A method and system for calculating node power carbon emission factors based on collaborative iteration

Abstract

The patent application provides a node power carbon emission factor calculation method and system based on collaborative iteration, comprising: acquiring energy management system data and intelligent scheduling management system data of each calculation party; based on the energy management system data and the intelligent scheduling management system data, a pre-constructed collaborative iteration model is collaboratively run by an initiator, a calculation party and a calculation collaboration party, and the power carbon emission factor of each calculation party in the node of the power system is calculated; wherein the collaborative iteration model is constructed based on the energy management system data and the intelligent scheduling management system data, and uses a collaborative iteration algorithm; the pre-collaborative iteration model is collaboratively run by the initiator, the calculation party and the calculation collaboration party, and the power carbon emission factor of each calculation party in the node of the power system can be quickly iteratively calculated.

Description

Technical Field

[0001] This invention patent application belongs to the field of electricity carbon emission factor technology, specifically relating to a method and system for calculating node electricity carbon emission factors based on collaborative iteration. Background Technology

[0002] Current methods for measuring carbon emissions from electricity consumption have several shortcomings. They primarily rely on average carbon emission factors to calculate indirect carbon emissions from electricity consumption. However, these average carbon emission factors are based on the average carbon content per kilowatt-hour of fuel consumed throughout the year, failing to reflect the spatiotemporal variations of carbon emission factors. As the proportion of clean energy generation continues to increase, the peak-to-valley difference in carbon emission factors will become increasingly large, leading to more significant measurement errors in average carbon emission factors. Furthermore, these methods cannot guide users to reduce carbon emissions in the power system by changing their electricity consumption behavior. By drawing on the theoretical foundation of power system collaborative analysis and considering the structural characteristics of power system collaboration, a cyclical iterative calculation method for carbon emission flows in actual power systems is proposed. This method enables minute-level calculations of carbon emission factors at each node (including power plants, transmission plants, and load plants).

[0003] Currently, the main methods for calculating the carbon emission factor (carbon emissions per unit of electricity) at each node in a power system include the cooperative iteration method. This method combines power system cooperative calculation methods with advanced power network analysis methods. Ignoring network losses, it establishes a basic cooperative iteration calculation method for power system carbon emission flows. However, cooperative iteration requires multiple rounds of iterative calculations based on pre-processed network data to ultimately calculate the carbon intensity factor for each node, resulting in a long computation time. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, this patent application proposes a method for calculating the nodal electricity carbon emission factor based on cooperative iteration, comprising:

[0005] Acquire energy management system data and intelligent scheduling management system data from each computing party;

[0006] Based on the energy management system data and the intelligent dispatch management system data, the power carbon emission factor of each node in the power system is calculated by the initiator, the computing party and the computing collaborator working together to run a pre-built collaborative iterative model.

[0007] The collaborative iterative model is constructed using a collaborative iterative algorithm based on energy management system data and intelligent dispatch management system data. Preferably, the collaborative iterative algorithm code includes a model parameter acquisition algorithm, a model component registration algorithm, a multi-party data interaction algorithm, and a collaborative iterative algorithm. The calculation of the electricity carbon emission factor of each node in the power system by the initiator, the computer, and the compute collaborator, based on the energy management system data and intelligent dispatch management system data, includes:

[0008] A model parameter acquisition algorithm based on a collaborative iterative model is used to acquire and process the energy management system data and intelligent scheduling management system data from each computing party.

[0009] Based on the processed energy management system data and intelligent dispatch management system data, combined with the multi-party interactive data algorithm and the model component registration algorithm, the algorithm is implemented by the initiator, the computing party and the computing collaborator working together to run the collaborative iteration logic, and the power carbon emission factor of each computing party's power system node is calculated.

[0010] The cooperative iterative logic implementation algorithm includes one or more of the following: a cooperative iterative logic implementation algorithm based on the number of loop iterations and a cooperative iterative logic implementation algorithm based on the minimum loss value.

[0011] Preferably, the calculation formulas for the electricity carbon emission factors of the nodes in the power system of each calculation party are as follows:

[0012]

[0013] Among them, I Bn Let Q be the carbon emission factor of the nth grid node B. Ht For the input power of the t-th power plant H connected to the grid node, I Ht Let Q be the carbon emission factor of the t-th power plant H connected to the grid node. Bc Let I be the input power of the c-th network node B. Bc Let be the carbon emission factor of the c-th grid node B.

[0014] Preferably, the model parameter acquisition algorithm using a collaborative iterative model, which acquires and processes the energy management system data and the intelligent dispatch management system data, includes:

[0015] A collaborative iterative model is used to generate model parameters according to set rules, and the energy management system data and the intelligent scheduling management system data are obtained and processed to obtain the processed energy management system data and intelligent scheduling management system data.

