An intelligent management method and system for power grid dispatching optimization

Abstract

The present application is suitable for power grid dispatching management technical field, provide a kind of power grid dispatching optimization intelligent management method and system.The present application obtains grid connection vehicle data by receiving power grid dispatching demand;Multiple grid connection resident vehicles and multiple grid connection temporary vehicles are divided;Demand intensity is determined;When low demand, multiple grid connection target vehicles are selected to participate in energy storage scheduling;When high demand, multiple grid connection supplement vehicles are selected for scheduling supplement.Multiple grid connection resident vehicles and multiple grid connection temporary vehicles can be divided, when low demand, the discharge loss cost of multiple grid connection resident vehicles is calculated, multiple grid connection target vehicles are selected to participate in energy storage scheduling;When high demand, the comprehensive influence value of multiple grid connection temporary vehicles is calculated, multiple grid connection supplement vehicles are selected for scheduling supplement, to realize more reasonable, actual scheduling allocation, balance the use cost of car owner, improve the application effect of new energy vehicle participating in power grid optimization scheduling.

Description

Technical Field

[0001] This invention belongs to the field of power grid dispatching and management technology, and in particular relates to an intelligent management method and system for power grid dispatching optimization. Background Technology

[0002] Power grid dispatch management is a management process that involves unified monitoring, analysis, decision-making, and control of various power resources, including power generation, transmission, distribution, energy storage, and adjustable loads, based on the operating status of the power system, load demand, power generation capacity, power grid structure, and safety and stability constraints. This process aims to ensure the safe, stable, economical, and efficient operation of the power system and achieve real-time balance and optimal allocation of electrical energy.

[0003] With the rapid growth of new energy vehicle ownership and the continuous development of vehicle-to-grid (V2G) technology, utilizing the energy storage capacity of new energy vehicle power batteries to participate in grid dispatch optimization has become an important research direction and application trend for building new power systems, improving the grid's flexible adjustment capabilities, and promoting the consumption of new energy. By aggregating new energy vehicles into distributed mobile energy storage resource pools, power can be fed back to the grid during peak load periods and absorbed during off-peak periods, thereby achieving functions such as peak shaving and valley filling, frequency and voltage regulation, demand response, and reserve capacity support.

[0004] However, most existing methods for new energy vehicles to participate in grid dispatch optimization are rather crude. They usually only select and allocate vehicles based on their current remaining power, which can easily lead to some vehicles being over-dispatched, increasing the cost of use for vehicle owners and thus reducing their enthusiasm for participating in dispatch. Furthermore, they do not distinguish between residential vehicles and temporary vehicles, and the dispatch strategies do not conform to the actual situation, which restricts the application effect of new energy vehicles participating in grid optimization dispatch. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent management method and system for power grid dispatch optimization, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: An intelligent management method for power grid dispatch optimization, the method specifically includes the following steps: Receive grid dispatch requests, perform planning analysis, and when vehicle-to-grid energy storage dispatch is required, combine active communication and associated communication to identify multiple grid-connected energy storage vehicles and obtain grid-connected vehicle data. The grid-connected vehicle data is analyzed for identity, and the multiple grid-connected energy storage vehicles are divided into multiple grid-connected residential vehicles and multiple grid-connected temporary vehicles. An intensity analysis is performed on the power grid dispatch demand to determine the demand intensity; When the demand intensity is low, the discharge loss cost of multiple grid-connected residential vehicles is calculated based on the grid-connected vehicle data, and multiple grid-connected target vehicles are selected to participate in vehicle-to-grid energy storage scheduling. When the demand intensity is high, in addition to multiple grid-connected residential vehicles participating in vehicle-to-grid energy storage scheduling, the comprehensive impact value of multiple temporary grid-connected vehicles is calculated, and multiple grid-connected supplementary vehicles are selected for scheduling supplementation.

[0007] As a further aspect of the present invention: receiving grid dispatch requirements, performing planning analysis, and when vehicle-to-grid energy storage dispatch is required, combining active communication and associated communication to identify multiple grid-connected energy storage vehicles and obtain grid-connected vehicle data specifically includes the following steps: Receive grid dispatch requests; Demand planning analysis is performed on the power grid dispatch requirements to determine whether vehicle-to-grid energy storage dispatch is required. When vehicle-to-grid energy storage dispatch is required, multiple grid-connected energy storage vehicles should be identified. Send active data requests to multiple grid-connected energy storage vehicles; Receive multiple proactive feedback data; Vehicle identification is performed on multiple active feedback data, and the multiple grid-connected energy storage vehicles are divided into multiple active feedback vehicles and multiple non-feedback vehicles, and the associated feedback vehicles of the multiple non-feedback vehicles are matched. By using multiple associated feedback vehicles, association communication is established with multiple non-feedback vehicles to obtain associated feedback data from the multiple non-feedback vehicles; The multiple active feedback data and the multiple associated feedback data are processed to obtain connected vehicle data.

[0008] As a further aspect of the present invention: the step of performing identity analysis on the grid-connected vehicle data to classify multiple grid-connected energy storage vehicles into multiple grid-connected residential vehicles and multiple grid-connected temporary vehicles specifically includes the following steps: Extract vehicle license plate data from the network-connected vehicle data; Based on a pre-set license plate database, the vehicle license plate data is analyzed for identity matching, and the identity matching results are recorded. Based on the identity matching results, the multiple grid-connected energy storage vehicles are divided into multiple grid-connected residential vehicles and multiple grid-connected temporary vehicles.

[0009] As a further aspect of the present invention: the intensity analysis of the power grid dispatch demand to determine the demand intensity specifically includes the following steps: The power grid dispatch demand is analyzed to determine the required discharge power and the duration of the demand. Import the preset intensity grading parameters; Based on the intensity grading parameters, the required discharge power and the required duration are compared to determine the required intensity.

