A substation carbon emission monitoring method and system

Abstract

The application provides a power substation carbon emission monitoring method and system, the method comprising: constructing a power substation digital twin model, optimizing the power substation digital twin model, and obtaining equipment state data and energy flow data according to the optimized power substation digital twin model; introducing a dynamic carbon flow factor to construct a carbon flow prediction equation according to the dynamic carbon flow factor, the equipment state data, and the energy flow data; calculating the carbon emissions of each node of the power substation according to the carbon flow prediction equation, and collecting the carbon emissions of each node of the power substation according to the time dimension to obtain carbon emission time series data; dividing each node into regions, setting corresponding carbon emission thresholds for different times and different regions, and monitoring the carbon emission time series data of the region according to the carbon emission threshold. The application can realize fine monitoring and abnormal early warning of power substation carbon emissions.

Description

Technical Field

[0001] This invention relates to the field of carbon emission monitoring technology, and in particular to a method and system for monitoring carbon emissions from substations. Background Technology

[0002] Against the backdrop of the power industry's low-carbon transformation, substations, as a key link in energy consumption and carbon emissions, require precise monitoring and dynamic management of their carbon emissions as a core technological need.

[0003] In existing technologies, substation carbon emission monitoring mainly relies on two types of methods: one is macro-level accounting based on statistical data, which estimates the total carbon emissions by using the regional power grid average emission factor or historical electricity consumption. This type of method does not consider the dynamic operating characteristics of equipment, resulting in significant deviations between the calculation results and actual emissions, especially in scenarios with a high proportion of renewable energy integration and frequent load fluctuations. The other type is a simplified carbon flow model, which allocates carbon emission responsibility by fixing emission factors, but ignores the real-time impact of parameters such as equipment temperature, load rate, and line losses on carbon emissions. For example, an increase in transformer temperature will significantly increase iron and copper losses, thereby changing the carbon emission intensity, and existing models cannot quantify such dynamic effects. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for monitoring carbon emissions in substations, which aims to solve the problem that traditional carbon emission monitoring technologies have large calculation errors due to the failure to consider the dynamic operating characteristics of equipment and ignore the real-time impact of many parameters of the substation on carbon emissions, making them difficult to apply to substation scenarios with real-time dynamic changes.

[0005] In a first aspect, the present invention provides a method for monitoring carbon emissions from a substation, the method comprising:

[0006] A digital twin model of a substation is constructed and optimized. Equipment status data and energy flow data are obtained based on the optimized digital twin model of the substation.

[0007] A dynamic carbon flow factor is introduced to construct a carbon flow prediction equation based on the dynamic carbon flow factor, the equipment status data, and the energy flow data.

[0008] The carbon emissions of each node in the substation are calculated based on the carbon flow prediction equation, and the carbon emissions of each node in the substation are summarized according to the time dimension to obtain carbon emission time series data.

[0009] Each node is divided into regions, and corresponding carbon emission thresholds are set for different regions at different times. The carbon emission time series data of the respective regions are monitored based on the carbon emission thresholds.

[0010] In some embodiments, the step of constructing a digital twin model of a substation includes:

[0011] Obtain substation topology information , For the set of substation nodes, For the substation side collection, among which, , , They are the 1st, 2nd, and 3rd respectively. 1 node , , Article 1, Article 2, and Article 3 are respectively. Edge;

[0012] Let the i-th node be associated with the device parameter vector set The set of parameter vectors associated with the j-th edge ,in, , , These are the parameter vectors of the 1st, 2nd, and 1st devices associated with the i-th node, respectively. , , These are the 1st, 2nd, and mth line parameter vectors associated with the j-th edge, respectively.

[0013] Acquire substation environmental data Let T be the temperature, h be the humidity, and w be the wind speed. Then, a digital twin model of the substation is constructed based on the following formula:

[0014] ;

[0015] in, For digital twin models of substations, It is a multiphysics coupling algorithm;

[0016] The output of the substation digital twin model is a set of device parameter vectors and a set of line parameter vectors for each node and edge at different times.

