significant limitations when dealing with distributed energy systems. They do not consider the impact of bidirectional power flows of distributed photovoltaic, energy storage and other equipment on the carbon flow path; they lack modeling of the interactive characteristics of carbon emissions from new elements such as electric vehicle charging piles and flexible loads, resulting in distorted node carbon flow calculations; and they rely on offline simulation calculations, making it impossible to achieve real-time carbon flow tracking at the minute level.

[0007] 3. The edge-end collaboration mechanism is weak, the centralized data processing platform has inherent defects, and data collection relies on periodic uploads (which makes it difficult to meet the needs of real-time carbon flow tracking). The computing power of edge devices is not effectively utilized, resulting in bandwidth pressure and processing delays caused by massive data uploads. The "cloud-edge-end" collaborative computing framework has not been established, and it cannot adapt to the distributed architecture characteristics of the park-level energy Internet.

[0008] 4. The carbon responsibility allocation mechanism is unscientific. Existing technologies lack the ability to trace carbon responsibility at multiple time scales and fail to distinguish between the dynamic carbon contribution differences between baseload power sources and peak-shaving power sources, resulting in unclear attribution of carbon emissions in the transmission and distribution links.

[0009] In view of this, the present invention is proposed to solve the above technical problems. Summary of the Invention

[0010] The purpose of the present invention is to provide a method and device for measuring electricity carbon emission flow based on edge collaboration, so as to solve the technical problems in the existing technology of power system carbon emission measurement, such as insufficient temporal and spatial resolution, lack of dynamic tracking capability, weak edge collaboration mechanism and unscientific carbon responsibility sharing mechanism.

[0011] The first object of the present invention is to provide a method for measuring electricity carbon emissions based on edge collaboration, comprising: Step 1: Data collection and processing: Based on the carbon emission model, data is collected and processed by edge devices to remove abnormal data and ensure the consistency of data from different devices. Step 2: Intelligent terminal processing and refined management: The software platform processes the collected data, analyzes the operating characteristics of the distributed photovoltaic power generation and energy storage system, optimizes management based on carbon emission intensity, and calculates the carbon potential of each node; Step 3: Edge collaboration and carbon emission sharing. The data terminal uploads the carbon potential of each node to the edge device, and calculates the carbon emissions based on the real-time load data of the node. All calculated carbon emissions are then uploaded to the software platform for in-depth analysis. Carbon emission responsibility is shared based on actual energy consumption, and finally node carbon factor-related data for each node in the park is generated.

[0012] Preferably, in step 1, based on the carbon emission model , data collection and processing for model parameters; Among them, the data type includes each node in the park, and the power data of each node in the park is collected in real time through edge devices.

[0013] Preferably, the calculation of carbon potential is based on a carbon emission flow model, which is based on power system flow analysis and tracks carbon emissions at each node and branch in the power system; The carbon potential is defined as: ; in, is the outflow power of the branch, is the carbon flow density of the branch, Inject power into the node, is the injected carbon flux density; Written in matrix form: ,in, is the N-dimensional unit row vector; Then, the carbon potential calculation formula of all nodes is: ; in, is the branch power flow distribution matrix, Inject the distribution matrix into the unit, is the node active flux matrix.

[0014] Preferably, the carbon emission flow model can be used to obtain: (1) Node carbon potential injected by the upper grid = Carbon emission intensity of the grid connection point ; (2) Calculate the carbon potential and carbon emissions of each node: Among them, the carbon potential of distributed photovoltaic ; (3) When the energy storage system is in the first charging stage, the carbon emission intensity of the electricity in the system meets the following requirements: ; in, e s ( T ) represents the carbon emission factor of the energy storage system when it switches from the charging state to the discharging state at time T; η represents the cycle efficiency of the energy storage element; e c ( t )and p c ( t ) represent the carbon flux density and active power injected into the branch at a certain moment when the energy storage element is in the charging state; e s ( t )and p s ( t ) represent the carbon flow density and active power flowing out of the branch at a certain moment when the energy storage element is in the discharge state; (4) When the energy storage system is discharged: for the injection node, the carbon potential of the energy storage injection node is output, and the injection power = distributed photovoltaic injection power + upper grid injection power + energy storage injection power; When the energy storage is a load node: output the carbon flow rate of the energy storage load point line, carbon and sulfur density, node potential, injection power = distributed photovoltaic injection power + upper grid injection power.

