Carbon metering method and system for an extra-high voltage substation

Abstract

The application discloses a carbon metering method and system for an extra-high voltage substation, belongs to the technical field of carbon emission monitoring and metering, obtains equipment operation parameters and historical energy consumption data, constructs an equipment energy conversion efficiency model; a carbon emission factor matrix is established in combination with an energy efficiency coefficient; the matrix is used to dynamically estimate equipment carbon emission, and an equipment level carbon emission estimation curve is generated; a full-station carbon emission correlation network is established by fusing an equipment topology structure and an energy flow path; on the basis, carbon emission intensities of the substation under different operation states are calculated; when the carbon emission intensity exceeds a set threshold value, early warning information is output, and a load optimization or energy efficiency adjustment strategy is executed; the method realizes dynamic perception, structured expression and intelligent response of carbon emission of the substation, and improves the accuracy of carbon emission monitoring and the energy efficiency management level.

Description

Technical Field

[0001] This invention relates to the field of carbon emission monitoring and measurement technology, specifically to a carbon measurement method and system for ultra-high voltage substations. Background Technology

[0002] As the global goals of peaking carbon emissions and achieving carbon neutrality continue to advance, the accuracy of carbon emission measurement in the power system, a significant source of carbon emissions, is crucial for carbon quota accounting, energy efficiency assessment, and policy formulation. Ultra-high voltage (UHV) substations, as vital nodes in the power grid, exhibit complex and concealed indirect carbon emissions during their operation. Traditional carbon measurement methods largely rely on estimating static equipment power factors and operating time, lacking dynamic capture of actual load conditions and energy conversion efficiency, making it difficult to accurately reflect their true carbon emission levels. Furthermore, existing technologies generally ignore the energy loss characteristics of substation equipment under high voltage and high frequency conditions, failing to achieve refined monitoring of dynamic changes in carbon emissions over time, load, and environmental factors. On the other hand, current carbon measurement methods are mostly limited to point measurements at the single equipment level, lacking systematicity and interconnectivity, and unable to form a comprehensive view of carbon emissions covering the entire substation's operational status, thus failing to meet the requirements of smart grids for timely, accurate, and traceable carbon data. Summary of the Invention

[0003] The purpose of this invention is to provide a carbon metering method and system for ultra-high voltage substations to address the shortcomings in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a carbon metering method for ultra-high voltage substations, comprising: Obtain operating parameters and historical energy consumption data of various equipment in ultra-high voltage substations; Based on the aforementioned operating parameters and historical energy consumption data, an energy conversion efficiency model for the equipment is constructed, and a carbon emission factor matrix is ​​established in conjunction with the energy efficiency coefficient. The carbon emission factor matrix is ​​used to dynamically estimate the carbon emissions corresponding to the unit energy consumption of each piece of equipment, and generate carbon emission estimation curves at the equipment level. Based on the carbon emission estimation curves of the equipment, and by integrating the equipment topology and energy flow transmission paths, a carbon emission correlation network for the entire station is established. Based on the entire station's carbon emission correlation network, the carbon emission intensity of the substation under different operating conditions is calculated; When carbon emission intensity exceeds a set threshold, an early warning message is output, and load optimization or energy efficiency adjustment strategies are implemented.

[0005] Preferably, the various types of equipment include transformers, circuit breakers, disconnect switches, parallel capacitors, and series reactors.

[0006] Preferably, the construction of the device energy conversion efficiency model includes: Based on the equipment's operating parameters and historical energy consumption data, the input power, output power, and load rate under typical operating conditions are extracted to construct a feature vector of the equipment's operating status. A response relationship model between the input and output power of the equipment is established by using piecewise regression and nonlinear fitting methods. The local deviation of the efficiency curve is corrected by combining the load fluctuation frequency, and an energy conversion efficiency surface plot is generated. Cluster analysis and outlier removal are performed on the energy conversion efficiency surface plot to extract efficiency evaluation factors and construct an energy conversion efficiency model.

[0007] Preferably, the step of establishing a carbon emission factor matrix by combining energy efficiency coefficients includes: Based on the constructed energy conversion efficiency model, the unit energy consumption and energy efficiency fluctuation range of various devices under different load rates are extracted, and the corresponding energy efficiency coefficient sequence is generated. Map and associate the energy efficiency coefficient of each device with the standard carbon emission coefficient corresponding to the type of energy it uses to construct an initial carbon emission factor list; The initial factors are corrected based on the equipment operating environment parameters; The modified carbon emission factors are matrixed according to equipment type, operating stage and time interval to generate a carbon emission factor matrix.

[0008] Preferably, the step of dynamically estimating the carbon emissions per unit energy consumption of each piece of equipment using the carbon emission factor matrix to generate a carbon emission estimation curve at the equipment level includes: Call the carbon emission factor in the carbon emission factor matrix that corresponds to the current operating status of the target equipment, and match the load rate, operating stage and environmental parameters at the current moment; Based on real-time energy consumption data of the equipment, the unit energy consumption is multiplied by the corresponding carbon emission factor according to the time slice to calculate the instantaneous carbon emission in each sampling period, forming carbon emission time series data; Multi-scale sliding window processing is applied to carbon emission time series data to extract emission change trends at different time scales; Based on the extracted trend data, carbon emission estimation curves at the equipment level are constructed, and key operating nodes are marked.