[0017] In some embodiments, the step of optimizing the substation digital twin model and obtaining equipment status data and energy flow data based on the optimized substation digital twin model includes:

[0018] Construct the objective function based on the following formula:

[0019] ;

[0020] in, Let the objective function be the digital twin model of the substation. , , All are weighting coefficients. , Let be the actual device parameter vector set and the predicted device parameter vector set for the i-th node, respectively. , Let be the actual line parameter vector set and the predicted line parameter vector set for the j-th edge, respectively. The total number of time periods. The complexity of the digital twin model of the substation;

[0021] To minimize To achieve this goal, the equipment parameter vector and line parameter vector in the substation digital twin model are optimized to obtain an optimized substation digital twin model.

[0022] In some embodiments, the step of introducing a dynamic carbon flow factor to construct a carbon flow prediction equation based on the dynamic carbon flow factor, the equipment status data, and the energy flow data includes:

[0023] The dynamic carbon flow factor includes a carbon intensity factor and a carbon transport efficiency factor.

[0024] The carbon flow prediction equation is constructed based on the following formula:

[0025] ;

[0026] in, Let be the carbon emissions of node n at time t. Let be the carbon intensity factor of node n at time t, and l be the total number of device parameter vectors in the device parameter vector set. The weights of the i-th device parameter vector corresponding to node n. This is the parameter vector of the i-th device at time t corresponding to node n. Let n be the input energy flow at time t. Let n be the set of edges adjacent to node n. Let be the carbon transmission efficiency factor of line e at time t, and m be the total number of line parameter vectors in the line parameter vector set. The weights of the j-th line parameter vector corresponding to line e, This is the vector of the j-th line parameter at time t corresponding to line e. Let be the energy flow transmitted by line e at time t.

[0027] In some embodiments, the carbon strength factor is calculated according to the following formula:

[0028] ;

[0029] in, Carbon emissions per unit of energy This is a temperature sensitivity coefficient used to represent the degree to which changes in equipment temperature affect carbon emissions. Let n be the temperature at time t. This refers to the current at time t predicted by the digital twin model of the substation at node n. Let n be the rated current. Load factor index;

[0030] The carbon transport efficiency factor is calculated using the following formula:

[0031] ;

[0032] in, As a benchmark carbon transport efficiency, Let e ​​be the energy loss rate of line e at time t. For line load sensitivity coefficient, This represents the maximum transmission capacity of line e.

[0033] In some embodiments, the steps of dividing each node into regions, setting corresponding carbon emission thresholds for different regions at different times, and monitoring the carbon emission time series data of the respective regions based on the carbon emission thresholds include:

[0034] The expression for carbon emission time series data is defined as follows:

[0035] ;

[0036] in, , , These are the times of node n at time n. , , Carbon emissions;

[0037] Assumptions and The corresponding carbon emission threshold is Then judge Is it valid?

[0038] like If true, then determine normal;

[0039] like If the condition is not met, a warning message will be issued indicating that carbon emissions have exceeded the standard.

[0040] Secondly, the present invention provides a substation carbon emission monitoring system, the system comprising:

[0041] The model building module is used to build a digital twin model of the substation, optimize the digital twin model of the substation, and obtain equipment status data and energy flow data based on the optimized digital twin model of the substation.

[0042] The prediction equation construction module is used to introduce a dynamic carbon flow factor to construct a carbon flow prediction equation based on the dynamic carbon flow factor, the equipment status data, and the energy flow data.

[0043] The carbon emission calculation module is used to calculate the carbon emissions of each node of the substation according to the carbon flow prediction equation, and to summarize the carbon emissions of each node of the substation according to the time dimension to obtain carbon emission time series data.

[0044] The monitoring and execution module is used to divide each node into regions, set corresponding carbon emission thresholds for different regions at different times, and monitor the carbon emission time series data of the region according to the carbon emission thresholds.

[0045] Thirdly, the present invention provides a storage medium that stores one or more programs that, when executed by a processor, implement the above-described substation carbon emission monitoring method.