[0015] Preferably, edge collaboration and carbon emission sharing include: Edge data transmission and collaborative computing: based on carbon emission model The data terminal uploads the carbon potential of each node to the edge device of each node. The edge device combines the real-time load data of the node. The edge device uploads the measured data to the terminal for in-depth analysis. Based on the actual energy consumption, the software platform automatically allocates the carbon emission responsibility.

[0016] Preferably, carbon emission responsibility is shared: the carbon emission flow of each node is shared according to the actual energy consumption.

[0017] Preferably, the principle of carbon emission responsibility sharing is based on the following elements: park load, distributed photovoltaics, energy storage and charging piles; Carbon emissions are allocated according to their electricity consumption and injected carbon factors. Based on the carbon emission data of each node calculated by edge equipment, the system generates the carbon emission situation of the park.

[0018] A second object of the present application is to provide a device for executing any of the aforementioned edge-to-edge collaboration-based power carbon emission flow metering methods, comprising: Hardware architecture, which includes edge devices. Metering equipment supports data collection and transmission of edge devices. Edge devices are connected to the central processor through electrical signals. Edge devices are used to realize data collection, storage and calculation, integrate the calculation of carbon emission flows and the collection of carbon emission data; The software platform receives data transmitted by the metering equipment and processes, calculates and analyzes the data for carbon emission accounting, node carbon potential calculation and allocation.

[0019] By adopting the above technical solution, the present invention has the following beneficial effects: 1. By collecting real-time power data from each node within the park and combining it with power flow analysis to calculate carbon emissions, the accuracy of carbon emissions calculations is ensured. The application of a carbon emission flow model can track the real-time carbon emissions of each node and branch, effectively improving the accuracy of carbon emission accounting.

[0020] 2. Utilizing edge devices and edge-to-edge collaboration technology, we achieve efficient coordination of data collection, carbon emissions calculation, and responsibility allocation. Edge-to-edge collaboration not only improves data transmission efficiency but also enables precise coordination of data between nodes, enhancing the system's real-time responsiveness.

[0021] 3. By comprehensively considering the charging and discharging characteristics of distributed photovoltaic green electricity and energy storage systems at the energy injection nodes, the energy consumption and carbon emissions of each energy-consuming entity in the park can be finely managed, and the carbon emissions of each energy-consuming entity can be calculated more accurately, thereby achieving more reasonable energy allocation and carbon emission control.

[0022] 4. Based on real-time energy consumption, the system automatically allocates carbon emission responsibilities. Through in-depth analysis of the carbon-to-carbon table, it generates data such as carbon flow rate and carbon flow density for each node, providing an accurate basis for subsequent management and decision-making, and contributing to the scientific management and optimization of the park's carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are part of this application and are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but do not constitute an undue limitation of the present invention. Obviously, the drawings described below are only some embodiments. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without inventive effort. In the accompanying drawings: Figure 1 This is a flowchart of a method for measuring electricity carbon emission flow based on edge collaboration provided in this embodiment of the application.

[0024] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but rather to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0025] The specific embodiments of the present invention will be further described in detail with reference to the accompanying drawings.

[0026] See also Figure 1 As shown, the embodiment of the present application provides a method for measuring electricity carbon emission flow based on edge collaboration, including: Step 1: Data collection and processing: Based on the carbon emission model, data is collected and processed by edge devices to remove abnormal data and ensure the consistency of data from different devices. Edge devices include electricity carbon meters, smart meters, and inverters. Step 2: Intelligent terminal processing and refined management. The software platform processes the collected data, analyzes the operating characteristics of the distributed photovoltaic power generation and energy storage system (power output, charge and discharge efficiency, and state of charge (SOC)), optimizes management based on carbon emission intensity, and calculates the carbon potential of each node. Based on the principle of carbon emission flow, for a given power system, the system grid structure connectivity, the number of generator units, and their carbon emission intensity are determined, and the node carbon potential of the generator is also determined. The network node connectivity can be further inferred from the node carbon potential of the nodes connected to the generator, and then the node carbon potential of the entire network. Based on this, the carbon emission flow density of each branch in the network is derived, and the carbon flow rate of each branch is calculated based on the known system power flow.

