Electric vehicle charging and discharging optimization method and device and nonvolatile storage medium

Abstract

The invention discloses an optimization method and device for charging and discharging of an electric vehicle and a nonvolatile storage medium. The method comprises the steps that vehicle data and operation data of a target electric vehicle are obtained, and the vehicle data comprise at least one of the following data: the battery capacity and the charging and discharging power limitation of the target electric vehicle; the vehicle data are input into a deterministic model, a charging and discharging plan corresponding to the target electric vehicle is output, and the deterministic model is used for adjusting the charging and discharging plan of the target electric vehicle with the minimum cost of an aggregation system where the target electric vehicle is located as the target; and inputting the vehicle data and the operation data into the hierarchical model, adjusting a charging and discharging plan of the target electric vehicle in the hierarchical model, and determining a target charging and discharging plan of the target electric vehicle. The technical problem that the optimization effect is poor due to the fact that an existing electric vehicle charging and discharging optimization method cannot effectively process the uncertainty of the driving mode of the electric vehicle and the uncertainty of the charging and discharging demand is solved.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to an optimization method and device for charging and discharging an electric vehicle and a non-volatile storage medium. Background Art

[0002] EV aggregation systems can provide grid stabilization services by optimizing the controlled charging and recharging of large-scale EV fleets. In the rapidly developing EV environment, the main responsibility of the aggregation system is to optimize the sailing / chasing operation of electricity at the lowest cost to meet the driving needs of EV owners while considering the uncertainties associated with the driving plans of all connected EVs. In this context, the aggregation system faces two major obstacles when dealing with large-scale EV fleets, first, evaluating the driving pattern uncertainty of each EV while determining the precise data of its internal and technical specifications, and second, selecting a robust computational scheme to actually optimize the actual EV fleet. Traditional EV aggregation optimization methods cannot effectively handle the uncertainty of EV driving patterns, the uncertainty of charging and discharging demands, and the random time of arrival at the aggregation system.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide an optimization method, device and non-volatile storage medium for charging and discharging of an electric vehicle, so as to at least solve the technical problem that the current optimization method for charging and discharging of an electric vehicle cannot effectively handle the uncertainty of the driving mode of the electric vehicle and the uncertainty of the charging and discharging demand, resulting in poor optimization effect.

[0005] According to one aspect of an embodiment of the present invention, there is provided a method for optimizing charging and discharging of an electric vehicle, comprising: acquiring vehicle data and operating data of a target electric vehicle, wherein the vehicle data includes at least one of the following: battery capacity and charging and discharging power limit of the target electric vehicle; inputting the vehicle data into a deterministic model, and outputting a charging and discharging plan corresponding to the target electric vehicle, wherein the deterministic model is used to adjust the charging and discharging plan of the target electric vehicle with the target electric vehicle's cost minimizing as the target of the aggregated system; inputting the vehicle data and operating data into a hierarchical model, and adjusting the charging and discharging plan of the target electric vehicle in the hierarchical model to determine a target charging and discharging plan for the target electric vehicle, wherein the hierarchical model includes an upper model and a lower model, wherein the upper model is used to adjust the charging and discharging plan with the aggregated system as the target based on the deterministic model according to uncertainties in the operating data, and the lower model is used to adjust the charging and discharging plan with the target electric vehicle operating normally under the worst case as the target based on uncertainties in the operating data.

[0006] Optionally, before inputting vehicle data into the deterministic model and outputting the charging and discharging plan corresponding to the target electric vehicle, it includes: setting a first objective function according to the cost of the aggregation system; setting a first constraint condition according to the deterministic factors corresponding to the electric vehicle, wherein the deterministic factors represent factors related to the electric vehicle itself that will not change during the operation of the vehicle; and constructing a deterministic model according to the first objective function and the first constraint condition.

[0007] Optionally, the first constraint in the deterministic model includes at least one of the following: a power limit on a single charge and discharge of the target electric vehicle, a battery capacity limit on the target electric vehicle, a limit on the state of charge of the electric vehicle battery, and a transmission capacity limit on the target electric vehicle.

[0008] Optionally, before inputting vehicle data and operating data into the hierarchical model and adjusting the charging and discharging plan of the target electric vehicle in the hierarchical model, the method includes: setting a second constraint condition based on the deterministic model to obtain an upper model in the hierarchical model, wherein the second constraint condition represents the constraint on the target electric vehicle during operation; and constructing a lower model in the hierarchical model according to the restriction conditions of the aggregation system with the goal of minimizing the impact of the target electric vehicle on the aggregation system.

[0009] Optionally, the second constraint condition includes at least one of the following: a transportation energy constraint condition of the electric vehicle, a power demand constraint condition of the electric vehicle, and a logical constraint condition, wherein the logical constraint condition characterizes that the transportation power of the electric vehicle is non-negative.

[0010] Optionally, the lower-layer model includes a worst availability planning model and a worst vehicle-grid interaction planning model.

[0011] Optionally, the following method is used to construct a worst availability planning model, including: setting a third objective function based on the amount of electricity stored in the target electric vehicle within a target duration; setting a third constraint based on the minimum available electricity of the target electric vehicle per hour and the limit on the number of available electric vehicles in the aggregation system; and constructing a worst availability planning model based on the third objective function and the third constraint.