[0046] Fourthly, the present invention provides an electronic device, the electronic device comprising a memory and a processor, wherein:

[0047] The memory is used to store computer programs;

[0048] When the processor executes the computer program stored in the memory, it implements the above-mentioned substation carbon emission monitoring method.

[0049] Compared with the prior art, the present invention has the following advantages:

[0050] This invention significantly improves the real-time performance, accuracy, and dynamic adaptability of substation carbon emission calculations by constructing a monitoring system that deeply integrates digital twins and dynamic carbon flow factors. It effectively solves problems such as large calculation errors and poor adaptability to renewable energy integration found in existing technologies. Specifically, a substation digital twin model is first constructed. This model achieves real-time mapping between equipment state parameters and line transmission parameters by integrating a multi-physics coupling algorithm (equipment state vector - line state vector). Then, a carbon intensity factor and a carbon transmission efficiency factor are introduced. The carbon intensity factor is constructed based on the equipment temperature sensitivity coefficient and load rate index. Its core principle is that an increase in equipment temperature or load rate leads to an increase in line loss, which in turn leads to an increase in carbon emissions per unit of energy, causing carbon emission intensity to grow exponentially. By quantifying these relationships, the carbon intensity factor can dynamically adjust the carbon emissions per unit of energy. The carbon transmission efficiency factor, on the other hand, is designed in conjunction with line loss rate and transmission power. When the access of new energy sources causes fluctuations in transmission power, this factor can correct the contribution of line loss to carbon emissions in real time. For example, during peak output of distributed photovoltaic power, the transmission power decreases, resulting in a reduction in line loss, and the carbon transmission efficiency factor is adjusted accordingly. This avoids the artificially high calculation caused by ignoring power fluctuations in traditional methods, thereby constructing a more accurate and comprehensive carbon flow prediction equation. Finally, the predicted carbon emissions are monitored in a targeted manner, which can be effectively applied to substation scenarios with real-time dynamic changes. Attached Figure Description

[0051] Figure 1 This is a flowchart of a substation carbon emission monitoring method according to an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of a substation carbon emission monitoring system according to an embodiment of the present invention.

[0053] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. 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. Unless otherwise defined, the technical or scientific terms used herein should have the ordinary meaning understood by those skilled in the art. The terms "comprising" and similar expressions used herein mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but does not exclude other elements or objects.

[0055] like Figure 1 As shown, an embodiment of the present invention proposes a method for monitoring carbon emissions from a substation, the method comprising steps S101 to S104, wherein:

[0056] Step S101: Construct a digital twin model of the substation, optimize the digital twin model of the substation, and obtain equipment status data and energy flow data based on the optimized digital twin model of the substation;

[0057] It should be noted that in this step, the substation topology information is obtained first. , For the set of substation nodes, For the substation side collection, among which, , , They are the 1st, 2nd, and 3rd respectively. 1 node , , Article 1, Article 2, and Article 3 are respectively. Edge;

[0058] Let the i-th node be associated with the device parameter vector set The set of parameter vectors associated with the j-th edge ,in, , , These are the parameter vectors of the 1st, 2nd, and 1st devices associated with the i-th node, respectively. , , These are the 1st, 2nd, and mth line parameter vectors associated with the j-th edge, respectively.

[0059] Acquire substation environmental data Let T be the temperature, h be the humidity, and w be the wind speed. Then, a digital twin model of the substation is constructed based on the following formula:

[0060] ;

[0061] in, For digital twin models of substations, It is a multiphysics coupling algorithm;

[0062] The output of the substation digital twin model is a set of device parameter vectors and a set of line parameter vectors for each node and edge at different times.

[0063] In summary, by clearly defining the substation topology (the set of nodes and edges) and associated parameters (equipment parameter vector sets and line parameter vector sets), and combining environmental data (temperature, humidity, and wind speed), a multi-physics coupled digital twin model was constructed, achieving real-time interactive mapping of electrical, thermal, and environmental parameters. For example, when increased temperature leads to increased line resistance, the model can dynamically correct line loss parameters, avoiding carbon emission calculation errors caused by neglecting environmental factors in traditional methods; when humidity changes affect equipment insulation performance, the model can synchronously adjust equipment failure probability parameters, improving the robustness of the monitoring system. This model provides a high-fidelity data foundation for subsequent optimization and dynamic carbon flow factor calculation, significantly improving the accuracy of carbon emission monitoring.