[0010] As a further aspect of the present invention: when the demand intensity is low, calculating the discharge loss cost of multiple grid-connected residential vehicles based on the grid-connected vehicle data, and selecting multiple grid-connected target vehicles to participate in vehicle-to-grid energy storage scheduling specifically includes the following steps: When the demand intensity is low, extract residential vehicle data of multiple connected residential vehicles from the connected vehicle data; Based on the residential vehicle data, calculate the discharge loss cost of multiple grid-connected residential vehicles; Compare the discharge loss costs of multiple vehicles and select multiple target vehicles for grid connection from the multiple grid-connected residential vehicles; Participation control of vehicle-to-grid energy storage scheduling for multiple grid-connected target vehicles.

[0011] As a further aspect of the present invention: the formula for calculating the discharge loss cost of the plurality of grid-connected residential vehicles is as follows: ; in, For the first The cost of discharge loss for a single connected residential vehicle. For the first The vehicle price of a resident's vehicle connected to the internet; The preset standard duration; For the first The remaining cycle life of the batteries in a connected residential vehicle. For the first The current energy storage of the batteries in a connected residential vehicle; For the first Maintenance coefficient of each resident's vehicle connected to the network.

[0012] As a further aspect of the present invention: when the demand intensity is high, in addition to multiple grid-connected residential vehicles participating in vehicle-to-grid energy storage scheduling, the comprehensive impact value of multiple temporary grid-connected vehicles is calculated, and multiple supplementary grid-connected vehicles are selected for scheduling supplementation, specifically including the following steps: When the demand intensity is high, extract temporary vehicle data of multiple temporary vehicles connected to the grid from the grid connection vehicle data; Participation control for vehicle-to-grid energy storage scheduling of multiple grid-connected residential vehicles; Based on the temporary vehicle data, calculate the combined impact value of multiple temporary grid-connected vehicles; The combined impact values ​​are compared, and multiple supplementary vehicles for grid connection are selected from the multiple temporary grid connection vehicles. Supplementary control is performed on the vehicle-to-grid energy storage scheduling of multiple grid-connected supplementary vehicles.

[0013] As a further aspect of the present invention: the formula for calculating the comprehensive impact value of the multiple temporary grid connection vehicles is as follows: ; ; ; in, For the first The overall impact value of each temporary vehicle connected to the network; For the first The cost of discharge loss for a temporary vehicle connected to the grid; For the first The urgency of stopping a temporary vehicle connected to the network; For the first Convenience for owners of temporary vehicles connected to the network; , and These are preset weighting coefficients; For the first The vehicle price of a temporary vehicle connected to the network; For the first The remaining cycle life of the battery of a temporary vehicle connected to the grid; For the first The current energy stored in the battery of a temporary connected vehicle; For the first Maintenance coefficient of each temporary vehicle connected to the network; This is the estimated scheduling duration; For the first The estimated remaining stay time for each temporary vehicle connected to the network.

[0014] An intelligent management system for power grid dispatch optimization, the system comprising a dispatch demand processing module, a grid-connected vehicle classification module, a demand intensity analysis module, a low-demand dispatch control module, and a high-demand dispatch control module, wherein: The dispatch demand processing module is used to receive grid dispatch demands, perform planning analysis, and when vehicle-to-grid energy storage dispatch is required, it combines active communication and associated communication to identify multiple grid-connected energy storage vehicles and obtain grid-connected vehicle data. The grid-connected vehicle classification module is used to perform identity analysis on the grid-connected vehicle data and classify multiple grid-connected energy storage vehicles into multiple grid-connected residential vehicles and multiple grid-connected temporary vehicles. The demand intensity analysis module is used to perform intensity analysis on the power grid dispatch demand and determine the demand intensity. The low-demand scheduling control module is used to calculate the discharge loss cost of multiple grid-connected residential vehicles based on the grid-connected vehicle data when the demand intensity is low, select multiple grid-connected target vehicles, and participate in vehicle-to-grid energy storage scheduling. The high-demand scheduling control module is used to calculate the comprehensive impact value of multiple temporary grid-connected vehicles and select multiple supplementary grid-connected vehicles for scheduling supplementation when the demand intensity is high, in addition to multiple grid-connected residential vehicles participating in vehicle-to-grid energy storage scheduling.

[0015] As a further aspect of the present invention: the low-demand scheduling control module specifically includes: The data extraction unit is used to extract resident vehicle data of multiple connected resident vehicles from the grid-connected vehicle data when the demand intensity is low. The discharge loss cost calculation unit is used to calculate the discharge loss cost of multiple grid-connected residential vehicles based on the residential vehicle data. The discharge loss cost comparison unit is used to compare multiple discharge loss costs and select multiple grid-connected target vehicles from multiple grid-connected residential vehicles. The scheduling participation control unit is used for participating in the scheduling of vehicle-to-grid energy storage for multiple grid-connected target vehicles.