[0012] Optionally, the following method is used to construct a worst-case vehicle-grid interaction planning model, including: setting a fourth objective function according to the impact of the target electric vehicle on the aggregation system; setting a fourth constraint condition according to the battery energy supply of the target electric vehicle under the worst case scenario; and constructing the worst-case vehicle-grid interaction planning model based on the fourth objective function and the fourth constraint condition.

[0013] According to another aspect of an embodiment of the present invention, there is also provided an optimization device for charging and discharging an electric vehicle, comprising: an acquisition module for acquiring vehicle data and operating data of a target electric vehicle, wherein the vehicle data includes at least one of the following: battery capacity and charging and discharging power limit of the target electric vehicle; an output module for inputting the vehicle data into a deterministic model and outputting a charging and discharging plan corresponding to the target electric vehicle, wherein the deterministic model is used to adjust the charging and discharging plan of the target electric vehicle with the goal of minimizing the cost of the aggregation system in which the target electric vehicle is located; a determination module for inputting the vehicle data into the deterministic model and outputting the charging and discharging plan corresponding to the target electric vehicle , wherein the deterministic model is used to adjust the charging and discharging plan of the target electric vehicle with the goal of minimizing the cost of the aggregation system where the target electric vehicle is located; the vehicle data and the operation data are input into the hierarchical model, and the charging and discharging plan of the target electric vehicle is adjusted in the hierarchical model to determine the target charging and discharging plan of the target electric vehicle, wherein the hierarchical model includes an upper model and a lower model, the upper model is used to adjust the charging and discharging plan with the aggregation system as the goal based on the deterministic model according to the uncertain factors in the operation data, and the lower model is used to adjust the charging and discharging plan with the goal of normal operation of the target electric vehicle under the worst case based on the uncertain factors in the operation data.

[0014] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, the non-volatile storage medium including a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute any one of the above-mentioned electric vehicle charging and discharging optimization methods.

[0015] According to another aspect of the embodiments of the present invention, a computer device is provided. The computer device includes a processor, and the processor is used to run a program. When the program is run, any one of the above-mentioned optimization methods for charging and discharging an electric vehicle is executed.

[0016] According to another aspect of the embodiments of the present invention, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, any one of the above-mentioned optimization methods for charging and discharging an electric vehicle is implemented.

[0017] In an embodiment of the present invention, an optimization method for charging and discharging an electric vehicle is adopted, by obtaining vehicle data and operating data of a target electric vehicle, wherein the vehicle data includes at least one of the following: battery capacity and charging and discharging power limit of the target electric vehicle; the vehicle data is input into a deterministic model, and a charging and discharging plan corresponding to the target electric vehicle is output, wherein the deterministic model is used to adjust the charging and discharging plan of the target electric vehicle with the minimum cost of the aggregation system where the target electric vehicle is located as the goal; the vehicle data and operating data are input into a hierarchical model, and the charging and discharging plan of the target electric vehicle is adjusted in the hierarchical model to determine the target charging and discharging plan of the target electric vehicle, wherein, The hierarchical model includes an upper model and a lower model. The upper model is used to adjust the charging and discharging plan based on the deterministic model according to the uncertainties in the operating data with the goal of the aggregate system. The lower model is used to adjust the charging and discharging plan based on the uncertainties in the operating data with the goal of the target electric vehicle operating normally under the worst case scenario. This achieves the purpose of optimizing the charging and discharging plan in combination with the uncertainties in the operation of electric vehicles, thereby achieving the technical effect of improving the optimization effect, and further solving the technical problem that the current optimization method for electric vehicle charging and discharging cannot effectively deal with the uncertainty of electric vehicle driving modes and the uncertainty of charging and discharging requirements, resulting in poor optimization effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0019] Figure 1 A hardware structure block diagram of a computer terminal for realizing an optimization method for charging and discharging an electric vehicle is shown;

[0020] Figure 2 is a schematic flow chart of an optimization method for charging and discharging an electric vehicle provided according to an embodiment of the present invention;

[0021] Figure 3 is a modeling flow chart of an optimization method for charging and discharging an electric vehicle provided according to an optional embodiment of the present invention;

[0022] Figure 4 It is a schematic diagram of the association between the upper and lower layers of the hierarchical model in the optimization method for charging and discharging of an electric vehicle provided according to an optional embodiment of the present invention;

[0023] Figure 5 It is a structural block diagram of an optimization device for charging and discharging an electric vehicle provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following interpretations:

[0027] Aggregation system: Aggregation system refers to electric vehicle aggregators, whose main goal is to improve overall efficiency and reduce costs by optimizing the charging and discharging plans of electric vehicles, or to obtain profits by adjusting loads and participating in the electricity market.

[0028] According to an embodiment of the present invention, a method embodiment of an optimization method for charging and discharging an electric vehicle is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal for realizing an optimization method for charging and discharging electric vehicles is shown. Figure 1As shown, the computer terminal 10 may include one or more (102a, 102b, ..., 102n are used to illustrate) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0030] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0031] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the optimization method for charging and discharging an electric vehicle in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the optimization method for charging and discharging an electric vehicle of the above-mentioned application program is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0032] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0033] Figure 2 FIG. 1 is a flow chart of an optimization method for charging and discharging an electric vehicle according to an embodiment of the present invention. Figure 2As shown, the method comprises the following steps:

[0034] Step S202, obtaining vehicle data and operation data of the target electric vehicle, wherein the vehicle data includes at least one of the following: battery capacity and charge and discharge power limit of the target electric vehicle.