[0064] Furthermore, in some embodiments, the device state vector includes the node's current, voltage, active power, reactive power, and operating temperature, and the line state vector includes the line's reactance, resistance, transmitted active power, transmitted reactive power, current, and power loss.

[0065] Furthermore, in some embodiments, the multi-physics coupling algorithm used in this step is an existing technology. The only difference is that the multi-physics coupling in this step refers to the comprehensive modeling of the device state vector set and the line state vector set. This is achieved by jointly analyzing and dynamically associating the electrical parameters (such as current and voltage) and thermal parameters (such as temperature) related to the device and the electrical parameters (such as resistance and reactance) of the line. For example, changes in device temperature will affect its electrical characteristic parameters, and the transmission state of the line will also affect the device's operating state. This will enable the accurate model construction under multi-physics coupling.

[0066] Furthermore, in some embodiments, during the construction of a digital twin model of a substation, the model may not fit the actual operation of the substation well and may be overly complex. Therefore, an objective function is constructed to comprehensively consider factors such as prediction error and model complexity. The prediction error is measured by the difference between the actual equipment parameter vector set and the predicted equipment parameter vector set, and the actual line parameter vector set and the predicted line parameter vector set. Model complexity reflects the computational load and number of parameters in the model. Then, with the goal of minimizing this objective function, the equipment parameter vectors and line parameter vectors in the model are adjusted and optimized, as follows:

[0067] Construct the objective function based on the following formula:

[0068] ;

[0069] in, Let the objective function be the digital twin model of the substation. , , All are weighting coefficients. , Let be the actual device parameter vector set and the predicted device parameter vector set for the i-th node, respectively. , Let be the actual line parameter vector set and the predicted line parameter vector set for the j-th edge, respectively. The total number of time periods. The complexity of the digital twin model of the substation is represented by the predicted equipment parameter vector set and the predicted line parameter vector set, which are the outputs of the digital twin model of the substation, while the actual equipment parameter vector set and the actual line parameter vector set are obtained through data collection.

[0070] To minimize To achieve this goal, the equipment parameter vector and line parameter vector in the substation digital twin model are optimized to obtain an optimized substation digital twin model.

[0071] Step S102: Introduce a dynamic carbon flow factor to construct a carbon flow prediction equation based on the dynamic carbon flow factor, the equipment status data, and the energy flow data;

[0072] It should be noted that traditional carbon emission calculation methods often use fixed emission factors, which cannot reflect the dynamic impact of equipment status and line transmission characteristics on carbon emissions during the actual operation of substations. For example, the operating status of equipment in a substation (such as load rate and temperature) is constantly changing, and the transmission conditions of the lines (such as transmission power and losses) also change accordingly. These factors will significantly affect carbon emissions. Furthermore, when the equipment load rate increases, its efficiency may decrease, increasing the carbon emissions per unit of energy; when the line transmission power changes, the line losses will also vary, thus affecting the calculation of carbon emissions.

[0073] Based on this, the dynamic carbon flow factor in this embodiment includes a carbon intensity factor and a carbon transmission efficiency factor. First, based on factors such as the impact of equipment temperature changes on carbon emissions (temperature sensitivity coefficient), real-time equipment temperature, predicted current, rated current, and load factor index, a specific formula is designed to quantify the impact of equipment operating status on carbon emissions per unit of energy. Furthermore, the carbon transmission efficiency factor is also designed using a specific formula based on parameters such as the line's baseline carbon transmission efficiency, energy loss rate, line load sensitivity coefficient, and maximum transmission capacity to correct the contribution of line transmission to carbon emissions in real time. Then, based on the dynamic carbon flow factor, equipment status data, and energy flow data, the parameters are substituted into the carbon flow prediction equation formula for calculation, thereby constructing a carbon flow prediction equation to calculate the carbon emissions of each node in the substation at a specific time. This allows for more accurate application to substation scenarios such as renewable energy integration and load fluctuations.

