V2G scheduling method and system based on double-layer optimization model

Abstract

The invention discloses a V2G scheduling method and system based on a double-layer optimization model, and the method comprises the steps: obtaining electric vehicle data and power grid load data of a target region, and screening a first electric vehicle set participating in scheduling according to the electric vehicle data; in the first optimization process, performing optimization scheduling on the first electric vehicle set based on the power grid load data and a pre-constructed first scheduling optimization model to obtain a first optimization result; in the second optimization process, secondary optimization scheduling is carried out on the first optimization result according to the electric vehicle data and a pre-constructed second scheduling optimization model, and a second optimization result is obtained; taking the second optimization result as the input of the first optimization process, and iterating the first optimization process and the second optimization process to preset times to obtain a final optimization result; and performing scheduling processing on the first electric vehicle set according to the final optimization result. According to the method provided by the invention, the V2G scheduling efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of smart grids, and in particular to a V2G scheduling method and system based on a double-layer optimization model. Background Art

[0002] With the rapid development of the electric vehicle industry, vehicle-to-grid (V2G) technology has shown great potential in the new power system and energy Internet. V2G technology allows electric vehicles to transfer the remaining power of the on-board battery to the power grid when not in use, providing auxiliary services such as peak load regulation and black start for the power grid. How to efficiently dispatch these electric vehicles is the main problem of the current vehicle-grid interaction V2G technology.

[0003] A significant drawback of the existing technology is the lack of flexibility and real-time performance of the dispatching strategy. Traditional electric vehicle dispatching methods are often based on fixed schedules and preset rules, which cannot fully adapt to the real-time changes in grid load and electric vehicle usage status. This static dispatching strategy may not only lead to an imbalance in grid load, but also reduce the charging and discharging efficiency of electric vehicle users. Summary of the invention

[0004] The present application aims to provide a V2G scheduling method and system based on a two-layer optimization model to improve the efficiency of V2G scheduling.

[0005] In order to solve the above technical problems, an embodiment of the present application provides a V2G scheduling method based on a two-layer optimization model, comprising the following steps:

[0006] Acquire electric vehicle data and grid load data of a target area, and select a first set of electric vehicles participating in the scheduling according to the electric vehicle data;

[0007] In the first optimization process, the first electric vehicle set is optimized and scheduled based on the power grid load data and a pre-built first scheduling optimization model to obtain a first optimization result, wherein the optimization goal of the first scheduling optimization model is to minimize the daily load variance of the power grid in the target area;

[0008] In the second optimization process, the first optimization result is subjected to secondary optimization scheduling according to the electric vehicle data and a pre-built second scheduling optimization model to obtain a second optimization result, wherein the optimization goal of the second scheduling optimization model is to minimize the charging and discharging cost of the first electric vehicle set;

[0009] Using the second optimization result as the input of the first optimization process, and obtaining a final optimization result by iterating the first optimization process and the second optimization process to a preset number of times;

[0010] The first electric vehicle set is dispatched according to the final optimization result.

[0011] As one preferred solution, the first electric vehicle set is optimized and dispatched based on the power grid load data and a pre-built first dispatch optimization model to obtain a first optimization result, including:

[0012] Determining the acquired charging and discharging power of each electric vehicle in the first electric vehicle set in each selected time period as a decision variable, and determining the daily load variance of the power grid as an objective function, so as to construct the first scheduling optimization model;

[0013] Constructing a transformer capacity constraint according to the transformer capacity limit during the charging and discharging process of each electric vehicle in the first electric vehicle set; constructing a charging and discharging power constraint according to the allowable adjustment range of the exchange power of each electric vehicle;

[0014] The first dispatch optimization model is processed according to the load power and average load power in each selected time period, the transformer capacity constraint and the charge and discharge power constraint to obtain a first optimization result.

[0015] As one of the preferred solutions, the first scheduling optimization model is expressed as:

[0016]

[0017] Among them, P A represents the load power during period t, P B represents the average load power during period t, n represents the number of electric vehicles, P iC Represents the discharging and charging power of the electric vehicle during period t.