[0035] In this step, the vehicle data of the target electric vehicle is obtained. The vehicle data here may be the battery capacity, the maximum power and the minimum power during charging and discharging in the target electric vehicle, and may also include the battery degradation cost of the target electric vehicle. The operation data may include the battery status and output power of the target electric vehicle during vehicle operation, and may also include the vehicle usage habits of the user corresponding to the target electric vehicle. These data will affect the charging and discharging of the target electric vehicle, among which battery capacity, charging and discharging power limit, etc. can be used as deterministic factors, while operation data, such as the user's driving preference, are uncertain factors.

[0036] Step S204, inputting vehicle data into a deterministic model, and outputting a charge and discharge plan corresponding to the target electric vehicle, wherein the deterministic model is used to adjust the charge and discharge plan of the target electric vehicle with the goal of minimizing the cost of the aggregation system where the target electric vehicle is located.

[0037] In this step, the vehicle data can be input into the deterministic model, which can be a model that adjusts the charging and discharging plan based on the deterministic factors in the target electric vehicle with the goal of minimizing the cost of the aggregation system where the target electric vehicle is located. Among them, the electric vehicle aggregation system is deterministically modeled, and the model includes objective functions and constraints. The basic goal of the electric vehicle aggregation system is to effectively achieve two goals, that is, to obtain the optimal charging and discharging scheduling mode for each connected electric vehicle, appropriately meet their physical limitations, and build an optimization model with the goal of minimizing the overall operating cost, specifically including the purchase and sale cost of energy, battery degradation cost, and breach of contract penalty cost.

[0038] Step S206, input the vehicle data and the operating data into the hierarchical model, adjust the charging and discharging plan of the target electric vehicle in the hierarchical model, and determine the target charging and discharging plan of the target electric vehicle, wherein the hierarchical model includes an upper model and a lower model, the upper model is used to adjust the charging and discharging plan based on the deterministic model according to the uncertainties in the operating data with the target of the aggregated system as the target, and the lower model is used to adjust the charging and discharging plan based on the uncertainties in the operating data with the target electric vehicle operating normally under the worst case as the target.

[0039] In this step, the vehicle data and operation data are input into the hierarchical model, wherein the hierarchical model includes an upper model and a lower model, wherein the upper model can be a model of an upper aggregation system established based on a deterministic model, and the upper model is different from the deterministic model in that the upper model takes into account the uncertainties of electric vehicles during operation. The lower model considers the specific availability and behavioral uncertainty of electric vehicles according to the uncertainties of the target electric vehicles, wherein specifically, the worst charging and discharging state and the emergency situation of reduced participation in grid interaction are taken into account. The charging and discharging of electric vehicles are optimized according to the above model to obtain a target charging and discharging plan. The charging and discharging plan refers to the charging and discharging schedule and power level formulated for each electric vehicle in the electric vehicle (EV) aggregation system. This plan is the core part of the electric vehicle aggregation optimization process, which aims to balance the grid load, maximize economic benefits, reduce battery degradation costs, and meet the charging and discharging needs of car owners. In a smart grid environment, the charging and discharging plan can be dynamically adjusted to respond to the real-time demand of the grid and market price fluctuations. For example, when the grid load is low, the plan may arrange for electric vehicles to charge to take advantage of cheap electricity; when the load is peak, electric vehicles may be dispatched to discharge to the grid to relieve peak pressure and earn income by participating in electricity market transactions. The hierarchical model improves the efficiency and enthusiasm of electric vehicles in participating in grid interaction and improves the optimization effect of charging and discharging plans.

[0040] Through the above steps, the purpose of optimizing the charging and discharging plan in combination with the uncertain factors of the electric vehicle during operation can be achieved, thereby realizing the technical effect of improving the optimization effect, and further solving the technical problem that the current optimization method of electric vehicle charging and discharging cannot effectively deal with the uncertainty of the electric vehicle driving mode and the uncertainty of the charging and discharging demand, resulting in poor optimization effect.

[0041] As an optional embodiment, before inputting vehicle data into a deterministic model and outputting a charging and discharging plan corresponding to a target electric vehicle, the method includes: setting a first objective function according to the cost of an aggregation system; setting a first constraint condition according to a deterministic factor corresponding to the electric vehicle, wherein the deterministic factor represents a factor related to the electric vehicle itself that will not change during the operation of the vehicle; and constructing a deterministic model according to the first objective function and the first constraint condition.

[0042] Optionally, the deterministic model can define a first objective function according to the basic goal of the electric vehicle aggregation system. The upper objective function aims to minimize the cost of the aggregation system, and then set the first constraint condition, which can set the constraint conditions in the deterministic model, including the power supply constraint of the aggregation system, the transmission capacity constraint, the electric vehicle battery state of charge constraint, the single charge and discharge constraint, the battery capacity constraint and the battery degradation cost constraint.

