Energy aggregation management method, system and medium in vehicle-grid interactive multi-energy collaboration

Abstract

The present application discloses a method, system and medium for managing energy aggregation in vehicle-grid interactive multi-energy collaboration, which mainly relates to the field of energy aggregation technology, and is used to solve the problem that the existing V2G vehicle fusion solution is mainly to calculate the total energy that can be returned, and it is difficult to achieve a positive support effect on the stability and reliability of the power grid except for reducing the impact of the impact load. It includes: obtaining the difference between the load demand and the adjustable load corresponding to the same preset time period; when the adjustable load is greater than or equal to the load demand, the power grid in the preset area directly responds to the scheduling plan corresponding to the load demand; when the adjustable load is less than the load demand, the load difference is obtained, and a scheduling instruction is issued to the V2G invitation organization module; through the V2G invitation organization module, a charging and discharging invitation is sent to the electric vehicles in the preset area to determine the charging and discharging amount and charging and discharging time of the selected vehicle in the preset time period.

Description

Technical Field

[0001] The present application relates to the field of energy aggregation management technology, and in particular to a method, system and medium for energy aggregation management in vehicle-grid interactive multi-energy collaboration. Background Art

[0002] The construction of the power system has promoted the rapid development of new elements such as electric vehicles. As an innovative and mobile energy storage resource, electric vehicles have demonstrated their unique multiple roles in the modern energy system. They are not only load nodes in the power grid, but also can draw electricity from the grid when needed to meet driving needs. More importantly, through V2G (Vehicle-to-Grid) technology, electric vehicles also have the ability to send electricity back to the grid. This means that at the right time and under the right conditions, electric vehicles can feed the stored electricity back to the grid. Large-scale electric vehicle grid connection as a way to increase the absorption of renewable energy in the grid is more economical and convenient than other ways, and can provide support for other users or systems.

[0003] At present, the existing power system mainly focuses on application scenarios such as charging and swapping integration and service integration. At present, the energy aggregation scheme in multi-energy synergy is mainly: energy equipment such as photovoltaic power and energy storage power are used for coordinated energy aggregation. The existing scheme for applying vehicle-grid interaction in power system energy aggregation is mainly: using the route optimization algorithm of the prediction module to guide V2G vehicles to the nearest V2G charging pile for charging and discharging, and then calculating the V2G vehicles that can be aggregated on the day and the energy returned to the grid by each aggregated V2G vehicle through the prediction module, and then calculating the total energy that all aggregated V2G vehicles can return to the grid.

[0004] The existing V2G vehicle integration solution is mainly to calculate the total energy that can be returned. In addition to reducing the impact of shock loads, it is difficult to achieve a positive support role for the stability and reliability of the power grid. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present application provides an energy aggregation management system, method and medium in vehicle-grid interactive multi-energy collaboration to solve the problem that the existing V2G vehicle fusion solution is mainly to calculate the total energy that can be returned, and in addition to reducing the impact of shock loads, it is difficult to achieve a positive support role for the stability and reliability of the power grid.

[0006] In a first aspect, the present application provides a method for energy aggregation management in vehicle-grid interactive multi-energy collaboration, the method comprising:

[0007] Collect historical load data of preset areas in preset time periods every day within historical time periods, and then predict the load demand in each preset time period of the next day; predict the photovoltaic power generation power, energy storage charging and discharging power, and electric vehicle charging and discharging power of the current power grid in each preset time period of the next day through historical power data, and then combine the basic load power and network supply load to obtain the adjustable load of each preset time period; obtain the difference between the load demand and the adjustable load corresponding to the same preset time period; when the adjustable load is greater than or equal to the load demand, the power grid in the preset area directly responds to the dispatch plan corresponding to the load demand; when the adjustable load is less than the load demand, obtain the load difference and issue a dispatch instruction to the V2G invitation organization module; send charging and discharging invitations to electric vehicles in the preset area through the V2G invitation organization module; obtain the battery status, remaining power, distance to the V2G charger, historical punctuality rate, and maximum battery charging and discharging power of the electric vehicles receiving the invitation, and then determine the selected vehicle; based on the load difference and the maximum battery charging and discharging power and remaining power of the selected vehicle, determine the charging and discharging amount and charging and discharging time of the selected vehicle in the preset time period.

