A method and system for constructing a long-term future power flow section in a power grid based on a multi-version model

Abstract

The application discloses a kind of based on the construction method and system of long-term future flow section in power grid of multi-version model.The method is based on model multi-version library to query future state model version information;According to future state model version information, corresponding model data and current real-time state model data are extracted in model multi-version library to form complete full model data by model splicing, and real-time state estimation mode data is inserted into full future model;Then, for the incremental equipment in future version model, automatic grid-connection processing is carried out relying on deep topological search technology, and complete initial mode data is given to the future model;Finally, according to multiple future model versions, the future section of multiple time scales is intelligently grouped, future boundary planning data is superimposed, and the construction of long-term future flow section is completed.The application can effectively improve the accuracy and reliability of future flow calculation.

Description

Technical Field

[0001] This invention relates to a method and system for constructing long-term future power flow profiles of a power grid based on a multi-version model, belonging to the field of power system data processing technology. Background Technology

[0002] Currently, the uncertainty on both the power source and load sides of the new power system is increasing. Resources such as wind, solar, and hydropower are affected by seasonal weather and meteorological conditions, exhibiting fluctuations across quarters, months, and days. While the power grid dispatching system's future-oriented analysis and decision-making functions, primarily based on daily timeframes, are largely mature, their forward-looking time is relatively short, resulting in insufficient foresight over longer timescales. This leads to a surge in pressure on short-cycle power grid balancing and renewable energy consumption. There is an urgent need to conduct future-oriented analysis of the power grid over longer timescales to guide the grid in making scientific and rational arrangements for power resources in advance on annual, monthly, and weekly timescales, thereby achieving the multiple objectives of ensuring safety, supply, and promoting consumption under the new power system.

[0003] Meanwhile, the operating system models on medium- and long-term timescales are constantly changing. Changes in the operating system model cannot be reflected in the future power grid model in real time, and recent changes in the future power grid model cannot be reflected in the long-term future power grid model, making it unsuitable for multi-period planning of the future power grid. Existing methods for constructing future power flow sections do not consider the impact of the future model, which inevitably affects the accuracy and reliability of future power flow calculations. Summary of the Invention

[0004] Purpose of the invention: To address the shortcomings of existing technologies, this invention provides a method and system for constructing medium- and long-term future power flow profiles of power grids based on multi-version models. To address the aforementioned problems, the invention fully considers the changes in future model versions during the generation of future profiles, automatically processes model increments for grid connection, and improves the accuracy and reliability of future power flow.

[0005] Technical solution: Firstly, a method for constructing medium- and long-term future power flow sections of a power grid based on a multi-version model, comprising the following steps:

[0006] Based on the set future time period for pre-analysis, query the future state model version information in the model multi-version library. The model refers to the data set of power grid equipment attributes and connection relationships. The model multi-version library stores model data generated by power grid equipment and lines based on commissioning / decommissioning plans for different time periods.

[0007] Based on the query of the future state model version information, the model data of the corresponding version is extracted from the model multi-version library and spliced ​​with the current real-time model data to form a complete full model data. At the same time, the real-time state estimation section corresponding to the real-time model is inserted into the full model. The real-time state estimation section is obtained based on the real-time measurement of the system.

[0008] For incremental devices in future versions of the model, deep topology search technology is used for automatic grid connection processing. Among them, decommissioned devices are treated as out of service, and the future model is given complete initial mode data.

[0009] Based on the complete model data and initial data of the future model, the future sections at multiple time scales are grouped and the future boundary planning data are overlaid to complete the construction of the medium- and long-term future power flow sections of the power grid.

[0010] Furthermore, the multi-version model library adopts an incremental data storage method. When storing data for each version of the model, only incremental data relative to the previous version is stored, including products to be put into operation / returned and time identifiers. When querying model data of a specific version, the model version at the corresponding time is determined by backtracking with the current model version according to the operation time, and the corresponding model data is reconstructed and obtained based on the incremental data.