[0018] As one of the preferred solutions, the transformer capacity constraint is expressed as:

[0019]

[0020] Among them, P l,t represents the base load during period t, S c Indicates the transformer capacity limit;

[0021] The charge and discharge power constraint is expressed as:

[0022] -P dmax ≤P iC ≤P cmax

[0023] Among them, P dmax Indicates the maximum discharge power, P cmax Indicates the maximum charging power.

[0024] As one of the preferred solutions, performing secondary optimization scheduling on the first optimization result according to the electric vehicle data and the pre-built second scheduling optimization model to obtain the second optimization result includes:

[0025] Taking the charging and discharging state of each electric vehicle in the first set of electric vehicles in each of the time periods as a decision variable and minimizing the charging and discharging cost as an objective function, a second scheduling optimization model is constructed;

[0026] The second scheduling optimization model is subjected to linear weighted processing, and the electric vehicle data is input into the second scheduling optimization model after the linear weighted processing for calculation to obtain a second optimization result.

[0027] Another embodiment of the present application provides a V2G scheduling system based on a two-layer optimization model, including:

[0028] An acquisition module, used to acquire electric vehicle data and grid load data of a target area, and select a first set of electric vehicles participating in the scheduling according to the electric vehicle data;

[0029] A first optimization module is used to optimize the scheduling of the first electric vehicle set based on the power grid load data and a pre-built first scheduling optimization model in a first optimization process to obtain a first optimization result, wherein the optimization goal of the first scheduling optimization model is to minimize the daily load variance of the power grid in the target area;

[0030] A second optimization module is used to perform secondary optimization scheduling on the first optimization result according to the electric vehicle data and a pre-built second scheduling optimization model in a second optimization process to obtain a second optimization result, wherein the optimization goal of the second scheduling optimization model is to minimize the charging and discharging cost of the first electric vehicle set;

[0031] an iteration module, configured to use the second optimization result as an input of the first optimization process, and obtain a final optimization result by iterating the first optimization process and the second optimization process to a preset number of times;

[0032] The scheduling module is used to schedule the first electric vehicle set according to the final optimization result.

[0033] As one preferred solution, the first optimization module is specifically used for:

[0034] Determining the acquired charging and discharging power of each electric vehicle in the first electric vehicle set in each selected time period as a decision variable, and determining the daily load variance of the power grid as an objective function, so as to construct the first scheduling optimization model;

[0035] Constructing a transformer capacity constraint according to the transformer capacity limit during the charging and discharging process of each electric vehicle in the first electric vehicle set; constructing a charging and discharging power constraint according to the allowable adjustment range of the exchange power of each electric vehicle;

[0036] The first dispatch optimization model is processed according to the load power and average load power in each selected time period, the transformer capacity constraint and the charge and discharge power constraint to obtain a first optimization result.

[0037] As one of the preferred solutions, the first scheduling optimization model is expressed as:

[0038]

[0039] Among them, P A represents the load power during period t, P B represents the average load power during period t, n represents the number of electric vehicles, P iC Represents the discharging and charging power of the electric vehicle during period t.

[0040] As one of the preferred solutions, the transformer capacity constraint is expressed as:

[0041]

[0042] Among them, P l,t represents the base load during period t, S c Indicates the transformer capacity limit;

[0043] The charge and discharge power constraint is expressed as:

[0044] -P dmax ≤P iC ≤P cmax

[0045] Among them, P dmax Indicates the maximum discharge power, P cmax Indicates the maximum charging power.

[0046] As one preferred solution, the second scheduling optimization module is specifically used to:

[0047] Taking the charging and discharging state of each electric vehicle in the first set of electric vehicles in each of the time periods as a decision variable and minimizing the charging and discharging time as an objective function, a second scheduling optimization model is constructed;

[0048] The second scheduling optimization model is subjected to linear weighted processing, and the electric vehicle data is input into the second scheduling optimization model after the linear weighted processing for calculation to obtain a second optimization result.

