A method, system and electronic device for constructing a virtual power plant energy scheduling model

Abstract

The present application relates to the technical field of energy dispatch, and in particular, to a method, system and electronic device for constructing a virtual power plant energy dispatch model. The method includes: obtaining the first location of each power plant and the second location of each substation area, obtaining the transmission relationship between each power plant and each substation area, constructing an energy dispatch model of the virtual power plant corresponding to the target area based on the first location, the second location and the transmission relationship between each power plant and each substation area, obtaining the target substation area with electricity demand, determining the target location where the target substation area is located, determining at least one shortest route based on the target location, performing fault pre-diagnosis detection on the at least one shortest route to obtain the target route, and performing electric energy dispatch and allocation based on the target route. By performing electric energy dispatch and allocation based on the determined shortest route and optimizing the electric energy allocation strategy, the efficient utilization of electric energy and the reduction of transmission loss can be achieved, and the overall operation efficiency of the power system can be improved.

Description

Technical Field

[0001] This application relates to the technical field of energy scheduling, and particularly to a method, system, and electronic device for constructing a virtual power plant energy scheduling model. Background Art

[0002] With the continuous growth of energy demand and the transformation of the energy structure, the power system is facing unprecedented challenges and opportunities. Especially against the backdrop of a significant increase in the proportion of renewable energy access, the stability and economy of the power system have been greatly tested. To effectively address these issues, constructing an efficient and intelligent virtual power plant energy scheduling model and improving the operating efficiency and reliability of the system by optimizing power distribution and determining the shortest route have become one of the hotspots in current power system research.

[0003] Within the framework of constructing and operating a virtual power plant energy scheduling model, the efficient distribution of electric energy plays a crucial role. Traditional direct distribution methods often neglect the path optimization in the electric energy transmission process, which results in an overly long path for the electric energy to flow through, thereby causing unnecessary electric energy losses. Against the current backdrop of energy shortage and energy conservation and emission reduction, how to reduce the electric energy consumption in the electric energy transmission process has become an urgent problem to be solved. Summary of the Invention

[0004] To reduce the electric energy consumption in the electric energy transmission process, this application provides a method, system, and electronic device for constructing a virtual power plant energy scheduling model.

[0005] In a first aspect, this application provides a method for constructing a virtual power plant energy scheduling model, adopting the following technical solution:

[0006] A method for constructing a virtual power plant energy scheduling model includes:

[0007] Obtain the first position of each power plant and the second position of each substation area in the target area;

[0008] Obtain the transmission relationship between each power plant and each substation area, where the transmission relationship indicates whether there is a transmission connection between the power plant and the substation area;

[0009] Based on the first position, the second position, and the transmission relationship between each power plant and each substation area, construct an energy scheduling model of the virtual power plant corresponding to the target area;

[0010] Obtain the target substation area with power demand;

[0011] In the energy scheduling model of the virtual power plant, determine the target position where the target substation area is located;

[0012] Based on the target position, determine at least one shortest route;

[0013] Perform fault pre - diagnosis detection on the at least one shortest route to obtain a target route;

[0014] Perform power scheduling and allocation based on the target route.

[0015] By adopting the above - mentioned technical solution, the first positions of each power plant and the second positions of each sub - station area in the target area, as well as their transmission relationships, can be obtained, and a detailed energy scheduling model of the virtual power plant can be constructed, providing clear and intuitive information. Then, real - time power demand monitoring is carried out to obtain the target sub - station areas with power demand to ensure the accuracy and timeliness of power distribution. The target position where the target sub - station area is located is determined in the energy scheduling model of the virtual power plant. After determining the target position, at least one shortest route is determined based on the target position. To ensure the stable availability of the selected at least one shortest route, fault pre - diagnosis detection is performed on the at least one shortest route to obtain a target route. That is, the obtained target route not only has a better distance from the target position, but also has better stable availability for power transmission, thus realizing efficient and stable power transmission. Power scheduling and allocation are performed based on the determined target route. By optimizing the power distribution strategy, efficient utilization of power and reduction of transmission losses can be achieved, and the overall operation efficiency of the power system can be improved.

[0016] In a possible implementation manner, the determining of at least one shortest route based on the target position includes:

[0017] Establish a first set corresponding to the first position and a second set corresponding to the second position;

[0018] Iterate over each second position in the second set to determine a target second position corresponding to the iteration number according to the iteration number;

[0019] Transfer the target second position corresponding to each iteration number from the second set to the first set;

[0020] When the second set is empty, stop the iteration;

[0021] Determine at least one shortest route according to the first set after iteration.

[0022] By adopting the above - mentioned technical solution, various factors can be comprehensively considered to determine the optimal path from the power plant to the target sub - station area, thus realizing efficient and stable power transmission. At the same time, by adopting an iterative method, various situations can be flexibly processed, including multi - path connections between power plants and sub - station areas, potential connections between sub - station areas, etc., so that the solution can adapt to various complex power system environments.

[0023] In a possible implementation, iterating over each second position in the second set to determine a target second position corresponding to the iteration count based on the iteration count includes:

[0024] Obtaining a target second position corresponding to a target iteration count, where the target iteration count is the previous iteration count of the current iteration count;

[0025] Determining the first distance between the target second position corresponding to the target iteration count and each second position in the second set, and determining the second distance between the first position and each second position in the second set;

[0026] Determining the second position with the shortest first distance and second distance as the target second position corresponding to the current iteration count.

[0027] By adopting the above technical solution, in each iteration, not only the distance (first distance) between the target second position corresponding to the target iteration count and each second position in the current second set is considered, but also the distance (second distance) between these second positions and the first position (power plant) is considered. This dual distance consideration makes the path selection more accurate and can ensure that the finally determined shortest route better meets the actual requirements and optimization goals.

[0028] In a possible implementation, the operation of retrieving the target second position corresponding to each iteration count from the second set into the first set includes:

[0029] Obtaining the power demand corresponding to each second position in the second set;

[0030] Generating a position label for the target second position corresponding to each iteration count based on the distance between the target second position corresponding to the target iteration count and each second position in the second set and the power demand corresponding to each second position;

[0031] Retrieving the target second position corresponding to each iteration count from the second set, and transferring the target second position corresponding to each iteration count and the position label corresponding to the target second position corresponding to each iteration count into the first set.

[0032] By adopting the above technical solution, by considering the power demand, more flexible power dispatching can be achieved. For example, when a power plant fails and the power supply decreases, the routing can be adjusted according to the power demand of the power grid areas to ensure that key power grid areas obtain sufficient power supply. At the same time, the introduction of position labels provides richer information, and the power demand status of the power grid areas and their position relationships in the power system can be understood based on these labels, so as to adjust the power generation plan, optimize power dispatching, etc.

[0033] In a possible implementation, determining at least one shortest route according to the iterated first set includes:

[0034] Based on the target second positions corresponding to each first position in the iterated first set and the target second positions corresponding to the target iteration times, determining the transmission start position and the transmission process positions of each path;

[0035] Based on the transmission start position and the transmission process positions of each path, determining at least one shortest route.