[0016] The processed energy management system data, intelligent dispatch management system data, and the calculated power carbon emission factors of the nodes in the power system are stored in the domain of each computing party.

[0017] The energy management system data includes: real-time monitoring data, equipment status data, historical operating data, forecast data, configuration data, and parameter data; the intelligent dispatch management system data includes: power outage event data, network topology data, customer information, real-time monitoring data, and historical operating data.

[0018] Preferably, the setting rules include at least one or more of the following: source rules, regional rules, data table rules, and data field rules; the model parameter acquisition algorithm generated according to the setting rules using a collaborative iterative model acquires and processes the energy management system data and the intelligent dispatch management system data to obtain processed energy management system data and intelligent dispatch management system data, including:

[0019] An algorithm for generating source-end rules is used to identify and access energy management system data and intelligent scheduling management system data from various computing parties.

[0020] An algorithm that uses regional rules to generate data sets differentiated parameters for each computing party based on their specific needs. The energy management system data and the intelligent dispatch management system data are then adjusted according to these regional differentiated parameters to generate the adjusted energy management system data and intelligent dispatch management system data.

[0021] An algorithm using data table rules is used to generate a data model from the adjusted energy management system data and intelligent dispatch management system data;

[0022] An algorithm based on data field rules is used to verify and transform the energy management system data and intelligent dispatch management system data in the data model according to the designed data verification and transformation mechanism, thereby generating unified standard energy management system data and intelligent dispatch management system data.

[0023] Preferably, based on the processed energy management system data and intelligent dispatch management system data, combined with the multi-party interactive data algorithm and the model component registration algorithm, the initiator, the computing party, and the computing collaborator collaboratively run the collaborative iterative algorithm to calculate the power carbon emission factor of each computing party's power system node, including:

[0024] Based on the processed energy management system data and intelligent scheduling management system data of each computing party, the SM4 encryption algorithm in the multi-party interaction data algorithm is used to encrypt the data that needs to be interacted, and obtain encrypted interaction data.

[0025] Based on the processed energy management system data and intelligent dispatch management system data of each computing party, the encrypted interactive data, and the algorithm components registered by the model component registration algorithm, the algorithm is implemented by the collaborative iteration logic run by the initiator, computing party, and computing collaborator to calculate the power carbon emission factor of each node in the power system.

[0026] Based on the same inventive concept, this patent application also provides a node power carbon emission factor calculation system based on collaborative iteration, including: a data acquisition module and a power carbon emission factor calculation module;

[0027] The data acquisition module is used to acquire energy management system data and intelligent scheduling management system data from each computing party;

[0028] The power carbon emission factor calculation module is used to calculate the power carbon emission factor of each node in the power system based on the energy management system data and the intelligent dispatch management system data, through the collaborative operation of a pre-built collaborative iterative model by the initiator, the calculation party and the calculation collaborator.

[0029] The collaborative iterative model is constructed based on energy management system data and intelligent dispatch management system data, using a collaborative iterative algorithm. Preferably, the collaborative iterative algorithm code of the power carbon emission factor calculation module includes a model parameter acquisition algorithm, a model component registration algorithm, a multi-party data interaction algorithm, and a collaborative iterative algorithm; the power carbon emission factor calculation module is specifically used for:

[0030] A model parameter acquisition algorithm based on a collaborative iterative model is used to acquire and process the energy management system data and intelligent scheduling management system data from each computing party.

[0031] Based on the processed energy management system data and intelligent dispatch management system data, combined with the multi-party interactive data algorithm and the model component registration algorithm, the algorithm is implemented by the initiator, the computing party and the computing collaborator working together to run the collaborative iteration logic, and the power carbon emission factor of each computing party's power system node is calculated.

[0032] The cooperative iterative logic implementation algorithm includes one or more of the following: a cooperative iterative logic implementation algorithm based on the number of loop iterations and a cooperative iterative logic implementation algorithm based on the minimum loss value.

[0033] Preferably, the calculation formula for the electricity carbon emission factor of each node in the power system of the electricity carbon emission factor calculation module is as follows:

[0034]

[0035] Among them, IBn Let Q be the carbon emission factor of the nth grid node B. Ht For the input power of the t-th power plant H connected to the grid node, I Ht Let Q be the carbon emission factor of the t-th power plant H connected to the grid node. Bc Let I be the input power of the c-th network node B. Bc Let be the carbon emission factor of the c-th grid node B.

