Layered hybrid power system carbon emission metering method and system

Abstract

The invention relates to the technical field of electric carbon emission, in particular to a hierarchical hybrid power system carbon emission metering method and system, and the method comprises the steps: obtaining a topological structure and power flow data of a power network in a target region; based on the topological attributes of the power network and the distribution characteristic parameters of the energy nodes, the power network is decoupled into a centralized calculation layer and a distributed calculation layer in a layered mode; the centralized calculation layer adopts a matrix calculation method to solve a first carbon emission factor by constructing a power flow distribution matrix related to power grid topology and a unit carbon emission intensity vector; the distributed calculation layer iteratively updates a second carbon emission factor through a power distribution relation based on the first node carbon emission factor issued by the centralized calculation layer; and integrating centralized and distributed calculation results to obtain a full-node carbon emission factor vector, a power grid side indirect carbon emission transfer amount and a load side indirect carbon emission amount. The objective of the invention is to realize layered decoupling of carbon emission and improve real-time efficiency and deployment cost of carbon emission metering.

Description

Technical Field

[0001] The present invention relates to the technical field of electricity carbon emissions, and in particular to a carbon emission metering method and system for a layered hybrid power system. Background Art

[0002] Building an accurate and efficient carbon emission measurement system for the power system has become a key technical support for achieving the "dual carbon" goals. The current mainstream carbon emission flow theory establishes a source-grid-load coordinated carbon emission responsibility allocation model, allocating carbon emissions from the power generation side to the user side through power flow tracking technology, forming a measurement method based on node carbon potential. Within this framework, existing technologies are mainly divided into two technical approaches: the first is a centralized carbon emission measurement system, which relies on full-network topology modeling and matrix operations to build a cloud-based carbon flow analysis platform, aggregating all-node power flow data to calculate carbon emission factors in real time; the second is a distributed carbon emission measurement system, which uses independently deployed smart meters and carbon meters, combined with edge computing and distributed optimization algorithms, to achieve localized calculation and interaction of carbon emission flows across nodes. Both technical approaches are based on the node carbon potential mapping mechanism to achieve spatial allocation of carbon emission responsibilities.

[0003] However, the above technical solutions have defects in terms of computational timeliness and engineering economy. In terms of timeliness, the centralized solution needs to handle high-dimensional operations on the topology matrix of the entire network. Due to the bottleneck of cloud computing power and communication delays, the minute-level calculation cycle is difficult to meet the second-level carbon potential tracking requirements; while the distributed solution relies on an iterative convergence mechanism, and its calculation rounds are exponentially related to the depth of the power grid topology. In large-scale networks, the iteration time is long, resulting in real-time failure. In terms of engineering economy, the centralized carbon emission metering system requires the development of different types of carbon metering information display devices, which cannot be integrated with existing electricity metering devices and require the construction of an independent communication network. It is difficult to implement and has high deployment costs. The distributed carbon emission metering system requires the deployment of distributed carbon metering devices on all lines within the metering range, resulting in hardware investment being uneconomical in scale and high transformation costs. Summary of the Invention

[0004] In order to achieve stratified decoupling of carbon emissions and improve the real-time efficiency and deployment cost of carbon emissions measurement, the present invention provides a stratified hybrid power system carbon emissions measurement method and system. The technical solutions adopted are as follows:

[0005] The technical solution of the first aspect of the present invention provides a method for measuring carbon emissions in a layered hybrid power system, the method comprising:

[0006] Obtain the topology and flow data of the power network in the target area;

[0007] Based on the topological properties of the power network and the characteristic parameters of energy node distribution, the power network is decoupled into a centralized computing layer and a distributed computing layer.

[0008] In the centralized computing layer, by constructing a power flow distribution matrix related to the power grid topology and a unit carbon emission intensity vector, a matrix calculation method is used to solve the first carbon emission factor;

[0009] In the distributed computing layer, based on the first node carbon emission factor sent down by the centralized computing layer, the second carbon emission factor is iteratively updated through the power distribution relationship;

[0010] By integrating the centralized and distributed computing results, a full-node carbon emission factor vector, the indirect carbon emission transfer amount on the power grid side, and the indirect carbon emissions on the load side are obtained.

[0011] Furthermore, based on the power network topology attributes and energy node distribution characteristic parameters, the power network is hierarchically decoupled into a centralized computing layer and a distributed computing layer; including:

[0012] According to the characteristic parameters such as voltage level, regional distribution, power source access point, load access point, and electro-carbon information measurement requirements, hierarchical decoupling characteristic parameters are defined;

[0013] A multi-attribute decision matrix is constructed to standardize the characteristic parameters;

[0014] The weighted summation method or the analytic hierarchy process is used to allocate weights to the characteristic parameters, and the hierarchical decoupling boundary is extracted according to the comprehensive scores of different hierarchical schemes;

[0015] According to the hierarchical decoupling boundary, the power network is divided into a centralized computing layer and a distributed computing layer.