[0074] Specifically, the carbon flow prediction equation is constructed based on the following formula:

[0075] ;

[0076] in, Let be the carbon emissions of node n at time t. Let be the carbon intensity factor of node n at time t, and l be the total number of device parameter vectors in the device parameter vector set. The weights of the i-th device parameter vector corresponding to node n. This is the parameter vector of the i-th device at time t corresponding to node n. Let n be the input energy flow at time t. Let n be the set of edges adjacent to node n. Let be the carbon transmission efficiency factor of line e at time t, and m be the total number of line parameter vectors in the line parameter vector set. The weights of the j-th line parameter vector corresponding to line e, This is the vector of the j-th line parameter at time t corresponding to line e. Let be the energy flow transmitted by line e at time t.

[0077] Furthermore, in some embodiments, the carbon strength factor is calculated according to the following formula:

[0078] ;

[0079] in, Carbon emissions per unit of energy This is a temperature sensitivity coefficient used to represent the degree to which changes in equipment temperature affect carbon emissions. Let n be the temperature at time t. This refers to the current at time t predicted by the digital twin model of the substation at node n. Let n be the rated current. Load factor index;

[0080] The carbon transport efficiency factor is calculated using the following formula:

[0081] ;

[0082] in, As a benchmark carbon transport efficiency, Let e ​​be the energy loss rate of line e at time t. For line load sensitivity coefficient, This represents the maximum transmission capacity of line e.

[0083] Step S103: Calculate the carbon emissions of each node in the substation according to the carbon flow prediction equation, and summarize the carbon emissions of each node in the substation according to the time dimension to obtain carbon emission time series data.

[0084] Step S104: Divide each node into regions, set corresponding carbon emission thresholds for different regions at different times, and monitor the carbon emission time series data of the respective regions according to the carbon emission thresholds.

[0085] It should be noted that in actual substation operation, industrial areas typically have high electricity consumption and equipment load rates, resulting in relatively high and somewhat regular carbon emission intensity. Commercial areas see electricity consumption concentrated during daytime business hours, with load fluctuations closely related to commercial activities. Residential areas primarily consume electricity in the morning and evening, and are significantly affected by seasons and weather. Furthermore, electricity demand and carbon emissions vary across different times, with significant differences in carbon emission characteristics between peak and off-peak periods. Using a uniform carbon emission monitoring standard would fail to accurately reflect the actual situation in each area, potentially leading to misjudgments or missed detections of abnormal emissions. Therefore, to achieve more accurate and effective carbon emission monitoring, it is necessary to divide each node into regions and set corresponding carbon emission thresholds based on the characteristics of different regions and times.

[0086] Specifically, the expression for carbon emission time series data is first defined as follows:

[0087] ;

[0088] in, , , These are the times of node n at time n. , , Carbon emissions;

[0089] Assumptions and The corresponding carbon emission threshold is Then judge Is it valid?

[0090] like If true, then determine normal;

[0091] like If the condition is not met, a warning message will be issued indicating that carbon emissions have exceeded the standard.

[0092] In summary, by constructing and optimizing a digital twin model of a substation, real-time dynamic acquisition of equipment status and energy flow data is achieved, solving the problem of insufficient accuracy caused by the reliance on static models in traditional monitoring methods. By introducing a dynamic carbon flow factor to construct a carbon flow prediction equation, the real-time impact of equipment operating parameters (such as temperature and load rate) and line transmission characteristics on carbon emissions can be quantified, breaking through the limitations of traditional fixed emission factors. By summarizing carbon emission data in the time dimension and dividing regional thresholds, refined monitoring and anomaly early warning at multiple spatiotemporal scales can be achieved.

