A grid carbon flow collaborative metering method and system based on a plug-and-play electric carbon meter

Abstract

The application discloses a kind of based on plug and play electric carbon table's power grid carbon flow collaborative metering method and system, belong to wisdom energy and carbon emission monitoring technical field, for solving the technical problem that present technique in power grid topology structure rigidity rigidification, cannot be flexibly expanded, in access new equipment, cannot achieve plug and play technology problem.The based on plug and play electric carbon table's power grid carbon flow collaborative metering method and system described in the application, when distributed resource accesses, electric carbon table initiates registration request containing equipment identification to system main station automatically;System main station identifies node type accordingly, determines adaptive strategy based on pre-stored node type and carbon emission metering strategy mapping relationship, and sends the carbon emission data generated to electric carbon table display, to realize the plug and play of electric carbon table, the flexible expansion of distributed resource and the efficient collaborative metering of power grid carbon flow.

Description

Technical Field

[0001] This invention relates to a method and system for coordinated metering of grid carbon flow based on a plug-and-play carbon meter, belonging to the field of smart energy and carbon emission monitoring technology. Background Technology

[0002] The power grid is an effective way to integrate distributed photovoltaic, wind power, energy storage and diverse loads. It is a key carrier for achieving the "dual carbon" goal. Accurate carbon emission monitoring within the power grid is the foundation for promoting its low-carbon operation, which is especially critical in industrial park microgrids.

[0003] There are many existing carbon monitoring solutions, such as Chinese patent CN120450734B, which provides a regional carbon monitoring solution. However, its monitoring zones are relatively fixed. When applied to microgrids in parks with dynamically changing topologies, it exposes obvious limitations: the existing system has fixed monitoring points, a rigid topology, and cannot be flexibly expanded. The metering rules are preset, and it cannot be plug-and-play when new equipment is added. When a photovoltaic array or an electric vehicle charging station is added to the grid, manual intervention is required to re-plan the monitoring sub-zone and configure the communication and carbon emission calculation logic. The implementation cycle is long and the cost is high, which cannot meet the flexible management needs of frequent access and exit of resources in the microgrid.

[0004] Current carbon monitoring solutions suffer from technical shortcomings, particularly the ambiguity in defining carbon liability. Existing technologies focus on calculating overall regional carbon intensity, lacking the ability to physically trace carbon flows within complex power grid topologies. In a power grid, electricity flows from multiple sources with varying carbon intensities to multiple loads, resulting in complex pathways. Current technologies cannot answer the core question of "from which specific sources does user A consume electricity, what is their respective share, and how much carbon emissions should they bear?" This leads to unfair carbon liability allocation and fails to support carbon accounting and green electricity consumption certification within industrial parks.

[0005] Therefore, there is an urgent need for a new method and system for measuring carbon emissions that can automatically adapt to dynamic changes in the power grid and achieve accurate carbon traceability and fair allocation.

[0006] The information disclosed in this background section is only for understanding the background of the inventive concept, and therefore may include information that does not constitute prior art. Summary of the Invention

[0007] To address the aforementioned problems or one of them, the present invention aims to provide a grid carbon flow collaborative metering method based on a plug-and-play carbon meter. When distributed resources are connected, the carbon meter automatically initiates a registration request containing a device identifier to the system master station. The system master station identifies the node type based on this, determines the adaptation strategy based on the pre-stored mapping relationship between node types and carbon emission metering strategies, and sends the generated carbon emission data to the carbon meter for display, thereby realizing plug-and-play functionality of the carbon meter, flexible expansion of distributed resources, and efficient collaborative metering of grid carbon flow.

[0008] To address the aforementioned problems or one of them, the second objective of this invention is to provide a grid carbon flow collaborative metering system based on a plug-and-play carbon meter. When distributed resources are connected, the carbon meter automatically initiates a registration request containing a device identifier to the system master station. The system master station identifies the node type based on this, determines the adaptation strategy based on the pre-stored mapping relationship between node types and carbon emission metering strategies, and sends the generated carbon emission data to the carbon meter for display, thereby realizing plug-and-play functionality of the carbon meter, flexible expansion of distributed resources, and efficient collaborative metering of grid carbon flow.

[0009] To achieve one of the above objectives, the first technical solution of the present invention is as follows: A method for coordinated metering of grid carbon flow based on a plug-and-play carbon meter includes the following steps: When a distributed resource is connected to the power grid, the plug-and-play carbon meter connected to it automatically initiates a registration request to the system master station. The registration request includes a device identifier. In response to the registration request, the system master station identifies the node type of the distributed resource based on the device identifier. Based on the pre-stored mapping relationship between node types and carbon emission metering strategies, it determines an appropriate carbon emission metering strategy and sends the carbon emission data generated according to the carbon emission metering strategy to the plug-and-play carbon meter for display.

[0010] As a preferred technical measure, the carbon emission metering strategy includes: For power generation nodes, carbon emission data is generated based on fixed values ​​or manufacturer-measured data; for load nodes, carbon emission data is generated based on the actual carbon intensity allocated to that node; for energy storage nodes, carbon emission data is generated during charging based on the actual carbon intensity allocated to that node. Carbon emission data; in the discharge state, carbon emission data is generated based on the cumulative carbon emissions during historical charging, or based on a fixed value. Alternatively, the carbon emission metering strategy includes: For power generation nodes, carbon emission data is generated based on fixed values ​​or actual measured data from manufacturers; for load nodes, carbon emission data is generated based on carbon intensity calculated using predictive carbon emission factors. For energy storage nodes, carbon emission data is generated based on carbon intensity calculated by predictive carbon emission factors during charging; and carbon emission data is generated based on the cumulative predicted carbon emissions during historical charging, or based on a fixed value, during discharging. Or / and, the system master station is equipped with a device resource description library, which contains carbon attribute tags; the carbon attribute tags include node type identifier, carbon intensity attribute identifier and load adjustability identifier; Or / and, the system master station sends an electricity metering strategy to the plug-and-play carbon meter according to the node type. The electricity metering strategy includes metering electricity consumption or metering power generation. The plug-and-play carbon meter measures and displays the electricity consumption or power generation according to the electricity metering strategy. Or / and, the system master station is used to control the charging and discharging of energy storage nodes, and the control can adapt to the access of plug-and-play devices.

[0011] As preferred technical measures, the following also include: The plug-and-play carbon meter collects real-time operating data of the nodes and uploads the real-time operating data and the grid node information of the nodes to the system master station. The real-time operating data includes the real-time power of the nodes. The system master station updates the global topology model of the microgrid based on the type of the distributed resources and the information of the connected power grid nodes.

[0012] Or / and, the system master station is used to control the charging and discharging of energy storage nodes, and the control is adapted to the access of plug-and-play devices, including: Construct a first function with the objective of minimizing the system's economic cost; Construct a second function with the objective of minimizing the total carbon emissions of the system; Constraints are set for the first function and the second function, including global power balance constraints. The global power balance constraints are described by a dynamic set of nodes and a dynamic set of branches. When a new device is detected to be connected, the set is automatically updated to reconstruct the network topology equation, thereby achieving plug-and-play functionality. The first and second functions are transformed into single-objective functions for solution to obtain the optimal charging and discharging power of each energy storage node during the scheduling period, and control commands are then issued.

[0013] As a preferred technical measure: Methods for generating carbon emission data based on the cumulative carbon emissions during historical charging include: Real-time statistics on the historical cumulative charging volume of energy storage devices and their corresponding total carbon emissions; The average carbon intensity of the energy storage device is calculated based on the total carbon emissions and the cumulative charging amount; carbon emission data for this discharge process is generated based on the average carbon intensity and the current discharge amount. Alternatively, methods for generating carbon emission data based on the cumulative carbon emissions during historical charging include: Using a first-in-first-out metering method, the electrical energy stored in the energy storage device is stratified and labeled according to the charging time sequence, and the grid carbon intensity corresponding to the charging time of each layer of electrical energy is recorded. When the energy storage device discharges, it prioritizes releasing the earliest stored energy layer. The carbon emission data for this discharge process is calculated by summing the actual discharge energy and the grid carbon intensity recorded by the power layer.