[0036] By adopting the above technical solution, through the iterative process, the transmission paths from the power plant (the first position) to each substation area (the target second position) are gradually determined, making the entire path planning process clear and definite, providing clear guidance for the operation and maintenance of the power system. Based on the iterated first set, it can be ensured that the determined routes are the shortest routes that meet the conditions, which helps to reduce the loss during power transmission, improve the energy utilization efficiency, and reduce the operation and maintenance costs.

[0037] In a possible implementation, performing a fault pre-diagnosis detection on the at least one shortest route to obtain a target route includes:

[0038] Obtaining the historical route information and the abnormal time nodes when route abnormalities occur in the historical route abnormal information, where the historical route information is all the normal operation information and the non-normal operation information that occurred to the at least one shortest route within the historical period;

[0039] Performing a fault analysis on the historical route information and the abnormal time nodes to obtain a fault pre-diagnosis standard;

[0040] After detecting the application instruction of the at least one shortest route, obtaining the route operation parameters of the at least one shortest route and generating a transmission parameter fluctuation graph based on the route operation parameters;

[0041] Based on the transmission parameter fluctuation graph, determining whether the parameter floating trend of the route operation parameters meets a preset floating trend condition. If not, determining the initial time node with a floating trend according to the transmission parameter fluctuation graph, and cutting the transmission parameter fluctuation graph into a first fluctuation graph and a second fluctuation graph according to the initial time node;

[0042] Determining the highest transmission rate and the lowest transmission rate of the first fluctuation graph, calculating the average value of the highest transmission rate and the lowest transmission rate, and using the calculated average transmission rate as the numerator and the time duration from the start time node of the first fluctuation graph to the initial time node as the denominator to obtain a first rate change value corresponding to the first fluctuation graph;

[0043] Determine the highest transmission rate and the lowest transmission rate of the second fluctuation graph, calculate the average value of the highest transmission rate and the lowest transmission rate, use the calculated average transmission rate as the numerator, and use the time duration between the initial time node and the termination time node of the second fluctuation graph as the denominator to obtain a second rate change value corresponding to the second fluctuation graph;

[0044] Perform fault pre-diagnosis on the at least one shortest route according to the first rate change value, the second rate change value, and the fault pre-diagnosis standard to obtain fault pre-diagnosis information;

[0045] Based on the fault pre-diagnosis information, mark the at least one shortest route, and gradually replace it with another shortest route or the next-level route second only to the at least one shortest route according to the distance between the route and the target location for fault pre-diagnosis cycling until the parameter fluctuation trend of the route operation parameters does not meet the preset fluctuation trend condition to obtain the target route.

[0046] In a possible implementation manner, the fault analysis of the historical route information and the abnormal time node to obtain the fault pre-diagnosis standard includes:

[0047] Determine the first route operation parameter and the second route operation parameter in the historical route information according to the abnormal time node. The first route operation parameter is the route operation parameter of the at least one shortest route from the electric energy transmission start time node to the target time node, and the second route operation parameter is the route operation parameter of the at least one shortest route from the target time node to the abnormal time node. The target time node is the initial time node when the change trend of the route operation parameter meets the preset condition within the preset time;

[0048] Generate a first transmission parameter fluctuation graph based on the first route operation parameter, determine the highest transmission rate and the lowest transmission rate in the first route operation parameter according to the first transmission parameter fluctuation graph, calculate the average value of the highest transmission rate and the lowest transmission rate, and use the calculated average transmission rate as the numerator and the time duration between the start time node and the target time node as the denominator to obtain a first transmission rate change value corresponding to the first route operation parameter;

[0049] Generate a second transmission parameter fluctuation graph based on the second routing operation parameters, determine the highest transmission rate and the lowest transmission rate in the second routing operation parameters according to the second transmission parameter fluctuation graph, calculate the average value of the highest transmission rate and the lowest transmission rate, and use the calculated average transmission rate as the numerator and the duration from the target time node to the abnormal time node as the denominator to obtain a second transmission rate change value corresponding to the second routing operation parameters;

[0050] Bind the ratio of the first transmission rate change value to the second transmission rate change value to the abnormal fault corresponding to the abnormal time node to obtain a fault pre-diagnosis standard.

[0051] In a second aspect, the present application provides a virtual power plant energy scheduling model construction system, adopting the following technical solution:

[0052] A virtual power plant energy scheduling model construction system includes:

[0053] A location acquisition module for acquiring the first location of each power plant and the second location of each substation area in the target area;

[0054] An association acquisition module for acquiring the transmission relationship between each power plant and each substation area, where the transmission relationship indicates whether there is a transmission connection between the power plant and the substation area;

[0055] A model construction module for constructing an energy scheduling model of the virtual power plant corresponding to the target area based on the first location, the second location, and the transmission relationship between each power plant and each substation area;

[0056] A substation area acquisition module for acquiring the target substation area with power demand;

[0057] A location determination module for determining the target location where the target substation area is located in the energy scheduling model of the virtual power plant;

[0058] A route determination module for determining at least one shortest route based on the target location;

[0059] A fault pre-diagnosis module for performing fault pre-diagnosis detection on the at least one shortest route to obtain a target route;

[0060] A scheduling allocation module for performing power energy scheduling and allocation based on the target route.

[0061] In a possible implementation manner, when the route determination module determines at least one shortest route based on the target location, it specifically is used for:

[0062] Establish a first set corresponding to the first location and a second set corresponding to the second location;

[0063] Iterate over each second position in the second set to determine the target second position corresponding to the iteration count based on the iteration count;

[0064] Retrieve the target second position corresponding to each iteration count from the second set to the first set;

[0065] When the second set is empty, stop iterating;

[0066] Determine at least one shortest route based on the first set after iteration.

[0067] In a possible implementation, when the route determination module iterates over each second position in the second set to determine the target second position corresponding to the iteration count, it specifically is used for:

[0068] Obtain the target second position corresponding to the target iteration count, where the target iteration count is the previous iteration count of the current iteration count;

[0069] Determine the first distance between the target second position corresponding to the target iteration count and each second position in the second set, and determine the second distance between the first position and each second position in the second set;

[0070] Determine the second position with the shortest first distance and the shortest second distance as the target second position corresponding to the current iteration count.

[0071] In another possible implementation, when the route determination module retrieves the target second position corresponding to each iteration count from the second set to the first set, it specifically is used for:

[0072] Obtain the power demand corresponding to each second position in the second set;

[0073] Generate a position label for the target second position corresponding to each iteration count based on the distance between the target second position corresponding to the target iteration count and each second position in the second set and the power demand corresponding to each second position;

[0074] Remove the target second position corresponding to each iteration count from the second set, and transfer the target second position corresponding to each iteration count and the position label corresponding to the target second position corresponding to each iteration count to the first set.

[0075] In another possible implementation, when the route determination module determines at least one shortest route based on the first set after iteration, it specifically is used for:

[0076] Determine the transmission start position and the transmission process position of each path based on the target second position corresponding to each first position in the iterated first set and the target second position corresponding to the target iteration count;

[0077] Determine at least one shortest route based on the transmission start position and the transmission process position of each path.