[0016] Furthermore, the centralized computing layer covers the power grid topology from the power source to the user access point before the preset voltage level, including generator sets, centralized energy storage, and transmission nodes; the distributed computing layer covers the power grid topology after the user access point of the preset voltage level, including electrical loads, distributed power sources, and energy storage nodes.

[0017] Furthermore, in the centralized computing layer, by constructing a power flow distribution matrix related to the power grid topology and a unit carbon emission intensity vector, a matrix calculation method is used to solve the first carbon emission factor, including:

[0018] Based on the node active power flux, branch power flow distribution, and unit carbon emission intensity, a node active power flux matrix, a branch power flow distribution matrix, a unit injection distribution matrix, and a unit carbon emission intensity vector are constructed;

[0019] A linear equation set is constructed and solved to obtain the first carbon emission factor of each node in the centralized computing layer.

[0020] Furthermore, the expression for solving the linear equation set is:

[0021]

[0022] Where, E N represents the node carbon emission factor vector; P N represents the node active flux matrix; represents the transpose of the branch power flow distribution matrix; represents the transpose of the unit injection distribution matrix; D G Represents the carbon emission intensity vector of the unit.

[0023] Furthermore, at the distributed computing layer, based on the first node carbon emission factor issued by the centralized computing layer, the second carbon emission factor is iteratively updated through the power allocation relationship, including:

[0024] Loading the first carbon emission factor issued by the centralized computing layer at the head-end node of the distributed computing layer;

[0025] Based on the power ratio between the inflow node and the outflow node, the upstream node sends the carbon emission factor in the iterative calculation process to the downstream node;

[0026] All distributed nodes perform parallel calculations until the change in the carbon emission factor of all nodes is less than the preset threshold, completing the iterative convergence.

[0027] Furthermore, the expression for iterative calculation of carbon emission factor is:

[0028]

[0029] Where, represents the carbon emission factor of node n at the k+1th iteration; I + represents the set of all loops flowing into node n; I - represents the set of all circuits outgoing from node n; P i→n represents the injected power from the incoming node to node n; P n→j represents the output power of node n flowing out of the loop; represents the carbon emission factor of the circuit flowing into node n.

[0030] The technical solution of the second aspect of the present invention provides a tiered hybrid power system carbon emission measurement system, which adopts the tiered hybrid power system carbon emission measurement method described in the technical solution of the first aspect of the present invention, and the system includes:

[0031] a data acquisition module configured to acquire topological structure and flow data of the power network in the target area;

[0032] A hierarchical decoupling module is configured to decouple the power network into a centralized computing layer and a distributed computing layer based on the power network topology attributes and energy node distribution characteristic parameters;

[0033] A centralized computing layer, configured to solve the first carbon emission factor by using a matrix calculation method through constructing a power flow distribution matrix related to the power grid topology and a unit carbon emission intensity vector;

[0034] A distributed computing layer, configured to iteratively update the second carbon emission factor based on the first node carbon emission factor issued by the centralized computing layer through a power distribution relationship;

[0035] A data aggregation module, configured to fuse the centralized and distributed computing results to obtain a full-node carbon emission factor vector, an indirect carbon emission transfer amount on the power grid side, and an indirect carbon emission amount on the load side.

[0036] Further, the system further includes:

[0037] Internet of Things electricity-carbon meters, which are dispersedly deployed at each loop node of the power network and include:

[0038] A centralized computing layer electricity-carbon meter, configured to receive and display the first carbon emission factor issued by the centralized computing layer;

[0039] A distributed computing layer electricity-carbon meter, which is communicatively connected to the carbon measurement module, configured to collect power data required for iterative calculation and upload the second carbon emission factor;

[0040] A carbon measurement module, which is embedded in the distributed computing layer electricity-carbon meter and configured to calculate the carbon emission amount of the distributed computing layer.

[0041] Further, the carbon measurement module is further configured to obtain the incoming power, outgoing power of the current node, and the carbon emission factor of the upstream node; calculate the carbon emission factor of the current node by weighted calculation according to the ratio of the incoming power to the outgoing power; send the calculation result to the downstream node to trigger the iterative update of the downstream node.