[0011] Furthermore, the future version model and the real-time model are stitched together to form complete full model data, including:

[0012] Obtain the current real-time model and state estimation measurement data. Based on the current real-time model and future state model version information, backtrack by time to obtain the future state model data at the corresponding specified time, including electrical equipment and its connection relationships.

[0013] Based on the future state model data, graphical management tools are used to draw graphics, which represent the power grid connection relationship in or between plants. The electrical equipment attributes include: equipment ID, equipment name and electrical terminal connection node number. A unique multi-version node number is determined for each graphical topology node based on the electrical terminal connection node number, and the node is entered into the database.

[0014] The future state model data is combined with the current real-time state model data to form the complete model data;

[0015] Insert the state estimation measurement data into the stitched full model and assign a portion of initial mode data to the full model. The state estimation measurement data includes: device ID, telemetry value, and teleindication value.

[0016] Furthermore, the rules for generating multi-version node numbers in the graph model are as follows:

[0017] Type+co_no+fac_type+fac_no+vl_no+fac_node

[0018] in,

[0019] Type indicates the generation method; 1 indicates local generation, and 2 indicates cloud download.

[0020] co_no is a six-digit region number;

[0021] fac_type is the type of plant / station, where 1 represents a substation, 2 represents a power plant, and 3 represents a converter station;

[0022] fac_no is the plant / station serial number;

[0023] vl_no is the voltage level type;

[0024] fac_node is the node number. The node numbers of different terminals of each device are different. If the node numbers of terminals of different devices are the same, it means that the terminals of the two devices with the same node number are connected together.

[0025] Furthermore, deep topology search technology is used for automatic grid connection processing, providing the future model with complete initial model data, including:

[0026] The incremental equipment includes newly added power plants, substations, generating units, transformers, and transmission lines;

[0027] For each incremental device, based on the topology connection relationship, the set of switch paths with the current electrical island energized bus is searched through the depth-first traversal rule, the shortest path to grid connection is selected, the switch state is closed, so that the new device can automatically connect to the grid, and the generator and the traditional units in the power plant are assigned initial active power values.

[0028] Furthermore, during the depth-first traversal search, the set of switch paths is divided into three categories: paths classified by power source, paths classified by load at the end of the path, and paths classified by branches passed through in the path. The shortest path is selected from these categories, the switch is assigned to the closed state, and measurements are applied to the generator equipment.

[0029] Furthermore, future cross-sections at multiple time scales are grouped and overlaid with future boundary planning data to complete the construction of medium- and long-term future power flow cross-sections of the power grid, including:

[0030] Based on the future state model version information, the future trend sections are grouped according to the corresponding set of future trend sections for each model version;

[0031] The future boundary planning data is accessed into the system via E-file. The future boundary planning data includes system load forecast, bus load forecast, new energy forecast, unit power generation plan, equipment maintenance plan, and power receiving plan data. The data is then verified for rationality and supplemented.

[0032] For each group, based on the complete model data and initial method data of the future model, combined with the future boundary plan data, the power flow cross section at multiple time scales is calculated.

[0033] Secondly, a system for constructing medium- and long-term future power flow sections of a power grid based on a multi-version model includes:

[0034] The future model version information query module is used to query the future state model version information in the future time period based on the model multi-version library according to the set pre-analysis future time period. The model refers to the data set of power grid equipment attributes and connection relationships. The model multi-version library stores model data generated by power grid equipment and lines based on the commissioning / decommissioning plans of different time periods.

[0035] The full model data construction module is used to extract the corresponding version of model data from the multi-version model library based on the query of future model version information, and to combine it with the current real-time model data to form complete full model data;

[0036] The initial mode data construction module is used to insert the real-time state estimation section corresponding to the real-time model into the full model. The real-time state estimation section is obtained based on the real-time measurement of the system. For incremental devices in future version models, the deep topology search technology is used to automatically connect them to the grid. Among them, decommissioned devices are treated as out of service, and the future model is given complete initial mode data.

[0037] The future power flow section construction module is used to group future sections at multiple time scales based on the complete model data and initial method data of the future model, and overlay future boundary plan data to complete the construction of the medium and long-term future power flow sections of the power grid.