[0036] Preferably, the electricity carbon emission factor calculation module employs a model parameter acquisition algorithm based on a collaborative iterative model to acquire and process the energy management system data and intelligent dispatch management system data from each calculation party, including:

[0037] A collaborative iterative model is used to generate model parameters according to set rules, and the energy management system data and the intelligent scheduling management system data are obtained and processed to obtain the processed energy management system data and intelligent scheduling management system data.

[0038] The processed energy management system data, intelligent dispatch management system data, and the calculated power carbon emission factors of the nodes in the power system are stored in the domain of each computing party.

[0039] The energy management system data includes: real-time monitoring data, equipment status data, historical operating data, forecast data, configuration data, and parameter data; the intelligent dispatch management system data includes: power outage event data, network topology data, customer information, real-time monitoring data, and historical operating data.

[0040] Preferably, the setting rules of the power carbon emission factor calculation module include at least one or more of the following: source-end rules, regional rules, data table rules, and data field rules; the power carbon emission factor calculation module uses a collaborative iterative model to obtain model parameters generated according to the set rules, acquires and processes the energy management system data and the intelligent dispatch management system data, and obtains the processed energy management system data and intelligent dispatch management system data, including:

[0041] An algorithm for generating source-end rules is used to identify and access energy management system data and intelligent scheduling management system data from various computing parties.

[0042] An algorithm that uses regional rules to generate data sets differentiated parameters for each computing party based on their specific needs. The energy management system data and the intelligent dispatch management system data are then adjusted according to these regional differentiated parameters to generate the adjusted energy management system data and intelligent dispatch management system data.

[0043] An algorithm using data table rules is used to generate a data model from the adjusted energy management system data and intelligent dispatch management system data;

[0044] An algorithm based on data field rules is used to verify and transform the energy management system data and intelligent dispatch management system data in the data model according to the designed data verification and transformation mechanism, thereby generating unified standard energy management system data and intelligent dispatch management system data.

[0045] Preferably, the electricity carbon emission factor calculation module, based on processed energy management system data and intelligent dispatch management system data, and combined with the multi-party interactive data algorithm and the model component registration algorithm, calculates the electricity carbon emission factor of each node in the power system through the collaborative iteration logic implemented by the initiator, the calculator, and the computing collaborator, including:

[0046] Based on the processed energy management system data and intelligent scheduling management system data of each computing party, the SM4 encryption algorithm in the multi-party interaction data algorithm is used to encrypt the data that needs to be interacted, and obtain encrypted interaction data.

[0047] Based on the processed energy management system data and intelligent dispatch management system data of each computing party, the encrypted interactive data, and the algorithm components registered by the model component registration algorithm, the algorithm is implemented by the collaborative iteration logic run by the initiator, computing party, and computing collaborator to calculate the power carbon emission factor of each node in the power system.

[0048] Compared with the closest prior art, the beneficial effects of this invention patent application are as follows:

[0049] This patent application provides a method and system for calculating node power carbon emission factors based on collaborative iteration, comprising: acquiring energy management system data and intelligent dispatch management system data of each computing party; and calculating the power carbon emission factors of each computing party's nodes in the power system by collaboratively running a pre-constructed collaborative iteration model based on the energy management system data and intelligent dispatch management system data, through the collaborative operation of the initiator, computing party, and computing collaborator; wherein, the collaborative iteration model is constructed based on the energy management system data and intelligent dispatch management system data using a collaborative iteration algorithm; this patent application, through the collaborative operation of the pre-collaborative iteration model by the initiator, computing party, and computing collaborator, can quickly iteratively calculate the power carbon emission factors of each computing party's nodes in the power system. Attached Figure Description

[0050] Figure 1 A schematic diagram of a method for calculating the carbon emission factor of node electricity based on collaborative iteration, provided for this patent application;

[0051] Figure 2 This invention patent application provides a schematic diagram of the node structure in a power system.

[0052] Figure 3 A flowchart of the collaborative iterative logic implementation algorithm based on the number of loop iterations provided for this invention patent application;

[0053] Figure 4 A flowchart of the collaborative iterative logic implementation algorithm based on minimum loss value provided for this invention patent application;

[0054] Figure 5 This is a schematic diagram of some model parameter codes provided in the patent application for this invention;

[0055] Figure 6 This invention patent application provides a partial code diagram illustrating multi-party data encryption and collaborative interaction.

[0056] Figure 7 A schematic diagram of the code for determining the calculation termination condition under multi-party collaboration in the collaborative iteration logic implementation code provided for the present invention patent application;

[0057] Figure 8 This is a schematic diagram of a node power carbon emission factor calculation system based on collaborative iteration, provided for this patent application. Detailed Implementation

[0058] The specific embodiments of this patent application will be further described in detail below with reference to the accompanying drawings.