[0014] As a preferred technical measure, the method for calculating the actual carbon intensity allocated to this node is as follows: Based on the global topology model, a node admittance matrix is ​​formed, and power flow calculation is performed using the real-time operating data to obtain the power flow distribution of each branch. Obtain carbon emission data for each power generation node; A carbon tracing algorithm based on power flow is adopted. Starting from the power generation node, the carbon emissions of the power generation node are distributed to downstream nodes according to the proportion of its power output in the power grid, until all load nodes are reached, so as to obtain the carbon intensity of each load node.

[0015] Or / and, the economic costs include conventional unit fuel costs, energy storage aging costs, electricity purchase and sale costs, and / or carbon emission penalty costs calculated based on line loss equivalence. The total carbon emissions include direct carbon emissions from conventional units and / or indirect carbon emissions from line losses calculated based on the system average carbon emission factor. The constraints also include energy storage device operation constraints and / or line transmission capacity constraints. Transforming the first and second functions into single-objective functions for solving means using a weighted summation method to normalize the first and second functions and then transform them into single-objective functions for solving.

[0016] As a preferred technical measure: The energy storage node includes the following measures when it is charging: Based on the real-time carbon emission factor of the access node, it is determined whether the current period is a high-carbon period. When it is a high-carbon period, the charging power of the energy storage node is reduced; otherwise, it is charged normally. An adjustable parameter N is configured to dynamically control the sensitivity of the high-carbon period judgment, limit the frequency of daily state switching, and use the hysteresis effect to filter the fluctuation of the real-time carbon emission factor to ensure that the single charging process is continuous and effective. The method for reducing the charging power of energy storage nodes includes: Based on the different time-of-use electricity prices, the energy storage nodes are controlled to charge in a paused or low-power mode, and an adjustable parameter T is configured to set the minimum time difference between power decisions, thereby limiting the switching frequency and taking into account equipment protection. At the same time, an interruption mechanism is set up. When a new plug-and-play carbon meter automatic registration request is detected, the current process is immediately terminated to prioritize the response to topology changes. By adjusting the N and T parameters, the energy storage nodes are switched to a control mode that improves carbon response sensitivity and extends the power adjustment interval, ensuring system stability while quickly adapting to the access of new nodes. Alternatively, the energy storage node in the charging state includes the following measures: Obtain the real-time carbon emission factor of the access node; Based on real-time carbon emission factors, determine whether the current period is a high-carbon period. If it is a high-carbon period, reduce the charging power of the energy storage nodes; otherwise, charge normally. The method for determining whether the current period is a high-carbon period is as follows: Real-time carbon emission factors are collected according to a preset sampling period; When the real-time carbon emission factor for N consecutive sampling periods is greater than or equal to the preset carbon emission threshold, it is determined that the current period is a high-carbon period. When a period is identified as high-carbon, if the real-time carbon emission factor is less than the preset carbon emission threshold for N consecutive sampling periods, the current period is considered to have ended. The method for reducing the charging power of the energy storage node includes: S1: Get the current time-of-use electricity price; S2: Compare the time-of-use electricity price with a first electricity price threshold and a second electricity price threshold, wherein the first electricity price threshold is higher than the second electricity price threshold; If the time-of-use electricity price is greater than or equal to the first electricity price threshold, then control the energy storage node to suspend charging or charge at the first power. If the time-of-use electricity price is less than or equal to the second electricity price threshold, then the energy storage node is controlled to charge at the second power. If the time-of-use electricity price is between the first electricity price threshold and the second electricity price threshold, then the current charging power of the energy storage node remains unchanged; Wherein, the first power < the second power < the normal charging power of the energy storage node; S3: Wait for a preset time T. During the waiting period, monitor in real time whether the interruption condition is met. The interruption condition includes: the system master station receives a new automatic registration request for the plug-and-play carbon meter. If the interruption condition is met, the current waiting is terminated and the system exits to end the current charging power reduction process. If the interruption condition is not met and the waiting time reaches T, return to step S1. The N and T are dynamically adjusted as follows: (a) Related to the maximum permissible number of daily switching operations for energy storage nodes, the closer the number of charging state switching operations that have occurred in the past 24 hours is to the maximum permissible number of daily switching operations, the larger the values ​​of N and T; (b) When the system master station receives a new automatic registration request for a plug-and-play carbon meter, the value of N is reduced and the value of T is increased. (c) When the system master station completes the automatic registration request processing of a new plug-and-play carbon meter for a preset time, or when it detects that the power grid operation status has not fluctuated within the preset time, the values ​​of N and T will be restored to their default values. As a preferred technical measure, the method for generating carbon emission data based on carbon intensity calculated from predictive carbon emission factors is as follows: Based on the global topology model, a node admittance matrix is ​​formed, and power flow calculation is performed using the real-time operating data to obtain the power flow distribution of each branch. Obtain carbon emission data for each power generation node; Obtain predictive carbon emission factors and calculate carbon emissions at power generation nodes based on these predictive carbon emission factors; A carbon tracing algorithm based on power flow is adopted. Starting from the power generation node, the carbon emissions of the power generation node are distributed to downstream nodes according to the proportion of its power output in the power grid, until all load nodes are reached, so as to obtain the carbon intensity of each load node.

[0017] Or / and, the method for obtaining the predictive carbon emission factor is as follows: Based on the time series of multimodal data, the predicted power generation and total load of each power generation node in future periods are obtained; Based on the power generation forecast and total load forecast of each power generation node, the expected consumption ratio of each energy type is obtained; By combining the expected absorption ratio of each energy type and the carbon intensity of each energy type, a predictive carbon emission factor is obtained.

[0018] As a preferred technical measure, the method for generating carbon emission data based on carbon intensity calculated from predictive carbon emission factors further includes: When the expected consumption ratio changes beyond a fixed or dynamic threshold, the predictive carbon emission factor is updated. Or / and, methods for generating carbon emission data based on carbon intensity calculated from predictive carbon emission factors also include: When a sudden new resource not covered by the prediction model is accessed via plug-and-play, the predictive carbon emission factor is corrected.

[0019] To achieve one of the above objectives, the second technical solution of the present invention is as follows: A grid carbon flow collaborative metering method based on plug-and-play carbon meters, applied to plug-and-play carbon meters, includes the following steps: When distributed resources are connected to the power grid, they automatically send a registration request to the system master station. The registration request includes the device identifier. The system receives and displays carbon emission data sent by the main station. The carbon emission data is generated as follows: in response to the registration request, the main station identifies the node type of the distributed resource based on the device identifier; based on the pre-stored mapping relationship between node types and carbon emission measurement strategies, it determines the appropriate carbon emission measurement strategy and displays the carbon emission data generated according to the carbon emission measurement strategy.

[0020] To achieve one of the above objectives, the third technical solution of the present invention is as follows: A grid carbon flow collaborative metering method based on plug-and-play carbon meters, applied to the system master station, includes the following steps: The system receives a registration request from a plug-and-play carbon meter. This registration request is automatically initiated by the plug-and-play carbon meter connected to the distributed resource when the resource is connected to the power grid, and includes a device identifier. In response to the registration request, the system identifies the node type of the distributed resource based on the device identifier. Based on a pre-stored mapping relationship between node types and carbon emission metering strategies, the system determines an appropriate carbon emission metering strategy and sends the carbon emission data generated according to this strategy to the plug-and-play carbon meter for display.