[0008] The energy aggregation management method in multi-energy collaboration provided by the embodiment of the present application uses the historical load data of the preset area in the preset time period collected every day in the historical time period to estimate the load demand in each preset time period of the next day through the prediction model. It is helpful to plan energy supply in advance to cope with load fluctuations in the power grid. By analyzing historical power data, the adjustable load of the station (such as energy storage facilities, dispatchable power plants, etc.) of the next day is predicted; the difference between the load demand and the adjustable load in each preset time period is calculated. If the required power is less than or equal to the adjustable load, it means that the existing adjustable resources of the station are sufficient to meet the demand and can directly respond to the load demand plan. If the required power is greater than the adjustable load, it means that additional energy resources are needed to supplement it, and then it is necessary to start the vehicle-grid interactive adjustment plan. Through the V2G invitation organization module, a charging and discharging invitation is sent to electric vehicles in the preset area. As a distributed energy storage unit, electric vehicles can be charged and discharged according to the needs of the power grid, thereby adjusting the load balance of the power grid. Configure the charge and discharge amount and charge and discharge time of electric vehicles to ensure that they can provide or absorb electrical energy when needed, thereby responding to the load demand plan. Through the above scheme: V2G electric vehicle energy resources can effectively participate in supplementing grid resources, improve the flexibility and resilience of the grid, and help ensure that the grid can maintain safe and stable operation when facing load fluctuations, faults or other challenges.

[0009] In one implementation of the present application, the method further includes:

[0010] Obtain the actual electricity price, actual response power and coefficient constants of each preset time period of the next day;

[0011] By formula:

[0012] , calculate the actual income; where i represents the i-th preset time period, and n represents the total amount of the preset time period;

[0013] By formula:

[0014] , calculate the V2G payment cost; among which, the discharge price and additional reward income are the data preset in the charge and discharge invitation;

[0015] By formula:

[0016] The total revenue of the station = income - V2G payment cost, and the total revenue of the station is obtained.

[0017] In one implementation of the present application, before sending a charge and discharge invitation to electric vehicles in a preset area through the V2G invitation organization module, the method further includes:

[0018] Electric vehicles in a preset area upload basic vehicle information to the registration program of the V2G invitation organization; when it is determined that the basic vehicle information meets the preset V2G standard, the electric vehicle registration is determined to be successful; and charging and discharging invitations are sent to electric vehicles that have successfully registered in the preset area through the V2G invitation organization module.

[0019] In one implementation of the present application, the battery status, remaining power, distance from the V2G charger, historical punctuality rate, and maximum battery charge and discharge power of the electric vehicle receiving the invitation are obtained, and then the selected vehicle is determined, specifically including:

[0020] The battery status, remaining power, distance from the V2G charger, historical punctuality rate, and maximum battery charge and discharge power of the electric vehicle receiving the invitation are used as input data; the constraint range of the remaining power is used as a constraint condition and input into the preset trained algorithm, and the electric vehicle that obtains the output is the selected vehicle.

[0021] In one implementation of the present application, based on the load difference and the maximum charge and discharge power and remaining power of the battery of the selected vehicle, the charge and discharge amount and charge and discharge time of the selected vehicle within a preset time period are determined, specifically including:

[0022] Obtain the historical punctuality rate corresponding to the selected vehicles, and then determine the order of each selected vehicle; based on the order of priority, determine in turn the operating time of the selected vehicles when they run at the maximum charge and discharge power of the battery until the remaining power equals the preset minimum power; when the operating time is less than the preset time period, determine the operating time as the charge and discharge time of the selected vehicle within the preset time period; when the operating time is greater than or equal to the preset time period, determine the preset time period as the charge and discharge time of the selected vehicle within the preset time period; after obtaining the operating time of each selected vehicle, calculate the charge and discharge amount of each selected vehicle based on the operating time and the maximum charge and discharge power; based on the order of priority, accumulate the charge and discharge amounts in turn, and determine the position of the selected vehicle whose accumulated value is greater than or equal to the load difference; issue a stop scheduling command to the selected vehicle after the position of the selected vehicle whose accumulated value is greater than or equal to the load difference, and reset the charge and discharge amount and charge and discharge time of the selected vehicle that issues the stop scheduling command to zero.