[0016] Compared with the prior art, the beneficial effects of the present invention are: (1) When the demand intensity is low, the present invention can calculate the discharge loss cost of multiple grid-connected residential vehicles, compare the multiple discharge loss costs, select multiple grid-connected target vehicles, and participate in vehicle-to-grid energy storage scheduling, so as to achieve more reasonable vehicle selection and scheduling allocation, avoid some vehicles being over-scheduled, thereby balancing the usage costs of each vehicle owner and effectively ensuring the enthusiasm of each vehicle owner to participate in scheduling. (2) When the demand intensity is high, the present invention can calculate the comprehensive impact value of multiple grid-connected temporary vehicles in addition to participating in the vehicle-to-grid energy storage scheduling of multiple grid-connected residential vehicles. By comparing the multiple comprehensive impact values, multiple grid-connected supplementary vehicles are selected for supplementary control of vehicle-to-grid energy storage scheduling, so as to realize the distinction and differentiated scheduling between residential vehicles and temporary vehicles, making the scheduling strategy more in line with the actual situation and improving the application effect of new energy vehicles participating in grid optimization scheduling. (3) When vehicle-to-grid energy storage scheduling is required, the present invention can send active data requests to multiple grid-connected energy storage vehicles, receive multiple active feedback data, perform vehicle identification, feedback division and association matching, and then communicate with multiple non-feedback vehicles through multiple matched associated feedback vehicles to obtain multiple associated feedback data, thereby organizing the grid-connected vehicle data, realizing the combination of active communication and associated communication, ensuring complete data acquisition, and providing comprehensive and accurate data support for subsequent identity analysis, strength analysis and scheduling planning. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0018] Figure 1 A flowchart illustrating the intelligent management method for power grid dispatch optimization provided in this embodiment of the invention; Figure 2 This is a flowchart illustrating the data acquisition process combining active communication and associated communication in the method provided by this embodiment of the invention; Figure 3 This is a flowchart illustrating the process of dividing a number of residential vehicles connected to the grid and a number of temporary vehicles connected to the grid in the method provided in this embodiment of the invention; Figure 4 A flowchart illustrating the process of performing strength analysis to determine the required strength in the method provided in this embodiment of the invention; Figure 5 This is a flowchart of the vehicle-to-grid energy storage scheduling plan during low demand periods provided in the embodiments of the present invention; Figure 6 This is a flowchart illustrating the scheduling supplementation during high demand in the method provided by the embodiments of the present invention; Figure 7 An application architecture diagram of an intelligent management system for power grid dispatch optimization provided in an embodiment of the present invention; Figure 8 This is a structural block diagram of the low-demand scheduling control module in the system provided in this embodiment of the invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] Understandably, in existing technologies, most methods for new energy vehicles to participate in grid dispatch optimization are rather crude. They typically rely solely on the vehicle's current remaining power to select and allocate vehicles, which can easily lead to over-dispatch of some vehicles, increasing the cost of use for vehicle owners and thus reducing their enthusiasm for participating in dispatch. Furthermore, they fail to distinguish between residential vehicles and temporary vehicles, and the dispatch strategies do not conform to the actual situation, thus restricting the application effect of new energy vehicles participating in grid optimization dispatch.

[0021] To address the aforementioned issues, this invention discloses an intelligent management method and system for power grid dispatch optimization. This method receives power grid dispatch requests, performs planning analysis, and when vehicle-to-grid energy storage dispatch is required, combines active and associated communication to identify multiple grid-connected energy storage vehicles and acquire their data. It then performs identity analysis on the grid-connected vehicle data, classifying the vehicles into multiple residential grid-connected vehicles and multiple temporary grid-connected vehicles. The method performs intensity analysis on the power grid dispatch requests to determine the demand intensity. When the demand intensity is low, it calculates the discharge loss cost of multiple residential grid-connected vehicles based on the vehicle data and selects multiple target grid-connected vehicles to participate in vehicle-to-grid energy storage dispatch. When the demand intensity is high, in addition to the multiple residential grid-connected vehicles participating in vehicle-to-grid energy storage dispatch, it calculates the comprehensive impact value of multiple temporary grid-connected vehicles and selects multiple supplementary grid-connected vehicles for dispatch supplementation. It can classify multiple grid-connected residential vehicles and multiple grid-connected temporary vehicles. Under low demand, it calculates the discharge loss cost of multiple grid-connected residential vehicles, selects multiple grid-connected target vehicles, and participates in energy storage dispatch. Under high demand, it calculates the comprehensive impact value of multiple grid-connected temporary vehicles, selects multiple grid-connected supplementary vehicles, and dispatches them to supplement the grid. This achieves a more reasonable and realistic dispatch allocation, balances the usage costs for vehicle owners, and improves the application effect of new energy vehicles in grid optimization dispatch.

[0022] Figure 1 A flowchart illustrating an intelligent management method for power grid dispatch optimization provided in an embodiment of the present invention. In this embodiment, the intelligent management method for power grid dispatch optimization specifically includes the following steps: Step S101: Receive grid dispatch requirements, perform planning analysis, and when vehicle-to-grid energy storage dispatch is required, combine active communication and associated communication to identify multiple grid-connected energy storage vehicles and obtain grid-connected vehicle data.

[0023] In this embodiment of the invention, a grid dispatching request is received, and a demand planning analysis is performed on the grid dispatching request to determine whether vehicle-to-grid energy storage dispatching is required. If vehicle-to-grid energy storage dispatching is required, multiple grid-connected energy storage vehicles in the parking lot are identified, and an active data request is generated and sent to the multiple grid-connected energy storage vehicles. Multiple active feedback data are received, and object identification is performed on the multiple active feedback data to determine the active feedback vehicles corresponding to the multiple active feedback data. Vehicles among the multiple grid-connected energy storage vehicles that do not provide feedback data are marked as non-feedback vehicles, thereby dividing the grid-connected energy storage vehicles into multiple active feedback vehicles and multiple non-feedback vehicles. At the same time, according to the communication relationship of the multiple non-feedback vehicles, the associated feedback vehicles corresponding to the multiple non-feedback vehicles are matched from the multiple active feedback vehicles. Then, temporary association communication links are established with the multiple corresponding non-feedback vehicles through the multiple associated feedback vehicles. Using the multiple associated feedback vehicles as an intermediate medium, the associated feedback data of the multiple non-feedback vehicles is obtained. Finally, the multiple active feedback data and the multiple associated feedback data are sorted to obtain grid-connected vehicle data.