[0021] To achieve one of the above objectives, the fourth technical solution of the present invention is as follows: A grid carbon flow collaborative metering system based on a plug-and-play carbon meter includes: Multiple plug-and-play carbon meters are used to connect distributed resources. When a distributed resource is connected to the power grid, the plug-and-play carbon meters connected to it automatically initiate a registration request to the system master station. The registration request includes a device identifier. The system master station is used to respond to the registration request, identify the node type of the distributed resource based on the device identifier, determine the appropriate carbon emission metering strategy based on the pre-stored mapping relationship between node types and carbon emission metering strategies, and send the carbon emission data generated according to the carbon emission metering strategy to the plug-and-play carbon meters for display.

[0022] Compared with existing technical solutions, this invention achieves plug-and-play functionality of the carbon meter and flexible expansion of distributed resources by automatically initiating a registration request containing device identifiers when distributed resources are accessed, and by having the system master station identify node types based on this, determine adaptation strategies based on pre-stored mapping relationships, and distribute carbon emission data. This collaborative mechanism utilizes device self-registration and dynamic strategy distribution technology to achieve plug-and-play functionality of the carbon meter and flexible expansion of distributed resources, solving the problem of manual intervention in the configuration of new devices, significantly improving the scalability and operation and maintenance efficiency of the system, and simultaneously achieving efficient collaborative metering of grid carbon flow. Furthermore, by introducing a carbon flow tracking algorithm based on physical currents, carbon emissions are closely linked to the physical flow path of electricity, achieving accurate and fair allocation of carbon responsibility. This ensures that the carbon emission data allocated to each load has a solid scientific basis and high credibility, solving the problem of carbon responsibility identification in existing technologies. This promotes the fairness and transparency of the carbon emission trading market and provides core data support for the refined low-carbon dispatching of the power grid and green energy use decisions on the user side. Attached Figure Description

[0023] Figure 1 This is a system architecture diagram of Embodiment 1 of the present invention; Figure 2 This is a flowchart illustrating the process of Embodiment 1 of the present invention; Figure 3 This is a schematic diagram illustrating the principle of carbon flow tracking and allocation in this invention; Figure 4 This is a flowchart of Example 5. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present 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 should fall within the scope of protection of this application. This invention covers any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of the invention as defined by the claims.

[0025] Example 1 like Figure 1 As shown, this embodiment describes a method for coordinated metering of grid carbon flow based on a plug-and-play carbon meter, including the following steps: When a distributed resource is connected to the power grid, the plug-and-play carbon meter connected to it automatically initiates a registration request to the system master station. The registration request includes a device identifier. In response to the registration request, the system master station identifies the node type of the distributed resource based on the device identifier. Based on the pre-stored mapping relationship between node types and carbon emission metering strategies, it determines an appropriate carbon emission metering strategy and sends the carbon emission data generated according to the carbon emission metering strategy to the plug-and-play carbon meter for display.

[0026] Specifically, the carbon emission measurement strategy includes: For power generation nodes, carbon emission data is generated based on a fixed value (e.g., 0) or manufacturer-measured data; for load nodes, carbon emission data is generated based on the actual carbon intensity allocated to that node; for energy storage nodes, carbon emission data is generated during charging based on the actual carbon intensity allocated to that node. Carbon emission data; In the discharge state, carbon emission data is generated based on the cumulative carbon emissions during historical charging, or based on a fixed value (such as 0). Furthermore, the system master station is equipped with a device resource description library, which contains carbon attribute tags. The carbon attribute tags include node type identifiers, carbon intensity attribute identifiers, and load adjustability identifiers. The carbon attribute tags can be as follows: Photovoltaic inverter: {Type: Power generation, Carbon intensity: 0, Dispatchable: No} Energy storage converter: {Type: Energy storage, Carbon intensity: Dynamic (inherits grid carbon intensity during charging and discharging), Dispatchable: Yes} Charging station: {Type: Load, Carbon intensity: Dynamic (determined by carbon flow calculation), Dispatchable: Yes} Or / and, the system master station sends an electricity metering strategy to the plug-and-play carbon meter according to the node type. The electricity metering strategy includes metering electricity consumption or metering power generation. The plug-and-play carbon meter measures and displays the electricity consumption or power generation according to the electricity metering strategy.

[0027] Specifically, this embodiment also includes: The plug-and-play carbon meter collects real-time operating data of the nodes and uploads the real-time operating data and the grid node information of the nodes to the system master station. The real-time operating data includes the real-time power of the nodes. The system master station updates the global topology model of the microgrid based on the type of the distributed resources and the information of the connected power grid nodes.

[0028] Furthermore, the method for calculating the actual carbon intensity allocated to this node is as follows: Based on the global topology model, a node admittance matrix is ​​formed, and power flow calculation is performed using the real-time operating data to obtain the power flow distribution of each branch. Obtain carbon emission data for each power generation node; A carbon tracing algorithm based on power flow is adopted. Starting from the power generation node, the carbon emissions of the power generation node are distributed to downstream nodes according to the proportion of its power output in the power grid, until all load nodes are reached, so as to obtain the carbon intensity of each load node.

[0029] The specific calculation formula is as follows, calculated by the carbon flow collaborative computing engine; in, For load nodes Shared carbon emissions; For power generation nodes Injection power; Its carbon strength; For power generation nodes Flow to load nodes The power; For power generation nodes Total output power.

[0030] Furthermore, the method for generating carbon emission data based on the cumulative carbon emissions during historical charging can employ an averaging method, specifically including: Real-time statistics on the historical cumulative charging volume of energy storage devices and their corresponding total carbon emissions; The average carbon intensity of the energy storage device is calculated based on the total carbon emissions and the cumulative charging amount; carbon emission data for this discharge process is generated based on the average carbon intensity and the current discharge amount. Alternatively, methods for generating carbon emission data based on historical cumulative carbon emissions during charging can employ a first-in, first-out (FIFO) approach, including: Using a first-in-first-out metering method, the electrical energy stored in the energy storage device is stratified and labeled according to the charging time sequence, and the grid carbon intensity corresponding to the charging time of each layer of electrical energy is recorded. When the energy storage device discharges, it prioritizes releasing the earliest stored energy layer. The carbon emission data for this discharge process is calculated by summing the actual discharge energy and the grid carbon intensity recorded by the power layer.

[0031] This embodiment achieves plug-and-play functionality for carbon meters and flexible expansion of distributed resources by automatically initiating a registration request containing device identifiers when distributed resources are accessed. The system master station identifies node types based on this, determines adaptation strategies based on pre-stored mapping relationships, and distributes carbon emission data through a collaborative mechanism. By utilizing device self-registration and dynamic strategy distribution technologies, it solves the problem of manual intervention in configuring new devices, significantly improves the system's scalability and operational efficiency, and simultaneously achieves efficient collaborative metering of grid carbon flow.

[0032] like Figure 2 As shown, this embodiment is applied to a microgrid in a park that initially includes one diesel engine (G1) and one factory load (L1), and now plans to add a photovoltaic system (PV) and an electric vehicle charging station (EV).

[0033] initial state The diesel engine G1 and the factory load L1 have been equipped with plug-and-play carbon meters and have been registered on the system master station.

[0034] The carbon flow collaborative computing engine maintains the current topology model and continuously calculates the carbon emissions (all from G1) corresponding to the electrical energy consumed by L1.

[0035] Plug and play process 1. Connection and Registration: Construction personnel connect the photovoltaic (PV) and charging station EV to the corresponding bus nodes of the microgrid. After power-on, their carbon meters automatically send a registration message to the system master station, which contains the device ID.

[0036] 2. Identification and Triggering: The main station matches the PV tag {Power Generation, 0} and the EV tag {Load, Dynamic} from the device resource description library based on the ID. This triggers the carbon flow collaborative calculation engine.

[0037] 3. Engine dynamic response: Update Topology: The engine adds PV and EV as new nodes to its internal digital topology model.