[0059] Example 1:

[0060] This invention patent application provides a method for calculating the nodal power carbon emission factor based on cooperative iteration, such as... Figure 1 As shown, it includes:

[0061] Step 1: Obtain energy management system data and intelligent dispatch management system data from each computing party;

[0062] Step 2: Based on the energy management system data and the intelligent dispatch management system data, the initiator, the computing party, and the computing collaborator jointly run a pre-built collaborative iterative model to calculate the power carbon emission factor of each computing party's power system node.

[0063] The collaborative iterative model is constructed using a collaborative iterative algorithm based on data from the energy management system and the intelligent scheduling management system.

[0064] The energy management system data in step 1 above includes real-time monitoring data, equipment status data, historical operating data, forecast data, configuration and parameter data, etc., mainly derived from 6 tables: plant / station table, unit table, base voltage table, line table, line endpoint table, and measurement value table; the intelligent dispatch management system data includes power outage event data, network topology data, customer information, real-time monitoring data, historical operating data, etc., mainly derived from 3 tables: plant / station table, line table, and base voltage table; this invention can obtain data from multiple calculation parties, laying a data foundation for subsequent calculation of the power system node's power carbon emission factor for each calculation party.

[0065] In one implementation, step 2 above includes:

[0066] A model parameter acquisition algorithm based on a collaborative iterative model is used to acquire and process the energy management system data and intelligent scheduling management system data from each computing party.

[0067] Based on the processed energy management system data and intelligent dispatch management system data, combined with the multi-party interactive data algorithm and the model component registration algorithm, the algorithm is implemented by the initiator, the computing party and the computing collaborator working together to run the collaborative iteration logic, and the power carbon emission factor of each computing party's power system node is calculated.

[0068] The cooperative iterative logic implementation algorithm includes one or more of the following: a cooperative iterative logic implementation algorithm based on the number of loop iterations and a cooperative iterative logic implementation algorithm based on the minimum loss value.

[0069] For example, the formulas for calculating the electricity carbon emission factor of nodes in the power system for each computing party are as follows:

[0070]

[0071] Among them, I Bn Let Q be the carbon emission factor of the nth grid node B. Ht For the input power of the t-th power plant H connected to the grid node, I Ht Let Q be the carbon emission factor of the t-th power plant H connected to the grid node. Bc Let I be the input power of the c-th network node B. Bc Let be the carbon emission factor of the c-th grid node B.

[0072] For example, the model parameter acquisition algorithm using a collaborative iterative model acquires and processes the energy management system data and the intelligent dispatch management system data, including:

[0073] A collaborative iterative model is used to generate model parameters according to set rules, and the energy management system data and the intelligent scheduling management system data are obtained and processed to obtain the processed energy management system data and intelligent scheduling management system data.

[0074] The processed energy management system data, intelligent dispatch management system data, and the calculated power carbon emission factors of the nodes in the power system are stored in the domain of each computing party.

[0075] The energy management system data includes: real-time monitoring data, equipment status data, historical operating data, forecast data, configuration data, and parameter data; the intelligent dispatch management system data includes: power outage event data, network topology data, customer information, real-time monitoring data, and historical operating data.

[0076] For example, the setting rules include at least one or more of the following: source rules, region rules, data table rules, and data field rules;

[0077] Example source rules (data source):

[0078] This specifies how to identify and access data sources from different provinces, including but not limited to electricity market data, power grid operation data, and environmental monitoring data. It defines standard protocols and interfaces for data access to ensure the stability and security of data transmission.

[0079] Regional rules (regional differences):

[0080] Explain how to adjust data processing strategies based on the specific circumstances of each region (such as power structure, environmental policies, and geographical features). Set regionally differentiated parameters to adapt to the specific needs and conditions of different regions.

[0081] Data table rules (data structure):

[0082] Define a unified data model, including the necessary data tables and the relationships between them. Clearly define the information categories contained in each data table, such as power generation, carbon emission factors, and electricity consumption.

[0083] Data field rules (data attributes):

[0084] Define the data type, format, naming rules, and constraints for each data field in the data model. Design data validation and transformation mechanisms to ensure data standardization and consistency.

[0085] For example, the model parameter acquisition algorithm using a collaborative iterative model, generated according to set rules, acquires and processes the energy management system data and the intelligent dispatch management system data to obtain processed energy management system data and intelligent dispatch management system data, including:

[0086] An algorithm for generating source-end rules is used to identify and access energy management system data and intelligent scheduling management system data from various computing parties;

[0087] An algorithm that uses regional rules to generate data sets differentiated parameters for each computing party based on their specific needs. The energy management system data and the intelligent dispatch management system data are then adjusted according to these regional differentiated parameters to generate the adjusted energy management system data and intelligent dispatch management system data.