[0038] Thirdly, a computer device includes one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, wherein when the programs are executed by the processors, they implement the steps of the method for constructing long-term future power flow sections of a power grid based on a multi-version model as described in the first aspect of the present invention.

[0039] Fourthly, a computer storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method for constructing medium- and long-term future power flow sections of a power grid based on a multi-version model as described in the first aspect of the present invention.

[0040] Beneficial effects:

[0041] (1) According to the selected future model version, the present invention extracts the corresponding model data from the model multi-version library and splices it with the current real-time power grid model data to form a complete full model data. At the same time, real-time state estimation data (such as load, power generation, etc.) are integrated into the full model data to ensure the accuracy and practicality of the model.

[0042] (2) This invention fully considers the changes in future model versions. For newly added power grid equipment, it automatically completes the grid connection process using deep topology search technology and assigns it initial operating parameters in the future model to ensure the integrity and accuracy of the future power grid model. According to different future model versions, the future power grid operating states at multiple time scales are intelligently grouped. By superimposing future boundary condition data, a medium- and long-term future power flow profile is finally constructed, improving the accuracy and reliability of future power flow calculations and providing a reliable basis for power grid planning, scheduling, and operation. Attached Figure Description

[0043] Figure 1 This is an overall flowchart of the method for constructing medium- and long-term future power flow sections of a power grid based on a multi-version model.

[0044] Figure 2 This diagram illustrates the storage and querying of model versions.

[0045] Figure 3 A flowchart for future model version node import and full model generation.

[0046] Figure 4 A schematic diagram showing the grouping of future trend cross sections. Detailed Implementation

[0047] The technical solutions in the embodiments of the present invention will now be clearly and completely described in conjunction with the accompanying drawings.

[0048] This invention proposes a method and system for constructing medium- and long-term future power flow sections of a power grid based on a multi-version model. In the context of this invention, "model" refers to a data set of power grid equipment attributes and connection relationships. A real-time model refers to a data set of power grid equipment attributes and connection relationships that are already in real-time operation. The model itself does not inherently possess a future state concept. However, because this invention's database uses incremental storage, the equipment attributes and connection relationships contained in the model at different times form different past-present-future states. Therefore, future state attributes are assigned to the model based on commissioning and decommissioning times, thus giving rise to the concept of a future state model. "Version" refers to separate version management primarily for graphics and topology. Each power plant will draw and store the corresponding version of the graphics file based on its own model changes, simultaneously forming the corresponding version's topology information. In this invention, medium- and long-term refer to time scales such as weeks, ten-day periods, months, and years, which differ from intraday short-term or ultra-short-term time concepts.

[0049] Reference Figure 1 This invention provides a method for constructing medium- and long-term future power flow sections of a power grid based on a multi-version model, comprising the following steps:

[0050] S1. Based on the set future time period for pre-analysis, query the future state model version information based on the model multi-version library;

[0051] The model multi-version repository stores model data from the past, present, and future. Future-state model data refers to data that, in the future, will see changes in the attributes and connections of equipment in the power network due to planned commissioning or decommissioning of lines / equipment. These plans and changes are events that will occur in the future relative to the present time; therefore, they are described as future-state model data in this invention. This invention uses incremental data storage, storing only incremental data between different model versions. When used, the model version at a specified time is generated by backtracking from the current model version according to the model's operation time. (Refer to...) Figure 2 Suppose a power grid model is updated from version M1 to M2, and then to M3. Incremental storage doesn't store the complete data for M1, M2, and M3; instead, it stores the changes from M1 to M2 (incremental data 1) and the changes from M2 to M3 (incremental data 2). This incremental data includes products to be put into operation / retired and their time stamps, forming corresponding version identifiers. To obtain the M2 version model, the system starts from M1 and uses incremental data 1 to reconstruct M2. To obtain the M3 version model, the system starts from M1 and uses incremental data 1 and incremental data 2 sequentially to reconstruct M3. This incremental data storage method is particularly effective when dealing with large and frequently updated power grid models, saving storage space and improving data processing efficiency.