[0009] Preferably, establishing a station-wide carbon emission correlation network includes: Analyze the physical connection relationships and electrical topology of various electrical equipment in UHV substations, and construct a directed energy transmission graph with transformers, busbars, switches and load terminals as nodes; Based on the carbon emission estimation curve of the equipment, the carbon emission time series of each node is embedded as an attribute into the topology graph to form a graph structure model with dynamic carbon emission characteristics. By combining the energy flow transmission path and the real-time power flow direction between nodes, the carbon emission transfer weight between adjacent devices is calculated, and a carbon emission impact factor matrix between nodes is constructed. Using carbon emission impact factors as edge weights, a station-wide carbon emission correlation network is generated to achieve a quantitative expression of the carbon emission contribution relationship between equipment.

[0010] Preferably, the calculation of the carbon emission intensity of the substation under different operating conditions includes: Based on the carbon emission correlation network of the entire site, the carbon emissions of all device nodes at the current moment are aggregated, and the total carbon emissions of the entire network are calculated in combination with the carbon emission impact factor. Collect real-time operating status parameters of the substation, including total load power, equipment operating mode and environmental conditions, construct operating status vectors and classify and identify operating stages; Based on the identified operating status, the total carbon emissions within the corresponding time window are divided by the total output energy of that stage to obtain the carbon emission value per unit energy, which is used as the carbon emission intensity under that operating status. By setting state weighting factors, the carbon emission intensity under each operating stage is weighted and aggregated to form a state-intensity mapping dataset.

[0011] Preferably, the execution of load optimization or energy efficiency adjustment strategies includes: When carbon emission intensity is detected to exceed the set threshold, a multi-dimensional early warning message is immediately output, including the equipment exceeding the emission limit, current status parameters, and suggested countermeasures. Based on the carbon emission correlation network of the whole station, identify the carbon emission impact path, prioritize key node equipment as the control target, and formulate load optimization schemes including reducing non-critical loads, optimizing operation combination or switching to high-efficiency mode; The selected strategy is pushed to the substation dispatch system, and feedback analysis is performed based on the changes in carbon emission intensity before and after the strategy is implemented, so as to correct and optimize the strategy.

[0012] The present invention also provides a carbon metering system for ultra-high voltage substations, comprising: Data acquisition module: Acquires operating parameters and historical energy consumption data of various equipment in UHV substations; Carbon emission factor matrix construction module: Based on the operating parameters and historical energy consumption data, construct the equipment energy conversion efficiency model, and establish the carbon emission factor matrix in combination with the energy efficiency coefficient; Estimation module: Utilizes the carbon emission factor matrix to dynamically estimate the carbon emissions corresponding to the unit energy consumption of each piece of equipment, generating carbon emission estimation curves at the equipment level; The associated network establishment module establishes a carbon emission association network for the entire station by integrating the equipment topology and energy flow transmission path based on the carbon emission estimation curves of the equipment. Carbon emission intensity calculation module: Based on the whole station carbon emission correlation network, calculate the carbon emission intensity of the substation under different operating conditions; Early warning feedback module: When carbon emission intensity exceeds the set threshold, it outputs early warning information and executes load optimization or energy efficiency adjustment strategies.

[0013] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention constructs a carbon emission factor matrix and equipment energy efficiency model for ultra-high voltage substations, realizing dynamic estimation and state identification of carbon emissions of all equipment in the station. For the first time, it deeply integrates energy flow path, electrical topology and carbon emission behavior, and establishes a carbon emission correlation network with time series attributes and structural correlation characteristics.

[0014] 2. The present invention proposes a carbon emission intensity assessment and intelligent response mechanism based on operating status, which can automatically output multi-dimensional early warning information when carbon emissions exceed the limit, identify high-impact emission paths, and link and execute load optimization and energy efficiency adjustment strategies to achieve dynamic closed-loop optimization of carbon emission control. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0016] Figure 1 This is a flowchart of the method of the present invention.

[0017] Figure 2 This is a flowchart of the system modules of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1, please refer to Figure 1 As shown in this embodiment, the carbon metering method for ultra-high voltage substations includes: Obtain operating parameters and historical energy consumption data of various equipment in ultra-high voltage substations; Based on the aforementioned operating parameters and historical energy consumption data, an energy conversion efficiency model for the equipment is constructed, and a carbon emission factor matrix is ​​established in conjunction with the energy efficiency coefficient. The carbon emission factor matrix is ​​used to dynamically estimate the carbon emissions corresponding to the unit energy consumption of each piece of equipment, and generate carbon emission estimation curves at the equipment level. Based on the carbon emission estimation curves of the equipment, and by integrating the equipment topology and energy flow transmission paths, a carbon emission correlation network for the entire station is established. Based on the entire station's carbon emission correlation network, the carbon emission intensity of the substation under different operating conditions is calculated; When carbon emission intensity exceeds a set threshold, an early warning message is output, and load optimization or energy efficiency adjustment strategies are implemented.