[0042] The present invention has the following beneficial effects:

[0043] The hierarchical hybrid power system carbon emission measurement method and system provided by the present invention decompose the power network into a centralized calculation layer and a distributed calculation layer based on the topological attributes of the power network and the distribution characteristic parameters of energy nodes. The complex power network can be decomposed into multiple simple-structured power networks, reducing the matrix calculation dimension of the centralized calculation layer and simplifying the network complexity of the distributed calculation layer, thus achieving efficient collaborative optimization of carbon emission measurement. In the centralized calculation layer, based on the power flow distribution matrix related to the power grid topology and the unit carbon emission intensity vector, a high-dimensional matrix calculation method is used to quickly solve the first carbon emission factor, reducing the calculation dimension of the whole network centralized calculation; in the distributed calculation layer, the second carbon emission factor is iteratively updated through the power distribution relationship, which can reduce the number of iterative rounds and thus improve the real-time performance. This method realizes the regional decomposition and regional deployment of the power network control scope. In the case of centralized deployment on the grid side, the carbon emission measurement system and device on the user side can be independently deployed, solving the problem of the adaptability of the existing technology to the real scenario; through the hybrid architecture, the deep integration of centralized global optimization and distributed fast response is realized, avoiding both the high computing power requirement and communication delay of the centralized scheme and overcoming the problem of the large number of iterative convergence times of the distributed architecture. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 It is the method flowchart of the hierarchical hybrid power system carbon emission measurement method provided by an embodiment of the present invention;

[0046] Figure 2 It is the hierarchical schematic diagram of the power system provided by an embodiment of the present invention.

[0047] Figure 3 It is the structural schematic diagram of the hierarchical hybrid power system carbon emission measurement system provided by an embodiment of the present invention;

[0048] Figure 4 It is the communication schematic diagram of the hierarchical hybrid power system carbon emission measurement system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, in conjunction with the accompanying drawings and preferred embodiments, a method and system for carbon emission measurement of a hierarchical hybrid power system proposed according to the present invention, including its specific implementation manner, structure, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0051] The following specifically describes the specific solution of a method and system for carbon emission measurement of a hierarchical hybrid power system provided by the present invention in conjunction with the accompanying drawings.

[0052] Please refer to Figure 1 , which shows a flowchart of a method for carbon emission measurement of a hierarchical hybrid power system provided by an embodiment of the present invention. The method includes:

[0053] Step S100: Obtain the topological structure and power flow data of the power network in the target area; specifically, in this embodiment, the injection power of the generator set, the load power, and the steady-state power flow distribution are collected in real time from the SCADA system; the GIS platform is used to analyze the distribution of power grid nodes, the topology of transmission lines, and the access locations of equipment, including but not limited to generator sets, distributed power sources, and energy storage systems; based on the fuel type database and the unit efficiency model, the carbon emission intensity of the generator set is calculated; the AMI system is used to obtain the power data of the user-side load access point, and the real-time output of the distributed power source and the energy storage system is monitored through the DERMS.

[0054] Step S200: Decouple the power network into a centralized calculation layer and a distributed calculation layer based on the topological attributes of the power network and the characteristic parameters of the energy node distribution; the characteristic parameters of the energy node distribution include the power source access point, the load access point, and the carbon-electricity measurement requirement.

[0055] Step S200 specifically includes:

[0056] Step S210: Define the hierarchical decoupling characteristic parameters according to the voltage level, regional distribution, power access point, load access point, and the characteristic parameters of the electro-carbon information measurement requirements; among them, the voltage level is used to divide the high-voltage side and the low-voltage side; the regional distribution can be determined based on the geographical distribution or the power grid dispatching area; the power access points include the access locations of traditional power plants such as thermal power and hydropower, new energy power stations such as photovoltaic and wind power, and centralized energy storage; the load access points, that is, the distribution of user load-intensive areas, are used as characteristic parameters; the electro-carbon measurement requirement is the dynamic update frequency of the carbon emission intensity, and different application scenarios and management requirements have different requirements for the update frequency of the carbon emission intensity;

[0057] Step S220: Construct a multi-attribute decision matrix and standardize the characteristic parameters; specifically, according to the preliminary analysis of the power network and different partitioning ideas, generate multiple candidate hierarchical decoupling schemes; use the voltage level, regional distribution, power access point, load access point, and electro-carbon information measurement requirements in Step S210 as evaluation attributes; the multi-attribute decision matrix can be expressed as:

[0058] D=(d ij )m×n

[0059] In the formula, m represents the number of candidate hierarchical decoupling schemes, and n represents the number of evaluation attributes; d ij represents the jth attribute value of the ith candidate scheme; since the dimensions of different evaluation attributes are different, for example, the voltage level is numerical, while the regional distribution may be in text description form, in order to be able to compare and analyze on the same scale, it is necessary to standardize the decision matrix. Using the normalization method, map its value to the interval of 0-1, and convert the attribute values with different dimensions into dimensionless standardized values r ij ;

[0060] Step S230: Use the weighted aggregation method or the analytic hierarchy process to assign weights to the characteristic parameters, and extract the hierarchical decoupling boundary according to the comprehensive scores of different hierarchical schemes;