[0049] Compared with the prior art, the beneficial effects of the embodiments of the present application are at least one of the following:

[0050] (1) Through the first optimization process, the scheme dispatches electric vehicles with the goal of minimizing the daily grid load variance in the target area. This helps to significantly reduce the fluctuation of grid load and make the grid operation more stable.

[0051] (2) Through a reasonable scheduling strategy, users can charge during periods when electricity prices are low, and choose to discharge or maintain battery power during periods when electricity prices are high, thus achieving the economically optimal solution.

[0052] (3) The solution realizes two-way interaction between electric vehicles and the power grid through V2G technology. This not only helps to balance the load of the power grid, but also provides auxiliary services such as peak load regulation and frequency regulation for the power grid, thereby improving the operating efficiency of the entire power system.

[0053] (4) By iterating the first optimization process and the second optimization process to a preset number of times, the scheme can continuously approach the optimal solution. This iterative optimization method improves the overall performance of the system and makes the scheduling strategy more reasonable and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flowchart of a V2G scheduling method based on a double-layer optimization model in one embodiment of the present application;

[0055] Figure 2 It is a schematic diagram of the V optimization process of a double-layer model in one embodiment of the present application;

[0056] Figure 3 It is a schematic diagram of a V2G scheduling system based on a double-layer optimization model in one embodiment of the present application. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0058] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0059] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used in this article are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application. The term "and / or" used in this article includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0060] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0061] An embodiment of the present application provides a polyurethane penetration grouting diffusion simulation method. For details, see Figure 1 , Figure 1 The flowchart of the V2G scheduling method based on the double-layer optimization model provided in one embodiment of the present application is shown. It includes steps S1 to S5:

[0062] S1. Acquire electric vehicle data and grid load data of a target area, and select a first set of electric vehicles to participate in scheduling according to the electric vehicle data;

[0063] S2. In a first optimization process, the first electric vehicle set is optimized and scheduled based on the power grid load data and a pre-constructed first scheduling optimization model to obtain a first optimization result, wherein the optimization goal of the first scheduling optimization model is to minimize the daily load variance of the power grid in the target area;

[0064] S3. In a second optimization process, the first optimization result is subjected to secondary optimization scheduling according to the electric vehicle data and a pre-constructed second scheduling optimization model to obtain a second optimization result, wherein the optimization goal of the second scheduling optimization model is to minimize the charging and discharging cost of the first electric vehicle set;

[0065] S4, using the second optimization result as the input of the first optimization process, and obtaining a final optimization result by iterating the first optimization process and the second optimization process to a preset number of times;

[0066] S5. Dispatching the first electric vehicle set according to the final optimization result.

[0067] It should be noted that although the traditional time-of-use electricity price strategy can improve the situation of increasing peak-to-valley difference and reducing load rate of power grid caused by disorderly charging of electric vehicles to a certain extent, it is easy to generate new load peaks, and the current multi-objective optimization strategy has poor peak-to-valley effect or low user participation, which affects the safety and reliability of the power grid. Therefore, proposing a reasonable and efficient electric vehicle charging and discharging scheduling strategy is the main problem of the current vehicle-grid interaction V2G technology.

[0068] This application studies V2G optimization scheduling, fully considers the needs of both the power grid and users, and proposes a scheduling strategy based on a two-layer optimization model. First, the first-layer model is determined to minimize the variance of the daily load of the power grid and the second-layer model is determined to minimize the charging cost of the owner and ensure the travel needs of users; then, the optimization results of the second layer are fed back to the first layer, so as to cycle the optimization and output the optimal result.

[0069] Before optimizing the dispatch, the control center needs to collect and screen the information on whether electric vehicles can participate in the dispatch. The information of electric vehicles that can participate in the dispatch is transmitted to the decision center for optimized dispatch, and the grid load and electric vehicle charging and discharging electricity price information are collected.

[0070] By controlling the charging and discharging power of electric vehicles at different time periods within a day, and considering the transformer capacity constraints and the charging and discharging power constraints of electric vehicles, the first-layer model is optimized and calculated, and the daily load curve is obtained.