[0024] It is understood that, in this embodiment of the invention, the grid dispatch demand is analyzed by combining the current grid load level, real-time power gap, frequency fluctuation, voltage deviation, energy storage resource operation status and preset dispatch target, and the dispatch type, dispatch capacity, dispatch duration and other information corresponding to the dispatch demand are determined. Among them, the dispatch type can be used to determine whether vehicle-to-grid energy storage dispatch is required.

[0025] It is understood that grid-connected energy storage vehicles are new energy vehicles that have established an electrical connection with the charging facility through the charging gun, have charging and discharging functions, and have a certain amount of releaseable energy storage capacity. When the vehicle establishes an electrical connection with the charging facility through the charging gun, it will automatically transmit simple data such as connection status, charging and discharging function, and current energy storage. Specifically, in the embodiments of the present invention, having a certain amount of releaseable energy storage capacity can be set to the current energy storage being 80% of the battery capacity.

[0026] It is understood that, in this embodiment of the invention, the communication relationships among multiple non-feedback vehicles include human-authorized communication relationships and brand-authorized communication relationships. Human-authorized communication relationships refer to the relationship where the owner of a non-feedback vehicle actively authorizes its non-feedback vehicle to have wireless communication permissions with other actively feeding vehicles. Specific scenarios include: A) When a family in the same community has two or more vehicles, a non-feedback vehicle with older functions has wireless communication permissions with an actively feeding vehicle with newer functions; B) A non-feedback vehicle visiting relatives or friends has wireless communication permissions with an actively feeding vehicle belonging to a resident in the community corresponding to the parking lot; C) A non-feedback vehicle engaged in temporary work (renovation work, appliance installation and repair work, community landscaping work, etc.) has wireless communication permissions with an actively feeding vehicle belonging to a resident in the community corresponding to the parking lot. Brand-authorized communication relationships, in the absence of human-authorized communication relationships, refer to the relationship where non-feedback vehicles of the same brand have wireless communication permissions with actively feeding vehicles, and the corresponding vehicle owners need to activate the same-brand wireless communication permissions for their vehicles beforehand.

[0027] Understandably, the grid-connected vehicle data includes multiple active feedback data and multiple related feedback data, all of which contain data such as the vehicle price, remaining cycle life of the vehicle battery, current energy storage capacity, brand, and model of the corresponding grid-connected energy storage vehicle.

[0028] Specifically, Figure 2 This is a flowchart illustrating the data acquisition process combining active communication and associated communication in the method provided by an embodiment of the present invention.

[0029] The specific steps involved in receiving grid dispatch requests, performing planning analysis, and combining active and associated communication when vehicle-to-grid energy storage dispatch is required to identify multiple grid-connected energy storage vehicles and obtain their data include: Step S1011: Receive power grid dispatch request; Step S1012: Perform demand planning analysis on the power grid dispatch demand to determine whether vehicle-to-grid energy storage dispatch is required; Step S1013: When vehicle-to-grid energy storage dispatch is required, determine multiple grid-connected energy storage vehicles; Step S1014: Send active data requests to multiple grid-connected energy storage vehicles; Step S1015: Receive multiple proactive feedback data; Step S1016: Perform vehicle identification on the multiple active feedback data, divide the multiple grid-connected energy storage vehicles into multiple active feedback vehicles and multiple non-feedback vehicles, and match the associated feedback vehicles of the multiple non-feedback vehicles. Step S1017: Through multiple associated feedback vehicles, perform associated communication with multiple non-feedback vehicles to obtain associated feedback data from multiple non-feedback vehicles; Step S1018: Process the multiple active feedback data and the multiple associated feedback data to obtain connected vehicle data.

[0030] Furthermore, the intelligent management method for power grid dispatch optimization also includes the following steps: Step S102: Perform identity analysis on the grid-connected vehicle data to divide the multiple grid-connected energy storage vehicles into multiple grid-connected residential vehicles and multiple grid-connected temporary vehicles.

[0031] In this embodiment of the invention, the license plate numbers of multiple grid-connected energy storage vehicles are extracted from the grid-connected vehicle data to obtain vehicle license plate data. Then, based on a preset license plate database, the multiple license plate numbers in the vehicle license plate data are matched for identity, and the identity matching results are recorded. Then, according to the identity matching results, the multiple grid-connected energy storage vehicles are divided into multiple grid-connected residential vehicles and multiple grid-connected temporary vehicles.

[0032] It is understood that vehicles connected to the parking lot are internal vehicles belonging to residents of the community corresponding to the parking lot; temporary vehicles connected to the parking lot are internal vehicles not belonging to residents of the community corresponding to the parking lot. In this embodiment of the invention, these may be vehicles of visiting relatives and friends, temporary work vehicles, etc.

[0033] Understandably, for temporary grid-connected vehicles among multiple grid-connected energy storage vehicles, the corresponding grid-connected vehicle data includes not only vehicle price, remaining cycle life of the vehicle battery, current energy storage capacity, brand, and model, but also data on the estimated remaining dwell time.

[0034] Specifically, Figure 3 The flowchart illustrates the method for dividing a number of residential vehicles connected to the grid and a number of temporary vehicles connected to the grid, as provided in the embodiments of the present invention.

[0035] Specifically, the step of performing identity analysis on the grid-connected vehicle data to classify multiple grid-connected energy storage vehicles into multiple grid-connected residential vehicles and multiple grid-connected temporary vehicles includes the following steps: Step S1021: Extract vehicle license plate data from the connected vehicle data; Step S1022: Based on a preset license plate database, perform identity matching analysis on the vehicle license plate data and record the identity matching results; Step S1023: According to the identity matching result, the multiple grid-connected energy storage vehicles are divided into multiple grid-connected residential vehicles and multiple grid-connected temporary vehicles.