[0027] Step 3: Edge collaboration and carbon emission sharing. The data terminal uploads the carbon potential of each node to the electricity carbon meter. The electricity carbon meter calculates the carbon emissions based on the real-time load data of the node, and then uploads all calculated carbon emissions to the software platform for in-depth analysis. The carbon emission responsibility is shared based on the actual energy consumption situation, and finally the node carbon factor (kgCO2 / kWh) related data of each node in the park is generated.

[0028] In the above solution, power data from each node in the park is collected in real time through edge devices, and combined with power system flow analysis, accurate carbon emission flow is calculated. Edge collaborative technology is used to complete data collection, carbon emission calculation and responsibility sharing in the terminal system, ensuring efficient collaboration and accurate calculation of data between different nodes. At the same time, the refined management of distributed photovoltaic and energy storage system characteristics optimizes energy scheduling and carbon emission calculation by detailed consideration of the charging and discharging efficiency and energy storage volt-state characteristics of distributed photovoltaic power generation and energy storage systems, further improving the accuracy and efficiency of carbon emission accounting.

[0029] In step 1, based on the carbon emission model ,Data collection and processing for model parameters, the boundary range of this paper considers a typical park; Among them, the data types include various nodes in the park, and the power data of each node in the park is collected in real time through edge devices. The nodes in the park include park loads, distributed photovoltaics, energy storage and charging piles.

[0030] Data Source Data types collected: Park nodes (park loads, distributed photovoltaics, energy storage, charging piles, etc.) collect data such as power, electricity, energy storage status, and charge and discharge efficiency in real time through smart meters, inverters, and other equipment.

[0031] Distributed photovoltaic data: distributed photovoltaic power generation and injection power.

[0032] Energy storage data: energy storage, energy storage charging and discharging power, efficiency, etc.

[0033] Grid data: power injected into each node of the grid, node type, node line topology, and carbon factor (carbon emission intensity of the grid).

[0034] Collection equipment Smart meters and inverters: Using the Internet of Things (IoT) to connect devices to data collection terminals, smart meters and inverters collect power data from each node.

[0035] Data processing Data cleaning and filtering: Remove abnormal data (such as incorrect power measurements, power outages, etc.).

[0036] Data synchronization: Each node uses different devices to collect data, and the data needs to be synchronized to ensure the uniformity of data from different devices.

[0037] The calculation of carbon potential is based on the carbon emission flow model, which is based on power system flow analysis and tracks the carbon emissions of each node and branch in the power system; The carbon potential is defined as: ; in, is the outflow power of the branch, is the carbon flow density of the branch, Inject power into the node, is the injected carbon flux density; Written in matrix form: ,in, is the N-dimensional unit row vector; Then, the carbon potential calculation formula of all nodes is: ; in, is the branch power flow distribution matrix, which is an N-order square matrix; is the unit injection distribution matrix, which is a K×N order square matrix; is the node active flux matrix, which is an N-order square matrix.

[0038] By comprehensively considering the charging and discharging characteristics of distributed photovoltaic green electricity and energy storage systems at the energy injection node, the energy consumption and carbon emissions of each energy-consuming entity in the park can be finely managed. This can more accurately calculate the carbon emissions of each energy-consuming entity, thereby achieving more reasonable energy distribution and carbon emission control. Based on the park's power grid topology and the power flow data of each node, the carbon emission flow model can be used to obtain: (1) Node carbon potential injected by the upper grid = Carbon emission intensity of the grid connection point ; (2) Calculate the carbon potential and carbon emissions of each node: Among them, the carbon potential of distributed photovoltaic ; (3) When the energy storage system is in the first charging stage, the carbon emission intensity of the electricity in the system meets the following requirements: ; in, e s ( T ) represents the carbon emission factor of the energy storage system when it switches from the charging state to the discharging state at time T; η represents the cycle efficiency of the energy storage element; e c ( t )and p c ( t) represent the carbon flux density and active power injected into the branch at a certain moment when the energy storage element is in the charging state; e s ( t )and p s ( t ) represent the carbon flow density and active power flowing out of the branch at a certain moment when the energy storage element is in the discharge state; (4) When the energy storage system is discharged: for the injection node, the carbon potential of the energy storage injection node is output, and the injection power = distributed photovoltaic injection power + upper grid injection power + energy storage injection power; When the energy storage is a load node: output the carbon flow rate of the energy storage load point line, carbon and sulfur density, node potential, injection power = distributed photovoltaic injection power + upper grid injection power.