[0043] The basic goal of the EV aggregation system is to improve its overall profitability by effectively accomplishing two goals: defining the optimal charging and discharging scheduling mode for each connected EV and appropriately meeting their physical limitations. Therefore, an optimization model is constructed with the goal of minimizing the overall operating cost, including the purchase and sale costs of energy. Battery degradation costs Breach of contract penalty costs The specific objective function is expressed as follows:

[0044]

[0045] in, is a set of decision variables, c e,t is the charging power provided by the electric vehicle at time t; d e,t represents the discharge power provided by the electric vehicle at time t, e e,t represents the state of charge of the battery of the electric vehicle at time t; represents the electricity price at time t; p t It represents the total amount of electricity provided by the aggregation system at time t. When it is greater than 0, it means that electricity is purchased from the grid during this period, otherwise it means that electricity is sold; for the degradation costs of electric vehicle batteries; A penalty levied for each kilowatt-hour of electricity produced that does not meet certain operating requirements; e,t is a slack variable for energy balance. A first constraint condition is set according to the vehicle data of the target electric vehicle, such as an aggregate system power supply constraint, a transmission capacity limit constraint, an electric vehicle battery state of charge constraint, a single charge and discharge limit constraint, etc. A deterministic model can be constructed according to the first objective function and the first constraint condition.

[0046] As an optional embodiment, the first constraint in the deterministic model includes at least one of the following: a power limit on a single charge and discharge of the target electric vehicle, a battery capacity limit on the target electric vehicle, a limit on the charge state of the electric vehicle battery, and a transmission capacity limit on the target electric vehicle.

[0047] Optionally, the first constraint condition is mainly based on the performance of the target electric vehicle itself and may include the following constraint conditions.

[0048] The power supply constraint of the aggregate system is the constraint on the power consumed by the aggregate system at time t.

[0049]

[0050] Among them, p t represents the total power provided by the aggregation system at time t, c e,t is the charging power provided by the electric vehicle at time t; de,t Represents the discharge power provided by the electric vehicle at time t.

[0051] Transmission capacity limit constraint, the capacity limit constraint on the connected feeder at time t.

[0052]

[0053] Among them, P S It represents the maximum capacity allowed to be transmitted when the aggregation system is connected to the power grid.

[0054] The state of charge constraint of the electric vehicle battery characterizes the change of the battery power of the electric vehicle battery at two adjacent moments.

[0055]

[0056] Among them, e e,t-1 is the charging power provided by the electric vehicle at time t-1; η e Indicates the power loss coefficient of electric vehicles during charging and discharging; c e,t Indicates the single charging power of electric vehicles; d e,t Indicates the single discharge power of an electric vehicle; Indicates expected value.

[0057] The single charge and discharge limit constraint refers to the maximum power limit of a single charge and discharge operation of an electric vehicle.

[0058]

[0059] in, It indicates the maximum allowable power of an electric vehicle on a single charge; Indicates the maximum allowable single discharge power of an electric vehicle.

[0060] Electric vehicle battery capacity limitation constraints refer to the maximum and minimum battery capacities for electric vehicle charging and discharging operations.

[0061]

[0062] in, It indicates the maximum power that electric vehicles are allowed to store; E -v Indicates the minimum power that an electric vehicle is allowed to store.

[0063] The electric vehicle battery degradation cost constraint refers to the degradation cost caused by the reduction in battery life due to each charging and discharging operation of the electric vehicle battery.

[0064]

[0065] in, is the total charging cost of electric vehicles; Indicates the available power of an electric vehicle.

[0066] As an optional embodiment, before inputting vehicle data and operation data into a hierarchical model and adjusting the charging and discharging plan of a target electric vehicle in the hierarchical model, it includes: setting a second constraint condition based on a deterministic model to obtain an upper model in the hierarchical model, wherein the second constraint condition represents a constraint on the target electric vehicle during operation; and constructing a lower model in the hierarchical model according to the restriction conditions of the aggregation system with the goal of minimizing the impact of the target electric vehicle on the aggregation system.

[0067] Optionally, in addition to deterministic factors, uncertainties of electric vehicles need to be evaluated, such as driving mode uncertainty, uncertainty in charging and discharging demand, and random time to reach the aggregation system. These uncertainties can be evaluated by setting up a hierarchical model. Based on the deterministic model, uncertainties can be introduced to construct a hierarchical optimization model. The upper optimization model mainly considers grid stability and economic benefits, while the lower optimization model considers the specific availability and behavioral uncertainty of electric vehicles. In the upper optimization, the aggregation system needs to determine the optimal charging and discharging plan based on the current grid state and predicted load demand to maximize economic benefits and smooth grid load. The scheduling plan generated by the upper model will guide the specific charging and discharging operations of electric vehicles to ensure that the entire system is optimal in terms of economy and stability. In the lower optimization, the model needs to consider the worst-case availability of electric vehicles to ensure that the entire system can still operate normally even when some electric vehicles are unavailable or the charging and discharging demand is reduced. The lower model generates a specific charging and discharging plan based on the upper scheduling plan and the specific conditions of each electric vehicle (such as battery status, user needs, etc.).

[0068] Specifically, the second objective function can be to enable the vehicle-grid interaction function based on the deterministic model and perform actions that can improve operating costs:

[0069]

[0070] in, is a set of decision variables; compared with the deterministic model, a e,t Indicates whether the electric vehicle can be charged and discharged, s e,t It represents the slack variable introduced by electric vehicles for stable charging, and the default power unit is kWh. The second constraint condition can include electric vehicle transportation energy constraint, power demand adjustment constraint, logic constraint, and availability scheduling constraint.