[0078] In another possible implementation, when the fault pre-diagnosis module performs fault pre-diagnosis detection on at least one shortest route to obtain a target route, it specifically is used for:

[0079] Obtain the historical route information and the abnormal time node when a route abnormality occurs in the historical route abnormality information, where the historical route information is all the normal operation information and the non-normal operation information that occurred to the at least one shortest route within a historical period;

[0080] Perform fault analysis on the historical route information and the abnormal time node to obtain a fault pre-diagnosis criterion;

[0081] After detecting the application instruction of the at least one shortest route, obtain the routing operation parameters of the at least one shortest route, and generate a transmission parameter fluctuation graph based on the routing operation parameters;

[0082] Based on the transmission parameter fluctuation graph, determine whether the parameter floating trend of the routing operation parameters meets a preset floating trend condition. If not, determine the initial time node with a floating trend according to the transmission parameter fluctuation graph, and cut the transmission parameter fluctuation graph into a first fluctuation graph and a second fluctuation graph according to the initial time node;

[0083] Determine the highest transmission rate and the lowest transmission rate of the first fluctuation graph, calculate the average value of the highest transmission rate and the lowest transmission rate, and use the calculated average transmission rate as the numerator and the time duration from the start time node to the initial time node of the first fluctuation graph as the denominator to obtain a first rate change value corresponding to the first fluctuation graph;

[0084] Determine the highest transmission rate and the lowest transmission rate of the second fluctuation graph, calculate the average value of the highest transmission rate and the lowest transmission rate, and use the calculated average transmission rate as the numerator and the time duration from the initial time node to the termination time node of the second fluctuation graph as the denominator to obtain a second rate change value corresponding to the second fluctuation graph;

[0085] Perform fault pre-diagnosis on the at least one shortest route according to the first rate change value, the second rate change value, and the fault pre-diagnosis criterion to obtain fault pre-diagnosis information;

[0086] Mark the at least one shortest route based on the fault pre-diagnosis information, and gradually replace it with another shortest route or the next-level route second only to the at least one shortest route according to the distance between the route and the target location for the fault pre-diagnosis loop until the parameter floating trend of the route operation parameters does not meet the preset floating trend condition, and obtain the target route.

[0087] In another possible implementation manner, when the fault pre-diagnosis module performs fault analysis on the historical route information and the abnormal time node to obtain the fault pre-diagnosis criterion, it is specifically used for:

[0088] Determine the first route operation parameter and the second route operation parameter in the historical route information according to the abnormal time node, where the first route operation parameter is the route operation parameter of the at least one shortest route from the starting time node of power transmission to the target time node, and the second route operation parameter is the route operation parameter of the at least one shortest route from the target time node to the abnormal time node, and the target time node is the initial time node when the change trend of the route operation parameter meets the preset condition within the preset time;

[0089] Generate a first transmission parameter fluctuation graph based on the first route operation parameter, determine the highest transmission rate and the lowest transmission rate in the first route operation parameter according to the first transmission parameter fluctuation graph, calculate the average value of the highest transmission rate and the lowest transmission rate, and use the calculated average transmission rate as the numerator and the time duration from the starting time node to the target time node as the denominator to obtain the first transmission rate change value corresponding to the first route operation parameter;

[0090] Generate a second transmission parameter fluctuation graph based on the second route operation parameter, determine the highest transmission rate and the lowest transmission rate in the second route operation parameter according to the second transmission parameter fluctuation graph, calculate the average value of the highest transmission rate and the lowest transmission rate, and use the calculated average transmission rate as the numerator and the time duration from the target time node to the abnormal time node as the denominator to obtain the second transmission rate change value corresponding to the second route operation parameter;

[0091] Bind the ratio of the first transmission rate change value to the second transmission rate change value to the abnormal fault corresponding to the abnormal time node to obtain the fault pre-diagnosis criterion.

[0092] In a third aspect, the present application provides an electronic device, adopting the following technical solution:

[0093] An electronic device, the electronic device includes:

[0094] At least one processor;

[0095] Memory;

[0096] At least one application program, where the at least one application program is stored in the memory and is configured to be executed by at least one processor, and the at least one application program is configured to: execute the virtual power plant energy scheduling model construction method described in the first aspect above.

[0097] In a fourth aspect, the present application provides a computer-readable storage medium, adopting the following technical solution:

[0098] A computer-readable storage medium, comprising: a computer program stored with the virtual power plant energy scheduling model construction method described in the first aspect above that can be loaded and executed by a processor.

[0099] In summary, the present application includes the following beneficial technical effects: By obtaining the first positions of each power plant and the second positions of each power distribution area in the target area, as well as the transmission relationships between them, a detailed virtual power plant energy scheduling model can be constructed, providing clear and intuitive information. Then, real-time monitoring of the power demand is carried out to obtain the target power distribution areas with power demand to ensure the accuracy and timeliness of power distribution. In the virtual power plant energy scheduling model, the target position where the target power distribution area is located is determined. After determining the target position, at least one shortest route is determined based on the target position. In order to ensure the stable availability of the selected at least one shortest route, a fault pre-diagnosis detection is carried out on the at least one shortest route to obtain the target route. That is, the obtained target route not only has a better distance to the target position, but also has better stable availability for power transmission, thereby realizing efficient and stable power transmission. Based on the determined target route, power scheduling and distribution are carried out. By optimizing the power distribution strategy, efficient utilization of power and reduction of transmission losses can be achieved, and the overall operation efficiency of the power system can be improved. Description of the Drawings

[0100] Figure 1 is a schematic flowchart of a virtual power plant energy scheduling model construction method provided by an embodiment of the present application;

[0101] Figure 2 is a schematic block diagram of a virtual power plant energy scheduling model construction system provided by an embodiment of the present application;

[0102] Figure 3 is a schematic diagram of an electronic device provided by an embodiment of the present application. Detailed Embodiments

[0103] The following will further describe the present application in detail with reference to the attached Figure 1 - attached Figure 3 diagrams.

[0104] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of this application without creative efforts belong to the scope of protection of this application.

[0105] To facilitate the understanding of the technical solutions proposed in this application, several elements introduced in the description of this application will be introduced here first. It should be understood that the following introduction only facilitates the understanding of these elements in order to understand the content of the embodiments of this application, and does not necessarily cover all possible situations.

[0106] With the continuous growth of energy demand and the transformation of the energy structure, the power system faces unprecedented challenges and opportunities. Especially in the context of a significant increase in the proportion of renewable energy access, the stability and economy of the power system have been greatly tested. To effectively solve these problems, constructing an efficient and intelligent energy scheduling model for virtual power plants and improving the operation efficiency and reliability of the system by optimizing the power distribution and determining the shortest route have become one of the hotspots in current power system research.

[0107] A virtual power plant is an energy management system based on advanced communication technologies and software systems. It can aggregate distributed resources such as distributed power sources, distributed energy storage, controllable loads, and electric vehicles in the power system, optimize them into an overall that can operate collaboratively, and uniformly manage and dispatch them. The emergence of this mode provides new ideas and methods for the optimal operation of the power system.