[0088] An algorithm using data table rules is used to generate a data model from the adjusted energy management system data and intelligent dispatch management system data;

[0089] An algorithm based on data field rules is used to verify and transform the energy management system data and intelligent dispatch management system data in the data model according to the designed data verification and transformation mechanism, thereby generating unified standard energy management system data and intelligent dispatch management system data.

[0090] For example, some algorithms using data field rules process data from the EMS system to generate a standard dataset, while others using data field rules process data from the OMS system to generate a standard dataset. The data processing code will handle the differences between data sources from various provinces, unifying the entire data processing process by designing data processing rules across four dimensions: source, province, data table, and data field.

[0091] By using the set rules in the model parameter acquisition algorithm, various types of data from all computing parties can be aggregated. Current data aggregation methods often face the problem of low processing efficiency when dealing with large-scale power grid data. Secondly, due to the lack of an effective coordination architecture, existing technologies face data compatibility and integration problems when integrating data from different sources and formats. These problems limit the efficiency and reliability of data aggregation, thereby affecting the calculation and analysis of carbon emission factors of the entire power system. This invention uses a model parameter acquisition algorithm to specifically process data from different sources and formats from various computing parties and place them into the domains of each computing party, facilitating subsequent calculations and improving the efficiency and reliability of data aggregation.

[0092] For example, based on the processed energy management system data and intelligent dispatch management system data, combined with the multi-party interactive data algorithm and the model component registration algorithm, the initiator, the computing party, and the computing collaborator collaboratively run the collaborative iterative algorithm to calculate the power carbon emission factor of each computing party's power system node, including:

[0093] Based on the processed energy management system data and intelligent scheduling management system data of each computing party, the SM4 encryption algorithm in the multi-party interaction data algorithm is used to encrypt the data that needs to be interacted, and thus obtain encrypted interaction data.

[0094] Based on the processed energy management system data and intelligent dispatch management system data of each computing party, the encrypted interactive data, and the algorithm components registered by the model component registration algorithm, the algorithm is implemented by the collaborative iteration logic run by the initiator, computing party, and computing collaborator to calculate the power carbon emission factor of each node in the power system.

[0095] For example, the collaborative iterative logic implementation algorithm includes an automatic boundary node identification algorithm, which can reduce the amount of interactive data.

[0096] The specific implementation process of step 2 is as follows:

[0097] The principle behind the above-described collaborative iterative logic implementation algorithm is as follows:

[0098] The main approach involves multiple iterative calculations to simulate the transfer of carbon carried by power flow along the power grid lines to lower-level nodes. Each calculation round represents one transfer of carbon concentration to a lower-level node. As the number of calculation rounds increases, the transferred carbon concentration gets closer to the final value, ultimately achieving an accurate calculation of the carbon emission factor for each node. Based on... Figure 2 The node structure in the power system shown is illustrated in Table 1 below. As can be seen from the specific calculation process of the network nodes, the factor results of each node gradually approach the final correct value with each calculation round.

[0099] Table 1 Detailed calculation process for network nodes

[0100]

[0101]

[0102] The cooperative iterative logic implementation algorithm includes: such as Figure 3 The algorithm shown is a cooperative iterative logic implementation based on the number of loop iterations, and as follows: Figure 4 The algorithm shown is based on the cooperative iterative logic implementation of minimum loss value;

[0103] The collaborative iteration algorithm includes: model parameter acquisition algorithm, multi-party interaction data algorithm, model component registration algorithm, and collaborative iteration logic implementation algorithm. All algorithms in this invention are implemented in code, and the corresponding code is model parameter code, model component code, multi-party interaction data code, and collaborative iteration logic implementation code.

[0104] Model parameter codes are primarily used to obtain the model's input parameters and store the data from each computational method in its own domain, such as... Figure 5 This provides partial model parameter code, avoiding the problem of the original technology requiring all data to be aggregated, which was difficult to implement.

[0105] The model component code is mainly used to register algorithm components on the FATE platform;

[0106] Multi-party data interaction code is mainly used to define encrypted collaborative data interaction among multiple parties during the computation process, such as... Figure 6 This is code for partially encrypted collaborative data interaction between multiple parties, using the SM4 encryption algorithm to achieve data exchange. SM4 is a block cipher algorithm with a block length of 128 bits and a key length of 128 bits.