[0023] In the second aspect, the present application provides an energy aggregation management system in vehicle-grid interactive multi-energy collaboration, the system comprising:

[0024] The regional load forecasting module is used to collect the historical load data of the preset area in the preset time period every day within the historical time period, and then predict the load demand in each preset time period of the next day; the market plan acquisition module is used to predict the photovoltaic power generation power, energy storage charging and discharging power, and electric vehicle charging and discharging power of the current power grid in each preset time period of the next day through historical power data, and then combine the basic load power and the network supply load to obtain the adjustable load of each preset time period; the market declaration module is used to obtain the difference between the load demand and the adjustable load corresponding to the same preset time period; when the adjustable load is greater than or equal to the load demand, the power grid in the preset area directly responds to the dispatching plan corresponding to the load demand; when the adjustable load is less than the load demand, the load difference is obtained and the dispatching instruction is issued to the V2G invitation organization module; the V2G invitation organization module is used to send charging and discharging invitations to electric vehicles in the preset area; obtain the battery status, remaining power, distance to the V2G charger, historical punctuality rate, and maximum battery charging and discharging power of the electric vehicle receiving the invitation, and then determine the selected vehicle; based on the load difference and the maximum battery charging and discharging power and remaining power of the selected vehicle, determine the charging and discharging amount and charging and discharging time of the selected vehicle in the preset time period.

[0025] In one implementation of the present application, the system further includes a revenue calculation module: used to obtain the actual electricity price, the actual response power and the coefficient constant of each preset time period in the preset time period of the next day;

[0026] By formula:

[0027] , calculate the actual income; where i represents the i-th preset time period, and n represents the total amount of the preset time period;

[0028] By formula:

[0029] , calculate the V2G payment cost; among which, the discharge price and additional reward income are the data preset in the charge and discharge invitation;

[0030] By formula:

[0031] The total revenue of the station = income - V2G payment cost, and the total revenue of the station is obtained.

[0032] In one implementation of the present application, the V2G invitation organization module includes a car registration unit, which is used to obtain basic vehicle information uploaded by electric vehicles in a preset area to the registration program of the V2G invitation organization; when it is determined that the basic vehicle information meets the preset V2G standard, it is determined that the electric vehicle is successfully registered; and charging and discharging invitations are sent to electric vehicles that have successfully registered in the preset area through the V2G invitation organization module.

[0033] In one implementation of the present application, the V2G invitation organization module includes a calculation module for obtaining the historical punctuality rate corresponding to the selected vehicles, and then determining the order of priority of each selected vehicle; based on the order of priority, determining in turn the operating time of the selected vehicles when the battery runs at the maximum charge and discharge power until the remaining power is equal to the preset minimum power; when the operating time is less than the preset time period, determining the operating time as the charge and discharge time of the selected vehicle within the preset time period; when the operating time is greater than or equal to the preset time period, determining the preset time period as the charge and discharge time of the selected vehicle within the preset time period; after obtaining the operating time of each selected vehicle, calculating the charge and discharge amount of each selected vehicle based on the operating time and the maximum charge and discharge power; based on the order of priority, accumulating the charge and discharge amounts in turn, and determining the position of the selected vehicle whose accumulated value is greater than or equal to the load difference; issuing a stop scheduling instruction to the selected vehicle after the position of the selected vehicle whose accumulated value is greater than or equal to the load difference, and resetting the charge and discharge amount and charge and discharge time of the selected vehicle that issued the stop scheduling instruction to zero.

[0034] In a third aspect, the present application provides a non-volatile computer storage medium on which computer instructions are stored. When the computer instructions are executed, they implement a method for energy aggregation management in vehicle-grid interactive multi-energy collaboration as described above.

[0035] Those skilled in the art can understand that the present application has at least the following beneficial effects:

[0036] The present application discloses a method, system and medium for energy aggregation management in vehicle-grid interactive multi-energy collaboration. The system predicts the load demand for the next day through historical data, and predicts the adjustable load for the next day through historical power data. Then, the difference between the load demand and the adjustable load corresponding to the preset time period is obtained. If the power demand in the same preset time period is <= the adjustable load of the station, the load demand plan can be directly responded to. If the power demand in the same preset time period is > the adjustable load of the station, it is necessary to add a vehicle-grid interactive adjustment plan. The V2G invitation organization module sends charging and discharging invitations to electric vehicles in a preset area, and then responds to the load demand plan by configuring the charging and discharging amount and charging and discharging time of the electric vehicles, thereby effectively participating in supplementing grid resources and ensuring the safe and stable operation of the grid. It solves the problem that the existing V2G vehicle fusion solution is mainly to calculate the total energy that can be returned, and it is difficult to achieve a positive support role for the stability and reliability of the grid except for reducing the impact of the impact load. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Some embodiments of the present disclosure are described below with reference to the accompanying drawings, in which:

[0038] Figure 1 It is a flow chart of an energy aggregation management method in vehicle-grid interactive multi-energy collaboration provided in an embodiment of the present application.