[0036] Furthermore, the intelligent management method for power grid dispatch optimization also includes the following steps: Step S103: Perform intensity analysis on the power grid dispatch demand to determine the demand intensity.

[0037] In this embodiment of the invention, by parsing the grid dispatch demand parameters, the required discharge power and duration of the vehicle-to-grid energy storage dispatch are determined, and preset intensity grading parameters are imported. Then, based on the intensity grading parameters, the required discharge power and duration of the demand are compared to determine the demand intensity. Specifically, if neither the required discharge power nor the required duration of the demand exceeds the corresponding benchmark value in the intensity grading parameters, the demand intensity is low; if the required discharge power and / or the required duration of the demand exceeds the corresponding benchmark value in the intensity grading parameters, the demand intensity is high.

[0038] It is understandable that the required discharge power corresponds to the scheduling capacity in the scheduling requirement; and the required duration corresponds to the scheduling duration in the scheduling requirement.

[0039] Specifically, Figure 4 The flowchart illustrates the process of performing strength analysis to determine the required strength in the method provided in this embodiment of the invention.

[0040] The step of performing intensity analysis on the power grid dispatch demand to determine the demand intensity specifically includes the following steps: Step S1031: Analyze the power grid dispatch demand to determine the required discharge power and the duration of the demand; Step S1032: Import the preset intensity grading parameters; Step S1033: Based on the intensity grading parameters, compare the required discharge power and the required duration to determine the required intensity.

[0041] Furthermore, the intelligent management method for power grid dispatch optimization also includes the following steps: Step S104: When the demand intensity is low, calculate the discharge loss cost of multiple grid-connected residential vehicles based on the grid-connected vehicle data, select multiple grid-connected target vehicles, and participate in vehicle-to-grid energy storage scheduling.

[0042] In this embodiment of the invention, when the demand intensity is low, residential vehicle data for multiple grid-connected residential vehicles is extracted from the grid-connected vehicle data. Then, based on the residential vehicle data, the discharge loss cost of these multiple grid-connected residential vehicles is calculated. Next, the multiple discharge loss costs are compared, and multiple target loss costs are selected from them in ascending order. Then, multiple grid-connected target vehicles corresponding to these target loss costs are selected from the multiple grid-connected residential vehicles. The participation control of vehicle-to-grid energy storage scheduling is then implemented for these multiple grid-connected target vehicles. Specifically, the calculation formula for the discharge loss cost of multiple grid-connected residential vehicles is as follows: ; in, For the first The cost of discharge loss for a single connected residential vehicle. For the first The vehicle price of a resident's vehicle connected to the internet; The preset standard duration; For the first The remaining cycle life of the batteries in a connected residential vehicle. For the first The current energy storage of the batteries in a connected residential vehicle; For the first Maintenance coefficient of each resident's vehicle connected to the network.

[0043] It is understandable that the maintenance coefficient is related to the vehicle's parts-to-vehicle ratio, maintenance costs, insurance costs, etc., and the higher the parts-to-vehicle ratio, the higher the maintenance costs, and the higher the insurance costs, the greater the maintenance coefficient. In this embodiment of the invention, by obtaining the vehicle's brand and model, the corresponding parts-to-vehicle ratio, maintenance costs, and insurance costs can be determined through Internet technology.

[0044] Specifically, Figure 5 This is a flowchart of the vehicle-to-grid energy storage scheduling plan during low demand periods, provided in the embodiments of the present invention.

[0045] Specifically, when the demand intensity is low, calculating the discharge loss cost of multiple grid-connected residential vehicles based on the grid-connected vehicle data, and selecting multiple grid-connected target vehicles to participate in vehicle-to-grid energy storage scheduling includes the following steps: Step S1041: When the demand intensity is low, extract the residential vehicle data of multiple connected residential vehicles from the connected vehicle data. Step S1042: Based on the residential vehicle data, calculate the discharge loss cost of multiple grid-connected residential vehicles; Step S1043: Compare the discharge loss costs of multiple vehicles and select multiple target vehicles for grid connection from the multiple grid-connected residential vehicles; Step S1044: Participation control of vehicle-to-grid energy storage scheduling for multiple grid-connected target vehicles.

[0046] Furthermore, the intelligent management method for power grid dispatch optimization also includes the following steps: Step S105: When the demand intensity is high, in addition to multiple grid-connected residential vehicles participating in vehicle-to-grid energy storage scheduling, calculate the comprehensive impact value of multiple grid-connected temporary vehicles, select multiple grid-connected supplementary vehicles, and schedule supplementary vehicles.

[0047] In this embodiment of the invention, under high demand conditions, in addition to participating in the vehicle-to-grid energy storage scheduling control of multiple grid-connected residential vehicles, temporary vehicle data of multiple grid-connected temporary vehicles is extracted from the grid-connected vehicle data. Then, based on the temporary vehicle data, the comprehensive impact value of the multiple grid-connected temporary vehicles is calculated. Subsequently, the multiple comprehensive impact values ​​are compared, and multiple target impact values ​​are selected from the multiple comprehensive impact values ​​in ascending order. Then, multiple grid-connected supplementary vehicles corresponding to the multiple target impact values ​​are selected from the multiple grid-connected temporary vehicles, and supplementary vehicle-to-grid energy storage scheduling control is performed on these multiple grid-connected supplementary vehicles. Specifically, the formula for calculating the comprehensive impact value of the multiple grid-connected temporary vehicles is as follows: ; ; ; in, For the first The overall impact value of each temporary vehicle connected to the network; For the first The cost of discharge loss for a temporary vehicle connected to the grid; For the first The urgency of stopping a temporary vehicle connected to the network; For the first Convenience for owners of temporary vehicles connected to the network; , and These are preset weighting coefficients; For the first The vehicle price of a temporary vehicle connected to the network; For the first The remaining cycle life of the battery of a temporary vehicle connected to the grid; For the first The current energy stored in the battery of a temporary connected vehicle; For the first Maintenance coefficient of each temporary vehicle connected to the network; This is the estimated scheduling duration; For the first The estimated remaining stay time for each temporary vehicle connected to the network.