[0071] As an optional embodiment, the second constraint condition includes at least one of the following: a transportation energy constraint condition of the electric vehicle, a power demand constraint condition of the electric vehicle, and a logical constraint condition, wherein the logical constraint condition characterizes that the transportation power of the electric vehicle is non-negative.

[0072] Optionally, the second constraint condition may include the following constraint conditions, for example:

[0073] The EV transport energy constraint, i.e. the energy required by the EV in each time frame, must be within the optimization range and should correspond to the expected power obtained by the EV per day.

[0074]

[0075] Among them, T e,t The total power required for electric vehicle transportation, is the expected amount of energy required to drive an electric vehicle on a daily basis.

[0076] The power demand adjustment constraint is originally an average distribution for all hours. Due to the existence of randomness, the power demand is adjusted based on the maximum storage energy calculation, as shown below:

[0077]

[0078] Among them, a e,t Indicates whether the electric vehicle can be charged and discharged. If it is available, it is 1, otherwise it is 0. Indicates the maximum amount of electricity stored in an electric vehicle, E -e Indicates the minimum amount of electricity that an electric vehicle can store.

[0079] The logical constraint is that the total transport power of electric vehicles is non-negative, and the constraints are as follows:

[0080]

[0081] The availability dispatch constraint, i.e. the energy supply of the EV battery in the worst case, is constrained as follows:

[0082]

[0083] in, is the total supplied power of the electric vehicle in the worst case. a e,t The underlying parameters, i.e., those provided by the lower-level model, provide the availability information of electric vehicles. The following two constraint formulas affect the participation of electric vehicles in grid dispatching from the perspectives of availability and interactivity, respectively.

[0084] Figure 3 is a modeling flow chart of an optimization method for charging and discharging an electric vehicle according to an optional embodiment of the present invention, such as Figure 3 As shown, the deterministic model of the electric vehicle aggregation system is first modeled, and then the upper model in the hierarchical model is constructed on the basis of the deterministic model, that is, the aggregation system uncertainty model, and then the lower model in the hierarchical model is constructed, that is, the schedulable and interactive modeling based on electric vehicles.

[0085] As an optional embodiment, the lower-layer model includes a worst availability planning model and a worst vehicle-grid interaction planning model.

[0086] Alternatively, the worst availability planning model and the worst vehicle-grid interaction planning model are two key components of the EV aggregation system optimization strategy considering uncertainty, which deal with the EV availability uncertainty and the EV-grid interaction uncertainty respectively to ensure the robustness and economy of the optimization plan.

[0087] Among them, the worst availability planning model aims to identify and solve the worst case scenario of the aggregation system when the charging demand of electric vehicles cannot be met when there is uncertainty in the availability of electric vehicles. The goal of the model is to ensure that the power demand of electric vehicles can still be met in the worst case by reducing the amount of electricity stored in the electric vehicle battery in one day, while minimizing the operating cost of the aggregation system. The worst vehicle-grid interaction planning model focuses on dealing with emergencies in which the interaction between electric vehicles and the grid is reduced, that is, when the participation of electric vehicles is reduced, how to formulate the best charging / discharging scheduling plan to minimize the cost increase caused by the decrease in the availability of electric vehicles. The goal of the model is to set the availability status of electric vehicles to 0, that is, they cannot participate in the charging and discharging tasks, based on the daily energy demand of electric vehicles, and optimize the charging and discharging operations to adapt to this worst case scenario. Through the combined use of these two models, the electric vehicle aggregation system can better cope with various uncertainties, including fluctuations in the charging and discharging demand of electric vehicles, changes in the willingness to participate in grid interaction, etc., thereby improving the operating efficiency and economic benefits of the entire system.

[0088] As an optional embodiment, the following method is used to construct a worst availability planning model, including: setting a third objective function based on the amount of electricity stored in the target electric vehicle within a target duration; setting a third constraint condition based on the minimum available electricity of the target electric vehicle per hour and the limit on the number of available electric vehicles in the aggregation system; and constructing a worst availability planning model based on the third objective function and the third constraint condition.

[0089] Alternatively, the worst availability planning model is when the aggregate system cannot accommodate EVs or meet their daily energy demands, and the parameter Λ eContains a set of worst availability plans where the charging power that the EVs receive from the aggregate system is very low and insufficient to charge the EV batteries within a specified time period or a specified optimization timeframe. Considering a set of uncertain availability sets where the availability of EVs is unknown, these worst sets can be transformed into feasible ones by applying the following optimization constraints:

[0090]

[0091] The third objective function is to reduce the amount of electricity stored in the electric vehicle battery in one day, which is calculated by determining the difference between the injected / recovered electricity and the actual power output efficiency of charging or discharging. is the total power supply of electric vehicles in the worst case; a′ e,t is a decision variable, indicating whether electric vehicles are available; H e represents the minimum number of time periods that the electric vehicle e needs to remain available during the optimization process, ζ e , B′ -e,t , The dual variable introduced for the constraint. The three third constraints represent the minimum available power of electric vehicles per hour, the maximum and minimum available number of electric vehicles, and the logical constraint of whether electric vehicles are available. The value given in the brackets after the colon is the dual variable.

[0092] As an optional embodiment, the following method is used to construct a worst-case vehicle-grid interaction planning model, including: setting a fourth objective function according to the impact of the target electric vehicle on the aggregation system; setting a fourth constraint condition according to the battery energy supply of the target electric vehicle under the worst case scenario; and constructing the worst-case vehicle-grid interaction planning model based on the fourth objective function and the fourth constraint condition.