[0107] Both the encryption and key expansion algorithms employ a 32-round nonlinear iterative structure, performing encryption operations in units of words (32 bits). Each iteration is a transformation function F. The SM4 algorithm has the same encryption / decryption structure, expressed as follows:

[0108]

[0109] Among them, X i For the i-th piece of data that needs to be encrypted, X i+1 For the (i+1)th data that needs to be encrypted, X i+2 For the (i+2)th data that needs to be encrypted, X i+3 For the (i+3)th data item that needs to be encrypted, X i+4 For intermediate output data, F is the transformation function, T is the operation of function T, Y0, Y1, Y2 and Y3 are all encrypted data, i is the number of data to be encrypted, and rk is the key.

[0110] like Figure 7 The image shows part of the code for determining the end condition of a multi-party collaborative calculation in the implementation of the collaborative iteration logic. Example: There are two calculation parties, each with 100 nodes. When both parties finish, it means that each party has calculated its own nodes. A total of 100 rounds of calculation are performed. The other party then performs multi-party calculations, round by round, for a total of 100 rounds.

[0111] When the above collaborative iteration model is running, there are three parties simultaneously carrying out collaborative computation: first, the computation initiator (Guest), there is only one initiator at runtime; second, the shared computation party (Host), there can actually be multiple shared parties, for example, 27 provinces can have 27 shared parties; and third, the computation collaborator (Arbitor), there is only one collaborator, which can be on the same server as the initiator.

[0112] After the collaborative iterative model based on the FATE platform finishes running, the results and process logs can be viewed through the interface. For data security, the initiator's electricity carbon emission factor can only be obtained and viewed on the initiator's server, and the co-calculator's electricity carbon emission factor can only be obtained and viewed on the co-calculator's server; the collaborating parties do not store any factor data. This invention, based on the original collaborative iterative algorithm, firstly adds encrypted collaborative interaction of multi-party data during the calculation process; secondly, it adds automatic identification of multi-party boundary nodes to reduce the amount of interactive data; and thirdly, it adds a calculation termination condition determination under multi-party collaboration to balance efficiency and calculation accuracy.

[0113] Example 2:

[0114] Based on the same inventive concept, this patent application also provides a nodal power carbon emission factor calculation system based on collaborative iteration, such as... Figure 8 As shown, it includes: a data acquisition module and an electricity carbon emission factor calculation module;

[0115] The data acquisition module is used to acquire energy management system data and intelligent scheduling management system data from each computing party;

[0116] The power carbon emission factor calculation module is used to calculate the power carbon emission factor of each node in the power system based on the energy management system data and the intelligent dispatch management system data, through the collaborative operation of a pre-built collaborative iterative model by the initiator, the calculation party and the calculation collaborator.

[0117] The collaborative iterative model is constructed using a collaborative iterative algorithm based on data from the energy management system and the intelligent scheduling management system.

[0118] Preferably, the collaborative iterative algorithm code of the electricity carbon emission factor calculation module includes a model parameter acquisition algorithm, a model component registration algorithm, a multi-party data interaction algorithm, and a collaborative iterative algorithm; the electricity carbon emission factor calculation module is specifically used for:

[0119] A model parameter acquisition algorithm based on a collaborative iterative model is used to acquire and process the energy management system data and intelligent scheduling management system data from each computing party.

[0120] Based on the processed energy management system data and intelligent dispatch management system data, combined with the multi-party interactive data algorithm and the model component registration algorithm, the algorithm is implemented by the initiator, the computing party and the computing collaborator working together to run the collaborative iteration logic, and the power carbon emission factor of each computing party's power system node is calculated.

[0121] The cooperative iterative logic implementation algorithm includes one or more of the following: a cooperative iterative logic implementation algorithm based on the number of loop iterations and a cooperative iterative logic implementation algorithm based on the minimum loss value.

[0122] Preferably, the calculation formula for the electricity carbon emission factor of each node in the power system of the electricity carbon emission factor calculation module is as follows:

[0123]

[0124] Among them, I Bn Let Q be the carbon emission factor of the nth grid node B. Ht For the input power of the t-th power plant H connected to the grid node, I Ht Let Q be the carbon emission factor of the t-th power plant H connected to the grid node. Bc Let I be the input power of the c-th network node B. Bc Let be the carbon emission factor of the c-th grid node B.

[0125] Preferably, the electricity carbon emission factor calculation module employs a model parameter acquisition algorithm based on a collaborative iterative model to acquire and process the energy management system data and intelligent dispatch management system data from each calculation party, including:

[0126] A collaborative iterative model is used to generate model parameters according to set rules, and the energy management system data and the intelligent scheduling management system data are obtained and processed to obtain the processed energy management system data and intelligent scheduling management system data.

[0127] The processed energy management system data, intelligent dispatch management system data, and the calculated power carbon emission factors of the nodes in the power system are stored in the domain of each computing party.