[0048] In an embodiment of the present invention, For the first The convenience for owners of temporary grid-connected vehicles depends on whether they have authorized energy storage dispatch and whether there is an emergency. Specifically, if the first... If a temporary vehicle connected to the network has been authorized and there is no urgent matter, then... The value is 0; if the first If a temporary vehicle connected to the network is unauthorized or has an emergency, then... The value is 10; if the first If a temporary vehicle connected to the network is unauthorized and there is an emergency, then... The value is 100.

[0049] Specifically, Figure 6 This is a flowchart illustrating the scheduling supplementation during periods of high demand in the method provided by this embodiment of the invention.

[0050] Specifically, when the demand intensity is high, in addition to multiple grid-connected residential vehicles participating in vehicle-to-grid energy storage dispatch, the comprehensive impact value of multiple temporary grid-connected vehicles is calculated, and multiple supplementary grid-connected vehicles are selected for dispatch supplementation. This includes the following steps: Step S1051: When the demand intensity is high, extract temporary vehicle data of multiple temporary vehicles connected to the grid from the grid connection vehicle data. Step S1052: Participation control of vehicle-to-grid energy storage scheduling for multiple grid-connected residential vehicles; Step S1053: Calculate the combined impact value of multiple temporary vehicles connected to the grid based on the temporary vehicle data; Step S1054: Compare the multiple comprehensive impact values ​​and select multiple grid connection supplementary vehicles from the multiple grid connection temporary vehicles; Step S1055: Perform supplementary control of vehicle-to-grid energy storage scheduling for multiple grid-connected supplementary vehicles.

[0051] Figure 7 This invention provides an application architecture diagram for an intelligent management system for power grid dispatch optimization. In this embodiment, the intelligent management system for power grid dispatch optimization includes: The dispatch demand processing module 101 is used to receive grid dispatch demands, perform planning analysis, and when vehicle-to-grid energy storage dispatch is required, it combines active communication and associated communication to identify multiple grid-connected energy storage vehicles and obtain grid-connected vehicle data.

[0052] In this embodiment of the invention, the scheduling demand processing module 101 receives grid scheduling demands and performs demand planning analysis on the grid scheduling demands to determine whether vehicle-to-grid energy storage scheduling is required. If vehicle-to-grid energy storage scheduling is required, it identifies multiple grid-connected energy storage vehicles in the parking lot at this time, generates active data requests, and sends the active data requests to the multiple grid-connected energy storage vehicles. By receiving multiple active feedback data and performing object identification on the multiple active feedback data, it identifies the active feedback vehicles corresponding to the multiple active feedback data, and marks the vehicles among the multiple grid-connected energy storage vehicles that have not fed back data as non-feedback vehicles. Thus, the grid-connected energy storage vehicles are divided into multiple active feedback vehicles and multiple non-feedback vehicles. At the same time, according to the communication relationship of the multiple non-feedback vehicles, it matches the associated feedback vehicles corresponding to the multiple non-feedback vehicles from the multiple active feedback vehicles. Then, through the multiple associated feedback vehicles, it establishes temporary associated communication relationships with the multiple corresponding non-feedback vehicles. Using the multiple associated feedback vehicles as intermediate media, it obtains the associated feedback data of the multiple non-feedback vehicles, and then organizes the multiple active feedback data and the multiple associated feedback data to obtain grid-connected vehicle data.

[0053] The grid-connected vehicle classification module 102 is used to perform identity analysis on the grid-connected vehicle data and classify multiple grid-connected energy storage vehicles into multiple grid-connected residential vehicles and multiple grid-connected temporary vehicles.

[0054] In this embodiment of the invention, the grid-connected vehicle classification module 102 extracts the license plate numbers of multiple grid-connected energy storage vehicles from the grid-connected vehicle data to obtain vehicle license plate data. Then, based on a preset license plate database, it performs identity matching on the multiple license plate numbers in the vehicle license plate data and records the identity matching results. Subsequently, according to the identity matching results, the multiple grid-connected energy storage vehicles are classified into multiple grid-connected residential vehicles and multiple grid-connected temporary vehicles.

[0055] The demand intensity analysis module 103 is used to perform intensity analysis on the power grid dispatch demand and determine the demand intensity.

[0056] In this embodiment of the invention, the demand intensity analysis module 103 analyzes the grid dispatch demand to determine the required discharge power and duration of vehicle-to-grid energy storage dispatch. It also imports preset intensity grading parameters and compares the required discharge power and duration of demand with the intensity grading parameters to determine the demand intensity. Specifically, if neither the required discharge power nor the required duration of demand exceeds the corresponding benchmark value in the intensity grading parameters, the demand intensity is low; if the required discharge power and / or the required duration of demand exceeds the corresponding benchmark value in the intensity grading parameters, the demand intensity is high.

[0057] The low-demand scheduling control module 104 is used to calculate the discharge loss cost of multiple grid-connected residential vehicles based on the grid-connected vehicle data when the demand intensity is low, select multiple grid-connected target vehicles, and participate in vehicle-to-grid energy storage scheduling.