[0061] Specifically, the weighted aggregation method aims at optimizing the system deployment cost and maximizing the carbon emission calculation time limit. First, directly set the weight w j through expert scoring or historical data statistics; calculate the comprehensive score of each scheme, which can be expressed as:

[0062]

[0063] In the formula, S i represents the comprehensive score of the ith scheme; w j represents the weight of the jth attribute value; according to this formula, select the scheme with the highest score as the hierarchical decoupling boundary;

[0064] As another implementation, when subjectively assigning weights considering the actual regional deployment requirements, this embodiment adopts the analytic hierarchy process. By constructing a hierarchical structure, the weights of each attribute are determined through pairwise comparison, and consistency checking is combined to ensure logical rationality. First, construct the target layer: the target is to determine the hierarchical decoupling scheme; the criterion layer: the attributes are used as the criterion layer factors; the scheme layer: the candidate hierarchical schemes are used as the scheme layer elements. The 1-9 scale method is used to make pairwise comparisons of the importance of the attributes to construct a judgment matrix, which can be expressed as:

[0065] A = [a i′j

[0066] In the formula, a i′j represents the importance of attribute i' relative to attribute j; this matrix satisfies a i′j = 1 / a ji′ ; Calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector, and after normalization, the weights of each attribute can be obtained; then perform consistency checking, and calculate the comprehensive score of each scheme based on the weights after consistency checking, and select the optimal scheme. The specific calculation method is similar to the principle of the weighted synthesis method, so it will not be elaborated here.

[0067] Step S240: Divide the power network into a centralized computing layer and a distributed computing layer according to the hierarchical decoupling boundary; specifically, the centralized computing layer covers the power grid topology from the power source to the grid connection point of users before the preset voltage level, including generating units, centralized energy storage, and transmission nodes; the distributed computing layer covers the power grid topology after the grid connection point of users at the preset voltage level, including electrical loads, distributed power sources, and energy storage nodes;

[0068] As Figure 2 shown, Figure 2 the source-network side centralized computing layer includes nodes 1 to 12, and the nodes of the load side distributed computing layer include nodes 13 and 14; it should be noted that hierarchical decoupling does not mean that each hierarchical part has only one. Multiple hierarchical layers in multiple regions can be carried out according to specific situations. For example, for the power network of a large city, it can be divided into multiple centralized computing layer and distributed computing layer regions according to different administrative regions or electrical load characteristics.

[0069] The following schematic is only for one embodiment and does not represent all hierarchical decoupling situations:

[0070] The source-network side centralized computing layer The calculation range is the power network from the power source to the power network before the grid connection point of 35kV / 10kV users, including traditional fossil energy power stations, new energy power stations, centralized energy storage power stations, grid transmission routes, and nodes; the preset voltage level can be dynamically adjusted according to the power grid topology;

[0071] ​Load - side distributed computing layer including power users, distributed power sources, and energy storage The calculation scope is the power network after the access points of 35kV / 10kV users, including electricity loads, distributed power sources, and distributed energy storage.

[0072] In this embodiment, based on the topological attributes of the power network and the characteristic parameters of the energy node distribution, the power network is hierarchically decoupled into a centralized computing layer and a distributed computing layer. In terms of computing efficiency, through hierarchical decoupling, the complex power network is decomposed into relatively simple centralized and distributed computing layers. The centralized computing layer can focus on processing large - scale and centralized power data and carbon emission calculations, reducing the calculation dimension. The distributed computing layer flexibly calculates the dispersed data of the user side and distributed energy sources. This hierarchical computing method can improve the calculation speed of the carbon emission measurement of the entire power network and meet the requirements of a relatively high dynamic update frequency of the carbon emission intensity. Secondly, in terms of system deployment and management, hierarchical decoupling realizes the regional decomposition and regional deployment of the power network control scope. In the case of centralized deployment on the grid side, the independent deployment of the carbon emission measurement system and device on the user side can be realized. The distributed computing layers in different regions can independently set carbon emission measurement devices and systems according to the electricity consumption characteristics and management requirements of this region, without relying on the centralized management system of the entire power network. This not only improves the flexibility of system deployment but also reduces the cost of system construction and maintenance. At the same time, by using a multi - attribute decision matrix and a scientific weight allocation method to determine the hierarchical decoupling boundary, multiple factors such as voltage level, regional distribution, power source access point, load access point, and carbon - electricity measurement requirements are fully considered, making the hierarchical decoupling scheme more scientific and reasonable, meeting the actual operation and management requirements of the power system. Finally, hierarchical decoupling is not limited to a single hierarchical mode and can be multi - region and multi - quantity hierarchical according to the actual situation of the power network. Whether it is a large and complex urban power grid or a relatively simple power grid, reasonable hierarchical decoupling can be used to optimize carbon emission measurement and power management. This flexibility makes this method have wide adaptability to real - world scenarios and can be better applied to different types of power systems to achieve accurate and efficient carbon emission measurement of the power system.