[0052] Continue to refer to Figure 2 Assuming the current model version is M3, then M1 and M2 are historical models. In the future, based on the planned number of new equipment commissioned / retired weekly, monthly, or other timescales, future model versions are created, and corresponding incremental data is overlaid. For example, the equipment commissioning plan is as follows: Day 5, a new transmission line L1 is commissioned, connecting substation A and substation B; Day 15, a new generator G1 is commissioned, connecting substation C. The equipment decommissioning plan is as follows: Day 20, an old transmission line L2 is decommissioned, which originally connected substation D and substation E. Therefore, model version M4 is created based on M3, including a version number and time scale, and incremental data is overlaid: information on the newly added transmission line L1, including line parameters (impedance, capacity, etc.). Model version M5 is created based on M4, including a version number and time scale, and information on generator G1 is added. Model version M6 is created based on M5, including a version number and time scale, and information on transmission line L2 is removed. This information is recorded in the model multi-version repository.

[0053] In this embodiment of the invention, the calculation of future tidal current sections for the next month (30 days) is set at a time interval of 1 hour, resulting in a total of 720 tidal current sections. There are 3 future model versions for the next month. Under the above assumptions, the information of the 3 model versions can be retrieved from the model multi-version library.

[0054] [version1 version2 version3](1)

[0055] S2. Based on the queried future state model version information, extract the corresponding model data and the current real-time state model data from the model multi-version library, and combine them to form complete full model data. Then, insert the real-time state estimation method data into the full model data.

[0056] This invention extracts corresponding model data (equipment information and connection relationships) from a multi-version model library based on future state model version information, performs graphical drawing using a new generation of smart grid dispatching system graphical management tools, and then stores the graphical topology nodes in the library. This models the relationships between electrical equipment as connection relationships, forming complete model data, and inserts real-time state estimation data into the assembled complete model. Specifically, refer to... Figure 3 This includes the following steps:

[0057] (1) Obtain the current real-time model and state estimation measurement data. Based on the current real-time model and future state model version information, backtrack by time to obtain the future state model data at the specified time, including electrical equipment and its connection relationship.

[0058] (2) Based on the new generation of smart grid dispatch system graphical management tool, draw the graphics of electrical equipment according to the future state model data, and then put the graphical topology nodes into the database.

[0059] The electrical equipment in the acquired future and real-time models includes: AC lines, busbars, transformers, circuit breakers, disconnect switches, grounding switches, capacitors, and reactors.

[0060] Electrical equipment attributes include equipment ID, equipment name, and electrical terminal connection node number. The electrical terminal connection node number is a unique identifier assigned to each electrical connection point in an electrical system or circuit diagram, used to represent the connection point of an electrical device, line, or component. The naming rules for node numbers (node ​​numbers) may vary depending on specific engineering standards, company regulations, or personal habits, but they should be unique to ensure that each connection point has a clear identification.

[0061] The node entry function of this invention is integrated into the graphical editor and is directly associated with the plant wiring diagram. It supports multiple drawing methods for plant wiring diagrams, mainly including: Figure 1Station: A single power grid wiring diagram includes only one substation; Multiple stations in one diagram: A single power grid wiring diagram includes more than one substation; Multiple diagrams for one station: A substation is represented by more than one power grid wiring diagram. The correspondence between diagrams and substations is maintained during the node entry process and recorded in the node entry substation diagram correspondence table.

[0062] Reference Figure 3 Based on the model version, the list of plants and stations to be included in the database is obtained from the graphic name. The plant and station ID and voltage level information are obtained, and the connection rules are checked, such as whether the voltage level matches and whether the equipment type is compatible. After the connection rules are checked, the connection status of the terminals of different plants and stations is checked to ensure that the connection is correct and the parent-child relationship is added correctly. Then, the multi-version node number of the graphic model is generated.

[0063] The present invention provides a multi-version node number generation method for graphical models: node numbers are entered into the database according to multiple temporal versions to ensure consistency of node numbers for terminals connected to different devices within the same version. The rules for generating multi-version node numbers for graphical models are as follows:

[0064] Type (1 bit) + co_no (6 bits) + fac_type (1 bit) + fac_no (4 bits) + vl_no (4 bits) + fac_node (3 bits)

[0065] in,

[0066] Type indicates the generation method; 1 indicates local generation, and 2 indicates cloud download.