[0108] In the energy scheduling model of a virtual power plant, power distribution is a key link. Traditional direct distribution methods often ignore the path optimization in the power transmission process, which may lead to too long paths for the power to flow through, thereby causing unnecessary power losses. In the current context of energy shortage and energy conservation and emission reduction, how to reduce the power consumption in the power transmission process has become an urgent problem to be solved.

[0109] In view of this, an embodiment of the present application provides a method for constructing a virtual power plant energy scheduling model. By constructing a power plant power supply scheduling model, real-time power demand monitoring is determined to obtain target regions with power demand, so as to ensure the accuracy and timeliness of power distribution. In the energy scheduling model of the virtual power plant, the target position where the target region is located is determined. After determining the target position, at least one shortest route is determined based on the target position, so as to determine the optimal path from the power plant to the target region, thereby realizing the efficient and stable transmission of electric energy. Based on the determined shortest route, power scheduling and distribution are carried out. By optimizing the power distribution strategy, efficient utilization of electric energy and reduction of transmission losses can be achieved, and the overall operation efficiency of the power system can be improved. In summary, the power distribution and shortest route optimization technology based on the energy scheduling model of the virtual power plant is an advanced power system optimization method. It can realize real-time monitoring and prediction of power demand, accurately determine the location of the target region, and determine the optimal power transmission path based on various factors.

[0110] See Figure 1 , an embodiment of the present application provides a method for constructing a virtual power plant energy scheduling model, which is executed by an electronic device. The method includes:

[0111] Step S10: Obtain the first position of each power plant and the second position of each region in the target area.

[0112] In the embodiment of the present application, the power plants are divided into regions according to the coverage area of each power plant. There is an overlapping area between the coverage area of a power plant and the coverage area of adjacent power plants. Therefore, in order to determine the ownership of the overlapping area, first obtain the corresponding position of each region (i.e., the second position), and then judge the ownership of the overlapping area based on the actual gap between the power plant position (i.e., the first position) and the region position, so as to determine the jurisdiction area corresponding to each power plant. When determining the target area, the target area is divided in combination with the jurisdiction area corresponding to each power plant and the load of the power plant. Avoid having an incomplete jurisdiction area within the target area, that is, the target area can contain multiple complete jurisdiction areas, but does not contain any partial jurisdiction area of the power plant area.

[0113] Specifically, ultra-wideband positioning technology or other high-precision positioning technologies can be used to obtain the accurate position information of each power plant and region in the target area, and through the dispatching system of the power grid, obtain the transmission relationship data between the power plant and the region.

[0114] Furthermore, a GIS (Geographic Information System) engine can be used to visually display the position information of the power plant and the region on the map, and use different icons or colors to distinguish the power plant and the region for easy identification.

[0115] Step S11: Obtain the transmission relationship between each power plant and each region.

[0116] Among them, the transmission relationship characterizes whether there is a transmission connection between the power plant and the distribution area.

[0117] Step S12: Based on the first position, the second position, and the transmission relationship between each power plant and each distribution area, construct an energy dispatching model for the virtual power plant corresponding to the target area.

[0118] Draw the transmission lines between the power plant and the distribution area on the map to represent their transmission relationship, and then on the GIS platform, integrate the power plant, the distribution area, and the transmission lines between them into an energy dispatching model of a virtual power plant. Specifically, different line types, colors, or thicknesses can be used to represent different attributes of the transmission lines (such as voltage level, transmission capacity, etc.).

[0119] Step S13: Obtain the target distribution area with electricity demand.

[0120] Step S14: In the energy dispatching model of the virtual power plant, determine the target position where the target distribution area is located.

[0121] Specifically, in the energy dispatching model of the virtual power plant, the spatial query of the GIS platform can be used to screen out the position of the target distribution area. Further, the map display function of the GIS platform can be used to highlight the screened target distribution area on the map, so as to visually determine its target position.

[0122] Step S15: Based on the target position, determine at least one shortest routing.

[0123] Among them, the routing refers to the path information for the electric energy to be sent from the source to the destination, and can also be understood as the process of moving the electric energy from the source location to the target location through the electric energy transmission path.

[0124] After knowing the positions of the power plant and the target distribution area in the energy dispatching model of the virtual power plant respectively, a shortest path can be determined based on their respective positions as the shortest routing, so as to optimize the transmission path and reduce the transmission loss on the premise of ensuring that the electric energy of all target distribution areas is satisfied.

[0125] Specifically, based on the power plant position of the power plant in the energy dispatching model of the virtual power plant, screen out the target position closest to the power plant position from each target position as the first target position, and then screen out the target position closest to the first target position from the target positions except the first target position as the second target position, and so on, to obtain a shortest routing.

[0126] Since a power plant is responsible for power supply to multiple distribution areas, when there are multiple power plant locations and multiple target locations, the first target location corresponding to each power plant location is screened out from each target location, and the second target location corresponding to each first target location is screened out, and so on, to obtain multiple shortest routes.

[0127] Step S16: Perform fault pre-diagnosis detection on at least one shortest route to obtain a target route.

[0128] Specifically, obtain the historical route information and the abnormal time node when a route abnormality occurs in the historical route abnormality information. The historical route information is all the normal operation information and the information that did not operate normally that occurred in at least one shortest route during the historical period. Perform fault analysis on the historical route information and the abnormal time node to obtain a fault pre-diagnosis standard. When an application instruction for at least one shortest route is detected, obtain the route operation parameters corresponding to at least one shortest route, and generate a transmission parameter fluctuation graph based on the route operation parameters. Determine whether the parameter fluctuation trend of the route operation parameters meets the preset fluctuation trend condition based on the transmission parameter fluctuation graph. If not, determine the initial time node with a floating trend according to the transmission parameter fluctuation graph, and cut the transmission parameter fluctuation graph into a first fluctuation graph and a second fluctuation graph according to the initial time node. Determine the highest transmission rate and the lowest transmission rate of the first fluctuation graph, calculate the average value of the highest transmission rate and the lowest transmission rate, and use the calculated average transmission rate as the numerator and the time duration from the start time node to the initial time node of the first fluctuation graph as the denominator to obtain the first rate change value corresponding to the first fluctuation graph. Determine the highest transmission rate and the lowest transmission rate of the second fluctuation graph, calculate the average value of the highest transmission rate and the lowest transmission rate, and use the calculated average transmission rate as the numerator and the time duration from the initial time node to the termination time node of the second fluctuation graph as the denominator to obtain the second rate change value corresponding to the second fluctuation graph. Perform fault pre-diagnosis on at least one shortest route according to the first rate change value, the second rate change value, and the fault pre-diagnosis standard to obtain fault pre-diagnosis information. Mark at least one shortest route based on the fault pre-diagnosis information, and gradually replace it with another shortest route or the next-level route second only to at least one shortest route according to the distance between the route and the target location for fault pre-diagnosis loop until the parameter fluctuation trend of the route operation parameters does not meet the preset fluctuation trend condition, and obtain the target route.