[0093] like Figure 2 As shown, one embodiment of the present invention proposes a substation carbon emission monitoring system, the system comprising:

[0094] The model building module 10 is used to build a digital twin model of the substation, optimize the digital twin model of the substation, and obtain equipment status data and energy flow data based on the optimized digital twin model of the substation.

[0095] The prediction equation construction module 20 is used to introduce a dynamic carbon flow factor to construct a carbon flow prediction equation based on the dynamic carbon flow factor, the equipment status data, and the energy flow data.

[0096] The carbon emission calculation module 30 is used to calculate the carbon emissions of each node of the substation according to the carbon flow prediction equation, and to summarize the carbon emissions of each node of the substation according to the time dimension to obtain carbon emission time series data.

[0097] The monitoring execution module 40 is used to divide each node into regions, set corresponding carbon emission thresholds for different regions at different times, and monitor the carbon emission time series data of the region according to the carbon emission thresholds.

[0098] In another aspect, the present invention also proposes a storage medium on which one or more programs are stored, which, when executed by a processor, implement the above-described substation carbon emission monitoring method.

[0099] In another aspect, the present invention also proposes an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to realize the above-mentioned substation carbon emission monitoring method.

[0100] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain stored, communicated, propagated, or transmitted programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0101] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0102] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0103] While embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations fall within the scope and spirit of the invention as set forth in the claims. Furthermore, the invention described herein may have other embodiments and can be implemented or carried out in various ways.

Claims

1. A method of monitoring carbon emissions from a substation, characterized by, The method comprises: a substation digital twin model is constructed, and the substation digital twin model is optimized, and equipment state data and energy flow data are obtained according to the optimized substation digital twin model; a dynamic carbon flow factor is introduced to construct a carbon flow prediction equation according to the dynamic carbon flow factor, the equipment state data and the energy flow data; the dynamic carbon flow factor comprises a carbon intensity factor and a carbon transmission efficiency factor; the carbon flow prediction equation is constructed according to the following formula: ； wherein, is the carbon emission of node n at time t, is the carbon intensity factor of node n at time t, and l is the total number of equipment parameter vectors in the equipment parameter vector set, is the weight of the i-th equipment parameter vector corresponding to node n, is the i-th equipment parameter vector corresponding to node n at time t, is the input energy flow of node n at time t, is the edge set adjacent to node n, is the carbon transmission efficiency factor of line e at time t, and m is the total number of line parameter vectors in the line parameter vector set, is the weight of the j-th line parameter vector corresponding to line e, is the j-th line parameter vector corresponding to line e at time t, is the transmission energy flow of line e at time t; the carbon intensity factor is calculated according to the following formula: ； wherein, is the unit energy carbon emission, is a temperature sensitivity coefficient, used to represent the degree of influence of the change of the device temperature on the carbon emission, is the temperature of the node n at the time t, is the current of the node n at the time t predicted by the digital twin model of the substation, is the rated current of the node n, is a load rate index; the carbon transmission efficiency factor is calculated according to the following formula: ； wherein, is the reference carbon transfer efficiency, is the energy loss rate of line e at time t, is the line load sensitivity coefficient, is the maximum transfer capacity of line e; carbon emissions of each node of the substation are calculated according to the carbon flow prediction equation, and carbon emission time series data is obtained by aggregating the carbon emissions of each node of the substation in the time dimension; each node is divided into regions, and corresponding carbon emission thresholds are set for different regions at different times, and carbon emission time series data of the regions is monitored according to the carbon emission thresholds.

2. The substation carbon emission monitoring method of claim 1, wherein, The step of constructing the substation digital twin model comprises: Obtain substation topology information , For the set of substation nodes, For the substation side collection, among which, , , They are the 1st, 2nd, and 3rd respectively. 1 node , , Article 1, Article 2, and Article 3 are respectively. Edge; Let the set of device parameter vectors associated with the ith node be Let the set of line parameter vectors associated with the jth edge be where , , are the 1st, 2nd, lth device parameter vectors associated with the ith node, respectively, , , are the 1st, 2nd, mth line parameter vectors associated with the jth edge, respectively. Obtaining substation environment data T is temperature, h is humidity, and w is wind speed, then a substation digital twin model is constructed according to the following formula: ； wherein, is a digital twin model of a substation, is a multi-physics coupling algorithm; The output of the substation digital twin model is a set of equipment parameter vectors and a set of line parameter vectors of each node and edge at different times.