[0038] Perform carbon flow calculations: such as Figure 3 As shown, the engine obtains the following real-time data: G1 output 30kW, PV output 20kW, total load L1 (40kW) + EV (10kW) = 50kW. Through power flow calculation and carbon flow tracing, it is concluded that of the 10kW load of the EV, 4kW comes from G1 and 6kW comes from PV. Generate and distribute policies: The strategy is set to "measure power generation, carbon emissions are always 0" for PV carbon meters. PV carbon meters measure power generation and display carbon emissions as 0. The policy is issued to the EV carbon meter: "Measure electricity consumption, your carbon emissions = (4kW * G1 carbon intensity) + (6kW * 0)"; the EV carbon meter measures electricity consumption and displays the carbon emissions, which are calculated and issued periodically by the main station.

[0039] Carbon flow co-metering: At this point, the carbon meter reading for factory load L1 also showed a change in its carbon emissions, because the power supply now includes zero-carbon PV, and the proportion of carbon emissions from G1 allocated to it has decreased.

[0040] The entire system automatically and accurately completed the connection of the new equipment to the network and the recalculation and allocation of carbon flow across the entire network without human intervention.

[0041] As can be seen from the above embodiments, the present invention perfectly solves the two major problems of "rigidity" and "fuzzy carbon responsibility" mentioned in the background technology, and realizes the self-evolution and accurate perception of the microgrid carbon monitoring system.

[0042] Example 2 This embodiment describes a grid carbon flow collaborative metering method based on a plug-and-play carbon meter. It improves the charging method of the energy storage node based on the first embodiment.

[0043] In existing technologies, the carbon liability attribution of energy storage nodes is ambiguous, and the real-time carbon emissions they bear during charging cannot be known. Therefore, their charging and discharging strategies are usually guided solely by economic electricity prices, i.e., charging during off-peak hours and discharging during peak hours. This single, purely economic strategy has significant drawbacks: First, it may lead to energy storage charging during periods of high overall grid carbon emission intensity (e.g., when thermal power units are operating at full load), essentially becoming a "consumer" of high-carbon electricity rather than a "regulator" of green energy, which runs counter to the macro-level goals of energy conservation and emission reduction. Secondly, this strategy fails to reflect the environmental value of energy storage, hinders the transformation of the power system towards low-carbon operation, and is incompatible with the country's long-term "dual-carbon" development needs. Thanks to the precise carbon metering method provided by this invention, the carbon responsibility of energy storage nodes is clearly defined. Therefore, this embodiment improves the charging method of energy storage nodes. Its strategy no longer blindly focuses on economic efficiency but takes carbon costs and economic costs into consideration together, thereby reducing its own carbon footprint during the charging process at the source. When the energy storage system discharges when needed, the released electrical energy has lower carbon emission attributes. This not only realizes the economic benefits of energy storage but also fully leverages its environmental benefits, which is key to promoting the low-carbon and efficient operation of the new power system.

[0044] The energy storage node described in this embodiment includes the following measures during the charging state: Based on the real-time carbon emission factor of the access node, it is determined whether the current period is a high-carbon period. When it is a high-carbon period, the charging power of the energy storage node is reduced; otherwise, it is charged normally. An adjustable parameter N is configured to dynamically control the sensitivity of the high-carbon period judgment, limit the frequency of daily state switching, and use the hysteresis effect to filter the fluctuation of the real-time carbon emission factor to ensure that the single charging process is continuous and effective. Specifically, the real-time carbon emission factor corresponds to the carbon intensity of the access node calculated in real time by the carbon flow collaborative computing engine. In a preferred embodiment of this example, the system master station directly uses the real-time carbon intensity value calculated in Example 1 and allocated to the access point where the energy storage node is located as the real-time carbon emission factor of that node. This factor fluctuates dynamically with changes in the power grid power structure (such as the proportion of thermal power and photovoltaic power), reflecting the cleanliness of the electricity obtained by the node at the current moment.

[0045] The method for reducing the charging power of energy storage nodes includes: Based on the different time-of-use electricity prices, the energy storage nodes are controlled to charge in a paused or low-power mode, and an adjustable parameter T is configured to set the minimum time difference between power decisions, thereby limiting the switching frequency and taking into account equipment protection. At the same time, an interruption mechanism is set up. When a new plug-and-play carbon meter automatic registration request is detected, the current process is immediately terminated to prioritize the response to topology changes. By adjusting the N and T parameters, the energy storage nodes are switched to a control mode that improves carbon response sensitivity and extends the power adjustment interval, ensuring system stability while quickly adapting to the access of new nodes. Specifically, the energy storage node in the charging state includes the following measures: Obtain the real-time carbon emission factor of the access node; Based on real-time carbon emission factors, determine whether the current period is a high-carbon period. If it is a high-carbon period, reduce the charging power of the energy storage nodes; otherwise, charge normally. The method for determining whether the current period is a high-carbon period is as follows: Real-time carbon emission factors are collected according to a preset sampling period; When the real-time carbon emission factor for N consecutive sampling periods is greater than or equal to the preset carbon emission threshold, it is determined that the current period is a high-carbon period. When a period is identified as high-carbon, if the real-time carbon emission factor is less than the preset carbon emission threshold for N consecutive sampling periods, the current period is considered to have ended. The method for reducing the charging power of the energy storage node includes: S1: Get the current time-of-use electricity price; S2: Compare the time-of-use electricity price with a first electricity price threshold and a second electricity price threshold, wherein the first electricity price threshold is higher than the second electricity price threshold; If the time-of-use electricity price is greater than or equal to the first electricity price threshold, then control the energy storage node to suspend charging or charge at the first power. If the time-of-use electricity price is less than or equal to the second electricity price threshold, then the energy storage node is controlled to charge at the second power. If the time-of-use electricity price is between the first and second electricity price thresholds, then the current charging power of the energy storage node remains unchanged; (amplitude hysteresis) Wherein, the first power < the second power < the normal charging power of the energy storage node; S3: Wait for a preset time T (timing delay). During the waiting process, monitor in real time whether the interruption condition is met. The interruption condition includes: the system master station receives a new automatic registration request from a plug-and-play carbon meter. If the interruption condition is met, immediately terminate the current waiting and exit to end the current charging power reduction process. If the interruption condition is not met and the waiting time reaches T, return to step S1. The N and T are dynamically adjusted as follows: (a) Related to the maximum number of daily switching operations of the energy storage node, the closer the number of charging state switching operations that have occurred in the past 24 hours is to the maximum number of daily switching operations, the larger the values ​​of N and T are; when the number of switching operations is close to the upper limit, the system response frequency is actively reduced by increasing N and T, which effectively avoids equipment overload and thus extends the service life of the energy storage node. (b) When the system master station receives a new automatic registration request for a plug-and-play carbon meter, the value of N is reduced and the value of T is increased. (c) When the system master station completes the automatic registration request processing of a new plug-and-play carbon meter for a preset time, or when it detects that the power grid operation status has not fluctuated within the preset time, the values ​​of N and T will be restored to their default values.

[0046] This embodiment effectively solves the problem of frequent jumps at critical points in traditional threshold judgment by introducing a dual-hysteresis judgment mechanism. Specifically, the system requires that the carbon emission factor meets the conditions for N consecutive sampling cycles before triggering a state switch. This symmetrical hysteresis design of "entry" and "exit" can effectively filter out the instantaneous fluctuation interference of grid carbon data and prevent high-frequency oscillations of energy storage nodes between "charging" and "power reduction" states, thereby protecting equipment life and maintaining grid stability.