[0039] Edge collaboration and carbon emission sharing include: Edge data transmission and collaborative computing: based on carbon emission model The data terminal uploads the carbon potential of each node to the electric carbon meter of each node. The electric carbon meter combines the real-time load data of the node to complete the carbon emission calculation of the node. The electric carbon meter uploads the measured data to the terminal for in-depth analysis. Based on the actual energy consumption, the software platform automatically allocates the carbon emission responsibility and generates carbon flow indicators, branch carbon flow rate (kgCO2 / kWh), branch carbon flow density (kgCO2 / h) and node carbon factor (kgCO2 / kWh) for each node in the park, providing accurate basis for subsequent management and decision-making.

[0040] Carbon emission responsibility allocation: The carbon emission flow of each node is allocated according to the actual energy consumption.

[0041] The principle of carbon emission responsibility sharing is based on the following elements: park load, distributed photovoltaics, energy storage and charging piles; Carbon emissions are allocated according to their electricity consumption and injected carbon factors. Based on the carbon emission data of each node calculated by edge equipment, the system generates the carbon emission situation of the park.

[0042] Furthermore, a device for executing the above-mentioned edge-to-edge collaboration-based electricity carbon emission flow metering method includes: Hardware architecture, which includes edge devices. Metering equipment supports data collection and transmission of edge devices. Edge devices are connected to the central processor through electrical signals. Edge devices are used to realize data collection, storage and calculation, integrate the calculation of carbon emission flows and the collection of carbon emission data; The software platform receives data transmitted by the metering equipment and processes, calculates and analyzes the data for carbon emission accounting, node carbon potential calculation and allocation.

[0043] Smart meters and inverters: Acquire and transmit real-time power, electricity consumption, energy storage status and other information of each node in the park.

[0044] Electricity carbon table: used to calculate node carbon emission data.

[0045] Edge devices (such as gateways): Merge central processing systems and edge devices to achieve data collection, storage and calculation, and integrate carbon emission flow calculation and carbon emission data aggregation functions.

[0046] The software platform is responsible for data processing, calculation and analysis tasks, and supports the efficient operation of the system.

[0047] Data collection, storage, and computing functions: Real-time collection of data from each node is performed through IoT technology.

[0048] Carbon emission flow calculation function: realizes carbon emission flow calculation, node carbon potential calculation and allocation functions.

[0049] This edge-to-edge collaborative power carbon emission flow metering method and device collects real-time data from each node in the industrial park and combines it with power flow analysis to accurately calculate carbon emissions. This edge-to-edge collaborative technology ensures efficient data collection, carbon emission calculation, and responsibility allocation. Furthermore, through refined management of the characteristics of distributed photovoltaic and energy storage systems, energy scheduling is optimized, further improving the accuracy and efficiency of carbon emission accounting. This addresses the issues of traditional carbon emission accounting methods, such as poor timeliness, coarse granularity, and low accuracy.

[0050] This specific embodiment is merely an explanation of the invention and is not a limitation of the invention. After reading this specification, those skilled in the art may make non-creative modifications to this embodiment as needed. However, as long as they are within the scope of protection of the invention, they are protected by patent law.