[0128] The energy management system data includes: real-time monitoring data, equipment status data, historical operating data, forecast data, configuration data, and parameter data; the intelligent dispatch management system data includes: power outage event data, network topology data, customer information, real-time monitoring data, and historical operating data.

[0129] Preferably, the setting rules of the power carbon emission factor calculation module include at least one or more of the following: source-end rules, regional rules, data table rules, and data field rules; the power carbon emission factor calculation module uses a collaborative iterative model to obtain model parameters generated according to the set rules, acquires and processes the energy management system data and the intelligent dispatch management system data, and obtains the processed energy management system data and intelligent dispatch management system data, including:

[0130] An algorithm for generating source-end rules is used to identify and access energy management system data and intelligent scheduling management system data from various computing parties.

[0131] An algorithm that uses regional rules to generate data sets differentiated parameters for each computing party based on their specific needs. The energy management system data and the intelligent dispatch management system data are then adjusted according to these regional differentiated parameters to generate the adjusted energy management system data and intelligent dispatch management system data.

[0132] An algorithm using data table rules is used to generate a data model from the adjusted energy management system data and intelligent dispatch management system data;

[0133] An algorithm based on data field rules is used to verify and transform the energy management system data and intelligent dispatch management system data in the data model according to the designed data verification and transformation mechanism, thereby generating unified standard energy management system data and intelligent dispatch management system data.

[0134] Preferably, the electricity carbon emission factor calculation module, based on processed energy management system data and intelligent dispatch management system data, and combined with the multi-party interactive data algorithm and the model component registration algorithm, calculates the electricity carbon emission factor of each node in the power system through the collaborative iteration logic implemented by the initiator, the calculator, and the computing collaborator, including:

[0135] Based on the processed energy management system data and intelligent scheduling management system data of each computing party, the SM4 encryption algorithm in the multi-party interaction data algorithm is used to encrypt the data that needs to be interacted, and obtain encrypted interaction data.

[0136] Based on the processed energy management system data and intelligent dispatch management system data of each computing party, the encrypted interactive data, and the algorithm components registered by the model component registration algorithm, the algorithm is implemented by the collaborative iteration logic run by the initiator, computing party, and computing collaborator to calculate the power carbon emission factor of each node in the power system.

[0137] Those skilled in the art will understand that embodiments of this patent application can be provided as methods, systems, or computer program products. Therefore, this patent application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this patent application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0138] This patent application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the patent application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0139] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this patent application and not to limit its scope of protection. Although the patent application has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this patent application, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the claims pending approval.

Claims

1. A method for calculating the nodal electricity carbon emission factor based on cooperative iteration, characterized in that, include: Acquire energy management system data and intelligent scheduling management system data from each computing party; Based on the energy management system data and the intelligent dispatch management system data, the power carbon emission factor of each node in the power system is calculated by the initiator, the computing party and the computing collaborator working together to run a pre-built collaborative iterative model. The collaborative iterative model is constructed using a collaborative iterative algorithm based on energy management system data and intelligent dispatch management system data. The collaborative iterative algorithm includes a model parameter acquisition algorithm, a model component registration algorithm, a multi-party data interaction algorithm, and a collaborative iterative logic implementation algorithm. Based on the energy management system data and the intelligent dispatch management system data, the initiator, the computer, and the compute collaborator collaboratively run a pre-built collaborative iterative model to calculate the power carbon emission factor of each computer's power system node, including: A model parameter acquisition algorithm based on a collaborative iterative model is used to acquire and process the energy management system data and intelligent scheduling management system data from each computing party. Based on the processed energy management system data and intelligent scheduling management system data of each computing party, the SM4 encryption algorithm in the multi-party interaction data algorithm is used to encrypt the data that needs to be interacted, and thus obtain encrypted interaction data. Based on the processed energy management system data and intelligent dispatch management system data of each computing party, the encrypted interactive data, and the algorithm components registered by the model component registration algorithm, the algorithm is implemented by the collaborative iteration logic run by the initiator, computing party, and computing collaborator to calculate the power carbon emission factor of each node in the power system. The cooperative iterative logic implementation algorithm includes one or more of the following: a cooperative iterative logic implementation algorithm based on the number of loop iterations and a cooperative iterative logic implementation algorithm based on the minimum loss value.

2. The method as described in claim 1, characterized in that, The formulas for calculating the electricity carbon emission factor of each node in the power system by each computing party are as follows: in, Let be the carbon emission factor of the nth grid node B. The input power of the t-th power plant H connected to the network node is... Let H be the carbon emission factor of the t-th power plant connected to the grid node. The input power of the c-th network node B, Let be the carbon emission factor of the c-th grid node B.