[0073] Step S300: In the centralized computing layer, by constructing a power flow distribution matrix related to the grid topology and a unit carbon emission intensity vector, using a matrix calculation method to solve the first carbon emission factor;

[0074] Step S300 specifically includes:

[0075] Step S301: Based on the nodal active power flux, branch power flow distribution, and unit carbon emission intensity, construct a nodal active power flux matrix, a branch power flow distribution matrix, a unit injection distribution matrix, and a unit carbon emission intensity vector;

[0076] Step S302: Construct a system of linear equations, whose expression is:

[0077]

[0078] In the formula, D N represents the node carbon emission factor vector; P N represents the node active power flux matrix, and the diagonal elements represent the active power injection of each node; represents the transpose of the branch power flow distribution matrix; represents the transpose of the unit injection distribution matrix; E G represents the unit carbon emission intensity vector, and the elements are the carbon emission intensity values of each generating unit.

[0079] Step S303: Solve the system of linear equations to obtain the first carbon emission factor of each node in the centralized calculation layer; specifically, the Gaussian elimination method or the LU decomposition method can be used for solving, and there may be differences in terms of calculation efficiency and numerical stability, etc. A suitable method can be selected according to the specific matrix scale and calculation requirements; according to the first carbon emission factor, the carbon emissions of the centralized calculation layer can be obtained;

[0080] In this embodiment, by transforming the complex carbon emission calculation problem of the power network into a problem of solving a system of linear equations, for a large-scale power network, the matrix calculation method has good scalability and parallel calculation potential, and can quickly solve the first carbon emission factor.

[0081] Step S400: In the distributed calculation layer, based on the first node carbon emission factor sent down by the centralized calculation layer, iteratively update the second carbon emission factor through the power distribution relationship; according to the second carbon emission factor, the carbon emissions of the distributed calculation layer can be obtained;

[0082] Step S400 specifically includes:

[0083] Step S401: Load the first carbon emission factor sent down by the centralized calculation layer at the head node of the distributed calculation layer;

[0084] Step S402: According to the power ratio relationship between the inflow node and the outflow node, the upstream node sends down the carbon emission factor in the iterative calculation process to the downstream node; according to the power data of the user-side load access point obtained by using the AMI system in step S100 and the real-time output data of distributed power sources and energy storage systems monitored by DERMS, clarify the power inflow and outflow situations of each node in the distributed calculation layer. For each node n, determine all loop sets I + of the inflow node n and -; The injected power of the incoming node to the node n can be obtained in real time by measuring the power sensor on the incoming line; the output power of the node outgoing loop can also be obtained by measuring the power sensor on the outgoing line.

[0085] Step S403: Distributed all nodes perform parallel calculations until the carbon emission factor change of all nodes is less than a preset threshold, completing iterative convergence. The expression for iterative calculation of the carbon emission factor is:

[0086]

[0087] Where, represents the carbon emission factor of node n at the k+1th iteration; I + represents the set of all loops flowing into node n; I - represents the set of all circuits outgoing from node n; P i→n represents the injected power from node i to node n; P n→j represents the output power of node n flowing out of the loop; Indicates the carbon emission factor flowing into the node n loop; calculate the downstream node The feedback is sent to the upstream node, triggering the upstream node to update the carbon emission factor of its downstream node; the upstream node recalculates the carbon emission factor flowing into itself based on the data fed back by the downstream node; after each iteration, the change in the carbon emission factor of all nodes is calculated. When the change in the carbon emission factor of all nodes is less than the preset threshold, the iteration converges, and the update of the carbon emission factor of all nodes in the distributed computing layer is completed. The preset threshold can be set according to actual needs and calculation accuracy requirements.

[0088] In this embodiment, the distributed computing layer iteratively updates the second carbon emission factor based on the first node carbon emission factor issued by the centralized computing layer through the power allocation relationship. The distributed architecture limits the iteration range to the low-voltage side network, which significantly reduces the iteration range compared to the unified iterative calculation of the entire network. This significantly reduces the number of iteration rounds and compresses the calculation cycle. In terms of hardware cost, only a lightweight carbon metering module needs to be deployed in the distributed computing layer, and there is no need to deploy a separate control center. The distributed computing layer can use the existing power equipment communication network and computing resources to iteratively calculate the carbon emission factor, without the need to build additional complex centralized control hardware facilities. Compared with the traditional centralized carbon emission metering system that requires a large-scale server cluster and a complex network architecture to process all data, this method effectively reduces hardware costs and improves the economy of the system. At the same time, the iterative calculation based on the real-time power allocation relationship can more accurately reflect the carbon emissions of each node in the distributed computing layer, more accurately calculate the carbon emission factor of each node, and provide a more reliable data basis for the subsequent calculation of indirect carbon emissions on the load side.