[0039] Figure 2 It is a schematic diagram of the internal structure of an energy aggregation management system in vehicle-grid interactive multi-energy collaboration provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] It should be understood by those skilled in the art that the embodiments described below are only preferred embodiments of the present disclosure, and do not mean that the present disclosure can only be implemented through the preferred embodiments. The preferred embodiments are only used to explain the technical principles of the present disclosure, and are not used to limit the protection scope of the present disclosure. Based on the preferred embodiments provided by the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work should still fall within the protection scope of the present disclosure.

[0041] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0042] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0043] The embodiment provides a method for energy aggregation management in vehicle-grid interactive multi-energy collaboration, such as Figure 1 As shown, the method provided in the embodiment of the present application mainly includes the following steps:

[0044] Step 110: Collect historical load data of a preset area in a preset time period every day within a historical time period, and then predict the load demand of each preset time period of the next day.

[0045] It should be noted that the historical load data may be data within 300 hours of the current moment. After obtaining the load demand, the present application may update the historical load data and revise the preliminary forecast results on the next day with 15 minutes as an optimization time period. The historical time period here may be 1 month.

[0046] In addition, the prediction scheme of the present application can be to use the daily historical load data of the preset area in the preset time period as sample data, use the sample data to train the preset genetic algorithm, and then use the trained preset genetic algorithm to predict the load demand for each preset time period of the next day.

[0047] Those skilled in the art will appreciate that the prediction scheme may also be implemented by any feasible neural network prediction algorithm.

[0048] Step 120: predict the photovoltaic power generation power, energy storage charging and discharging power, and electric vehicle charging and discharging power of the current power grid in each preset time period the next day through historical power data, and then combine the basic load power and network supply load to obtain the adjustable load for each preset time period.

[0049] It should be noted that the historical power data includes photovoltaic power generation power, energy storage charging and discharging power, and electric vehicle charging and discharging power.

[0050] It should be noted that, based on historical power data, the scheme of predicting the photovoltaic power generation power, energy storage charging and discharging power, and electric vehicle charging and discharging power in each preset time period of the next day of the current power grid can be realized by training the existing neural network algorithm. For example, the historical power data is used as sample data, and the initial neural network algorithm is trained through the sample data to obtain the trained neural network algorithm, and then the photovoltaic power generation power, energy storage charging and discharging power, and electric vehicle charging and discharging power in each preset time period of the next day of the current power grid are obtained.

[0051] In addition, those skilled in the art can calculate the photovoltaic power generation power according to actual needs.

[0052] The specific calculation process can be:

[0053] Obtain photovoltaic parameter conversion efficiency, obtain the current light intensity provided by the micro-meteorological module, and obtain the photovoltaic area;

[0054] By formula:

[0055] Photovoltaic power generation power = photovoltaic area × current light intensity × photovoltaic parameter conversion efficiency, and the photovoltaic power generation power is obtained.

[0056] It should be noted that the micro-meteorological module is an existing device or apparatus capable of collecting current light intensity.

[0057] In addition, those skilled in the art can calculate the energy storage charging and discharging power according to actual needs.

[0058] Discharge capacity (kW) = battery capacity (Ah) × voltage (V) / 1000 / discharge time (h) × discharge efficiency (%).

[0059] Charging capacity (kW) = battery capacity (Ah) × voltage (V) / 1000 / charging time (h) × charging efficiency (%).

[0060] The above-mentioned battery capacity, voltage, discharge time, charge time, charge efficiency and discharge efficiency are all preset values.

[0061] In addition, the specific solution for obtaining the adjustable load for each preset time period is: through the formula: adjustable load for each preset time period = predicted photovoltaic power generation power + energy storage charging and discharging power + electric vehicle charging and discharging power - basic load power - grid supply load, the adjustable load for each preset time period is obtained; here the basic load power and the grid supply load can be directly obtained by technical personnel in this field.

[0062] Step 130, obtain the difference between the load demand and the adjustable load corresponding to the same preset time period; when the adjustable load is greater than or equal to the load demand, the power grid in the preset area directly responds to the dispatch plan corresponding to the load demand; when the adjustable load is less than the load demand, obtain the load difference and issue a dispatch instruction to the V2G invitation organization module.