[0020] To achieve accurate measurement of carbon emissions from ultra-high voltage substations, it is first necessary to obtain the operating parameters and historical energy consumption data of various electrical equipment throughout the station, which will serve as the basis for subsequent carbon emission estimation and model training.

[0021] First, based on the asset management numbers and operating conditions of various typical electrical equipment (including but not limited to transformers, circuit breakers, disconnectors, parallel capacitors, and series reactors) in UHV substations, a multi-dimensional equipment information index model is constructed. By calling the station's asset management system (AMS) and the power equipment history database, static information such as the model, rated parameters, commissioning time, and maintenance records of various types of equipment is systematically archived, and a unique identifier is established for each type of equipment to facilitate subsequent data matching and time series alignment.

[0022] Secondly, for the dynamic acquisition of equipment operating parameters, the system connects to the substation energy management system (EMS), supervisory control and data acquisition system (SCADA), and fault recording system. Through the collaborative action of high-frequency sampling and edge computing units, it collects key operating indicators of each device in real time, including voltage, current, active power, reactive power, power factor, operating frequency, load rate, cooling status, and oil temperature. To improve data quality, the system is designed with a data redundancy filtering mechanism. By comparing the sampling results from multiple sensors, abnormal fluctuation values ​​are eliminated, and the data from different devices are normalized to unify the units and sampling period.

[0023] In terms of historical energy consumption data extraction, the system retrieves operational data from the past three to five years and performs data fitting and correction based on multi-dimensional information such as seasonal load fluctuation patterns, maintenance plans, and fault records. For missing or interrupted data segments, interpolation is performed using a moving average algorithm based on adjacent time windows and a device group comparison method to ensure data continuity. In addition, for high-energy-consuming equipment, such as main transformers and cooling systems, an energy efficiency coefficient calculation method is introduced, which evaluates the energy consumption per unit time as a ratio to the transmission load and generates standardized equipment energy consumption curves.

[0024] For example, when estimating the energy consumption of a transformer during operation, the system multiplies its active power by the operating time to obtain a basic energy consumption value. Simultaneously, it introduces load fluctuation factors and temperature rise coefficients to correct for energy efficiency deviations under non-rated operating conditions, thereby achieving a more accurate estimate of actual energy consumption. Based on this, the energy consumption data of all equipment is archived in chronological order to form a complete historical energy consumption dataset.

[0025] To improve the accuracy of carbon emission measurement in ultra-high voltage substations, this invention, based on the acquisition of various electrical equipment operating parameters and historical energy consumption data, further constructs an equipment-level energy conversion efficiency model to reflect the energy efficiency performance of the equipment under different operating conditions.

[0026] First, based on the collected historical operating data, the system selects representative operating conditions and extracts and normalizes key operating parameters. These parameters include the equipment's input power, output power, and real-time load rate. By sampling and analyzing operating data under different time periods and load levels, a feature vector of the equipment's operating status is constructed. This feature vector serves as the input for energy conversion modeling and can comprehensively reflect the equipment's performance characteristics under various operating conditions.

[0027] Secondly, piecewise regression and nonlinear fitting methods are used to model the relationship between input power and output power. For the linearly correlated region, the least squares method is used to fit the linear model; for the nonlinear region with efficiency fluctuations or power saturation characteristics, polynomial fitting or radial basis function (RBF) networks are introduced to model and fit the data to obtain a response curve that better reflects reality. Simultaneously, in establishing the response model, the frequency influence of load fluctuations during equipment operation is considered, and a load disturbance correction factor is constructed to correct local deviations in the efficiency curve and eliminate statistical biases caused by short-term impact loads.

[0028] After completing the response relationship modeling, the system constructs a three-dimensional input space using input power, output power, and load rate, and generates an energy conversion efficiency surface plot of the equipment using energy conversion efficiency as the output variable. This surface plot can be used to visually display the energy efficiency change trend under different operating conditions, reflecting the energy efficiency fluctuation characteristics of the equipment under light load, rated, and overload conditions.

[0029] To further refine the key parameters of the model, the system performs cluster analysis on the generated energy efficiency surface plots. Density clustering methods (such as DBSCAN) are used to identify the backbone regions of the data distribution, and outliers and isolated points are removed to improve the model's robustness. Subsequently, representative efficiency factors for each cluster center region are extracted, including indicators such as maximum efficiency, minimum efficiency, typical load efficiency, and critical load efficiency. Finally, a device energy conversion efficiency model that adapts to time and state changes is constructed. This model not only supports efficiency estimation at the single-device level but can also be extended to the overall energy efficiency assessment of device groups.