3. The method as described in claim 1, characterized in that, The model parameter acquisition algorithm using a collaborative iterative model acquires and processes the energy management system data and the intelligent dispatch management system data, including: A collaborative iterative model is used to generate model parameters according to set rules, and the energy management system data and the intelligent scheduling management system data are obtained and processed to obtain the processed energy management system data and intelligent scheduling management system data. The processed energy management system data, intelligent dispatch management system data, and the calculated power carbon emission factors of the nodes in the power system are stored in the domain of each computing party. The energy management system data includes: real-time monitoring data, equipment status data, historical operating data, forecast data, configuration data, and parameter data; the intelligent dispatch management system data includes: power outage event data, network topology data, customer information, real-time monitoring data, and historical operating data.

4. The method as described in claim 3, characterized in that, The defined rules include at least one or more of the following: source rules, regional rules, data table rules, and data field rules; the model parameter acquisition algorithm generated according to the defined rules using a collaborative iterative model acquires and processes the energy management system data and the intelligent dispatch management system data to obtain processed energy management system data and intelligent dispatch management system data, including: An algorithm for generating source-end rules is used to identify and access energy management system data and intelligent scheduling management system data from various computing parties; An algorithm that uses regional rules to generate data sets differentiated parameters for each computing party based on their specific needs. The energy management system data and the intelligent dispatch management system data are then adjusted according to these regional differentiated parameters to generate the adjusted energy management system data and intelligent dispatch management system data. An algorithm using data table rules is used to generate a data model from the adjusted energy management system data and intelligent dispatch management system data; An algorithm based on data field rules is used to verify and transform the energy management system data and intelligent dispatch management system data in the data model according to the designed data verification and transformation mechanism, thereby generating unified standard energy management system data and intelligent dispatch management system data.

5. A nodal electricity carbon emission factor calculation system based on cooperative iteration, characterized in that, include: Data acquisition module and electricity carbon emission factor calculation module; The data acquisition module is used to acquire energy management system data and intelligent scheduling management system data from each computing party; The power carbon emission factor calculation module is used to calculate the power carbon emission factor of each node in the power system based on the energy management system data and the intelligent dispatch management system data, through the collaborative operation of a pre-built collaborative iterative model by the initiator, the calculation party and the calculation collaborator. The collaborative iterative model is constructed using a collaborative iterative algorithm based on energy management system data and intelligent dispatch management system data. The collaborative iterative algorithm of the electricity carbon emission factor calculation module includes a model parameter acquisition algorithm, a model component registration algorithm, a multi-party data interaction algorithm, and a collaborative iterative logic implementation algorithm; the electricity carbon emission factor calculation module is specifically used for: A model parameter acquisition algorithm based on a collaborative iterative model is used to acquire and process the energy management system data and intelligent scheduling management system data from each computing party. Based on the processed energy management system data and intelligent scheduling management system data of each computing party, the SM4 encryption algorithm in the multi-party interaction data algorithm is used to encrypt the data that needs to be interacted, and thus obtain encrypted interaction data. Based on the processed energy management system data and intelligent dispatch management system data of each computing party, the encrypted interactive data, and the algorithm components registered by the model component registration algorithm, the algorithm is implemented by the collaborative iteration logic run by the initiator, computing party, and computing collaborator to calculate the power carbon emission factor of each node in the power system. The cooperative iterative logic implementation algorithm includes one or more of the following: a cooperative iterative logic implementation algorithm based on the number of loop iterations and a cooperative iterative logic implementation algorithm based on the minimum loss value.

6. The system as described in claim 5, characterized in that, The calculation formulas for the electricity carbon emission factors of each node in the power system in the electricity carbon emission factor calculation module are as follows: in, Let be the carbon emission factor of the nth grid node B. The input power of the t-th power plant H connected to the network node is... Let H be the carbon emission factor of the t-th power plant connected to the grid node. The input power of the c-th network node B, Let be the carbon emission factor of the c-th grid node B.

7. The system as described in claim 5, characterized in that, The electricity carbon emission factor calculation module employs a model parameter acquisition algorithm based on a collaborative iterative model to acquire and process the energy management system data and intelligent dispatch management system data from each calculation party, including: A collaborative iterative model is used to generate model parameters according to set rules, and the energy management system data and the intelligent scheduling management system data are obtained and processed to obtain the processed energy management system data and intelligent scheduling management system data. The processed energy management system data, intelligent dispatch management system data, and the calculated power carbon emission factors of the nodes in the power system are stored in the domain of each computing party. The energy management system data includes: real-time monitoring data, equipment status data, historical operating data, forecast data, configuration data, and parameter data; the intelligent dispatch management system data includes: power outage event data, network topology data, customer information, real-time monitoring data, and historical operating data.

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