[0093] Optionally, the worst-case vehicle-grid interaction plan is modeled and solved. When the availability of electric vehicles is uncertain within the optimization time range, the aforementioned method is not sufficient to gain benefits. In order to deal with such emergencies, the charging / discharging scheduling configuration plan of the aggregation system is enforced, which must be optimal and should be smart enough to cope with the worst-case electric vehicle availability scheduling. This is mainly when the number of electric vehicles available for V2G operation decreases or the interaction of electric vehicles with the market decreases. Due to these optimal load scheduling configuration plans, although the role of the aggregation system in the market is still limited, it can significantly manage the demand of the power grid and electric vehicles, where the fourth objective function is expressed as:

[0094]

[0095] The fourth constraint is as follows:

[0096]

[0097] Among them, compared with the formula in the worst availability scheduling model, the two are structurally the same except for the objective function, which reduces the coordination between vehicles and the energy market. The objective function is to set the availability state of the electric vehicle to 0 when the electric vehicle performs the charging or discharging task according to the scheduling plan of the aggregation system, and it is an operation performed after considering the daily energy required for driving.

[0098] Figure 4 is a schematic diagram of the association between the upper and lower layers of the hierarchical model in the optimization method for charging and discharging an electric vehicle provided according to an optional embodiment of the present invention, such as Figure 4 As shown, where c e,t is the charging power provided by the electric vehicle at time t; d e,t represents the discharge power provided by the electric vehicle at time t, is the total power supply of the electric vehicle in the worst case, a e,t Indicates whether the electric vehicle can be charged and discharged. a e,t The bottom-level parameters provide the availability information of electric vehicles. The vehicle data and operation data are input into the hierarchical model, which will be processed by the upper-level model to adjust the charging and discharging plan. e,t ,d e,t It can be passed into the lower-level model, specifically, it is input into the worst availability planning model and the worst vehicle-grid interaction planning model respectively. The worst availability planning model will get It is passed to the upper model for further adjustment. The worst-case vehicle-network interaction planning model will obtain a e,t The data is passed to the upper-level model for further adjustment until the final charging and discharging plan is obtained.

[0099] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0100] Through the description of the above implementation methods, those skilled in the art can clearly understand that the optimization method for charging and discharging of electric vehicles according to the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course, by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0101] According to an embodiment of the present invention, there is also provided an electric vehicle charging and discharging optimization device for implementing the above-mentioned electric vehicle charging and discharging optimization method. Figure 5 is a structural block diagram of an optimization device for charging and discharging an electric vehicle according to an embodiment of the present invention, such as Figure 5 As shown, the electric vehicle charging and discharging optimization device includes: an acquisition module 52, an output module 54 and a determination module 56. The electric vehicle charging and discharging optimization device is described below.

[0102] The acquisition module 52 is used to acquire vehicle data and operation data of the target electric vehicle, wherein the vehicle data includes at least one of the following: battery capacity and charge and discharge power limit of the target electric vehicle.

[0103] The output module 54 is connected to the acquisition module 52, and is used to input vehicle data into the deterministic model and output the charging and discharging plan corresponding to the target electric vehicle, wherein the deterministic model is used to adjust the charging and discharging plan of the target electric vehicle with the goal of minimizing the cost of the aggregation system where the target electric vehicle is located.

[0104] The determination module 56 is connected to the output module 54, and is used to input the vehicle data and the operation data into the hierarchical model, adjust the charge and discharge plan of the target electric vehicle in the hierarchical model, and determine the target charge and discharge plan of the target electric vehicle, wherein the hierarchical model includes an upper model and a lower model, the upper model is used to adjust the charge and discharge plan based on the deterministic model according to the uncertain factors in the operation data with the target of the aggregate system as the target, and the lower model is used to adjust the charge and discharge plan based on the uncertain factors in the operation data with the target electric vehicle operating normally under the worst case as the target.

[0105] It should be noted that the acquisition module 52, output module 54 and determination module 56 correspond to steps S202 to S206 in the embodiment, and the examples and application scenarios implemented by the multiple modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that the above modules, as part of the device, can be run in the computer terminal 10 provided in the embodiment.

[0106] An embodiment of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one network device among multiple network devices of a computer network. The computer device includes a memory and a processor.

[0107] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the method and device for optimizing the charging and discharging of electric vehicles in the embodiments of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned optimization method for charging and discharging of electric vehicles. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include a memory remotely arranged relative to the processor, and these remote memories can be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0108] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: obtain vehicle data and operation data of the target electric vehicle, wherein the vehicle data includes at least one of the following: battery capacity and charge and discharge power limit of the target electric vehicle; input the vehicle data into the deterministic model, and output the charge and discharge plan corresponding to the target electric vehicle, wherein the deterministic model is used to adjust the charge and discharge plan of the target electric vehicle with the goal of minimizing the cost of the aggregate system where the target electric vehicle is located; input the vehicle data and operation data into the hierarchical model, adjust the charge and discharge plan of the target electric vehicle in the hierarchical model, and determine the target charge and discharge plan of the target electric vehicle, wherein the hierarchical model includes an upper model and a lower model, the upper model is used to adjust the charge and discharge plan with the aggregate system as the target based on the deterministic model according to the uncertainties in the operation data, and the lower model is used to adjust the charge and discharge plan with the target electric vehicle operating normally under the worst case as the target based on the uncertainties in the operation data.