[0030] Based on the established energy conversion efficiency model, this invention further incorporates energy efficiency coefficients to construct a carbon emission factor matrix reflecting the operating states of various equipment in ultra-high voltage substations. This matrix is ​​used to convert the energy consumption behavior of electrical equipment into an equivalent carbon emission estimate, enabling refined mapping and dynamic adjustment of carbon emissions.

[0031] First, the system extracts the unit energy consumption range of various devices under different load rates based on the energy conversion efficiency model. For example, for a certain transformer, the input power required for its unit output electrical energy may differ at load rates of 50%, 75%, and 100%. The system quantifies this difference, forming a sequence of energy efficiency coefficients encompassing multiple load levels. Each energy efficiency coefficient represents the input electrical energy intensity required for a unit output energy, reflecting the energy consumption efficiency of the equipment under specific operating conditions.

[0032] Next, the system maps and associates the energy efficiency coefficient of the equipment with the carbon emission coefficient corresponding to the energy form used. The carbon emission coefficient can be set according to national standards or industry guidelines; for example, the carbon emission corresponding to 1 kilowatt-hour of electricity is a certain number of grams of carbon dioxide equivalent. For different energy paths in a substation, such as mains power input, backup diesel generator systems, or photovoltaic auxiliary systems, the system matches their standard carbon emission coefficients. Multiplying the energy efficiency coefficient by the corresponding carbon factor yields the initial carbon emission factor corresponding to a unit of energy output, constructing an initial carbon emission factor list.

[0033] To enhance the environmental adaptability of carbon emission factors, the system introduces an operating environment correction mechanism. Specifically, the coupling relationship between the energy efficiency coefficient and the carbon emission factor is adjusted based on parameters such as temperature, humidity, wind speed, and operating season of the equipment's environment. For example, in high-temperature environments, the energy consumption of the equipment's cooling system increases, overall energy efficiency decreases, and the corresponding carbon emission factor will be increased; while during winter light-load operation, some equipment losses decrease, and the carbon emission factor can be appropriately lowered. This type of correction is achieved by constructing a polynomial correction function between environmental variables and the carbon emission factor or by using an interpolation algorithm based on an empirical model.

[0034] Finally, the system performs matrix processing on the corrected carbon emission factors based on dimensions such as equipment type (e.g., transformer, circuit breaker), operating phase (start-up, stable, peak load), and time interval (daily, weekly, monthly), generating a carbon emission factor matrix with time-series and hierarchical structure. Each element in the matrix represents the carbon emission per unit of energy consumption for a specific device under a specific state, which can serve as the core input for real-time carbon metering and strategy optimization.

[0035] This carbon emission factor matrix is ​​not only applicable to carbon estimation at the single-device level, but can also be used to build a carbon emission correlation network within the station, supporting cross-device energy flow tracking and emission attribution, and providing a data foundation for carbon emission control, intelligent scheduling and energy-saving decision-making.

[0036] To achieve refined and dynamic management of carbon emissions from ultra-high voltage substations, this invention, based on the construction of a carbon emission factor matrix, further proposes a dynamic carbon emission estimation method oriented towards equipment operating status. By performing time-series calculations on the carbon emissions corresponding to unit energy consumption of equipment, carbon emission estimation curves at the equipment level are generated, providing data support for energy efficiency assessment and carbon emission investigation.

[0037] This invention first acquires the operating status information of the target equipment through a real-time monitoring system. This status information includes the current load rate, operating stage (e.g., startup, steady state, overload), and environmental parameters (e.g., ambient temperature, humidity, altitude). Using this status information as a retrieval criterion, the system calls upon the carbon emission factor corresponding to that status from a pre-established carbon emission factor matrix.

[0038] The carbon emission factor matrix is ​​organized in three dimensions: the horizontal axis represents the equipment operation stage, the vertical axis represents the load rate range, and the depth represents environmental correction parameters. Each element corresponds to the carbon emission value per unit of energy consumption. By matching the three-dimensional state vectors, the emission factors of the target equipment under its current state can be accurately selected.

[0039] For example, if a transformer operates at 75% load, steady state, and ambient temperature of 35°C, the system will retrieve the carbon emission factor α75 at the corresponding position in the factor matrix, which represents the carbon emission amount corresponding to the unit of electrical energy consumption under this state.

[0040] After matching the carbon emission factor, the system further calculates carbon emissions by combining real-time energy consumption data. Using a set sampling period (e.g., 1 minute or 5 minutes), the system multiplies the energy consumption value of the device within each period to obtain the carbon emissions for that time slice. The calculation expression is: Instantaneous carbon emissions = Unit energy consumption in that period × Carbon emission factor under the current state; where unit energy consumption is the electrical energy input of the device within the period (e.g., in kWh), and the calculation result is in grams of carbon dioxide equivalent.

[0041] Because equipment may be affected by factors such as load disturbances, environmental changes, or system switching during operation, carbon emission sequences may exhibit short-term abnormal fluctuations. To extract more representative emission trend information, this invention introduces a multi-scale sliding window analysis method to extract trends and denoise the carbon emission sequences.