[0129] For the embodiments of the present application, during the power transmission process of the router, if an abnormality occurs at a certain moment, then in the period of time before this moment, the router operating parameters will be inconsistent with the normal state, that is, the router operating parameters symbolizing the precursor of the abnormality. However, since the router operating parameters symbolizing the precursor of the abnormality change slightly, it is difficult to accurately capture them. The embodiments of the present application adopt big data and data processing technologies to extract the representative data that can indicate that a fault is about to occur and the normal data that does not indicate a fault from the historical data. By using the algorithm of the transmission data ratio, the corresponding fault pre-diagnosis standard is obtained, and then the current router transmission situation is analyzed. If it can be corresponding and matched with the fault pre-diagnosis standard, it means there is a possibility of a fault, thereby improving the recognition accuracy of fault pre-diagnosis for at least one shortest router, and at the same time, it can also detect the stable availability of at least one shortest router.

[0130] In addition, after identifying a fault, it is necessary to mark the current router for fault pre-diagnosis, switch to another router that is second only to the current router in distance from the target location, and continue to loop through the fault pre-diagnosis detection of the other router until the parameter floating trend of the router operating parameters meets the preset floating trend condition, then determine this router as the target router. If the parameter floating trend of the router operating parameters of the other router still does not meet the preset floating trend condition, then pre-diagnose the other router according to the fault pre-diagnosis method of the current router to determine the fault type of the other router, and at the same time switch to another router that is second only to the other router in distance from the target location for loop fault pre-diagnosis detection until a router whose parameter floating trend of the router operating parameters meets the preset floating trend condition is obtained, and define this router as the target router.

[0131] In the embodiments of the present application, the preset floating trend condition is the critical range value of the floating of the router operating parameters formulated by the staff according to the actual power transmission situation.

[0132] Specifically, perform fault analysis on historical routing information and abnormal time nodes to obtain a fault pre-diagnosis standard, including: determining a first routing operation parameter and a second routing operation parameter in the historical routing information according to the abnormal time node. The first routing operation parameter is the routing operation parameter of at least one shortest route from the starting time node of power transmission to the target time node, and the second routing operation parameter is the routing operation parameter of at least one shortest route from the target time node to the abnormal time node. The target time node is the initial time node when the change trend of the routing operation parameter meets the preset conditions within the preset time. Generate a first transmission parameter fluctuation diagram based on the first routing operation parameter, and determine the highest transmission rate and the lowest transmission rate in the first routing operation parameter according to the first transmission parameter fluctuation diagram. Calculate the average value of the highest transmission rate and the lowest transmission rate, and use the calculated average transmission rate as the numerator and the time duration from the starting time node to the target time node as the denominator to obtain a first transmission rate change value corresponding to the first routing operation parameter. Generate a second transmission parameter fluctuation diagram based on the second routing operation parameter, and determine the highest transmission rate and the lowest transmission rate in the second routing operation parameter according to the second transmission parameter fluctuation diagram. Calculate the average value of the highest transmission rate and the lowest transmission rate, and use the calculated average transmission rate as the numerator and the time duration from the target time node to the abnormal time node as the denominator to obtain a second transmission rate change value corresponding to the second routing operation parameter. Bind the ratio of the first transmission rate change value to the second transmission rate change value to the abnormal fault corresponding to the abnormal time node to obtain the fault pre-diagnosis standard.

[0133] For the embodiments of this application, the manifestation form of the fault pre-diagnosis standard is: ratio - abnormal fault type, and the ratio represents a range. For example, if different abnormal fault types correspond to multiple different ratios, then take the maximum value and the minimum value of the multiple different ratios to form a range critical point, so as to form a range value, that is, ratio range - abnormal fault type.

[0134] Step S17: Perform power scheduling and allocation based on the target route.

[0135] After obtaining the target route, based on the electric energy required by each target substation area, the electric energy output by the power plant is sent to the first position, the second position... in sequence until the electric energy demands of all target substation areas are met.

[0136] For the embodiments of the present application, by obtaining the first positions of each power plant and the second positions of each power grid substation area in the target area, as well as the transmission relationships between them, a detailed energy dispatching model of the virtual power plant can be constructed, providing clear and intuitive information. Then, real-time monitoring of power demand is carried out to obtain the target power grid substation areas with power demand, so as to ensure the accuracy and timeliness of power distribution. In the energy dispatching model of the virtual power plant, the target position where the target power grid substation area is located is determined. After determining the target position, at least one shortest route is determined based on the target position. In order to ensure the stable availability of the at least one shortest route selected and determined, a fault pre-diagnosis detection is carried out on the at least one shortest route to obtain the target route. That is, the obtained target route not only has a relatively optimal distance from the target position, but also has better stable availability for power transmission, thereby realizing the efficient and stable transmission of electric energy. Based on the determined target route, power dispatching and distribution are carried out. By optimizing the power distribution strategy, efficient utilization of electric energy and reduction of transmission losses can be achieved, and the overall operation efficiency of the power system can be improved.

[0137] In a possible implementation manner of the embodiments of the present application, in step S15 above, when determining at least one shortest route based on the target position, determining at least one shortest route based on the target position may specifically include:

[0138] Establish a first set corresponding to the first positions and a second set corresponding to the second positions;

[0139] Iterate through each second position in the second set to determine the target second position corresponding to the iteration number according to the iteration number;

[0140] Transfer the target second position corresponding to each iteration number from the second set to the first set;

[0141] When the second set is empty, stop the iteration;

[0142] Determine at least one shortest route according to the first set after iteration.

[0143] Specifically, a first set and a second set are established. An empty first set (used to store the second positions selected during the iteration, that is, the power grid substation areas) and a second set containing all second positions (power grid substation areas) are initialized.

[0144] Furthermore, start iterating through each second position (power grid substation area) in the second set. In each iteration, based on an algorithm (such as Dijkstra's algorithm, Floyd-Warshall algorithm, or Bellman-Ford algorithm, etc.), calculate the shortest path from each first position (power plant) to the second position (power grid substation area) in the current iteration. And according to the iteration number and the algorithm result, determine one or more target second positions (power grid substation areas) that have the shortest path in a certain sense under the current iteration.

[0145] Further, the target second position corresponding to each iteration number is removed from the second set and added to the first set. After each iteration, check whether the second set is empty. If the second set is empty, it means that all the second positions (substation areas) have been processed, and the iteration ends.

[0146] After the iteration is completed, the first set contains all the target second positions (substation areas) of the determined shortest paths. And at least one shortest routing from the power plant to the substation area is constructed according to the target second positions in the first set and the shortest path information between them and the first position (power plant).

[0147] In a possible implementation manner of the embodiment of the present application, in the above embodiment, each second position in the second set is iterated to determine the target second position corresponding to the iteration number according to the iteration number. Specifically, it may include:

[0148] Obtain the target second position corresponding to the target iteration number, where the target iteration number is the previous iteration number of the current iteration number;

[0149] Determine the respective first distances between the target second position corresponding to the target iteration number and each second position in the second set, and determine the respective second distances between the first position and each second position in the second set;

[0150] Determine the second position with the shortest first distance and second distance as the target second position corresponding to the current iteration number.