[0071] Preferably, in one embodiment of the present invention, the optimizing scheduling of the first electric vehicle set based on the power grid load data and the pre-built first scheduling optimization model to obtain a first optimization result includes:

[0072] Determining the acquired charging and discharging power of each electric vehicle in the first electric vehicle set in each selected time period as a decision variable, and determining the daily load variance of the power grid as an objective function, so as to construct the first scheduling optimization model;

[0073] Constructing a transformer capacity constraint according to the transformer capacity limit during the charging and discharging process of each electric vehicle in the first electric vehicle set; constructing a charging and discharging power constraint according to the allowable adjustment range of the exchange power of each electric vehicle;

[0074] The first dispatch optimization model is processed according to the load power and average load power in each selected time period, the transformer capacity constraint and the charge and discharge power constraint to obtain a first optimization result.

[0075] Specifically, by taking the charging and discharging power of electric vehicles in each time period as the control variable, the V2G optimization scheduling strategy on the power grid side mainly takes peak shaving and valley filling as the goal, and establishes the objective function of minimizing the daily load variance. The 24 hours of a day are divided into 96 time periods, each of which is 15 minutes, and the obtained data are sorted according to the time intervals of t=1, 2, 3, ..., 96.

[0076] As one of the preferred solutions, the first scheduling optimization model is expressed as:

[0077]

[0078] Among them, P A represents the load power during period t, P B represents the average load power during period t, n represents the number of electric vehicles, P iC Represents the discharging and charging power of the electric vehicle during period t.

[0079] Preferably, in one embodiment of the present invention, the transformer capacity constraint is expressed as:

[0080]

[0081] Among them, P l,t represents the base load during period t, S c Indicates the transformer capacity limit;

[0082] The charge and discharge power constraint is expressed as:

[0083] -P dmax ≤P iC ≤P cmax

[0084] Among them, P dmax Indicates the maximum discharge power, P cmax Indicates the maximum charging power.

[0085] Preferably, in one embodiment of the present invention, performing secondary optimization scheduling on the first optimization result according to the electric vehicle data and the pre-built second scheduling optimization model to obtain the second optimization result includes:

[0086] Taking the charging and discharging state of each electric vehicle in the first set of electric vehicles in each of the time periods as a decision variable and minimizing the charging and discharging cost as an objective function, a second scheduling optimization model is constructed;

[0087] The second scheduling optimization model is subjected to linear weighted processing, and the electric vehicle data is input into the second scheduling optimization model after the linear weighted processing for calculation to obtain a second optimization result.

[0088] Specifically, the second level optimization is performed on the basis of the first level optimization charging and discharging model. According to the time-of-use electricity price, V2G will bring price difference benefits to users. The goal is to minimize the charging and discharging cost of car owners. The second scheduling optimization model is expressed as:

[0089]

[0090] Based on the user's travel needs, the SOC objective function of the electric vehicle after charging and discharging is as follows:

[0091]

[0092] Among them, S1 represents the charging parameter, when charging, S1=1, S2=0, S2 represents the discharging parameter, when discharging, S1=0, S2=1, G 1t represents the charging electricity price during period t, G 2t represents the discharge electricity price in period t, T represents the length of the unit control time period, E c Indicates the charging efficiency of the vehicle battery, E d represents the discharge efficiency of the vehicle battery, S0,i represents the initial SOC of the i-th vehicle, A i represents the battery capacity of the i-th vehicle.

[0093] Furthermore, linear weighted processing is performed in the second scheduling optimization model, which is expressed as:

[0094]

[0095] Among them, F represents the multi-objective function of electric vehicles, λ1 represents the weighted coefficient of function f1, λ2 represents the weighted coefficient of function f2, f1 max represents the maximum value of function f1, f2 max Represents the maximum value of function f2.

[0096] The scheduling strategy of the second scheduling optimization is mainly subject to the following constraints:

[0097] (1) Available time constraints

[0098] Electric vehicles are not always connected to the grid 24 hours a day, 96 hours a day, so it is necessary to set the acceptable scheduling time constraints for electric vehicles:

[0099] T in ≤T V2G ≤T out

[0100] Where, T in represents the access time of electric vehicles participating in orderly charging, T V2G It indicates the time available for scheduling when electric vehicles participate in orderly charging and discharging.