3. The substation carbon emission monitoring method of claim 2, wherein, The step of optimizing the substation digital twin model and obtaining equipment state data and energy flow data according to the optimized substation digital twin model comprises: a target function is constructed according to the following formula: ； wherein, is a target function about the digital twin model of the substation, , , are weight coefficients, , are a set of actual device parameter vectors, a set of predicted device parameter vectors of the i-th node, respectively, , are a set of actual line parameter vectors, a set of predicted line parameter vectors of the j-th edge, respectively, is a total number of time periods, is a complexity of the digital twin model of the substation; to minimize optimizing the equipment parameter vector and the line parameter vector in the digital twin model of the transformer substation to obtain an optimized digital twin model of the transformer substation.

4. The substation carbon emission monitoring method of claim 3, wherein, The step of dividing each node into regions, setting corresponding carbon emission thresholds for different regions at different times, and monitoring carbon emission time series data of the regions according to the carbon emission thresholds comprises: the expression of the carbon emission time series data is defined as: ； wherein, , , are the carbon emissions of node n at time , , ; Assume that The corresponding carbon emission threshold is Then determine Whether it is true; If is true, then determine normal; If If not, an early warning information of exceeding carbon emission is sent out.

5. A substation carbon emission monitoring system characterized by, The system comprises: a model construction module for constructing a substation digital twin model, optimizing the substation digital twin model, and obtaining equipment state data and energy flow data according to the optimized substation digital twin model; a prediction equation construction module for introducing a dynamic carbon flow factor to construct a carbon flow prediction equation according to the dynamic carbon flow factor, the equipment state data and the energy flow data; the dynamic carbon flow factor comprises a carbon intensity factor and a carbon transmission efficiency factor; the carbon flow prediction equation is constructed according to the following formula: ； in, Let be the carbon emissions of node n at time t. Let be the carbon intensity factor of node n at time t, and l be the total number of device parameter vectors in the device parameter vector set. The weights of the i-th device parameter vector corresponding to node n. This is the parameter vector of the i-th device at time t corresponding to node n. Let n be the input energy flow at time t. Let n be the set of edges adjacent to node n. Let be the carbon transmission efficiency factor of line e at time t, and m be the total number of line parameter vectors in the line parameter vector set. The weights of the j-th line parameter vector corresponding to line e, This is the vector of the j-th line parameter at time t corresponding to line e. Let e ​​be the energy flow transmitted by line e at time t; the carbon intensity factor is calculated according to the following formula: ； wherein, is the unit energy carbon emission, is a temperature sensitivity coefficient, used to represent the degree of influence of the change of the device temperature on the carbon emission, is the temperature of node n at time t, is the current of node n at time t predicted by the digital twin model of the substation, is the rated current of node n, is a load rate index; the carbon transmission efficiency factor is calculated according to the following formula: ； wherein, is the reference carbon transfer efficiency, is the energy loss rate of line e at time t, is the line load sensitivity coefficient, is the maximum transfer capacity of line e; a carbon emission calculation module for calculating carbon emissions of each node of the substation according to the carbon flow prediction equation, and aggregating the carbon emissions of each node of the substation in the time dimension to obtain carbon emission time series data; a monitoring execution module for dividing each node into regions, setting corresponding carbon emission thresholds for different regions at different times, and monitoring carbon emission time series data of the regions according to the carbon emission thresholds.

6. A storage medium, characterized by The storage medium stores one or more programs which are executed by the processor to implement the substation carbon emission monitoring method of any one of claims 1-4.

7. An electronic device, comprising: The electronic device comprises a memory and a processor, wherein: the memory is used to store a computer program; The processor is configured to implement the substation carbon emission monitoring method according to any one of claims 1-4 when executing the computer program stored in the memory.

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