[0067] co_no is a six-digit region number;

[0068] fac_type is the type of plant / station, where 1 represents a substation, 2 represents a power plant, and 3 represents a converter station;

[0069] fac_no is the plant / station serial number;

[0070] vl_no is the voltage level type;

[0071] fac_node is the node number. The node numbers of different terminals of each device are different. If the node numbers of terminals of different devices are the same, it means that the terminals of the two devices with the same node number are connected together.

[0072] (3) After a node is added to the database, the multi-version model repository will store all model data and node data contained in the graph. At this time, all model data stored in the model repository is the model data that can be maintained for the current and future versions. When it is necessary to use model data of a certain future version, the future model management service can merge the future model data and the current real-time model data into a complete full model data according to the specified future model version.

[0073] (4) Simultaneously, the state estimation profile corresponding to the real-time model is inserted into the full model data, assigning a portion of initial mode data to the full model. The state estimation profile includes: device ID, telemetry value, and teleindication value. The state estimation profile is obtained based on real-time system measurements.

[0074] S3. For incremental devices in future versions of the model, automatic grid connection is performed using deep topology search technology. Decommissioned devices are treated as out of service, and the future model is given complete initial configuration data.

[0075] Incremental equipment includes newly added power plants, substations, generating units, transformers, and transmission lines;

[0076] Automatic grid connection processing: Based on the topology connection relationship, the system automatically searches for the set of switch paths between various new devices and the current live busbar of the electrical island, selects the shortest path for grid connection, closes the switch, and enables the new devices to automatically connect to the network.

[0077] Using a depth-first search, all power supply paths are obtained and categorized into three types: paths classified by power source, paths classified by the load at the path's endpoint, and paths classified by the branches they pass through. The paths are represented as follows:

[0078] [Path1|CB2...CB n (2)

[0079] path1 represents a new internet access path for the newly added device, and CBn represents the set of all switches for the internet access path.

[0080] In addition: Initial active power values ​​are assigned to generators and conventional units in power plants, where P0 = 0.8P for gas turbine units. max For coal-fired power units, P0 = 0.5P max ;P max p0 represents the generator's maximum output, and p0 represents the initial active power of the unit.

[0081] The complete full model consists of a real-time model and a future incremental model. The initial method corresponds to the real-time method and the method corresponding to the future incremental model. Here, the real-time method is obtained by acquiring real-time state estimation, and the data corresponding to the future incremental model is obtained by assigning the switch of the Internet path to the closed state through deep topology search technology, while the generator equipment is measured.

[0082] S4. Based on the complete model data and initial method data of the future model, group the future sections at multiple time scales, overlay the future boundary plan data, and complete the construction of the medium- and long-term future tidal current sections.

[0083] The planned boundary data includes: system load forecast, bus load forecast, new energy forecast (marketing, non-centralized dispatch, centralized dispatch), unit power generation plan, equipment maintenance plan, power receiving plan data, etc., used to construct medium- and long-term future power flow profiles. The format is shown in Table 1, where "time" represents the moment and "value" represents the value; the values ​​for different data types vary at different times. All data are accessed through E-files (created or generated according to E language specifications), with a data granularity of one hour per point (the time interval can be flexibly set); data rationality verification and supplementation are conducted to ensure data integrity and accuracy.

[0084] Table 1: Composition format of boundary data

[0085] Device ID <![CDATA[Time1]]> <![CDATA[Time2]]> … <![CDATA[Time n ]]> Planned data values <![CDATA[Value1]]> <![CDATA[Value2]]> … <![CDATA[Value n ]]>

[0086] Different model versions have different network structures, so each model version will correspond to a set of future power flow cross-sections. The grouping is shown in Formula 3, with each model version corresponding to multiple power flow cross-sections. `vesion` represents different model versions, `m` is the number of versions, and `case1` to `case2` represent different model versions. n This represents the corresponding future trend section.