[0101] (2) State of charge value constraints

[0102]

[0103] In the formula, S min Indicates the lower limit of normal charge (S min =0.3), S max Indicates the upper limit of normal charge (S max =1).

[0104] (3) Constraints on the lower limit of time-of-use electricity prices

[0105] Users have limited tolerance for charging costs, so the upper and lower limits of the charging and discharging time-of-use electricity price need to be set as follows:

[0106] P tmin ≤P t ≤P tmaX

[0107] Where P tmin represents the lower limit of the charging electricity price in the t period, P tmax Indicates the upper limit of the charging electricity price in the tth period.

[0108] The first optimization result is used as a constraint to carry out the second-level optimization strategy based on user-side demand. The optimization calculation is based on the minimum charging and discharging cost of the car owner, and the factors of electric vehicle use demand and electricity price fluctuation are considered to obtain the optimization result of the second-level model. For details, please refer to Figure 2 , Figure 2 Shown is a schematic diagram of the optimization process of a double-layer model provided in one embodiment of the present application.

[0109] The optimization results of the charging and discharging power of electric vehicles in each time period obtained by the second layer model are P iCFeedback is given to the upper layer for the next cycle, and the upper and lower layer models are repeatedly iterated until the result is satisfactory, and then the loop is exited to obtain the optimal solution.

[0110] The final optimization result is fed back to the intelligent charging device, and the intelligent charging device and its cooperating electric vehicles execute the scheduling arrangement after the strategy optimization.

[0111] Compared with the prior art, the beneficial effects of the embodiments of the present application are at least one of the following:

[0112] (1) Through the first optimization process, the scheme dispatches electric vehicles with the goal of minimizing the daily grid load variance in the target area. This helps to significantly reduce the fluctuation of grid load and make the grid operation more stable.

[0113] (2) Through a reasonable scheduling strategy, users can charge during periods when electricity prices are low, and choose to discharge or maintain battery power during periods when electricity prices are high, thus achieving the economically optimal solution.

[0114] (3) The solution realizes two-way interaction between electric vehicles and the power grid through V2G technology. This not only helps to balance the load of the power grid, but also provides auxiliary services such as peak load regulation and frequency regulation for the power grid, thereby improving the operating efficiency of the entire power system.

[0115] (4) By iterating the first optimization process and the second optimization process to a preset number of times, the scheme can continuously approach the optimal solution. This iterative optimization method improves the overall performance of the system and makes the scheduling strategy more reasonable and efficient.

[0116] Another embodiment of the present application provides a V2G scheduling system based on a two-layer optimization model. For details, see Figure 3 , Figure 3 The schematic diagram of the V2G scheduling system based on the double-layer optimization model provided in one embodiment of the present application is shown, which includes the following modules: an acquisition module 11, a first optimization module 12, a second optimization module 13, an iteration module 14, and a scheduling module 15, wherein

[0117] An acquisition module 11 is used to acquire electric vehicle data and grid load data of a target area, and select a first set of electric vehicles participating in the scheduling according to the electric vehicle data;

[0118] A first optimization module 12 is used to optimize the scheduling of the first electric vehicle set based on the power grid load data and a pre-built first scheduling optimization model in a first optimization process to obtain a first optimization result, wherein the optimization goal of the first scheduling optimization model is to minimize the daily load variance of the power grid in the target area;

[0119] A second optimization module 13 is used to perform secondary optimization scheduling on the first optimization result according to the electric vehicle data and a pre-built second scheduling optimization model in a second optimization process to obtain a second optimization result, wherein the optimization goal of the second scheduling optimization model is to minimize the charging and discharging cost of the first electric vehicle set;

[0120] An iteration module 14, configured to use the second optimization result as an input of the first optimization process, and obtain a final optimization result by iterating the first optimization process and the second optimization process to a preset number of times;

[0121] The scheduling module 15 is used to schedule the first electric vehicle set according to the final optimization result.