[0047] Building upon this foundation, the charging power of energy storage nodes is reduced using amplitude hysteresis and timing hysteresis. The former filters out minor fluctuations in the judgment conditions, while the latter locks in the minimum duration of the control action. The combination of these two significantly improves the robustness and stability of the entire charging power control method. During high-carbon periods, the system further incorporates time-of-use pricing for tiered responses: when electricity prices are high, energy storage nodes suspend charging or charge at extremely low power, thereby minimizing the absorption of high-carbon-emission electricity while avoiding high economic costs; conversely, when electricity prices are low, charging at moderate power is permitted, ensuring basic economic benefits while minimizing the impact of energy storage nodes on the high-carbon power grid, thus achieving a dynamic balance between environmental responsibility and economic costs.

[0048] Furthermore, this method achieves deep synergy with the plug-and-play mechanism. When the system master station receives a new plug-and-play carbon meter registration request, it often indicates an impending change in the power grid topology. This could mean the connection of new loads or the grid connection of distributed green power stations, leading to a reduction in carbon intensity. To address this change, the system dynamically reduces the value of the judgment parameter N, thereby lowering the threshold for high-carbon periods. This allows energy storage nodes to more sensitively detect and quickly capture fluctuations in grid carbon intensity, ensuring a rapid carbon response when the grid environment undergoes sudden changes. It promptly determines whether to reduce power charging during high-carbon periods or continue charging normally. Simultaneously, the system increases the value of the waiting time parameter T, proactively extending the power adjustment interval. This fast-sensing, slow-execution control mode ensures effective carbon response. By employing an interrupt mechanism in conjunction with adjustable parameters N and T, the interrupt mechanism takes effect immediately upon detecting a new plug-and-play carbon meter registration request. This forcibly terminates the current power adjustment waiting process and allows for direct and convenient dynamic adjustment of the values ​​of adjustable parameters N and T to change the charging mode. This synergistic effect enables the energy storage system to respond rapidly in a "fast sensing, slow execution" mode at critical moments when the grid topology changes abruptly. The aim is to provide the necessary operational stability for the energy storage system during the transition period of grid topology reconfiguration, avoiding frequent start-ups and shutdowns or power oscillations caused by instantaneous fluctuations on the grid side or interference from new equipment connections. When new equipment is connected, adopting this strategy across all energy storage nodes in the network can effectively improve system stability. Thus, while ensuring rapid response to grid changes, it effectively balances the operational safety and lifespan of energy storage devices.

[0049] Depending on the scenario, those skilled in the art can adjust the values ​​of sampling period, N, and T. For reference, the sampling period can be 1-5 minutes, and the value of N can be 3-10. The value of N is directly related to the sampling period. If the sampling period is short, the data fluctuation may be more frequent, and the value of N can be set slightly larger. The value of T can be 2-5 minutes.

[0050] Example 3 This embodiment describes a method for coordinated metering of grid carbon flow based on a plug-and-play carbon meter, which differs from Embodiment 1 in that: The system master station is used to control the charging and discharging of energy storage nodes, and can adapt to the access of plug-and-play devices during control.

[0051] Specifically, the system master station is used to control the charging and discharging of energy storage nodes, and the control is adaptable to the access of plug-and-play devices, including: Construct a first function with the objective of minimizing the system's economic cost; Construct a second function with the objective of minimizing the total carbon emissions of the system; Constraints are set for the first function and the second function, including global power balance constraints. The global power balance constraints are described by a dynamic set of nodes and a dynamic set of branches. When a new device is detected to be connected, the set is automatically updated to reconstruct the network topology equation, thereby achieving plug-and-play functionality. The first and second functions are transformed into single-objective functions for solution to obtain the optimal charging and discharging power of each energy storage node during the scheduling period, and control commands are then issued.

[0052] Specifically, the economic costs include conventional unit fuel costs, energy storage aging costs, electricity purchase and sale costs, and / or carbon emission penalty costs calculated based on line loss equivalence. The total carbon emissions include direct carbon emissions from conventional units and / or indirect carbon emissions from line losses calculated based on the system average carbon emission factor. The constraints also include energy storage device operation constraints and / or line transmission capacity constraints. Transforming the first and second functions into single-objective functions for solving means using a weighted summation method to normalize the first and second functions and then transform them into single-objective functions for solving.

[0053] Specifically, the bi-objective optimization function model for charging and discharging control of energy storage nodes includes the following decision variables (all power variables are non-negative real numbers and are subject to their respective upper and lower limits): The specific steps include: Step 1.1: Construct a system based on economic cost Minimize the first function with the objective as the goal; Traditional unit fuel costs: ,in Cost coefficient. Energy storage charging and discharging aging cost: , This is the unit power aging cost coefficient.

[0054] Electricity purchase and sales costs: , It is a time-of-use electricity price.

[0055] Line loss equivalent carbon emission penalty cost: ,in branch road Active power loss, The proportion of line loss borne by the user side. For carbon trading prices; The branch index number is used to identify a specific transmission line in a power system; The set of all branches (transmission lines) in the system. For example, if the system has 5 lines, then... .

[0056] Step 1.2: Construct a system based on total carbon emissions. Minimize the second function with the objective; Traditional units The carbon emission intensity (kgCO2 / kWh) is 0 for renewable energy units.

[0057] : Scheduling period length (usually 1 hour).

[0058] System average carbon emission factor (kgCO2 / kWh).

[0059] Branch road Active power loss (kW).

[0060] Step 2: Set the constraints for the first function and the second function as follows: (1) Global power balance constraints Meaning: The sum of all power generation nodes (traditional units + renewable energy + energy storage discharge) plus the net power purchased from the external power grid equals the sum of the electricity consumption of all load nodes plus all line losses; : The index number of the load node, used to identify a specific electricity user or centralized load in the system.

[0061] The set of all load nodes in the system. For example, if there are 10 user nodes, then... .

[0062] Load node During the period The active power consumption (kW) of the system is the sum of the power consumption of all load nodes, which is the total load of the system. (2) Dynamic topology adaptation constraints (supports plug and play) Define a dynamic node set and branch road collection When a new device is connected via a plug-and-play carbon meter, both sets are automatically expanded. For any node... The node power balance equation is: : The index number of a branch line, used to identify a specific transmission line in a power system.

[0063] Inflow node The set of branch paths.

[0064] Outflow node The set of branch paths.

[0065] Branch road Sent from the other end to the node Power (inflow).

[0066] Branch road From node Power sent to the other end (outflow).

[0067] :node The power of the generator or energy storage is injected.

[0068] :node Power output from load or energy storage charging (outflow).

[0069] This constraint form does not change with topology changes, thus supporting plug-and-play for new devices; (3) Upper and lower limits of output of traditional units :unit The minimum technical output is the minimum output required for stable operation of the unit. Outputs below this value may lead to unstable combustion or excessively low efficiency.

[0070] :unit The maximum rated output is the maximum output that the unit can achieve under safe operating conditions (usually equal to the installed capacity).

[0071] (4) Operational constraints of energy storage equipment Charge and discharge power limits: , Cannot be charged and discharged simultaneously: State of charge recursion: SoC Upper and Lower Limits: in: Energy storage devices Maximum permissible charging power (equipment rating), in kW.

[0072] Energy storage devices Maximum permissible discharge power (equipment rated value), unit kW.

[0073] : Binary variable, 1 represents energy storage During the period It is in charging state; 0 indicates that it is not charging.

[0074] : Binary variable, 1 represents energy storage During the period The device is in a discharging state; 0 indicates that it is not discharging.

[0075] This indicates that charging and discharging cannot occur simultaneously, ensuring that the energy storage will not be charged and discharged at the same time.

[0076] Energy storage devices During the period The state of charge at the end of the charge period, i.e., the percentage of usable energy remaining at rated capacity, expressed as % or kWh. : Lower limit of state of charge (to prevent over-discharge, usually taken as 0.1~0.2), unit.

[0077] : Upper limit of state of charge (to prevent overcharging, usually taken as 0.9~0.95), unit.

[0078] (5) Line transmission capacity constraints Branch road During the period The transmitted active power (kW). It can be a positive value (representing a reference direction) or a negative value (representing the opposite direction). Absolute value. This indicates the actual transmitted power.