[0063] It should be noted that if the power demand is less than or equal to the adjustable load, it means that the existing adjustable resources of the station are sufficient to meet the demand and can directly respond to the load demand plan. If the power demand is greater than the adjustable load, it means that additional energy resources are needed to supplement it, and then the vehicle-grid interactive adjustment plan needs to be started. The V2G invitation organization module is a server that can support V2G technology.

[0064] Step 140: Send a charge and discharge invitation to electric vehicles in a preset area through a V2G invitation organization module; obtain the battery status, remaining power, distance to the V2G charger, historical punctuality rate, and maximum charge and discharge power of the electric vehicle receiving the invitation, and then determine the selected vehicle; based on the load difference and the maximum charge and discharge power and remaining power of the battery of the selected vehicle, determine the charge and discharge amount and charge and discharge time of the selected vehicle within a preset time period.

[0065] It should be noted that the V2G invitation organization module sends charging and discharging invitations to electric vehicles in the preset area. As distributed energy storage units, electric vehicles can be charged and discharged according to the needs of the power grid, thereby adjusting the load balance of the power grid. The charging and discharging amount and charging and discharging time of electric vehicles are configured to ensure that they can provide or absorb electric energy when needed, thereby responding to the load demand plan.

[0066] In addition, the present application can set up a registration program in the V2G invitation organization module to perform electric vehicle registration communication, and then send the charging and discharging invitation to the successfully registered electric vehicle.

[0067] The specific implementation process may be: electric vehicles in a preset area upload basic vehicle information to the registration program of the V2G invitation organization; when it is determined that the basic vehicle information meets the preset V2G standard, the electric vehicle registration is determined to be successful; and a charging and discharging invitation is sent to the electric vehicles that have successfully registered in the preset area through the V2G invitation organization module.

[0068] In the step: obtaining the battery status, remaining power, distance from the V2G charger, historical punctuality rate, and maximum battery charge and discharge power of the electric vehicle receiving the invitation, and then determining the selected vehicle, specifically, can be:

[0069] The battery status, remaining power, distance from the V2G charger, historical punctuality rate, and maximum battery charge and discharge power of the electric vehicle receiving the invitation are used as input data; the constraint range of the remaining power is used as a constraint condition and input into the preset trained algorithm, and the electric vehicle that obtains the output is the selected vehicle.

[0070] In addition, it should be noted that the process of training the algorithm can be to use the battery status, remaining power, distance from the V2G charger, historical punctuality rate, maximum battery charge and discharge power of the electric vehicles that received the invitation within the historical time period and the information of the marked selected vehicles (battery status, remaining power, distance from the V2G charger, historical punctuality rate, maximum battery charge and discharge power) as samples to train neural network algorithms and other algorithms, thereby obtaining a trained algorithm.

[0071] In the step, based on the load difference and the maximum charge and discharge power and remaining power of the battery of the selected vehicle, the charge and discharge amount and charge and discharge time of the selected vehicle within the preset time period are determined, which can be specifically:

[0072] Obtain the historical punctuality rate corresponding to the selected vehicles, and then determine the order of each selected vehicle; based on the order of priority, determine in turn the operating time of the selected vehicles when they run at the maximum charge and discharge power of the battery until the remaining power equals the preset minimum power; when the operating time is less than the preset time period, determine the operating time as the charge and discharge time of the selected vehicle within the preset time period; when the operating time is greater than or equal to the preset time period, determine the preset time period as the charge and discharge time of the selected vehicle within the preset time period; after obtaining the operating time of each selected vehicle, calculate the charge and discharge amount of each selected vehicle based on the operating time and the maximum charge and discharge power; based on the order of priority, accumulate the charge and discharge amounts in turn, and determine the position of the selected vehicle whose accumulated value is greater than or equal to the load difference; issue a stop scheduling command to the selected vehicle after the position of the selected vehicle whose accumulated value is greater than or equal to the load difference, and reset the charge and discharge amount and charge and discharge time of the selected vehicle that issues the stop scheduling command to zero.

[0073] In addition, this application can also process funds according to the settlement results to complete the allocation and payment of income. The specific process can be:

[0074] Obtain the actual electricity price, actual response power and coefficient constants of each preset time period of the next day;

[0075] By formula:

[0076] , calculate the actual income; where i represents the i-th preset time period, and n represents the total amount of the preset time period.

[0077] By formula:

[0078] , calculate the V2G payment cost; among them, the discharge price and additional reward income are the preset data in the charging and discharging invitation.