[0042] The system sets multiple time scale windows, such as 5 minutes, 1 hour, and 1 day, to perform moving average processing on the carbon emission series at different scales. For each window width, the system calculates the average carbon emission value within the window by sliding it sequentially from the start point of the series, and outputs a smoothed trend curve. Multi-scale processing can take into account both short-term response and long-term evolution, facilitating comprehensive analysis of the carbon emission characteristics of the equipment.

[0043] In addition, to identify non-periodic abnormal events (such as sudden overload or equipment failure), the system introduces a standard deviation threshold method. Within a sliding window, when the current value deviates from the average by more than a certain multiple of the standard deviation, it is marked as an outlier and subsequently annotated in the estimated curve.

[0044] After trend extraction, the system integrates carbon emission trends at different time scales to construct a carbon emission estimation curve at the equipment level. This estimation curve, with time on the horizontal axis and carbon emissions on the vertical axis, presents the trend of carbon emission changes during equipment operation.

[0045] To enhance the interpretability of the curves, the system automatically identifies and labels the following key operational nodes: Startup peak: The sudden increase in carbon emissions caused by the rise in power during the initial power-up phase of the equipment; Steady-state operating range: After the equipment has stabilized, carbon emissions enter a relatively constant range; Load abrupt change node: emission transition when the power load of equipment changes drastically; Abnormal fluctuation segment: Abnormal points that exceed the preset emission fluctuation threshold should be prompted to be checked by operation and maintenance personnel.

[0046] To achieve systematic and structured identification and management of carbon emissions from ultra-high voltage substations, this invention, based on generating carbon emission estimation curves for each piece of equipment, further integrates the topology of electrical equipment within the substation with energy flow transmission paths, and proposes a method for constructing a carbon emission correlation network at the substation level.

[0047] First, the system analyzes the main electrical wiring diagram and automated monitoring data of the UHV substation, identifying the physical connections and operational communication methods of various major equipment within the substation. The equipment involved includes, but is not limited to, main transformers, busbars, disconnect switches, circuit breakers, parallel capacitors, series reactors, DC converter valve assemblies, and various load terminals.

[0048] Based on the above information, a directed graph structure for energy transmission within the station is constructed. This graph uses equipment as nodes and electrical connections as directed edges, with directions defined according to the energy flow path. For example, the connection from the main transformer to the busbar represents the direction of electrical energy output from the transformer to the busbar, forming a directed edge. For multi-level connections or branch structures, a graph layering strategy is adopted, grouping different levels of equipment structures into different depth layers of the graph to ensure good parsingability and hierarchy of the graph model.

[0049] In addition, to support time-series modeling, each node in the graph structure is configured with a timestamp mechanism to synchronize subsequently embedded carbon emission data.

[0050] Based on the established topology graph structure, the system calls the previously generated carbon emission estimation curves for each device and embeds their carbon emission time series as node attributes into the graph model. Each graph node contains not only static information (such as device type, rated power, and connection relationships) but also dynamic attributes, namely carbon emission time series data.

[0051] For example, if the carbon emission estimation curve of a main transformer between 08:00 and 10:00 is a set of continuous values, then the attribute of that node on the corresponding time axis is that carbon emission curve segment.

[0052] The system employs a sliding time window mechanism to update the time series, enabling the graph structure to reflect the changing trends of carbon emissions at each node in real time. Furthermore, to prevent incomplete topological attributes due to missing data, the system includes a missing data compensation module, using linear interpolation or an averaging method based on adjacent nodes to smoothly fill in the time series data.

[0053] To characterize the carbon emission impact relationships between devices, this invention combines energy flow transmission path and power flow direction information to establish a carbon emission impact factor matrix between nodes. This matrix represents the relative impact of upstream equipment carbon emissions on the operating carbon load of downstream equipment.

[0054] First, the system uses a power flow analysis module to obtain the direction and proportion of active power transmission between nodes in real time. For example, if the real-time power flow from transformer A to bus B is P1, and bus B outputs power P2 to capacitor C, then the system uses the power distribution ratio P2 / (P1+P2) as the basis for connection strength.

[0055] Secondly, the system combines connection strength with the carbon emissions of upstream equipment to calculate the carbon emission impact factor. , representing the carbon emission transmission weight per unit time from device i to device j. The calculation expression is as follows: Carbon Emission Impact Factor = (Carbon emissions per unit time of device i × Power flow ratio of i→j) / Total received power of device j; This factor is presented in a dimensionless form, and the higher the value, the greater the "contribution" of upstream devices to the carbon emissions of downstream devices. The carbon emission impact factor matrix is ​​formed by summing the impact factors among all nodes, which is used for the subsequent edge weight configuration of the graph network.