[0079] Branch road The maximum permissible transmission power (i.e., the thermal stability limit capacity of the line, in kW). Exceeding this value may cause the line to overheat or even be damaged.

[0080] Step 3: Transform the first and second functions into single-objective functions for solving.

[0081] The weights can be dynamically adjusted (e.g., based on carbon prices).

[0082] , The minimum value obtained from optimizing each individual objective is used for normalization.

[0083] When the new equipment is plug-and-play, the engine is updated. Simply call the solver again.

[0084] The key parameters in the above formula are shown in the table below. The method described in this embodiment possesses exceptional flexibility and scalability. By constructing dynamic node and branch sets to describe power balance constraints, it automatically updates the network topology equations when new equipment is connected, achieving true "plug-and-play" functionality without the need for manual remodeling. Simultaneously, it achieves deep synergistic optimization of economic efficiency and low carbon emissions. The objective function not only encompasses traditional fuel and electricity purchase and sale costs but also innovatively incorporates the carbon emission penalty costs equivalent to energy storage aging and line losses, making carbon footprint tracking more comprehensive and accurate. Furthermore, the well-developed energy storage and line transmission constraints effectively ensure the physical security of the power grid and delay equipment aging. Combined with a weighted summation normalization solution mechanism, the system can also dynamically adjust scheduling weights based on real-time carbon prices or policy demands, exhibiting both scientific decision-making capabilities and flexibility for practical engineering applications.

[0085] Example 4 like Figure 4 As shown, this embodiment describes a grid carbon flow collaborative metering method based on a plug-and-play carbon meter. Unlike the carbon emission metering strategy in Embodiment 1, its carbon emission metering strategy includes: For power generation nodes, carbon emission data is generated based on fixed values ​​(such as 0) or actual measured data from manufacturers; for load nodes, carbon emission data is generated based on carbon intensity calculated from predictive carbon emission factors. For energy storage nodes, carbon emission data is generated based on carbon intensity calculated by predictive carbon emission factors during charging; and carbon emission data is generated based on the cumulative predicted carbon emissions during historical charging, or based on a fixed value (such as 0) during discharging. Compared to the ex-post response mode that relies solely on real-time monitoring in existing technologies, this embodiment introduces a predictive carbon emission factor to construct a future-oriented prediction and adjustment mechanism. This mechanism presents users or the dispatch system with the expected carbon emission intensity of each node in the power grid within a specific future time period. This enables low-carbon guidance for users' electricity consumption behavior and peak-shaving scheduling of grid-side energy storage resources, transforming carbon metering from a simple data recording mode to a low-carbon command mode.

[0086] Specifically, the method for generating carbon emission data based on carbon intensity calculated from predictive carbon emission factors is as follows: Based on the global topology model, a node admittance matrix is ​​formed, and power flow calculation is performed using the real-time operating data to obtain the power flow distribution of each branch. Obtain carbon emission data for each power generation node; Obtain predictive carbon emission factors and calculate carbon emissions at power generation nodes based on these predictive carbon emission factors; A carbon tracing algorithm based on power flow is adopted. Starting from the power generation node, the carbon emissions of the power generation node are distributed to downstream nodes according to the proportion of its power output in the power grid, until all load nodes are reached, so as to obtain the carbon intensity of each load node.

[0087] The specific calculation formula can be as follows, calculated by the carbon flow collaborative computing engine; in, For load nodes Shared carbon emissions; For power generation nodes Injection power; It is a predictive carbon emission factor; For power generation nodes Flow to load nodes The power; For power generation nodes Total output power.

[0088] Furthermore, the method for obtaining the predictive carbon emission factor is as follows: Based on the time series of multimodal data, the predicted power generation and total load of each power generation node in future periods are obtained; Based on the power generation forecast and total load forecast of each power generation node, the expected consumption ratio of each energy type is obtained; By combining the expected absorption ratio of each energy type and the carbon intensity of each energy type, a predictive carbon emission factor is obtained.

[0089] Specifically, The time series of the multimodal data may include: time-series operational data modes, meteorological and environmental data modes, and calendar feature data modes.

[0090] The time-series operation data mode includes power generation data of each power generation node and total network load data within a preset time period (e.g., 72 hours). The meteorological environment data modality includes high-precision weather forecast data obtained from the meteorological service center, specifically covering wind speed, light intensity and ambient temperature data within a preset future time period (such as 48 hours). The calendar feature data modality includes a date type identifier generated based on the system clock, used to distinguish between weekdays, holidays, and special power supply guarantee days.

[0091] The above data comes from smart meters / carbon meters, meteorological service centers, and the system's internal clock, forming the data foundation for the predictive model.

[0092] A time-series forecasting model is used to obtain the predicted power generation and total load for each power generation node in future time periods. The time-series forecasting model can employ a Long Short-Term Memory (LSTM) network. Its input is the time series of the aforementioned multimodal data, and its output is the predicted power generation for future time periods (e.g., one hour in the future). and total load forecast Hourly data was chosen because it matches the power grid dispatch cycle and is readily available. To reduce computational load, total load forecasting was performed by predicting the total inlet power of the park, rather than predicting and summing the loads of massive users one by one. Historical data was used during the model training phase to minimize the mean square error between the predicted and actual values.

[0093] Based on the predicted power generation and total load values, the predicted energy structure can be obtained, and the expected energy consumption ratio can be calculated. Expected absorption ratio The calculation formula is: ,in Energy type; for Forecasted electricity generation values ​​for each energy type; This means adding up the predicted power generation for all energy types. The predictive carbon emission factor is calculated as follows: The carbon emission intensity factor representing the k-th energy category is derived from manufacturer measurements or from standards published by national or industry regulatory authorities.

[0094] To guide users and further enable flexible waste disposal, a carbon sensitivity coefficient is introduced. This coefficient is based on the load. The system sets a historical response level based on willingness to participate in low-carbon dispatching. Specifically, for high-quality users with a high degree of cooperation in low-carbon dispatching, the system assigns them a smaller carbon sensitivity coefficient. This provides carbon liability reductions at the carbon emission accounting level, effectively creating a virtual discount effect. Through this mechanism, the environmental rights requirements of highly sensitive loads for green electricity can be prioritized in the carbon flow allocation algorithm without altering the physical power flow distribution. The formula is as follows: Furthermore, the method for generating carbon emission data based on carbon intensity calculated from predictive carbon emission factors also includes: When the expected consumption ratio changes beyond a fixed or dynamic threshold, the predictive carbon emission factor is updated. The fixed threshold can be 5%; or it can be a dynamic threshold. As a criterion, The dynamic threshold mechanism enables more intelligent calculation of carbon emission factors, adaptively captures fluctuations in new energy sources, and significantly improves the system's practicality and high-precision operation level.

[0095] Dynamic threshold Its calculation can be based on the variance of the historical rate of change in energy structure. ,For example When the predicted absorption rate changes At that time, an update is triggered proactively.

[0096] It is the mean; used to reflect the average level of the rate of change in the energy structure over a past period of time. Standard deviation; used to reflect the magnitude of data fluctuation; when the predicted rate of change in energy structure deviates significantly from the normal fluctuation range ( When this occurs, the predictive carbon emission factor is updated.

[0097] Furthermore, methods for generating carbon emission data based on carbon intensity calculated from predictive carbon emission factors also include: When a sudden new resource not covered by the prediction model is accessed via plug-and-play, the predictive carbon emission factor is corrected.

[0098] The correction process may specifically include: in response to a plug-and-play access command for a sudden new resource, firstly identifying the energy type and access node attributes of the resource, and collecting its current grid-connected power data in real time; then directly superimposing the collected real-time power values ​​onto the predicted power generation value or the predicted total load value, thereby changing the energy structure and the expected absorption ratio. Updated to obtain more accurate predictive carbon emission factors. This eliminates prediction bias caused by accessing unmodeled resources.