[0122] Preferably, in one embodiment of the present invention, the first optimization module is specifically used to:

[0123] Determining the acquired charging and discharging power of each electric vehicle in the first electric vehicle set in each selected time period as a decision variable, and determining the daily load variance of the power grid as an objective function, so as to construct the first scheduling optimization model;

[0124] Constructing a transformer capacity constraint according to the transformer capacity limit during the charging and discharging process of each electric vehicle in the first electric vehicle set; constructing a charging and discharging power constraint according to the allowable adjustment range of the exchange power of each electric vehicle;

[0125] The first dispatch optimization model is processed according to the load power and average load power in each selected time period, the transformer capacity constraint and the charge and discharge power constraint to obtain a first optimization result.

[0126] Preferably, in one embodiment of the present invention, the first scheduling optimization model is expressed as:

[0127]

[0128] Among them, P A represents the load power during period t, P B represents the average load power during period t, n represents the number of electric vehicles, P iC Represents the discharging and charging power of the electric vehicle during period t.

[0129] Preferably, in one embodiment of the present invention, the transformer capacity constraint is expressed as:

[0130]

[0131] Among them, P l,t represents the base load during period t, Sc Indicates the transformer capacity limit;

[0132] The charge and discharge power constraint is expressed as:

[0133] -P dmax ≤P iC ≤P cmax

[0134] Among them, P dmax Indicates the maximum discharge power, P cmax Indicates the maximum charging power.

[0135] Preferably, in one embodiment of the present invention, the second scheduling optimization module is specifically used to:

[0136] Taking the charging and discharging state of each electric vehicle in the first set of electric vehicles in each of the time periods as a decision variable and minimizing the charging and discharging time as an objective function, a second scheduling optimization model is constructed;

[0137] The second scheduling optimization model is subjected to linear weighted processing, and the electric vehicle data is input into the second scheduling optimization model after the linear weighted processing for calculation to obtain a second optimization result.

[0138] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A V2G scheduling method based on a two-layer optimization model, characterized in that: The following steps are involved: Acquire electric vehicle data and grid load data of a target area, and select a first set of electric vehicles participating in the scheduling according to the electric vehicle data; In the first optimization process, the first electric vehicle set is optimized and scheduled based on the power grid load data and a pre-built first scheduling optimization model to obtain a first optimization result, wherein the optimization goal of the first scheduling optimization model is to minimize the daily load variance of the power grid in the target area; In the second optimization process, the first optimization result is subjected to secondary optimization scheduling according to the electric vehicle data and a pre-built second scheduling optimization model to obtain a second optimization result, wherein the optimization goal of the second scheduling optimization model is to minimize the charging and discharging cost of the first electric vehicle set; Using the second optimization result as the input of the first optimization process, and obtaining a final optimization result by iterating the first optimization process and the second optimization process to a preset number of times; The first electric vehicle set is dispatched according to the final optimization result.

2. The V2G scheduling method based on the double-layer optimization model according to claim 1 is characterized in that: The step of optimizing the first electric vehicle set based on the power grid load data and the pre-built first scheduling optimization model to obtain a first optimization result includes: Determining the acquired charging and discharging power of each electric vehicle in the first electric vehicle set in each selected time period as a decision variable, and determining the daily load variance of the power grid as an objective function, so as to construct the first scheduling optimization model; Constructing a transformer capacity constraint according to the transformer capacity limit during the charging and discharging process of each electric vehicle in the first electric vehicle set; constructing a charging and discharging power constraint according to the allowable adjustment range of the exchange power of each electric vehicle; The first dispatch optimization model is processed according to the load power and average load power in each selected time period, the transformer capacity constraint and the charge and discharge power constraint to obtain a first optimization result.

3. The V2G scheduling method based on the double-layer optimization model according to claim 2 is characterized in that: The first scheduling optimization model is expressed as: Among them, P A represents the load power during period t, P B represents the average load power during period t, n represents the number of electric vehicles, P iC Represents the discharging and charging power of the electric vehicle during period t.