[0058] In this embodiment of the invention, when the demand intensity is low, the low-demand scheduling control module 104 extracts residential vehicle data for multiple grid-connected residential vehicles from the grid-connected vehicle data. Based on this data, it calculates the discharge loss cost of these vehicles. Then, it compares these discharge loss costs and selects multiple target loss costs from them in ascending order. Finally, it selects multiple grid-connected target vehicles corresponding to these target loss costs and performs vehicle-to-grid energy storage scheduling control on these target vehicles. Specifically, the calculation formula for the discharge loss cost of multiple grid-connected residential vehicles is as follows: ; in, For the first The cost of discharge loss for a single connected residential vehicle. For the first The vehicle price of a resident's vehicle connected to the internet; The preset standard duration; For the first The remaining cycle life of the batteries in a connected residential vehicle. For the first The current energy storage of the batteries in a connected residential vehicle; For the first Maintenance coefficient of each resident's vehicle connected to the network.

[0059] Specifically, Figure 8 This is a structural block diagram of the low-demand scheduling control module 104 in the system provided in this embodiment of the invention.

[0060] Specifically, the low-demand scheduling control module 104 includes: The data extraction unit 1041 is used to extract residential vehicle data of multiple connected residential vehicles from the connected vehicle data when the demand intensity is low. The discharge loss cost calculation unit 1042 is used to calculate the discharge loss cost of multiple grid-connected residential vehicles based on the residential vehicle data. The discharge loss cost comparison unit 1043 is used to compare multiple discharge loss costs and select multiple grid-connected target vehicles from multiple grid-connected residential vehicles. The scheduling participation control unit 1044 is used for participation control of vehicle-to-grid energy storage scheduling for multiple grid-connected target vehicles.

[0061] Furthermore, the intelligent management system for power grid dispatch optimization also includes: The high-demand scheduling control module 105 is used to calculate the comprehensive impact value of multiple temporary grid-connected vehicles and select multiple grid-connected supplementary vehicles for scheduling supplementation when the demand intensity is high, in addition to multiple grid-connected residential vehicles participating in vehicle-to-grid energy storage scheduling.

[0062] In this embodiment of the invention, when the demand intensity is high, the high-demand scheduling control module 105, in addition to participating in the vehicle-to-grid energy storage scheduling control of multiple grid-connected residential vehicles, extracts temporary vehicle data of multiple grid-connected temporary vehicles from the grid-connected vehicle data. Based on the temporary vehicle data, it calculates the comprehensive impact value of the multiple grid-connected temporary vehicles. Then, it compares the multiple comprehensive impact values ​​and selects multiple target impact values ​​from them in ascending order. Subsequently, it selects multiple grid-connected supplementary vehicles corresponding to the multiple target impact values ​​from the multiple grid-connected temporary vehicles and performs supplementary vehicle-to-grid energy storage scheduling control on these supplementary vehicles. Specifically, the formula for calculating the comprehensive impact value of the multiple grid-connected temporary vehicles is as follows: ; ; ; in, For the first The overall impact value of each temporary vehicle connected to the network; For the first The cost of discharge loss for a temporary vehicle connected to the grid; For the first The urgency of stopping a temporary vehicle connected to the network; For the first Convenience for owners of temporary vehicles connected to the network; , and These are preset weighting coefficients; For the first The vehicle price of a temporary vehicle connected to the network; For the first The remaining cycle life of the battery of a temporary vehicle connected to the grid; For the first The current energy stored in the battery of a temporary connected vehicle; For the first Maintenance coefficient of each temporary vehicle connected to the network; This is the estimated scheduling duration; For the first The estimated remaining stay time for each temporary vehicle connected to the network.

[0063] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent management method for power grid dispatch optimization, characterized in that, The method specifically includes the following steps: Receive grid dispatch requests, perform planning analysis, and when vehicle-to-grid energy storage dispatch is required, combine active communication and associated communication to identify multiple grid-connected energy storage vehicles and obtain grid-connected vehicle data. The grid-connected vehicle data is analyzed for identity, and the multiple grid-connected energy storage vehicles are divided into multiple grid-connected residential vehicles and multiple grid-connected temporary vehicles. An intensity analysis is performed on the power grid dispatch demand to determine the demand intensity; When the demand intensity is low, the discharge loss cost of multiple grid-connected residential vehicles is calculated based on the grid-connected vehicle data, and multiple grid-connected target vehicles are selected to participate in vehicle-to-grid energy storage scheduling. When the demand intensity is high, in addition to multiple grid-connected residential vehicles participating in vehicle-to-grid energy storage scheduling, the comprehensive impact value of multiple temporary grid-connected vehicles is calculated, and multiple grid-connected supplementary vehicles are selected for scheduling supplementation.

2. The intelligent management method for power grid dispatch optimization according to claim 1, characterized in that, The process of receiving grid dispatch requests, performing planning analysis, and combining active and associated communication when vehicle-to-grid energy storage dispatch is required to identify multiple grid-connected energy storage vehicles and obtain their data includes the following steps: Receive grid dispatch requests; Demand planning analysis is performed on the power grid dispatch requirements to determine whether vehicle-to-grid energy storage dispatch is required. When vehicle-to-grid energy storage dispatch is required, multiple grid-connected energy storage vehicles should be identified. Send active data requests to multiple grid-connected energy storage vehicles; Receive multiple proactive feedback data; Vehicle identification is performed on multiple active feedback data, and the multiple grid-connected energy storage vehicles are divided into multiple active feedback vehicles and multiple non-feedback vehicles, and the associated feedback vehicles of the multiple non-feedback vehicles are matched. By using multiple associated feedback vehicles, association communication is established with multiple non-feedback vehicles to obtain associated feedback data from the multiple non-feedback vehicles; The multiple active feedback data and the multiple associated feedback data are processed to obtain connected vehicle data.

3. The intelligent management method for power grid dispatch optimization according to claim 1, characterized in that, The step of performing identity analysis on the grid-connected vehicle data to classify multiple grid-connected energy storage vehicles into multiple grid-connected residential vehicles and multiple grid-connected temporary vehicles specifically includes the following steps: Extract vehicle license plate data from the network-connected vehicle data; Based on a pre-set license plate database, the vehicle license plate data is analyzed for identity matching, and the identity matching results are recorded. Based on the identity matching results, the multiple grid-connected energy storage vehicles are divided into multiple grid-connected residential vehicles and multiple grid-connected temporary vehicles.