[0099] The correction process can employ a unique update mechanism: when a new distributed resource (such as a planned charging pile) whose power is already included in the current prediction range is connected, the system proceeds according to the original plan, and the model's prediction value remains unchanged; when a sudden new resource not covered by the prediction model is connected, the system immediately initiates a short-term (such as the next 15 minutes) prediction process based on the Autoregressive Integral Moving Average (ARIMA) model to quickly estimate the impact of the device on global power and carbon intensity, and to instantly correct the generated predictive carbon emission factors to ensure the real-time accuracy of carbon flow calculation.

[0100] Through the aforementioned deep integration and innovative design, this embodiment not only possesses plug-and-play flexibility and predictive triggering foresight, but also solves the collaborative technical problems arising from the combination of the two (such as real-time updates of the prediction model and guided optimization of the metering strategy), providing a solution that does not exist in the prior art, and significantly improving the system's intelligence level and practical application value.

[0101] Example 5 This embodiment describes a grid carbon flow collaborative metering method based on a plug-and-play carbon meter, applied to a plug-and-play carbon meter, including the following steps: When distributed resources are connected to the power grid, they automatically send a registration request to the system master station. The registration request includes the device identifier. The system receives and displays carbon emission data sent by the main station. The carbon emission data is generated as follows: in response to the registration request, the main station identifies the node type of the distributed resource based on the device identifier; based on the pre-stored mapping relationship between node types and carbon emission measurement strategies, it determines the appropriate carbon emission measurement strategy and displays the carbon emission data generated according to the carbon emission measurement strategy.

[0102] Other details not covered in this embodiment are the same as those described in embodiments one to four, and will not be repeated here.

[0103] Example 6 This embodiment describes a grid carbon flow collaborative metering method based on a plug-and-play carbon meter, applied to the system master station, including the following steps: The system receives a registration request from a plug-and-play carbon meter. This registration request is automatically initiated by the plug-and-play carbon meter connected to the distributed resource when the resource is connected to the power grid, and includes a device identifier. In response to the registration request, the system identifies the node type of the distributed resource based on the device identifier. Based on a pre-stored mapping relationship between node types and carbon emission metering strategies, the system determines an appropriate carbon emission metering strategy and sends the carbon emission data generated according to this strategy to the plug-and-play carbon meter for display.

[0104] Other details not covered in this embodiment are the same as those described in embodiments one to four, and will not be repeated here.

[0105] Example 7 This embodiment describes a grid carbon flow collaborative metering system based on a plug-and-play carbon meter, including: Multiple plug-and-play carbon meters are used to connect distributed resources. When a distributed resource is connected to the power grid, the plug-and-play carbon meters connected to it automatically initiate a registration request to the system master station. The registration request includes a device identifier. The system master station is used to respond to the registration request, identify the node type of the distributed resource based on the device identifier, determine the appropriate carbon emission metering strategy based on the pre-stored mapping relationship between node types and carbon emission metering strategies, and send the carbon emission data generated according to the carbon emission metering strategy to the plug-and-play carbon meters for display.

[0106] Other details not covered in this embodiment are the same as those described in embodiments one to four, and will not be repeated here.

[0107] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features within the scope of the technology disclosed in the present invention; and these modifications or substitutions will not cause the substance of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any modifications or equivalent substitutions that do not deviate from the spirit and scope of the present invention should be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for coordinated metering of grid carbon flow based on a plug-and-play carbon meter, characterized in that, Includes the following steps: When a distributed resource is connected to the power grid, the plug-and-play carbon meter connected to it automatically initiates a registration request to the system master station. The registration request includes the device identifier. In response to the registration request, the system master station identifies the node type of the distributed resource based on the device identifier; based on the pre-stored mapping relationship between node types and carbon emission metering strategies, it determines the appropriate carbon emission metering strategy and sends the carbon emission data generated according to the carbon emission metering strategy to the plug-and-play carbon meter for display.

2. The grid carbon flow collaborative metering method based on plug-and-play carbon meters as described in claim 1, characterized in that... , The carbon emission measurement strategy includes: For power generation nodes, carbon emission data is generated based on fixed values ​​or actual measured data from manufacturers. For load nodes, carbon emission data is generated based on the actual carbon intensity allocated to that node; For energy storage nodes, carbon emission data is generated based on the actual carbon intensity allocated to the node during charging; and carbon emission data is generated based on the cumulative carbon emissions during historical charging, or based on a fixed value, during discharging. Alternatively, the carbon emission metering strategy includes: For power generation nodes, carbon emission data is generated based on fixed values ​​or actual measured data from manufacturers. For load nodes, their carbon emission data are generated based on carbon intensity calculated using predictive carbon emission factors. For energy storage nodes, carbon emission data is generated based on carbon intensity calculated by predictive carbon emission factors during charging; and carbon emission data is generated based on the cumulative predicted carbon emissions during historical charging, or based on a fixed value, during discharging. Or / and, the system master station is equipped with a device resource description library, which contains carbon attribute tags; the carbon attribute tags include node type identifier, carbon intensity attribute identifier and load adjustability identifier; Or / and, the system master station sends an electricity metering strategy to the plug-and-play carbon meter according to the node type. The electricity metering strategy includes metering electricity consumption or metering power generation. The plug-and-play carbon meter measures and displays the electricity consumption or power generation according to the electricity metering strategy. Or / and, the system master station is used to control the charging and discharging of energy storage nodes, and the control can adapt to the access of plug-and-play devices.

3. The grid carbon flow collaborative metering method based on a plug-and-play carbon meter as described in claim 2, characterized in that... Also includes: The plug-and-play carbon meter collects real-time operating data of the nodes and uploads the real-time operating data and the grid node information of the nodes to the system master station. The real-time operating data includes the real-time power of the nodes. The system master station updates the global topology model of the microgrid based on the type of the distributed resources and the information of the connected power grid nodes; Or / and, the system master station is used to control the charging and discharging of energy storage nodes, and the control is adapted to the access of plug-and-play devices, including: Construct a first function with the objective of minimizing the system's economic cost; Construct a second function with the objective of minimizing the total carbon emissions of the system; Constraints are set for the first function and the second function, including global power balance constraints. The global power balance constraints are described by a dynamic set of nodes and a dynamic set of branches. When a new device is detected to be connected, the set is automatically updated to reconstruct the network topology equation, thereby achieving plug-and-play functionality. The first and second functions are transformed into single-objective functions for solution to obtain the optimal charging and discharging power of each energy storage node during the scheduling period, and control commands are then issued.

4. The grid carbon flow collaborative metering method based on a plug-and-play carbon meter as described in claim 3, characterized in that, The method for generating carbon emission data based on the cumulative carbon emissions during historical charging includes: real-time statistics of the historical cumulative charging amount of the energy storage device and its corresponding total carbon emissions; calculating the current average carbon intensity of the energy storage device based on the total carbon emissions and the cumulative charging amount; and generating carbon emission data for the current discharge process based on the average carbon intensity and the current discharge amount. Alternatively, methods for generating carbon emission data based on the cumulative carbon emissions during historical charging include: using a first-in, first-out (FIFO) metering method to stratify and label the electrical energy stored in the energy storage device according to the charging time sequence, and recording the grid carbon intensity corresponding to the charging time of each layer of electrical energy; when the energy storage device discharges, the earliest stored layer of electrical energy is released first; and the carbon emission data of this discharge process is calculated by accumulating the actual discharge energy and the grid carbon intensity recorded for that layer of electrical energy. Or / and, the method for calculating the actual carbon intensity allocated to that node is as follows: Based on the global topology model, a node admittance matrix is ​​formed, and power flow calculation is performed using the real-time operating data to obtain the power flow distribution of each branch. Obtain carbon emission data for each power generation node; A carbon flow tracing algorithm based on power flow is adopted. Starting from the power generation node, the carbon emissions of the power generation node are distributed to downstream nodes according to the distribution ratio of its power output in the power grid, until all load nodes are obtained, thus obtaining the carbon intensity of each load node. Or / and, the economic costs include conventional unit fuel costs, energy storage aging costs, electricity purchase and sale costs, and / or carbon emission penalty costs calculated based on line loss equivalence. The total carbon emissions include direct carbon emissions from conventional units and / or indirect carbon emissions from line losses calculated based on the system average carbon emission factor. The constraints also include energy storage device operation constraints and / or line transmission capacity constraints. Transforming the first and second functions into single-objective functions for solving means using a weighted summation method to normalize the first and second functions and then transform them into single-objective functions for solving.