[0087]

[0088] In the embodiment, the three model versions are divided into three groups, as shown in formula (4);

[0089]

[0090] Based on the grouping, and combining future state model data and future boundary plans, batch future power flow calculations are performed to construct multi-section future power flows. The specific methods of power flow calculation are not the focus of this invention and will not be elaborated here. (Refer to...) Figure 4 In this case study, a 30-day timeframe with an hourly granularity was used to calculate 24 cross-sections per day, resulting in a total of 720 future power flow cross-sections. Three model versions were used, each corresponding to a set of future power flow cross-sections. This allows for an accurate assessment of the long-term power flow cross-sections in the power grid.

[0091] Based on the above-described scheme, the present invention provides a method for constructing medium- and long-term future power flow sections of a power grid based on a multi-version model. In the generation of future sections, the changes in future model versions are fully considered, and the model increments are automatically processed for grid connection, thereby improving the accuracy and reliability of future power flow.

[0092] Based on the same technical concept as the method embodiments, the present invention also provides a system for constructing medium- and long-term future power flow sections of a power grid based on a multi-version model, comprising:

[0093] The future model version information query module is used to query the future state model version information in the future time period based on the model multi-version library according to the set pre-analysis future time period. The model refers to the data set of power grid equipment attributes and connection relationships. The model multi-version library stores model data generated by power grid equipment and lines based on the commissioning / decommissioning plans of different time periods.

[0094] The full model data construction module is used to extract the corresponding version of model data from the multi-version model library based on the query of future model version information, and to combine it with the current real-time model data to form complete full model data;

[0095] The initial mode data construction module is used to insert the real-time state estimation section corresponding to the real-time model into the full model. The real-time state estimation section is obtained based on the real-time measurement of the system. For incremental devices in future version models, the deep topology search technology is used to automatically connect them to the grid. Among them, decommissioned devices are treated as out of service, and the future model is given complete initial mode data.

[0096] The future power flow section construction module is used to group future sections at multiple time scales based on the complete model data and initial method data of the future model, and overlay future boundary plan data to complete the construction of the medium and long-term future power flow sections of the power grid.

[0097] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus (systems), computer devices, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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.

[0098] This invention is described with reference to a flowchart of a method according to embodiments of the invention. It should be understood that each step in the flowchart and combinations thereof 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 process. Figure 1 A device for a function specified in one or more processes.

[0099] 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 The function specified in one or more processes.

[0100] 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 Steps of a specified function in one or more processes.

Claims

1. A method for constructing a long-term future power flow section in a power grid based on a multi-version model, characterized in that, The method comprises the following steps: According to the future time period set for pre-analysis, query the future state model version information in the future time period based on the model multi-version library, the model refers to the data set of power grid equipment attributes and connection relationship, and the model multi-version library stores model data generated by the power grid equipment and line based on the commissioning / retirement plan in different time periods; According to the queried future state model version information, extract the model data of the corresponding version in the model multi-version library, and splice the current real-time state model data to form complete full model data, and insert the real-time state estimation section corresponding to the real-time state into the full model, wherein the real-time state estimation section is obtained according to the system real-time measurement; For the incremental equipment in the future version model, the deep topological search technology is used for automatic grid connection processing, wherein the retired equipment is treated as a shutdown, and the future model is given complete initial mode data; According to the complete model data and the initial mode data of the future model, the future sections of multiple time scales are grouped, the future boundary plan data is superimposed, and the construction of the long-term future power flow section in the power grid is completed.

2. The method of claim 1, wherein, The model multi-version library adopts an incremental data storage mode, and each version of model data is stored only by storing incremental data relative to the previous version, including to-be-commissioned / retired products and time identifiers; when querying the specified version of model data, the model version corresponding to the time point is determined by backtracking according to the operation time of the current model version, and the corresponding model data is obtained according to the incremental data.