[0109] Optionally, the processor may also execute program code for the following steps: before inputting vehicle data into the deterministic model and outputting a charging and discharging plan corresponding to the target electric vehicle, the steps include: setting a first objective function based on the cost of the aggregation system; setting a first constraint condition based on the deterministic factors corresponding to the electric vehicle, wherein the deterministic factors represent factors related to the electric vehicle itself that will not change during the operation of the vehicle; and constructing a deterministic model based on the first objective function and the first constraint condition.

[0110] Optionally, the processor may also execute the program code of the following steps: the first constraint in the deterministic model includes at least one of the following: a power limit for a single charge and discharge of the target electric vehicle, a battery capacity limit for the target electric vehicle, a limit on the charge state of the electric vehicle battery, and a transmission capacity limit for the target electric vehicle.

[0111] Optionally, the processor may also execute program code for the following steps: before inputting vehicle data and operating data into the hierarchical model and adjusting the charging and discharging plan of the target electric vehicle in the hierarchical model, the steps include: setting a second constraint condition based on a deterministic model to obtain an upper model in the hierarchical model, wherein the second constraint condition represents a constraint on the target electric vehicle during operation; and constructing a lower model in the hierarchical model according to the constraints of the aggregation system with the goal of minimizing the impact of the target electric vehicle on the aggregation system.

[0112] Optionally, the processor may also execute the program code of the following steps: the second constraint condition includes at least one of the following: the transportation energy constraint condition of the electric vehicle, the power demand constraint condition of the electric vehicle and the logical constraint condition, wherein the logical constraint condition characterizes that the transportation power of the electric vehicle is non-negative.

[0113] Optionally, the processor may also execute program code of the following steps: the lower-layer model includes a worst availability planning model and a worst vehicle-grid interaction planning model.

[0114] Optionally, the processor may also execute program code of the following steps: constructing a worst availability planning model using the following method, including: setting a third objective function based on the amount of electricity stored in the target electric vehicle within a target duration; setting a third constraint based on the minimum available electricity of the target electric vehicle per hour and the limit on the number of available electric vehicles in the aggregation system; and constructing a worst availability planning model based on the third objective function and the third constraint.

[0115] Optionally, the processor may also execute program code of the following steps: constructing a worst-case vehicle-grid interaction planning model using the following method, including: setting a fourth objective function according to the impact of the target electric vehicle on the aggregation system; setting a fourth constraint condition according to the battery energy supply of the target electric vehicle under the worst-case scenario; and constructing a worst-case vehicle-grid interaction planning model based on the fourth objective function and the fourth constraint condition.

[0116] According to an embodiment of the present invention, a method for optimizing the charging and discharging of an electric vehicle is provided, wherein the vehicle data and operating data of a target electric vehicle are obtained, wherein the vehicle data includes at least one of the following: the battery capacity and the charging and discharging power limit of the target electric vehicle; the vehicle data is input into a deterministic model, and a charging and discharging plan corresponding to the target electric vehicle is output, wherein the deterministic model is used to adjust the charging and discharging plan of the target electric vehicle with the minimum cost of the aggregation system where the target electric vehicle is located as the goal; the vehicle data and operating data are input into a hierarchical model, and the charging and discharging plan of the target electric vehicle is adjusted in the hierarchical model to determine the target charging and discharging plan of the target electric vehicle, wherein The hierarchical model includes an upper model and a lower model. The upper model is used to adjust the charging and discharging plan based on the deterministic model according to the uncertainties in the operating data with the goal of the aggregate system. The lower model is used to adjust the charging and discharging plan based on the uncertainties in the operating data with the goal of the target electric vehicle operating normally under the worst case scenario. The purpose of optimizing the charging and discharging plan in combination with the uncertainties in the operation of electric vehicles is achieved, thereby achieving the technical effect of improving the optimization effect, and further solving the technical problem that the current optimization method for electric vehicle charging and discharging cannot effectively deal with the uncertainty of electric vehicle driving modes and the uncertainty of charging and discharging requirements, resulting in poor optimization effects.

[0117] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a non-volatile storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0118] The embodiment of the present invention further provides a non-volatile storage medium. Optionally, in this embodiment, the non-volatile storage medium can be used to store the program code executed by the electric vehicle charging and discharging optimization method provided in the above embodiment.

[0119] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0120] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining vehicle data and operating data of the target electric vehicle, wherein the vehicle data includes at least one of the following: battery capacity and charging and discharging power limit of the target electric vehicle; inputting the vehicle data into a deterministic model, and outputting a charging and discharging plan corresponding to the target electric vehicle, wherein the deterministic model is used to adjust the charging and discharging plan of the target electric vehicle with the goal of minimizing the cost of the aggregated system in which the target electric vehicle is located; inputting the vehicle data and operating data into a hierarchical model, adjusting the charging and discharging plan of the target electric vehicle in the hierarchical model, and determining a target charging and discharging plan for the target electric vehicle, wherein the hierarchical model includes an upper model and a lower model, the upper model is used to adjust the charging and discharging plan with the aggregated system as the target based on the deterministic model according to uncertainties in the operating data, and the lower model is used to adjust the charging and discharging plan with the target electric vehicle operating normally under the worst case as the target based on uncertainties in the operating data.