[0089] Step S500: Integrate the centralized and distributed computing results to obtain the carbon emission factor vector of all nodes, the indirect carbon emission transfer amount on the grid side, and the indirect carbon emission amount on the load side; specifically, the management and calculation platform synchronously obtains carbon emission factor data from the Internet of Things electricity-carbon meters in the centralized computing layer (high voltage side) and the distributed computing layer (low voltage side) through the power communication network: the centralized computing layer is the first carbon emission factor and the carbon emission amount of the centralized computing layer, which is directly solved through matrix calculation; the distributed computing layer is the second carbon emission factor and the carbon emission amount of the distributed computing layer, which is obtained through iterative update; then, in the order of the numbers of the power network nodes, the carbon emission factor data of all nodes are sorted to construct the carbon emission factor vector of all nodes, which can be expressed as:

[0090] E Ni =[e N1 ,e N2 ,e N3 …e Ni T

[0091] In the formula, e Ni represents the carbon emission factor of the i-th node; T represents transpose; finally, the carbon emission factors obtained by centralized and distributed calculations are integrated into one vector to comprehensively reflect the carbon emission situation of each node in the entire power network; for the calculation of the indirect carbon emission transfer amount on the grid side, it is necessary to consider the power transmission situation of each branch in the power network and the difference in carbon emission factors between nodes, and accumulate the carbon emission transfer amounts of all branches to obtain the indirect carbon emission transfer amount on the grid side; the calculation of the indirect carbon emission amount on the load side is based on the power consumption of the load node and the carbon emission factor of this node, and the indirect carbon emission amounts of all load nodes are accumulated to obtain the indirect carbon emission amount on the load side.

[0092] ​In a preferred embodiment of the present invention, the power grid of 220 kV and above is calculated using the centralized algorithm, and the power grid below 220 kV is calculated using the distributed algorithm; at the same time, according to the change of the power grid topology, the hierarchical boundary is dynamically updated. For example, the newly added 220 kV node is included in the centralized calculation layer. This embodiment fuses the calculation results of the centralized calculation layer and the distributed calculation layer, and can comprehensively cover the carbon emission information of all nodes from the high-voltage side to the low-voltage side of the power network. By constructing the carbon emission factor vector of all nodes, the carbon emission characteristics of each node in the entire power network are presented completely, avoiding the problem of missing or inaccurate carbon emission measurement caused by only considering part of the network levels; the calculation of the indirect carbon emission transfer amount on the grid side can accurately evaluate the carbon emission transfer situation caused by different carbon emission characteristics of different nodes during the power transmission process. The calculation of the indirect carbon emission amount on the load side can accurately calculate the carbon emission situation on the user side. Finally, it provides accurate data basis for formulating reasonable user carbon emission management strategies and energy conservation and emission reduction measures, thereby effectively reducing the overall carbon emission level of the power system.

[0093] In summary, the hierarchical hybrid power system carbon emission measurement method and system provided by the present invention, based on the topological attributes of the power network and the distribution characteristic parameters of energy nodes, decouple the power network into a centralized calculation layer and a distributed calculation layer hierarchically, and can decompose the complex power network into multiple simple-structured power networks, realizing the reduction of the matrix calculation dimension of the centralized calculation layer and the simplification of the network complexity of the distributed calculation layer, and realizing the efficient collaborative optimization of carbon emission measurement. In the centralized calculation layer, based on the power flow distribution matrix of the power grid topology and the unit carbon emission intensity vector, a high-dimensional matrix calculation method is used to quickly solve the first carbon emission factor, reducing the calculation dimension of the whole-network centralized calculation; in the distributed calculation layer, the second carbon emission factor is iteratively updated through the power distribution relationship, and the number of iterative rounds can be reduced, thereby improving the real-time performance. This method realizes the regional decomposition and regional deployment of the power network control range. In the case of centralized deployment on the grid side, the carbon emission measurement system and device on the user side can be independently deployed, solving the problem of the adaptability of the existing technology to the real scenario; through the hybrid architecture, the deep integration of centralized global optimization and distributed fast response is realized, avoiding both the high computing power requirement and communication delay of the centralized scheme and overcoming the iterative convergence problem of the distributed architecture.

[0094] Please refer to Figure 3 , which shows a schematic structural diagram of a hierarchical hybrid power system carbon emission measurement system provided by an embodiment of the present invention. The system includes:

[0095] A data acquisition module, configured to acquire the topological structure and power flow data of the power grid within the target area; specifically, it acquires the injection power of generating units, load power, and steady-state power flow distribution from the SCADA system in real time; uses the GIS platform to analyze the grid node distribution, transmission line topology, and equipment access locations, including but not limited to generating units, distributed power sources, and energy storage systems; calculates the carbon emission intensity of generating units based on the fuel type database and unit efficiency model; uses the AMI system to obtain the power data of the user-side load access points, and monitors the real-time output of distributed power sources and energy storage systems through the DERMS.