4. The intelligent management method for power grid dispatch optimization according to claim 1, characterized in that, The process of performing intensity analysis on the power grid dispatch demand to determine the demand intensity specifically includes the following steps: The power grid dispatch demand is analyzed to determine the required discharge power and the duration of the demand. Import the preset intensity grading parameters; Based on the intensity grading parameters, the required discharge power and the required duration are compared to determine the required intensity.

5. The intelligent management method for power grid dispatch optimization according to claim 1, characterized in that, When the demand intensity is low, the process of calculating the discharge loss cost of multiple grid-connected residential vehicles based on the grid-connected vehicle data, selecting multiple target grid-connected vehicles, and participating in vehicle-to-grid energy storage scheduling specifically includes the following steps: When the demand intensity is low, extract residential vehicle data of multiple connected residential vehicles from the connected vehicle data; Based on the residential vehicle data, calculate the discharge loss cost of multiple grid-connected residential vehicles; Compare the discharge loss costs of multiple vehicles and select multiple target vehicles for grid connection from the multiple grid-connected residential vehicles; Participation control of vehicle-to-grid energy storage scheduling for multiple grid-connected target vehicles.

6. The intelligent management method for power grid dispatch optimization according to claim 5, characterized in that, The formula for calculating the discharge loss cost of multiple grid-connected residential vehicles is as follows: ； in, For the first The cost of discharge loss for a single connected residential vehicle. For the first The vehicle price of a resident's vehicle connected to the internet; The preset standard duration; For the first The remaining cycle life of the batteries in a connected residential vehicle. For the first The current energy storage of the batteries in a connected residential vehicle; For the first Maintenance coefficient of each resident's vehicle connected to the network.

7. The intelligent management method for power grid dispatch optimization according to claim 6, characterized in that, When the demand intensity is high, in addition to multiple grid-connected residential vehicles participating in vehicle-to-grid energy storage dispatch, the comprehensive impact value of multiple temporary grid-connected vehicles is calculated, and multiple supplementary grid-connected vehicles are selected for dispatch supplementation. This specifically includes the following steps: When the demand intensity is high, extract temporary vehicle data of multiple temporary vehicles connected to the grid from the grid connection vehicle data; Participation control for vehicle-to-grid energy storage scheduling of multiple grid-connected residential vehicles; Based on the temporary vehicle data, calculate the combined impact value of multiple temporary grid-connected vehicles; The combined impact values ​​are compared, and multiple supplementary vehicles for grid connection are selected from the multiple temporary grid connection vehicles. Supplementary control is performed on the vehicle-to-grid energy storage scheduling of multiple grid-connected supplementary vehicles.

8. The intelligent management method for power grid dispatch optimization according to claim 7, characterized in that, The formula for calculating the combined impact value of multiple temporary grid connection vehicles is as follows: ； ； ； in, For the first The overall impact value of each temporary vehicle connected to the network; For the first The cost of discharge loss for a temporary vehicle connected to the grid; For the first The urgency of stopping a temporary vehicle connected to the network; For the first Convenience for owners of temporary vehicles connected to the network; , and These are preset weighting coefficients; For the first The vehicle price of a temporary vehicle connected to the network; For the first The remaining cycle life of the battery of a temporary vehicle connected to the grid; For the first The current energy stored in the battery of a temporary connected vehicle; For the first Maintenance coefficient of each temporary vehicle connected to the network; This is the estimated scheduling duration; For the first The estimated remaining stay time for each temporary vehicle connected to the network.

9. An intelligent management system for power grid dispatch optimization, characterized in that, The system includes a scheduling demand processing module, a vehicle grid connection classification module, a demand intensity analysis module, a low-demand scheduling control module, and a high-demand scheduling control module, wherein: The dispatch demand processing module is used to receive grid dispatch demands, perform planning analysis, and when vehicle-to-grid energy storage dispatch is required, it combines active communication and associated communication to identify multiple grid-connected energy storage vehicles and obtain grid-connected vehicle data. The grid-connected vehicle classification module is used to perform identity analysis on the grid-connected vehicle data and classify multiple grid-connected energy storage vehicles into multiple grid-connected residential vehicles and multiple grid-connected temporary vehicles. The demand intensity analysis module is used to perform intensity analysis on the power grid dispatch demand and determine the demand intensity. The low-demand scheduling control module is used to calculate the discharge loss cost of multiple grid-connected residential vehicles based on the grid-connected vehicle data when the demand intensity is low, select multiple grid-connected target vehicles, and participate in vehicle-to-grid energy storage scheduling. The high-demand scheduling control module is used to calculate the comprehensive impact value of multiple temporary grid-connected vehicles and select multiple supplementary grid-connected vehicles for scheduling supplementation when the demand intensity is high, in addition to multiple grid-connected residential vehicles participating in vehicle-to-grid energy storage scheduling.

10. The intelligent management system for power grid dispatch optimization according to claim 9, characterized in that, The low-demand scheduling control module specifically includes: The data extraction unit is used to extract residential vehicle data of multiple connected residential vehicles from the connected vehicle data when the demand intensity is low. The discharge loss cost calculation unit is used to calculate the discharge loss cost of multiple grid-connected residential vehicles based on the residential vehicle data. The discharge loss cost comparison unit is used to compare multiple discharge loss costs and select multiple grid-connected target vehicles from multiple grid-connected residential vehicles. The scheduling participation control unit is used for participating in the vehicle-to-grid energy storage scheduling of multiple grid-connected target vehicles.

Images