3. The method of claim 2, wherein, The future version model and the real-time model are spliced to form complete full model data, including: Obtain the current real-time model and state estimation measurement data, obtain the future state model data corresponding to the specified time point according to the current real-time model and the future state model version information, and backtrace according to the time, including electrical equipment and its connection relationship; According to the future state model data, perform graph drawing by using a graph management tool, represent the power grid connection relationship in or between plants by using a graph, the electrical equipment attributes include: equipment ID, equipment name and electrical terminal connection node number, determine a unique graph model multi-version node number for each graph topological node according to the electrical terminal connection node number, and perform node storage; Splice the future state model data and the current real-time state model data to form full model data; Insert the state estimation measurement data into the spliced full model to give the full model a part of initial mode data, and the state estimation measurement data includes: equipment ID, telemetry value and remote signaling value.

4. The method of claim 2, wherein, The generation rule of the graph model multi-version node number is as follows: Type+co_no+fac_type+fac_no+vl_no+fac_node Type is the generation mode, 1 represents local generation, and 2 represents cloud download; co_no is a six-digit regional number; fac_type is the plant station type, 1 represents a substation, 2 represents a power plant, and 3 represents a converter station; fac_no is the plant station serial number; vl_no is the voltage level type; ​ fac_node is node number, the node number of different terminals of each device is different, if the node numbers of different device terminals are same, it means that the terminals with same node number of the two devices are connected together.

5. The method of claim 1, wherein, The automatic grid-connection processing is performed by using the deep topology search technology, and the future model is given complete initial mode data, including: The incremental equipment includes newly added power plants, substations, units, transformers and transmission lines; For each incremental equipment, the switch path set connected with the live bus of the current electrical island is searched according to the topological connection relationship by using the depth-first traversal rule, the shortest grid-connection path is selected, the switch state is closed, the newly added equipment is automatically connected to the grid, and the active initial value is given to the traditional unit in the generator and power plant.

6. The method of claim 5, wherein, When the depth-first traversal search is performed, the switch path set is divided into three categories: the path according to the power supply, the path according to the terminal load, and the path according to the branch passed in the path, the shortest path is selected, the switch is given a correct state, and the measurement of the generator equipment is performed.

7. The method of claim 1, wherein, The future sections of multiple time scales are grouped, the future boundary plan data is superimposed, and the construction of the long-term future power flow section of the power grid is completed, including: According to the future state model version information, each model version is grouped into a group of future power flow sections; The future boundary plan data is imported into the system through an E file, the future boundary plan data includes system load prediction, bus load prediction, new energy prediction, unit generation plan, equipment maintenance plan and power receiving plan data, and the data is reasonably checked and supplemented; For each group, the future model complete model data and initial mode data are calculated in combination with the future boundary plan data. 8.A system for constructing long-term future flow section in power grid based on multi-version model, characterized in that, Including: A future model version information query module is configured to query, based on a model multi-version library, future state model version information in a future time period set for pre-analysis, wherein the model refers to a data set of device attributes and connection relationships of the power grid, and the model multi-version library stores model data generated based on commissioning / retirement plans of devices and lines in different time periods; A full model data construction module is configured to extract model data of a corresponding version from the model multi-version library based on the queried future state model version information, and splice the model data with current real-time state estimation sections to form complete full model data; An initial mode data construction module is configured to insert real-time state estimation sections corresponding to a real-time state of a model into the full model, wherein the real-time state estimation sections are obtained based on real-time measurements of the system, and incremental equipment in a future version model is automatically connected to the grid by using a deep topology search technology, wherein the retired equipment is treated as a shutdown, and the future model is given complete initial mode data; A future power flow section construction module is configured to group future sections of multiple time scales based on complete model data and initial mode data of a future model, superimpose future boundary plan data, and complete construction of long-term future power flow sections of the power grid.

9. A computer device, comprising: The apparatus comprises one or more processors; memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs, when executed by the processors, implement the steps of the method for constructing mid-long term future power flow contours in a power grid based on multi-version models according to any one of claims 1-7.

10. A computer storage medium having stored thereon a computer program, characterized in that The computer programs, when executed by the processors, implement the steps of the method for constructing mid-long term future power flow contours in a power grid based on multi-version models according to any one of claims 1-7.

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