[0096] A hierarchical decoupling module, configured to hierarchically decouple the power grid into a centralized calculation layer and a distributed calculation layer based on the topological attributes of the power grid and the characteristic parameters of the energy node distribution;

[0097] The centralized calculation layer is configured to solve the first carbon emission factor by using a matrix calculation method through constructing a power flow distribution matrix related to the grid topology and a unit carbon emission intensity vector;

[0098] The distributed calculation layer is configured to iteratively update the second carbon emission factor based on the power distribution relationship according to the first node carbon emission factor issued by the centralized calculation layer;

[0099] A data aggregation module, configured to fuse the centralized and distributed calculation results to obtain the full-node carbon emission factor vector, the indirect carbon emission transfer amount on the grid side, and the indirect carbon emissions on the load side.

[0100] Please refer to Figure 4 , the system further includes:

[0101] Internet of Things electricity-carbon meters, which are dispersedly deployed at each loop node of the power grid, including:

[0102] The centralized calculation layer electricity-carbon meter is used to receive and display the first carbon emission factor issued by the centralized calculation layer; specifically, it is installed at the hub nodes of the high-voltage power grid above 220 kV, receives the first carbon emission factor issued by the management platform through the power communication network; integrates a high-resolution display screen to display the carbon emission factor and the carbon emission responsibility of the associated line in real time; only requires a basic communication module and no local computing ability, reducing the hardware cost;

[0103] The distributed calculation layer electricity-carbon meter is communicatively connected to the carbon measurement module and is used to collect the power data required for iterative calculation and upload the second carbon emission factor;

[0104] The carbon measurement module is embedded in the distributed calculation layer electricity-carbon meter and is used to calculate the carbon emissions of the distributed calculation layer.

[0105] The hierarchical hybrid power system carbon emission measurement system provided in this embodiment can only deploy Internet of Things electricity carbon meters in the centralized calculation layer to realize the display of carbon emission information of centralized circuits / nodes; in the distributed calculation layer, Internet of Things electricity carbon meters and carbon measurement modules are deployed at the same time, and there is no need to deploy a separate control center; the communication of the power system carbon emission measurement device adopts the existing communication system for power measurement, and there is no need to set up a separate communication network, effectively reducing the deployment cost and solving the problems of difficult deployment and high cost in the prior art; on the other hand, this system solves the compatibility between centralized calculation and distributed calculation, and only one type of electricity carbon measurement product can be used throughout the region, without distinguishing the types of electricity carbon measurement devices; different electricity carbon measurement devices only need to meet the communication protocol and basic functions of this system to realize device access, solving the compatibility problem in the prior art; at the same time, the local power grid topology structure, power flow and carbon emission data can be stored only at the management and calculation platform, and the carbon emission information is only displayed through communication at the specific circuit, thereby effectively enhancing information security; the communication of the power system carbon emission measurement device adopts the existing communication system for power measurement, and there is no need to set up a separate communication network, thereby solving the security problem.

[0106] Preferably, the carbon measurement module is further configured to obtain the incoming power, outgoing power of the current node and the carbon emission factor of the upstream node; calculate the carbon emission factor of the current node by weighted calculation according to the ratio of the incoming power to the outgoing power; send the calculation result to the downstream node to trigger the iterative update of the downstream node.

[0107] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0108] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A carbon emission measurement method for a hierarchical hybrid power system, characterized in that, The method includes: Obtaining the topological structure and power flow data of the power network within the target area; Based on the topological attributes of the power network and the characteristic parameters of the energy node distribution, decoupling the power network into a centralized calculation layer and a distributed calculation layer in a hierarchical manner; In the centralized calculation layer, by constructing a power flow distribution matrix related to the power grid topology and a unit carbon emission intensity vector, using a matrix calculation method to solve the first carbon emission factor; In the distributed calculation layer, based on the first node carbon emission factor issued by the centralized calculation layer, iteratively updating the second carbon emission factor through the power distribution relationship; Fusing the centralized and distributed calculation results to obtain the full-node carbon emission factor vector, the indirect carbon emission transfer amount on the grid side, and the indirect carbon emission amount on the load side.

2. The hierarchical hybrid power system carbon emission measurement method according to claim 1, characterized in that, Based on the topological attributes of the power network and the characteristic parameters of the energy node distribution, decoupling the power network into a centralized calculation layer and a distributed calculation layer in a hierarchical manner, including: Defining hierarchical decoupling characteristic parameters according to the voltage level, regional distribution, power source access point, load access point, and characteristic parameters of the electro-carbon information measurement requirements; Constructing a multi-attribute decision matrix and standardizing the characteristic parameters; Using the weighted total method or the analytic hierarchy process to assign weights to the characteristic parameters, and extracting the hierarchical decoupling boundary according to the comprehensive scores of different hierarchical schemes; According to the hierarchical decoupling boundary, dividing the power network into a centralized calculation layer and a distributed calculation layer.