[0151] Specifically, during the iteration process, at the first iteration (iteration number is 0), since there is no target second position corresponding to the previous iteration number, the target second position at this time is the power plant position as the starting point. In subsequent iterations, the target second position corresponding to the target iteration number is the target second position determined in the previous iteration.

[0152] Further, calculate the direct distance from the first position to each second position in the second set (or the shortest path distance calculated according to the power grid topology structure), denoted as the second distance. For each unvisited second position, calculate its total distance. If this is the first iteration, the total distance is the second distance; if it is not the first iteration, the total distance is the sum of the first distance and the second distance. Then, among all the unvisited second positions, select the second position with the smallest total distance as the target second position corresponding to the current iteration number.

[0153] In a possible implementation manner of the embodiment of the present application, in the above embodiment, the target second position corresponding to each iteration number is retrieved from the second set to the first set. Specifically, it may include:

[0154] Obtain the power demand corresponding to each second position in the second set;

[0155] Generate a position label for the target second position corresponding to each iteration number based on the distance between the target second position corresponding to the target iteration number and each second position in the second set and the power demand corresponding to each second position;

[0156] Remove the target second position corresponding to each iteration number from the second set, and transfer the target second position corresponding to each iteration number and the position label corresponding to the target second position corresponding to each iteration number into the first set.

[0157] Specifically, obtain the power demand corresponding to each second position in the second set, and generate a demand label corresponding to each second position based on the power demand. Then, the target second position corresponding to the current iteration number can be determined according to an iterative algorithm (such as a greedy algorithm, Dijkstra algorithm, etc.). Traverse all second positions in the second set, and calculate the distance between the target second position corresponding to the current iteration number and each second position in the second set, so as to generate a distance label corresponding to each second position based on the distance between the target second position corresponding to the current iteration number and each second position in the second set. Based on the distance label and demand label corresponding to each second position, obtain the position label corresponding to the target second position corresponding to the current iteration number, so as to obtain the position label corresponding to the target second position corresponding to each iteration number. Among them, the position label includes the power demand corresponding to the target second position and the distance corresponding to each second position.

[0158] After obtaining the target second position corresponding to each iteration number, when removing the target second position from the second set, form a data set with the target second position corresponding to each iteration number and the position label corresponding to the target second position corresponding to each iteration number, and transfer them into the first set together.

[0159] A possible implementation manner of the embodiment of the present application. In the above embodiment, determining at least one shortest route according to the iterated first set includes:

[0160] Based on the target second position corresponding to each first position in the iterated first set and the target second position corresponding to the target iteration number, determine the transmission start position and the transmission process position of each path;

[0161] Based on the transmission start position and the transmission process position of each path, determine at least one shortest route.

[0162] Specifically, for each element in the first set, its transmission start position is the first position (power plant). For each element in the first set, its transmission process positions are all the intermediate nodes on the path represented by that element. These intermediate nodes may be one or more and are arranged in the order from the first position to the target second position to obtain a target sequence. Exemplarily, if the number of iterations is 3, the target sequence is the first position - the target second position corresponding to iteration number 1 - the target second position corresponding to iteration number 2 - the target second position corresponding to iteration number 3.

[0163] Further, take the first position in the target sequence as the transportation start position, take the target second position corresponding to the maximum number of iterations as the transportation end position, and take the target second positions corresponding to the remaining iteration numbers as the transportation process positions. Based on the transportation start position, transportation process positions, and transportation end position, the shortest routing is obtained.

[0164] Even further, when there are multiple first positions, based on the order arrangement from each first position to the target second position, multiple target sequences are obtained, and based on the multiple target sequences, multiple routings are obtained.

[0165] The above embodiments introduce a method for constructing a virtual power plant energy scheduling model from the perspective of the method process. The following embodiments introduce a system for constructing a virtual power plant energy scheduling model from the perspective of virtual modules or virtual units. For details, see the following embodiments.

[0166] See Figure 2 , a system 20 for constructing a virtual power plant energy scheduling model may specifically include: a location acquisition module 21, an association acquisition module 22, a model construction module 23, a substation area acquisition module 24, a location determination module 25, a routing determination module 26, a fault pre-diagnosis module 27, and a scheduling allocation module 28, where:

[0167] A system 20 for constructing a virtual power plant energy scheduling model, includes:

[0168] The location acquisition module 21 is configured to acquire the first position of each power plant and the second position of each substation area in the target area;

[0169] The association acquisition module 22 is configured to acquire the transmission relationship between each power plant and each substation area, and the transmission relationship indicates whether there is a transmission connection between the power plant and the substation area;

[0170] The model construction module 23 is configured to construct an energy scheduling model of the virtual power plant corresponding to the target area based on the first position, the second position, and the transmission relationship between each power plant and each substation area;

[0171] The substation area acquisition module 24 is configured to acquire the target substation areas with power demand;

[0172] A location determination module 25, configured to determine a target location where a target substation area is located in an energy dispatch model of a virtual power plant;

[0173] A route determination module 26, configured to determine at least one shortest route based on the target location;

[0174] A fault pre-diagnosis module 27, configured to perform fault pre-diagnosis detection on at least one shortest route to obtain a target route;

[0175] A dispatch allocation module 28, configured to perform power energy dispatch allocation based on the target route.

[0176] In a possible implementation manner of the embodiment of the present application, when the route determination module 26 determines at least one shortest route based on the target location, it is specifically configured to:

[0177] Establish a first set corresponding to a first location and a second set corresponding to a second location;

[0178] Iterate through each second location in the second set to determine a target second location corresponding to the iteration count based on the iteration count;

[0179] Transfer the target second location corresponding to each iteration count from the second set to the first set;

[0180] When the second set is empty, stop the iteration;

[0181] Determine at least one shortest route according to the first set after iteration.

[0182] In another possible implementation manner of the embodiment of the present application, when the route determination module 26 iterates through each second location in the second set to determine a target second location corresponding to the iteration count based on the iteration count, it is specifically configured to:

[0183] Obtain the target second location corresponding to the target iteration count, where the target iteration count is the previous iteration count of the current iteration count;

[0184] Determine the first distance between the target second location corresponding to the target iteration count and each second location in the second set, and determine the second distance between the first location and each second location in the second set;

[0185] Determine the second location with the shortest first distance and second distance as the target second location corresponding to the current iteration count.

[0186] In another possible implementation manner of the embodiment of the present application, when the route determination module 26 transfers the target second location corresponding to each iteration count from the second set to the first set, it is specifically configured to:

[0187] Obtain the power demand corresponding to each second position in the second set;

[0188] Based on the distances between the target second position corresponding to the target iteration number and each second position in the second set, and the power demand corresponding to each second position, generate a position label for the target second position corresponding to each iteration number;

[0189] Remove the target second position corresponding to each iteration number from the second set, and transfer the target second position corresponding to each iteration number and the position label corresponding to the target second position corresponding to each iteration number to the first set.