[0079] By formula:

[0080] The total revenue of the station = income - V2G payment cost, and the total revenue of the station is obtained.

[0081] Based on the above description, it can be seen that this embodiment collects the historical load data of the preset area in the preset time period every day in the historical time period, and estimates the load demand in each preset time period of the next day through the prediction model. It is helpful to plan energy supply in advance to cope with the load fluctuation of the power grid. By analyzing the historical power data, the adjustable load of the station (such as energy storage facilities, dispatchable power plants, etc.) of the next day is predicted; the difference between the load demand and the adjustable load in each preset time period is calculated. If the required power is less than or equal to the adjustable load, it means that the existing adjustable resources of the station are sufficient to meet the demand and can directly respond to the load demand plan. If the required power is greater than the adjustable load, it means that additional energy resources are needed to supplement it. At this time, it is necessary to start the vehicle-grid interactive adjustment plan. Through the V2G invitation organization module, a charging and discharging invitation is sent to the electric vehicles in the preset area. As a distributed energy storage unit, electric vehicles can be charged and discharged according to the needs of the power grid, thereby adjusting the load balance of the power grid. Configure the charge and discharge amount and charge and discharge time of electric vehicles to ensure that they can provide or absorb electrical energy when needed, thereby responding to the load demand plan. Through the above scheme: V2G electric vehicle energy resources can effectively participate in supplementing power grid resources and improve the flexibility and resilience of the power grid. Helps ensure that the power grid can maintain safe and stable operation when facing load fluctuations, faults or other challenges.

[0082] In addition, this application Figure 2 An energy aggregation management system for vehicle-grid interactive multi-energy collaboration is provided in the embodiment of the present application. Figure 2 As shown, the system provided in the embodiment of the present application mainly includes:

[0083] The regional load forecasting module 210 is used to collect the historical load data of a preset area in a preset time period every day within the historical time period, and then predict the load demand in each preset time period of the next day.

[0084] The market plan acquisition module 220 is used to predict the photovoltaic power generation power, energy storage charging and discharging power, and electric vehicle charging and discharging power of each preset time period of the next day of the current power grid through historical power data, and then combine the basic load power and network supply load to obtain the adjustable load for each preset time period.

[0085] The market declaration module 230 is used to obtain the difference between the load demand and the adjustable load corresponding to the same preset time period; when the adjustable load is greater than or equal to the load demand, the power grid in the preset area directly responds to the dispatch plan corresponding to the load demand; when the adjustable load is less than the load demand, the load difference is obtained and a dispatch instruction is issued to the V2G invitation organization module 240.

[0086] The V2G invitation organization module 240 is used to send charging and discharging invitations to electric vehicles in a preset area; obtain the battery status, remaining power, distance to the V2G charger, historical punctuality rate, and maximum battery charging and discharging power of the electric vehicle receiving the invitation, and then determine the selected vehicle; based on the load difference and the maximum battery charging and discharging power and remaining power of the selected vehicle, determine the charging and discharging amount and charging and discharging time of the selected vehicle within a preset time period.

[0087] The V2G invitation organization module 240 includes a car registration unit, which is used to obtain basic vehicle information uploaded by electric vehicles in a preset area to the registration program of the V2G invitation organization; when it is determined that the basic vehicle information meets the preset V2G standard, it is determined that the electric vehicle is successfully registered; and the V2G invitation organization module 240 sends charging and discharging invitations to electric vehicles that have successfully registered in the preset area.

[0088] The V2G invitation organization module 240 includes a calculation module for obtaining the historical punctuality rate corresponding to the selected vehicles, and then determining the order of each selected vehicle; based on the order of priority, determining in turn the operating time of the selected vehicle when the battery runs at the maximum charge and discharge power until the remaining power is equal to the preset minimum power; when the operating time is less than the preset time period, determining the operating time as the charge and discharge time of the selected vehicle within the preset time period; when the operating time is greater than or equal to the preset time period, determining the preset time period as the charge and discharge time of the selected vehicle within the preset time period; after obtaining the operating time of each selected vehicle, calculating the charge and discharge amount of each selected vehicle based on the operating time and the maximum charge and discharge power; based on the order of priority, accumulating the charge and discharge amounts in turn, and determining the position of the selected vehicle whose accumulated value is greater than or equal to the load difference; issuing a stop scheduling instruction to the selected vehicle after the position of the selected vehicle whose accumulated value is greater than or equal to the load difference, and resetting the charge and discharge amount and charge and discharge time of the selected vehicle that issued the stop scheduling instruction to zero.