[0056] Based on the carbon emission impact factor matrix, the system assigns weights to the edges in the topological graph structure to generate a complete station-wide carbon emission correlation network. This network is essentially a weighted directed graph, where: Nodes represent equipment units and possess carbon emission time-series attributes; Edges represent energy flow connections, and edge weights are carbon emission impact factors; The overall network structure has a dynamic property that evolves over time and can be updated as device status is updated.

[0057] To further achieve accurate assessment and phased management of carbon emissions from ultra-high voltage substations, this invention proposes a carbon emission intensity calculation method based on operating status, building upon the established carbon emission correlation network for the entire substation.

[0058] In this step, the system first retrieves device node data from the overall carbon emission correlation network to summarize the instantaneous carbon emissions of each node at the current moment. The carbon emissions of each node are calculated by multiplying its real-time unit energy consumption by the matched carbon emission factor, with the unit being grams of carbon dioxide equivalent. To further reflect the indirect transmission relationship of carbon emissions between devices, the system incorporates the carbon emission impact factor in the edge weights to perform weighted correction on the impact of upstream emissions on downstream devices.

[0059] Specifically, let the local carbon emissions of the i-th device node be... Its upstream device node j has an influence factor. Then the total carbon emissions of device i are: The summation range is all upstream nodes j connected to i.

[0060] Repeat the above calculation for all device nodes, and for all... Summing these values ​​gives the total carbon emissions of the entire station at the current moment. .

[0061] While calculating total emissions, the system needs to collect real-time operating parameters of the substation to identify its current operating status. These parameters mainly include: Total load power within the station (kW); Operating modes of various equipment (e.g., running / standby / maintenance / fault); Environmental conditions (such as ambient temperature, relative humidity, air pressure, etc.).

[0062] The system constructs the above information into a state vector S(t), representing the operating state at time t. By comparing it with a preset operating state classification model, it determines which operating stage the system is currently in, for example: Basic operation phase: stable load and moderate temperature; Peak operating phase: Load approaches or exceeds rated value; Energy-saving operation phase: Some equipment is in sleep or low load; Abnormal operation phase: There is equipment failure or abnormal energy consumption.

[0063] The classification model can be implemented based on rule-based methods, clustering algorithms, or machine learning models, and supports offline training and online identification. The state identification results will be used for the next step of carbon emission intensity calculation.

[0064] Carbon emission intensity is defined as the amount of carbon emissions per unit of output energy, and it is an important indicator for evaluating the energy efficiency and carbon efficiency of a substation. During calculation, the system extracts the total carbon emissions Etotal(Δt) within the corresponding time window for each identified operating phase, and obtains the total output energy Pout(Δt) of the entire station within that phase (in kWh).

[0065] The carbon emission intensity (Cintensity) for this operational phase can be calculated using the following formula: Where: Etotal(Δt) is the total cumulative carbon emissions during this period; Pout(Δt) is the total output power of the substation during the corresponding time period; and Cintensity is the carbon emission intensity.

[0066] This method can be used to determine the carbon emission intensity of substations under different load levels, equipment combinations, and climatic conditions, reflecting the actual differences in carbon efficiency under different operating conditions.

[0067] For example, carbon emission intensity may remain at a low level under basic operating conditions; however, during peak load periods, carbon emission intensity increases significantly due to decreased equipment efficiency and increased cooling system load.

[0068] To comprehensively evaluate the overall carbon efficiency of a substation, this invention further introduces a state weighting factor to weight and aggregate the carbon emission intensity at each stage. The weighting factor can be set based on the following principles: Runtime percentage: The proportion of time spent in the runtime phase within the total runtime cycle; Load contribution percentage: The degree to which the load contributes to the total power output during this stage; Emissions sensitivity weight: The degree of impact on total carbon emissions at this stage; Let the carbon emission intensity of the k-th operating state be . The corresponding weighting factor is The weighted total station-wide periodic carbon emission intensity The calculation is as follows: , where k is the index of all running states.

[0069] Finally, the system constructs a state-intensity mapping dataset with time as the horizontal axis and operating states and corresponding carbon emission intensities as data points. This dataset can demonstrate the trend of carbon emissions changing with operating states and provide a basis for optimizing scheduling strategies.

[0070] To enhance the response capability of ultra-high voltage substations to abnormal carbon emissions during operation, this invention, based on real-time calculation and monitoring of carbon emission intensity, further proposes a load optimization and energy efficiency adjustment strategy that integrates early warning response, carbon emission impact path identification, and closed-loop optimization control.

[0071] When the real-time carbon emission intensity Cintensity(t) calculated by the system exceeds the set threshold Cthreshold(s) corresponding to the current operating state (where s is the current operating state category), the carbon emission exceedance warning mechanism is immediately triggered. The system will retrieve relevant data from the carbon emission intensity calculation module, the station-wide carbon emission correlation network, and the equipment operation monitoring module to generate a structured multi-dimensional warning information package, specifically including: List of equipment exceeding standards: Identifies key equipment whose carbon emissions suddenly increase within a unit of time and exceed the statistical baseline; Current status parameters include the current operating status vector, load level, environmental conditions, and device operating mode. Recommended countermeasures: Based on the equipment type and past experience, the system will automatically recommend possible response actions, such as switching operating modes, adjusting power allocation, or lowering load priority.