[0190] Another possible implementation manner of the embodiment of the present application. When the routing determination module 26 determines at least one shortest route according to the iterated first set, it specifically is used for:

[0191] Based on the target second position corresponding to each first position in the iterated first set and the target second position corresponding to the target iteration number, determine the transmission start position and the transmission process position of each path;

[0192] Based on the transmission start position and the transmission process position of each path, determine at least one shortest route.

[0193] Another possible implementation manner of the embodiment of the present application. When the fault pre-diagnosis module 27 performs fault pre-diagnosis detection on at least one shortest route to obtain a target route, it specifically is used for:

[0194] Obtain the historical route information and the abnormal time node when a route abnormality occurs in the historical route abnormal information. The historical route information is all normal operation information and non-normal operation information that occurred to at least one shortest route in the historical period;

[0195] Perform fault analysis on the historical route information and the abnormal time node to obtain a fault pre-diagnosis standard;

[0196] After detecting the application instruction of at least one shortest route, obtain the route operation parameters of at least one shortest route, and generate a transmission parameter fluctuation graph based on the route operation parameters;

[0197] Based on the transmission parameter fluctuation graph, determine whether the parameter floating trend of the route operation parameters meets the preset floating trend condition. If not, determine the initial time node with a floating trend according to the transmission parameter fluctuation graph, and cut the transmission parameter fluctuation graph into a first fluctuation graph and a second fluctuation graph according to the initial time node;

[0198] Determine the highest transmission rate and the lowest transmission rate of the first fluctuation graph, calculate the average value of the highest transmission rate and the lowest transmission rate, use the calculated average transmission rate as the numerator, and use the time duration between the start time node and the initial time node of the first fluctuation graph as the denominator to obtain the first rate change value corresponding to the first fluctuation graph;

[0199] Determine the highest transmission rate and the lowest transmission rate of the second fluctuation graph, calculate the average value of the highest transmission rate and the lowest transmission rate, use the calculated average transmission rate as the numerator, and use the time duration between the initial time node and the termination time node of the second fluctuation graph as the denominator to obtain the second rate change value corresponding to the second fluctuation graph;

[0200] Perform fault pre-diagnosis on at least one shortest route according to the first rate change value, the second rate change value, and the fault pre-diagnosis standard to obtain fault pre-diagnosis information;

[0201] Mark at least one shortest route based on the fault pre-diagnosis information, and gradually replace it with another shortest route or the next-level route second only to at least one shortest route according to the distance between the route and the target location for fault pre-diagnosis loop until the parameter fluctuation trend of the route operation parameters does not meet the preset fluctuation trend condition to obtain the target route.

[0202] In another possible implementation manner of the embodiment of the present application, when the fault pre-diagnosis module 27 performs fault analysis on the historical route information and the abnormal time node to obtain the fault pre-diagnosis standard, it is specifically used for:

[0203] Determine the first route operation parameter and the second route operation parameter in the historical route information according to the abnormal time node. The first route operation parameter is the route operation parameter of at least one shortest route from the electric energy transmission start time node to the target time node, and the second route operation parameter is the route operation parameter of at least one shortest route from the target time node to the abnormal time node. The target time node is the initial time node when the change trend of the route operation parameter meets the preset condition within the preset time;

[0204] Generate a first transmission parameter fluctuation graph based on the first route operation parameter, determine the highest transmission rate and the lowest transmission rate in the first route operation parameter according to the first transmission parameter fluctuation graph, calculate the average value of the highest transmission rate and the lowest transmission rate, and use the calculated average transmission rate as the numerator and the time duration between the start time node and the target time node as the denominator to obtain the first transmission rate change value corresponding to the first route operation parameter;

[0205] Generate a second transmission parameter fluctuation graph based on the second routing operation parameters, determine the highest transmission rate and the lowest transmission rate in the second routing operation parameters according to the second transmission parameter fluctuation graph, calculate the average value of the highest transmission rate and the lowest transmission rate, and use the calculated average transmission rate as the numerator and the time duration from the target time node to the abnormal time node as the denominator to obtain the second transmission rate change value corresponding to the second routing operation parameters;

[0206] Bind the ratio of the first transmission rate change value to the second transmission rate change value to the abnormal fault corresponding to the abnormal time node to obtain a fault pre-diagnosis standard.

[0207] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0208] See Figure 3 In addition, this embodiment of the present application also introduces an electronic device from the perspective of an entity device. As Figure 3 shown, Figure 3 the electronic device 300 shown includes a processor 301 and a memory 303. Among them, the processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may further include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation to this embodiment of the present application.

[0209] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in connection with the disclosure of the present application. The processor 301 may also be a combination that realizes computing functions, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0210] The bus 302 may include a path for transmitting information between the above components. The bus 302 can be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus 302 can be divided into an address bus, a data bus, a control bus, etc. For the sake of simplicity of representation, Figure 3 it is only represented by a thick line in Figure 3 , but it does not mean that there is only one bus or one type of bus.

[0211] The memory 303 can be a ROM (Read Only Memory), or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory), or other types of dynamic storage devices that can store information and instructions. It can also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0212] The memory 303 is used to store the application program code for implementing the solution of this application, and is controlled by the processor 301 for execution. The processor 301 is used to execute the application program code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0213] Among them, the electronic device includes but is not limited to: mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc., and can also be a server, etc. Figure 3 The electronic device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of this application.

[0214] The embodiments of this application provide a computer-readable storage medium, on which a computer program is stored. When it runs on a computer, it enables the computer to execute the corresponding content in the foregoing method embodiments.