[0089] Revenue calculation module: used to obtain the actual electricity price, actual response power and coefficient constants of each preset time period in the next day;

[0090] By formula:

[0091] , calculate the actual income; where i represents the i-th preset time period, and n represents the total amount of the preset time period.

[0092] By formula:

[0093] , calculate the V2G payment cost; among them, the discharge price and additional reward income are the preset data in the charging and discharging invitation.

[0094] By formula:

[0095] The total revenue of the station = income - V2G payment cost, and the total revenue of the station is obtained.

[0096] In addition, an embodiment of the present application also provides a non-volatile computer storage medium on which executable instructions are stored. When the executable instructions are executed, an energy aggregation management method in vehicle-grid interactive multi-energy collaboration as described above is implemented.

[0097] So far, the technical solutions of the present disclosure have been described in combination with the above multiple embodiments, but it is easy for those skilled in the art to understand that the protection scope of the present disclosure is not limited to these specific embodiments. Without departing from the technical principles of the present disclosure, those skilled in the art can split and combine the technical solutions in the above-mentioned various embodiments, and can also make equivalent changes or replacements to the relevant technical features. Any changes, equivalent replacements, improvements, etc. made within the technical concept and / or technical principle of the present disclosure will fall within the protection scope of the present disclosure.

Claims

1. A method for energy aggregation management in vehicle-grid interactive multi-energy collaboration, characterized in that: The method comprises: Collect the historical load data of the preset area in the preset time period every day within the historical time period, and then predict the load demand of each preset time period of the next day; Through historical power data, the photovoltaic power generation power, energy storage charging and discharging power, and electric vehicle charging and discharging power of the current power grid in each preset time period of the next day are predicted, and then combined with the basic load power and grid supply load, the adjustable load of each preset time period is obtained; Obtain the difference between the load demand and the adjustable load corresponding to the same preset time period; when the adjustable load is greater than or equal to the load demand, the power grid in the preset area directly responds to the dispatch plan corresponding to the load demand; when the adjustable load is less than the load demand, obtain the load difference and issue a dispatch instruction to the V2G invitation organization module; Send charging and discharging invitations to electric vehicles in a preset area through the V2G invitation organization module; obtain the battery status, remaining power, distance from the V2G charger, historical punctuality rate, and maximum battery charging and discharging power of the electric vehicle receiving the invitation, and then determine the selected vehicle; based on the load difference and the maximum battery charging and discharging power and remaining power of the selected vehicle, determine the charging and discharging amount and charging and discharging time of the selected vehicle within a preset time period, specifically including: Obtain the historical punctuality rate corresponding to the selected vehicles, and then determine the order of each selected vehicle; based on the order of priority, determine in turn the operating time of the selected vehicles when they run at the maximum charge and discharge power of the battery until the remaining power equals the preset minimum power; when the operating time is less than the preset time period, determine the operating time as the charge and discharge time of the selected vehicle within the preset time period; when the operating time is greater than or equal to the preset time period, determine the preset time period as the charge and discharge time of the selected vehicle within the preset time period; after obtaining the operating time of each selected vehicle, calculate the charge and discharge amount of each selected vehicle based on the operating time and the maximum charge and discharge power; based on the order of priority, accumulate the charge and discharge amounts in turn, and determine the position of the selected vehicle whose accumulated value is greater than or equal to the load difference; issue a stop scheduling command to the selected vehicle after the position of the selected vehicle whose accumulated value is greater than or equal to the load difference, and reset the charge and discharge amount and charge and discharge time of the selected vehicle that issues the stop scheduling command to zero.

2. The energy aggregation management method in vehicle-grid interactive multi-energy collaboration according to claim 1 is characterized in that: The method further comprises: Obtain the actual electricity price, actual response power and coefficient constants of each preset time period of the next day; By formula: , calculate the actual income; where i represents the i-th preset time period, and n represents the total amount of the preset time period; By formula: , calculate the V2G payment cost; among which, the discharge price and additional reward income are the data preset in the charge and discharge invitation; By formula: The total revenue of the station = income - V2G payment cost, and the total revenue of the station is obtained.

3. The energy aggregation management method in vehicle-grid interactive multi-energy collaboration according to claim 1 is characterized in that: Before sending a charge and discharge invitation to electric vehicles in a preset area through the V2G invitation organization module, the method further includes: The electric vehicles in the preset area upload the basic vehicle information to the registration program of the V2G invitation organization; when it is determined that the basic vehicle information meets the preset V2G standard, the electric vehicle registration is determined to be successful; The V2G invitation organization module sends charging and discharging invitations to electric vehicles that have successfully registered in the preset area.