4. The V2G scheduling method based on the double-layer optimization model according to claim 2 is characterized in that: The transformer capacity constraint is expressed as: Among them, P l,t represents the base load during period t, S c Indicates the transformer capacity limit; The charge and discharge power constraint is expressed as: -P dmax ≤P iC ≤P cmax Among them, P dmax Indicates the maximum discharge power, P cmax Indicates the maximum charging power.

5. The V2G scheduling method based on the double-layer optimization model according to claim 1 is characterized in that: The performing secondary optimization scheduling on the first optimization result according to the electric vehicle data and the pre-built second scheduling optimization model to obtain the second optimization result includes: Taking the charging and discharging state of each electric vehicle in the first set of electric vehicles in each of the time periods as a decision variable and minimizing the charging and discharging cost as an objective function, a second scheduling optimization model is constructed; The second scheduling optimization model is subjected to linear weighted processing, and the electric vehicle data is input into the second scheduling optimization model after the linear weighted processing for calculation to obtain a second optimization result.

6. A V2G scheduling system based on a two-layer optimization model, characterized in that: Includes the following modules: An acquisition module, used to acquire electric vehicle data and grid load data of a target area, and select a first set of electric vehicles participating in the scheduling according to the electric vehicle data; A first optimization module is used to optimize the scheduling of the first electric vehicle set based on the power grid load data and a pre-built first scheduling optimization model in a first optimization process to obtain a first optimization result, wherein the optimization goal of the first scheduling optimization model is to minimize the daily load variance of the power grid in the target area; A second optimization module is used to perform secondary optimization scheduling on the first optimization result according to the electric vehicle data and a pre-built second scheduling optimization model in a second optimization process to obtain a second optimization result, wherein the optimization goal of the second scheduling optimization model is to minimize the charging and discharging cost of the first electric vehicle set; an iteration module, configured to use the second optimization result as an input of the first optimization process, and obtain a final optimization result by iterating the first optimization process and the second optimization process to a preset number of times; The scheduling module is used to schedule the first electric vehicle set according to the final optimization result.

7. The V2G scheduling system based on the double-layer optimization model according to claim 6 is characterized in that: The first optimization module is specifically used for: Determining the acquired charging and discharging power of each electric vehicle in the first electric vehicle set in each selected time period as a decision variable, and determining the daily load variance of the power grid as an objective function, so as to construct the first scheduling optimization model; Constructing a transformer capacity constraint according to the transformer capacity limit during the charging and discharging process of each electric vehicle in the first electric vehicle set; constructing a charging and discharging power constraint according to the allowable adjustment range of the exchange power of each electric vehicle; The first dispatch optimization model is processed according to the load power and average load power in each selected time period, the transformer capacity constraint and the charge and discharge power constraint to obtain a first optimization result.

8. The V2G scheduling system based on the double-layer optimization model according to claim 7 is characterized in that: The first scheduling optimization model is expressed as: Among them, P A represents the load power during period t, P B represents the average load power during period t, n represents the number of electric vehicles, P iC Represents the discharging and charging power of the electric vehicle during period t.

9. The V2G scheduling system based on the double-layer optimization model according to claim 7, characterized in that: The transformer capacity constraint is expressed as: Among them, P l,t represents the base load during period t, S c Indicates the transformer capacity limit; The charge and discharge power constraint is expressed as: -P dmax ≤P iC ≤P cmax Among them, P dmax Indicates the maximum discharge power, P cmax Indicates the maximum charging power.

10. The V2G scheduling system based on the double-layer optimization model according to claim 6, characterized in that: The second scheduling optimization module is specifically used for: Taking the charging and discharging state of each electric vehicle in the first set of electric vehicles in each of the time periods as a decision variable and minimizing the charging and discharging time as an objective function, a second scheduling optimization model is constructed; The second scheduling optimization model is subjected to linear weighted processing, and the electric vehicle data is input into the second scheduling optimization model after the linear weighted processing for calculation to obtain a second optimization result.

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