3. The layered hybrid power system carbon emission measurement method according to claim 1, characterized in that The centralized calculation layer covers the power grid topology from the power source to the user access point before the preset voltage level, including generator sets, centralized energy storage, and transmission nodes; the distributed calculation layer covers the power grid topology after the user access point of the preset voltage level, including electrical loads, distributed power sources, and energy storage nodes.

4. The hierarchical hybrid power system carbon emission measurement method according to claim 1, characterized in that In the centralized calculation layer, by constructing a power flow distribution matrix related to the power grid topology and a unit carbon emission intensity vector, using a matrix calculation method to solve the first carbon emission factor, including: Based on the node active power flux, branch power flow distribution, and unit carbon emission intensity, constructing a node active power flux matrix, a branch power flow distribution matrix, a unit injection distribution matrix, and a unit carbon emission intensity vector; Constructing and solving a linear equation system to obtain the first carbon emission factor of each node in the centralized calculation layer.

5. The hierarchical hybrid power system carbon emission measurement method according to claim 4, wherein, The expression for solving the linear equation system is: where E N represents the node carbon emission factor vector; P N represents the node active power flux matrix; represents the transpose of the branch power flow distribution matrix; represents the transpose of the unit injection distribution matrix; E G represents the unit carbon emission intensity vector.

6. The carbon emission measurement method for the hierarchical hybrid power system according to any one of claims 1 to 5, characterized in that In the distributed calculation layer, based on the first node carbon emission factor issued by the centralized calculation layer, iteratively updating the second carbon emission factor through the power distribution relationship, including: Loading the first carbon emission factor issued by the centralized calculation layer at the head node of the distributed calculation layer; According to the power ratio relationship between the inflow node and the outflow node, the upstream node issues the carbon emission factor during the iterative calculation to the downstream node; Performing parallel calculation for all nodes in the distributed layer until the change amount of the carbon emission factor of all nodes is less than the preset threshold, and completing the iterative convergence.

7. The carbon emission measurement method for the hierarchical hybrid power system according to claim 6, characterized in that, The expression for the iterative calculation of the carbon emission factor is: Wherein, represents the carbon emission factor of node n at the (k + 1)-th iteration; I + represents the set of all loops flowing into node n; I - represents the set of all loops flowing out of node n; P i→n represents the injection power of the inflow node to node n; P n→j represents the output power of the loop flowing out of node n; represents the carbon emission factor of the loop flowing into node n.

8. Hierarchical hybrid power system carbon emission measurement system, characterized in that Adopting the hierarchical hybrid power system carbon emission measurement method according to any one of claims 1 to 7, the system includes: A data acquisition module configured to acquire the topological structure and power flow data of the power network within the target area; The hierarchical decoupling module is configured to hierarchically decouple the power grid into a centralized computing layer and a distributed computing layer based on the topological attributes of the power grid and the characteristic parameters of the energy node distribution; The centralized computing layer is configured to solve the first carbon emission factor by using a matrix calculation method through constructing a power flow distribution matrix related to the power grid topology and a unit carbon emission intensity vector; The distributed computing layer is configured to iteratively update the second carbon emission factor based on the first node carbon emission factor issued by the centralized computing layer through the power distribution relationship; The data aggregation module is configured to fuse the centralized and distributed computing results to obtain the full-node carbon emission factor vector, the indirect carbon emission transfer amount on the grid side, and the indirect carbon emissions on the load side.

9. The hierarchical hybrid power system carbon emission measurement system according to claim 8, wherein The system further includes: The Internet of Things electricity-carbon meter, which is dispersedly deployed at each loop node of the power grid, including: The centralized computing layer electricity-carbon meter, which is used to receive and display the first carbon emission factor issued by the centralized computing layer; The distributed computing layer electricity-carbon meter, which is communicatively connected to the carbon measurement module, and is used to collect the power data required for iterative calculation and upload the second carbon emission factor; The carbon measurement module, which is embedded in the distributed computing layer electricity-carbon meter, and is used to calculate the carbon emissions of the distributed computing layer.

10. The hierarchical hybrid power system carbon emission measurement system according to claim 9, wherein, The carbon measurement module is further used to obtain the incoming power, outgoing power of the current node, and the carbon emission factor of the upstream node; calculate the carbon emission factor of the current node by weighted calculation according to the ratio of the incoming power to the outgoing power; send the calculation result to the downstream node to trigger the iterative update of the downstream node.

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