5. The grid carbon flow collaborative metering method based on a plug-and-play carbon meter as described in claim 2, characterized in that, The energy storage node includes the following measures when it is charging: Based on the real-time carbon emission factor of the access node, it is determined whether the current period is a high-carbon period. When it is a high-carbon period, the charging power of the energy storage node is reduced; otherwise, it is charged normally. An adjustable parameter N is configured to dynamically control the sensitivity of the high-carbon period judgment, limit the frequency of daily state switching, and use the hysteresis effect to filter the fluctuation of the real-time carbon emission factor to ensure that the single charging process is continuous and effective. The method for reducing the charging power of energy storage nodes includes: Based on the different time-of-use electricity prices, the energy storage nodes are controlled to charge in a paused or low-power mode, and an adjustable parameter T is configured to set the minimum time difference between power decisions, thereby limiting the switching frequency and taking into account equipment protection. At the same time, an interruption mechanism is set up. When a new plug-and-play carbon meter automatic registration request is detected, the current process is immediately terminated to prioritize the response to topology changes. By adjusting the N and T parameters, the energy storage nodes are switched to a control mode that improves carbon response sensitivity and extends the power adjustment interval, ensuring system stability while quickly adapting to the access of new nodes. Alternatively, the energy storage node in the charging state includes the following measures: Obtain the real-time carbon emission factor of the access node; Based on real-time carbon emission factors, determine whether the current period is a high-carbon period. If it is a high-carbon period, reduce the charging power of the energy storage nodes; otherwise, charge normally. The method for determining whether the current period is a high-carbon period is as follows: Real-time carbon emission factors are collected according to a preset sampling period; When the real-time carbon emission factor for N consecutive sampling periods is greater than or equal to the preset carbon emission threshold, it is determined that the current period is a high-carbon period. When a period is identified as high-carbon, if the real-time carbon emission factor is less than the preset carbon emission threshold for N consecutive sampling periods, the current period is considered to have ended. The method for reducing the charging power of the energy storage node includes: S1: Get the current time-of-use electricity price; S2: Compare the time-of-use electricity price with a first electricity price threshold and a second electricity price threshold, wherein the first electricity price threshold is higher than the second electricity price threshold; If the time-of-use electricity price is greater than or equal to the first electricity price threshold, then control the energy storage node to suspend charging or charge at the first power. If the time-of-use electricity price is less than or equal to the second electricity price threshold, then the energy storage node is controlled to charge at the second power. If the time-of-use electricity price is between the first electricity price threshold and the second electricity price threshold, then the current charging power of the energy storage node remains unchanged; Wherein, the first power < the second power < the normal charging power of the energy storage node; S3: Wait for a preset time T. During the waiting period, monitor in real time whether the interruption condition is met. The interruption condition includes: the system master station receives a new automatic registration request for the plug-and-play carbon meter. If the interruption condition is met, the current waiting is terminated and the system exits to end the current charging power reduction process. If the interruption condition is not met and the waiting time reaches T, return to step S1. The N and T are dynamically adjusted as follows: The values ​​of N and T are larger as the number of charging state transitions that have occurred in the past 24 hours is closer to the maximum allowed number of transitions per day, which is related to the maximum allowed number of transitions per day. When the system master station receives a new automatic registration request for a plug-and-play carbon meter, it decreases the value of N and increases the value of T. After the system master station completes the automatic registration request processing of a new plug-and-play carbon meter for a preset time, or when it detects that the power grid operation status has not fluctuated within the preset time, the values ​​of N and T will be restored to their default values.

6. The grid carbon flow collaborative metering method based on a plug-and-play carbon meter as described in claim 3, characterized in that... ； The method for generating carbon emission data based on carbon intensity calculated from predictive carbon emission factors is as follows: Based on the global topology model, a node admittance matrix is ​​formed, and power flow calculation is performed using the real-time operating data to obtain the power flow distribution of each branch. Obtain carbon emission data for each power generation node; Obtain predictive carbon emission factors and calculate carbon emissions at power generation nodes based on these predictive carbon emission factors; A carbon flow tracing algorithm based on power flow is adopted. Starting from the power generation node, the carbon emissions of the power generation node are distributed to downstream nodes according to the distribution ratio of its power output in the power grid, until all load nodes are obtained, thus obtaining the carbon intensity of each load node. Or / and, the method for obtaining the predictive carbon emission factor is as follows: Based on the time series of multimodal data, the predicted power generation and total load of each power generation node in future periods are obtained; Based on the power generation forecast and total load forecast of each power generation node, the expected consumption ratio of each energy type is obtained; By combining the expected absorption ratio of each energy type and the carbon intensity of each energy type, a predictive carbon emission factor is obtained.

7. The grid carbon flow collaborative metering method based on a plug-and-play carbon meter as described in claim 6, characterized in that: The method for generating carbon emission data based on carbon intensity calculated from predictive carbon emission factors also includes: When the expected consumption ratio changes beyond a fixed or dynamic threshold, the predictive carbon emission factor is updated. Or / and, methods for generating carbon emission data based on carbon intensity calculated from predictive carbon emission factors also include: When a sudden new resource not covered by the prediction model is accessed via plug-and-play, the predictive carbon emission factor is corrected.

8. A method for coordinated metering of grid carbon flow based on a plug-and-play carbon meter, applied to a plug-and-play carbon meter, characterized in that... Includes the following steps: When distributed resources are connected to the power grid, they automatically send a registration request to the system master station. The registration request includes the device identifier. The system receives and displays carbon emission data sent by the main station. The carbon emission data is generated as follows: in response to the registration request, the main station identifies the node type of the distributed resource based on the device identifier; based on the pre-stored mapping relationship between node types and carbon emission measurement strategies, it determines the appropriate carbon emission measurement strategy and displays the carbon emission data generated according to the carbon emission measurement strategy.

9. A method for coordinated metering of power grid carbon flow based on a plug-and-play carbon meter, applied to a system master station, characterized in that, Includes the following steps: Receive a registration request from a plug-and-play carbon meter; the registration request is automatically initiated by the plug-and-play carbon meter connected to the distributed resource when it is connected to the power grid, and the registration request includes a device identifier; In response to the registration request, the node type of the distributed resource is identified based on the device identifier; based on the pre-stored mapping relationship between node types and carbon emission metering strategies, an appropriate carbon emission metering strategy is determined, and the carbon emission data generated according to the carbon emission metering strategy is sent to the plug-and-play carbon meter for display.

10. A grid carbon flow collaborative metering system based on a plug-and-play carbon meter, characterized in that, include: Multiple plug-and-play carbon meters are used to connect distributed resources. When a distributed resource is connected to the power grid, the plug-and-play carbon meters connected to it automatically initiate a registration request to the system master station. The registration request includes the device identifier. The system master station is used to respond to the registration request, identify the node type of the distributed resource according to the device identifier; determine the appropriate carbon emission metering strategy based on the pre-stored mapping relationship between node types and carbon emission metering strategies, and send the carbon emission data generated according to the carbon emission metering strategy to the plug-and-play carbon meter for display.

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