[0072] The warning information will be pushed to dispatchers or automatic control modules through the station control platform, smart terminals or remote interfaces to ensure that the system responds to abnormal carbon emission intensity as soon as possible.

[0073] After the early warning information is output, the system enters the optimization decision-making phase. First, based on the carbon emission correlation network of the entire station, the impact path identification algorithm is executed. This algorithm traverses the carbon emission path centered on the over-limit equipment, identifies its upstream high-impact factor equipment and energy flow transmission links, and constructs a carbon emission contribution sub-graph.

[0074] For the identified critical node devices, the system prioritizes them for control and formulates optimization plans based on the following strategies: Reduce non-critical loads: For auxiliary equipment or some interruptible loads that do not affect power supply safety, such as lighting systems and backup cooling devices, the system is advised to implement phased current limiting or intermittent operation based on their carbon emission intensity and load contribution ratio.

[0075] Optimize operational combinations: By analyzing the operating status of redundant devices in the equipment network, identify parallel devices with low operating efficiency and recommend temporarily shutting them down or switching them to backup units with higher operating efficiency. For example, for main transformer groups operating in parallel, prioritize devices with low unit energy consumption and currently operating within their optimal load range.

[0076] Switching to High-Efficiency Operating Mode: If some devices support multiple operating modes (such as energy-saving mode, peak mode, standby mode, etc.), the system will recommend switching to a low-carbon operating mode based on the carbon emission estimation curve and energy efficiency model. For example, cooling equipment can switch to energy-saving mode in a short time to reduce energy consumption.

[0077] After formulating the optimization strategy, the system will push the optimization control commands to the substation dispatching system or automation control system via the communication interface. Specific methods include: Directly issue load dispatch instructions to controllable equipment; The strategy will be executed after manual confirmation (for systems with human-machine collaborative control requirements). Configure policy execution priorities and recovery conditions to ensure that the safe operation of the system is not affected.

[0078] Within a set time period ΔT after the strategy is executed, the system reassesses the carbon emission intensity Cintensity(t+ΔT) and compares it with the data before optimization. If the carbon emission intensity decreases significantly, it indicates that the strategy is effective; if the change is not significant or a rebound occurs, the system performs the following operations based on the feedback results: Strategy fine-tuning: Adjusting the load reduction ratio and expanding the range of control targets; Solution switching: Select another superior strategy from the candidate solution set and execute it; Path reconfiguration: Re-identify carbon emission pathways and dynamically adjust the weights of carbon emission factors.

[0079] Through the above closed-loop feedback mechanism, the system can achieve adaptive adjustment and optimization iteration, thereby improving the actual execution effect of the load control strategy.

[0080] Example 2, please refer to Figure 2 As shown in this embodiment, the carbon metering system for ultra-high voltage substations includes: Data acquisition module: Acquires operating parameters and historical energy consumption data of various equipment in UHV substations; Carbon emission factor matrix construction module: Based on the operating parameters and historical energy consumption data, construct the equipment energy conversion efficiency model, and establish the carbon emission factor matrix in combination with the energy efficiency coefficient; Estimation module: Utilizes the carbon emission factor matrix to dynamically estimate the carbon emissions corresponding to the unit energy consumption of each piece of equipment, generating carbon emission estimation curves at the equipment level; The associated network establishment module establishes a carbon emission association network for the entire station by integrating the equipment topology and energy flow transmission path based on the carbon emission estimation curves of the equipment. Carbon emission intensity calculation module: Based on the whole station carbon emission correlation network, calculate the carbon emission intensity of the substation under different operating conditions; Early warning feedback module: When carbon emission intensity exceeds the set threshold, it outputs early warning information and executes load optimization or energy efficiency adjustment strategies.