4. The energy aggregation management method in vehicle-grid interactive multi-energy collaboration according to claim 1 is characterized in that: Obtain the battery status, remaining power, distance to the V2G charger, historical punctuality rate, and maximum battery charge and discharge power of the electric vehicle receiving the invitation, and then determine the selected vehicle, including: The battery status, remaining power, distance from the V2G charger, historical punctuality rate, and maximum battery charge and discharge power of the electric vehicle receiving the invitation are used as input data; the constraint range of the remaining power is used as a constraint condition and input into the preset trained algorithm, and the electric vehicle that obtains the output is the selected vehicle.

5. An energy aggregation management system for vehicle-grid interactive multi-energy collaboration, characterized in that: The system comprises: The regional load forecasting module is used to collect the historical load data of the preset area in the preset time period every day within the historical time period, and then predict the load demand of each preset time period of the next day; The market plan acquisition module is used to predict the photovoltaic power generation power, energy storage charging and discharging power, and electric vehicle charging and discharging power of the current power grid in each preset time period the next day through historical power data, and then combine the basic load power and network supply load to obtain the adjustable load for each preset time period; The market declaration module is used to obtain the difference between the load demand and the adjustable load corresponding to the same preset time period; when the adjustable load is greater than or equal to the load demand, the power grid in the preset area directly responds to the dispatch plan corresponding to the load demand; when the adjustable load is less than the load demand, the load difference is obtained and a dispatch instruction is issued to the V2G invitation organization module; The V2G invitation organization module is used to send charging and discharging invitations to electric vehicles in a preset area; obtain the battery status, remaining power, distance from the V2G charger, historical punctuality rate, and maximum battery charging and discharging power of the electric vehicle receiving the invitation, and then determine the selected vehicle; based on the load difference and the maximum battery charging and discharging power and remaining power of the selected vehicle, determine the charging and discharging amount and charging and discharging time of the selected vehicle within a preset time period; The V2G invitation organization module includes a calculation module for obtaining the historical punctuality rate corresponding to the selected vehicles, and then determining the order of each selected vehicle; based on the order of priority, determining in turn the operating time of the selected vehicles when the battery runs at the maximum charge and discharge power until the remaining power is equal to the preset minimum power; when the operating time is less than the preset time period, determining the operating time as the charge and discharge time of the selected vehicle within the preset time period; when the operating time is greater than or equal to the preset time period, determining the preset time period as the charge and discharge time of the selected vehicle within the preset time period; after obtaining the operating time of each selected vehicle, calculating the charge and discharge amount of each selected vehicle based on the operating time and the maximum charge and discharge power; based on the order of priority, accumulating the charge and discharge amounts in turn, and determining the position of the selected vehicle whose accumulated value is greater than or equal to the load difference; issuing a stop scheduling instruction to the selected vehicle after the position of the selected vehicle whose accumulated value is greater than or equal to the load difference, and resetting the charge and discharge amount and charge and discharge time of the selected vehicle that issued the stop scheduling instruction to zero.

6. The energy aggregation management system in vehicle-grid interactive multi-energy collaboration according to claim 5 is characterized in that: The system also includes a revenue calculation module: Used to obtain the actual electricity price, actual response power and coefficient constants of each preset time period of the next day; By formula: , calculate the actual income; where i represents the i-th preset time period, and n represents the total amount of the preset time period; By formula: , calculate the V2G payment cost; among which, the discharge price and additional reward income are the data preset in the charge and discharge invitation; By formula: The total revenue of the station = income - V2G payment cost, and the total revenue of the station is obtained.

7. The energy aggregation management system in vehicle-grid interactive multi-energy collaboration according to claim 5 is characterized in that: The V2G invitation organization module includes a car registration unit, Used to obtain basic vehicle information uploaded by electric vehicles in a preset area to the registration program of the V2G invitation organization; when it is determined that the basic vehicle information meets the preset V2G standard, the electric vehicle is successfully registered; The V2G invitation organization module sends charging and discharging invitations to electric vehicles that have successfully registered in the preset area.

8. A non-volatile computer storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed, they implement a method for energy aggregation management in vehicle-grid interactive multi-energy collaboration as described in any one of claims 1-4.

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