Claims

1. A method for measuring electricity carbon emission flow based on edge collaboration, characterized in that: include: Step 1: Data collection and processing: Based on the carbon emission model, data is collected and processed by edge devices to remove abnormal data and ensure the consistency of data from different devices. Step 2: Intelligent terminal processing and refined management: The software platform processes the collected data, analyzes the operating characteristics of the distributed photovoltaic power generation and energy storage system, optimizes management based on carbon emission intensity, and calculates the carbon potential of each node; Step 3: Edge collaboration and carbon emission sharing. The data terminal uploads the carbon potential of each node to the edge device, and calculates the carbon emissions based on the real-time load data of the node. All calculated carbon emissions are then uploaded to the software platform for in-depth analysis. Carbon emission responsibility is shared based on actual energy consumption, and finally node carbon factor-related data for each node in the park is generated.

2. The method for measuring electricity carbon emission flow based on edge collaboration according to claim 1 is characterized in that: In step 1, based on the carbon emission model , data collection and processing for model parameters; Among them, the data type includes each node in the park, and the power data of each node in the park is collected in real time through edge devices.

3. The method for measuring electricity carbon emission flow based on edge collaboration according to claim 2 is characterized in that: The carbon potential is calculated based on a carbon emission flow model, which is based on power system flow analysis and tracks carbon emissions at each node and branch in the power system. The carbon potential is defined as: ; in, is the outflow power of the branch, is the carbon flow density of the branch, Inject power into the node, is the injected carbon flux density; Written in matrix form: ,in, is the N-dimensional unit row vector; Then, the carbon potential calculation formula of all nodes is: ; in, is the branch power flow distribution matrix, Inject the distribution matrix into the unit, is the node active flux matrix.

4. The method for measuring electricity carbon emission flow based on edge collaboration according to claim 3 is characterized in that: The carbon emission flow model can be obtained as follows: (1) Node carbon potential injected by the upper grid = Carbon emission intensity of the grid connection point ; (2) Calculate the carbon potential and carbon emissions of each node: Among them, the carbon potential of distributed photovoltaic ; (3) When the energy storage system is in the first charging stage, the carbon emission intensity of the electricity in the system meets the following requirements: ; in, e s ( T ) represents the carbon emission factor of the energy storage system when it switches from the charging state to the discharging state at time T; η represents the cycle efficiency of the energy storage element; e c ( t )and p c ( t ) represent the carbon flux density and active power injected into the branch at a certain moment when the energy storage element is in the charging state; e s ( t )and p s ( t ) represent the carbon flow density and active power flowing out of the branch at a certain moment when the energy storage element is in the discharge state; (4) When the energy storage system is discharged: for the injection node, the carbon potential of the energy storage injection node is output, and the injection power = distributed photovoltaic injection power + upper grid injection power + energy storage injection power; When the energy storage is a load node: output the carbon flow rate of the energy storage load point line, carbon and sulfur density, node potential, injection power = distributed photovoltaic injection power + upper grid injection power.

5. The method for measuring electricity carbon emission flow based on edge collaboration according to claim 4 is characterized in that: The edge collaboration and carbon emission sharing include: Edge data transmission and collaborative computing: based on carbon emission model The data terminal uploads the carbon potential of each node to the edge device of each node. The edge device combines the real-time load data of the node. The edge device uploads the measured data to the terminal for in-depth analysis. Based on the actual energy consumption, the software platform automatically allocates the carbon emission responsibility.

6. The method for measuring electricity carbon emission flow based on edge collaboration according to claim 4 is characterized in that: The carbon emission responsibility allocation is to allocate the carbon emission flow of each node according to the actual energy consumption.

7. The method for measuring electricity carbon emission flow based on edge collaboration according to claim 5 is characterized in that: The principle of carbon emission responsibility sharing is based on the following elements: park load, distributed photovoltaic, energy storage and charging piles; Carbon emissions are allocated according to their electricity consumption and injected carbon factors. Based on the carbon emission data of each node calculated by edge equipment, the system generates the carbon emission situation of the park.

8. A device for executing the method for measuring electric carbon emission flow based on edge collaboration according to any one of claims 1 to 7, characterized in that: include: The hardware architecture includes edge devices, the metering devices support data collection and transmission of the edge devices, the edge devices are electrically connected to the central processor, and the edge devices are used to implement data collection, storage, and calculation, integrate the calculation of carbon emission flows, and aggregate carbon emission data; A software platform receives data transmitted by the metering equipment and processes, calculates and analyzes the data for carbon emission accounting, node carbon potential calculation and allocation.

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