[0081] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A carbon metering method for ultra-high voltage substations, characterized in that: include: Obtain operating parameters and historical energy consumption data of various equipment in ultra-high voltage substations; Based on the aforementioned operating parameters and historical energy consumption data, an energy conversion efficiency model for the equipment is constructed, and a carbon emission factor matrix is ​​established in conjunction with the energy efficiency coefficient, including: Based on the constructed energy conversion efficiency model, the unit energy consumption and energy efficiency fluctuation range of various devices under different load rates are extracted, and the corresponding energy efficiency coefficient sequence is generated. Map and associate the energy efficiency coefficient of each device with the standard carbon emission coefficient corresponding to the type of energy it uses to construct an initial carbon emission factor list; The initial factors are corrected based on the equipment operating environment parameters; Each modified carbon emission factor is matrixed according to equipment type, operating stage and time interval to generate a carbon emission factor matrix. The carbon emission factor matrix is ​​used to dynamically estimate the carbon emissions per unit of energy consumption for each piece of equipment, generating carbon emission estimation curves at the equipment level, including: Call the carbon emission factor in the carbon emission factor matrix that corresponds to the current operating status of the target equipment, and match the load rate, operating stage and environmental parameters at the current moment; Based on real-time energy consumption data of the equipment, the unit energy consumption is multiplied by the corresponding carbon emission factor according to the time slice to calculate the instantaneous carbon emission in each sampling period, forming carbon emission time series data; Multi-scale sliding window processing is applied to carbon emission time series data to extract emission change trends at different time scales; Based on the extracted trend data, construct carbon emission estimation curves at the equipment level and mark key operating nodes; Based on the carbon emission estimation curves of the aforementioned equipment, and by integrating the equipment topology and energy flow transmission paths, a station-wide carbon emission correlation network is established, including: Analyze the physical connection relationships and electrical topology of various electrical equipment in UHV substations, and construct a directed energy transmission graph with transformers, busbars, switches and load terminals as nodes; Based on the carbon emission estimation curve of the equipment, the carbon emission time series of each node is embedded as an attribute into the topology graph to form a graph structure model with dynamic carbon emission characteristics. By combining the energy flow transmission path and the real-time power flow direction between nodes, the carbon emission transfer weight between adjacent devices is calculated, and a carbon emission impact factor matrix between nodes is constructed. Using carbon emission impact factors as edge weights, a station-wide carbon emission correlation network is generated to achieve a quantitative expression of the carbon emission contribution relationship between equipment; Based on the aforementioned whole-station carbon emission correlation network, the carbon emission intensity of the substation under different operating conditions is calculated, including: Based on the carbon emission correlation network of the entire site, the carbon emissions of all device nodes at the current moment are aggregated, and the total carbon emissions of the entire network are calculated in combination with the carbon emission impact factor. Collect real-time operating status parameters of the substation, including total load power, equipment operating mode and environmental conditions, construct operating status vectors and classify and identify operating stages; Based on the identified operating status, the total carbon emissions within the corresponding time window are divided by the total output energy of that stage to obtain the carbon emission value per unit energy, which is used as the carbon emission intensity under that operating status. By setting state weight factors, the carbon emission intensity under each operating stage is weighted and aggregated to form a state-intensity mapping dataset; When carbon emission intensity exceeds a set threshold, an early warning message is output, and load optimization or energy efficiency adjustment strategies are implemented.

2. The carbon metering method for ultra-high voltage substations according to claim 1, characterized in that: The various types of equipment include transformers, circuit breakers, disconnect switches, parallel capacitors, and series reactors.

3. The carbon metering method for ultra-high voltage substations according to claim 2, characterized in that: The constructed device energy conversion efficiency model includes: Based on the equipment's operating parameters and historical energy consumption data, the input power, output power, and load rate under typical operating conditions are extracted to construct a feature vector of the equipment's operating status. A response relationship model between the input and output power of the equipment is established by using piecewise regression and nonlinear fitting methods. The local deviation of the efficiency curve is corrected by combining the load fluctuation frequency, and an energy conversion efficiency surface plot is generated. Cluster analysis and outlier removal are performed on the energy conversion efficiency surface plot to extract efficiency evaluation factors and construct an energy conversion efficiency model.

4. The carbon metering method for ultra-high voltage substations according to claim 1, characterized in that: The execution of load optimization or energy efficiency adjustment strategies includes: When carbon emission intensity is detected to exceed the set threshold, a multi-dimensional early warning message is immediately output, including the equipment exceeding the emission limit, current status parameters, and suggested countermeasures. Based on the carbon emission correlation network of the whole station, identify the carbon emission impact path, prioritize key node equipment as the control target, and formulate load optimization schemes including reducing non-critical loads, optimizing operation combination or switching to high-efficiency mode; The selected strategy is pushed to the substation dispatch system, and feedback analysis is performed based on the changes in carbon emission intensity before and after the strategy is implemented, so as to correct and optimize the strategy.

5. A carbon metering system for ultra-high voltage substations, used to implement the carbon metering method for ultra-high voltage substations as described in any one of claims 1-4, characterized in that: include: Data acquisition module: Acquires operating parameters and historical energy consumption data of various equipment in UHV substations; Carbon emission factor matrix construction module: Based on the operating parameters and historical energy consumption data, construct the equipment energy conversion efficiency model, and establish the carbon emission factor matrix in combination with the energy efficiency coefficient; Estimation module: Utilizes the carbon emission factor matrix to dynamically estimate the carbon emissions corresponding to the unit energy consumption of each piece of equipment, generating carbon emission estimation curves at the equipment level; The associated network establishment module establishes a carbon emission association network for the entire station by integrating the equipment topology and energy flow transmission path based on the carbon emission estimation curves of the equipment. Carbon emission intensity calculation module: Based on the whole station carbon emission correlation network, calculate the carbon emission intensity of the substation under different operating conditions; Early warning feedback module: When carbon emission intensity exceeds the set threshold, it outputs early warning information and executes load optimization or energy efficiency adjustment strategies.

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