[0215] It should be understood that although the steps in the flowchart of the accompanying drawings are shown sequentially according to the indication of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless there is a clear indication in this document, there is no strict order restriction for the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0216] The above are only some embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A method for constructing a virtual power plant energy scheduling model, characterized in that: include: Obtain a first position of each power plant and a second position of each substation in the target area; Obtaining a transmission relationship between each power plant and each substation, wherein the transmission relationship indicates whether there is a transmission connection between the power plant and the substation; Based on the first location, the second location, and the transmission relationship between each power plant and each substation, construct an energy dispatch model of the virtual power plant corresponding to the target area; Acquire the target area with power demand; In the energy dispatch model of the virtual power plant, the target location of the target substation is determined; Based on the target location, determining at least one shortest route; The determining, based on the target location, at least one shortest route comprises: Based on the power plant position in the energy dispatch model of the virtual power plant, the target position closest to the power plant position is selected from various target positions as the first target position, and the target position closest to the first target position is selected from various target positions except the first target position as the second target position, and so on, to obtain a shortest route; When there are multiple power plant locations and multiple target locations, a first target location corresponding to each power plant location is selected from each target location, and a second target location corresponding to each first target location is selected, and so on, to obtain multiple shortest routes; Performing fault pre-diagnosis detection on the at least one shortest route to obtain a target route; The performing fault pre-diagnosis detection on the at least one shortest route to obtain a target route includes: Acquire historical routing information and an abnormal time node when the routing abnormality occurs in the historical routing abnormality information, wherein the historical routing information is all normal operation information and abnormal operation information of the at least one shortest route occurring in a historical period; Performing fault analysis on the historical routing information and abnormal time nodes to obtain fault pre-diagnosis criteria; After detecting the application instruction of the at least one shortest route, obtaining the route operation parameters related to the at least one shortest route, and generating a transmission parameter fluctuation graph based on the route operation parameters; Based on the transmission parameter fluctuation graph, judging whether the parameter floating trend of the routing operation parameter meets the preset floating trend condition; if not, determining the initial time node where the floating trend exists according to the transmission parameter fluctuation graph, and cutting the transmission parameter fluctuation graph into a first fluctuation graph and a second fluctuation graph according to the initial time node; Determine the highest transmission rate and the lowest transmission rate of the first wave graph, calculate the average of the highest transmission rate and the lowest transmission rate, and use the calculated transmission rate average as a numerator and the time length between the start time node and the initial time node of the first wave graph as a denominator to obtain a first rate change value corresponding to the first wave graph; Determine the highest transmission rate and the lowest transmission rate of the second wave graph, calculate the average of the highest transmission rate and the lowest transmission rate, and use the calculated transmission rate average as the numerator and the time length between the initial time node and the end time node of the second wave graph as the denominator to obtain a second rate change value corresponding to the second wave graph; Performing fault pre-diagnosis on the at least one shortest route according to the first rate change value, the second rate change value, and the fault pre-diagnosis standard to obtain fault pre-diagnosis information; Marking the at least one shortest route based on the fault pre-diagnosis information, and gradually changing to another shortest route or a next-level route next to the at least one shortest route according to the distance between the route and the target location to perform a fault pre-diagnosis cycle until the parameter floating trend of the route operation parameter does not meet the preset floating trend condition, and the target route is obtained; Electric energy scheduling and allocation is performed based on the target route.

2. The method for constructing a virtual power plant energy scheduling model according to claim 1, characterized in that: The performing fault analysis on the historical routing information and abnormal time nodes to obtain a fault pre-diagnosis standard includes: Determine a first route operation parameter and a second route operation parameter in the historical route information according to the abnormal time node, wherein the first route operation parameter is a route operation parameter of the at least one shortest route from the start time node of power transmission to the target time node, and the second route operation parameter is a route operation parameter of the at least one shortest route from the target time node to the abnormal time node, and the target time node is an initial time node at which a change trend of the route operation parameter within a preset time meets a preset condition; generating a first transmission parameter fluctuation graph based on the first routing operation parameter, determining a maximum transmission rate and a minimum transmission rate in the first routing operation parameter according to the first transmission parameter fluctuation graph, calculating the average of the maximum transmission rate and the minimum transmission rate, and using the calculated transmission rate average as a numerator and the time length between the start time node and the target time node as a denominator to obtain a first transmission rate change value corresponding to the first routing operation parameter; generating a second transmission parameter fluctuation graph based on the second routing operation parameter, determining a maximum transmission rate and a minimum transmission rate in the second routing operation parameter according to the second transmission parameter fluctuation graph, calculating the average of the maximum transmission rate and the minimum transmission rate, and using the calculated transmission rate average as a numerator and the time length between the target time node and the abnormal time node as a denominator to obtain a second transmission rate change value corresponding to the second routing operation parameter; The ratio of the first transmission rate change value to the second transmission rate change value is bound to the abnormal fault corresponding to the abnormal time node to obtain a fault pre-diagnosis standard.

3. A virtual power plant energy scheduling model construction system, characterized in that: include: A location acquisition module, used to acquire a first location of each power plant and a second location of each substation in a target area; An association acquisition module, used to acquire the transmission relationship between each power plant and each substation, wherein the transmission relationship indicates whether there is a transmission connection between the power plant and the substation; A model building module, used to build an energy dispatch model of a virtual power plant corresponding to a target area based on the first location, the second location, and the transmission relationship between each power plant and each substation; A station area acquisition module is used to acquire a target station area with electric energy demand; A location determination module is used to determine the target location of the target substation in the energy dispatch model of the virtual power plant; A route determination module, used to determine at least one shortest route based on the target location; When the route determination module determines at least one shortest route based on the target location, it is specifically used to: Based on the power plant position in the energy dispatch model of the virtual power plant, the target position closest to the power plant position is selected from various target positions as the first target position, and the target position closest to the first target position is selected from various target positions except the first target position as the second target position, and so on, to obtain a shortest route; When there are multiple power plant locations and multiple target locations, a first target location corresponding to each power plant location is selected from each target location, and a second target location corresponding to each first target location is selected, and so on, to obtain multiple shortest routes; A fault pre-diagnosis module, used for performing fault pre-diagnosis detection on the at least one shortest route to obtain a target route; When the fault pre-diagnosis module performs fault pre-diagnosis detection on the at least one shortest route to obtain the target route, it is specifically used to: Acquire historical routing information and an abnormal time node when the routing abnormality occurs in the historical routing abnormality information, wherein the historical routing information is all normal operation information and abnormal operation information of the at least one shortest route occurring in a historical period; Performing fault analysis on the historical routing information and abnormal time nodes to obtain fault pre-diagnosis criteria; After detecting the application instruction of the at least one shortest route, obtaining the route operation parameters related to the at least one shortest route, and generating a transmission parameter fluctuation graph based on the route operation parameters; Based on the transmission parameter fluctuation graph, judging whether the parameter floating trend of the routing operation parameter meets the preset floating trend condition; if not, determining the initial time node where the floating trend exists according to the transmission parameter fluctuation graph, and cutting the transmission parameter fluctuation graph into a first fluctuation graph and a second fluctuation graph according to the initial time node; Determine the highest transmission rate and the lowest transmission rate of the first wave graph, calculate the average of the highest transmission rate and the lowest transmission rate, and use the calculated transmission rate average as a numerator and the time length between the start time node and the initial time node of the first wave graph as a denominator to obtain a first rate change value corresponding to the first wave graph; Determine the highest transmission rate and the lowest transmission rate of the second wave graph, calculate the average of the highest transmission rate and the lowest transmission rate, and use the calculated transmission rate average as the numerator and the time length between the initial time node and the end time node of the second wave graph as the denominator to obtain a second rate change value corresponding to the second wave graph; Performing fault pre-diagnosis on the at least one shortest route according to the first rate change value, the second rate change value, and the fault pre-diagnosis standard to obtain fault pre-diagnosis information; Marking the at least one shortest route based on the fault pre-diagnosis information, and gradually changing to another shortest route or a next-level route next to the at least one shortest route according to the distance between the route and the target location to perform a fault pre-diagnosis cycle until the parameter floating trend of the route operation parameter does not meet the preset floating trend condition, and the target route is obtained; A scheduling and allocation module is used to perform electric energy scheduling and allocation based on the target route.

4. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the virtual power plant energy scheduling model construction method described in any one of claims 1 to 2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the method for constructing a virtual power plant energy scheduling model as described in any one of claims 1 to 2.

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