[0121] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: before inputting vehicle data into the deterministic model and outputting the charging and discharging plan corresponding to the target electric vehicle, including: setting a first objective function based on the cost of the aggregation system; setting a first constraint condition based on the deterministic factors corresponding to the electric vehicle, wherein the deterministic factors represent factors related to the electric vehicle itself that will not change during the operation of the vehicle; and constructing a deterministic model based on the first objective function and the first constraint condition.

[0122] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: the first constraint in the deterministic model includes at least one of the following: a power limit condition for a single charge and discharge of the target electric vehicle, a battery capacity limit condition for the target electric vehicle, a limit condition on the charge state of the electric vehicle battery, and a transmission capacity limit condition for the target electric vehicle.

[0123] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: before inputting vehicle data and operating data into the hierarchical model and adjusting the charging and discharging plan of the target electric vehicle in the hierarchical model, including: setting a second constraint condition based on the deterministic model to obtain an upper model in the hierarchical model, wherein the second constraint condition represents a constraint on the target electric vehicle during operation; and constructing a lower model in the hierarchical model according to the constraints of the aggregation system with the goal of minimizing the impact of the target electric vehicle on the aggregation system.

[0124] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: the second constraint includes at least one of the following: a transportation energy constraint of the electric vehicle, a power demand constraint of the electric vehicle, and a logical constraint, wherein the logical constraint characterizes that the transportation power of the electric vehicle is non-negative.

[0125] Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for executing the following steps: the lower layer model includes a worst availability planning model and a worst vehicle-grid interaction planning model.

[0126] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: constructing a worst availability planning model using the following method, including: setting a third objective function based on the amount of electricity stored in the target electric vehicle within a target duration; setting a third constraint based on the minimum available electricity of the target electric vehicle per hour and the limit on the number of available electric vehicles in the aggregation system; and constructing a worst availability planning model based on the third objective function and the third constraint.

[0127] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: constructing a worst-case vehicle-grid interaction planning model using the following method, including: setting a fourth objective function based on the impact of the target electric vehicle on the aggregation system; setting a fourth constraint based on the battery energy supply of the target electric vehicle in the worst case; and constructing the worst-case vehicle-grid interaction planning model based on the fourth objective function and the fourth constraint.

[0128] An embodiment of the present invention also provides a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can achieve: obtaining vehicle data and operating data of a target electric vehicle, wherein the vehicle data includes at least one of the following: battery capacity and charging and discharging power limit of the target electric vehicle; inputting the vehicle data into a deterministic model, and outputting a charging and discharging plan corresponding to the target electric vehicle, wherein the deterministic model is used to adjust the charging and discharging plan of the target electric vehicle with the goal of minimizing the cost of the aggregated system where the target electric vehicle is located; inputting the vehicle data and operating data into a hierarchical model, adjusting the charging and discharging plan of the target electric vehicle in the hierarchical model, and determining the target charging and discharging plan of the target electric vehicle, wherein the hierarchical model includes an upper model and a lower model, the upper model is used to adjust the charging and discharging plan with the aggregated system as the target based on the deterministic model according to the uncertainties in the operating data, and the lower model is used to adjust the charging and discharging plan with the target electric vehicle operating normally under the worst case as the target based on the uncertainties in the operating data.

[0129] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0130] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0131] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0133] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0134] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0135] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing charging and discharging of an electric vehicle, characterized in that: include: Acquiring vehicle data and operation data of a target electric vehicle, wherein the vehicle data includes at least one of the following: battery capacity and charge / discharge power limits of the target electric vehicle; Inputting the vehicle data into a deterministic model, and outputting a charge and discharge plan corresponding to the target electric vehicle, wherein the deterministic model is used to adjust the charge and discharge plan of the target electric vehicle with the goal of minimizing the cost of the aggregation system where the target electric vehicle is located; The vehicle data and the operating data are input into a hierarchical model, and the charging and discharging plan of the target electric vehicle is adjusted in the hierarchical model to determine the target charging and discharging plan of the target electric vehicle, wherein the hierarchical model includes an upper model and a lower model, the upper model is used to adjust the charging and discharging plan based on the deterministic model according to the uncertainties in the operating data with the aggregation system as the target, and the lower model is used to adjust the charging and discharging plan based on the uncertainties in the operating data with the target electric vehicle operating normally under the worst case as the target.

2. The method according to claim 1, characterized in that Before inputting the vehicle data into the deterministic model and outputting the charging and discharging plan corresponding to the target electric vehicle, the method includes: setting a first objective function according to the cost of the aggregation system; According to the deterministic factors corresponding to the electric vehicle, a first constraint condition is set, wherein the deterministic factors represent factors related to the electric vehicle itself that will not change during the operation of the vehicle; The deterministic model is constructed according to the first objective function and the first constraint condition.

3. The method according to claim 2, characterized in that The first constraint in the deterministic model includes at least one of the following: a power limit for a single charge and discharge of the target electric vehicle, a battery capacity limit for the target electric vehicle, a limit on the state of charge of the electric vehicle battery, and a transmission capacity limit for the target electric vehicle.

4. The method according to claim 2, characterized in that: Before inputting the vehicle data and the operation data into the hierarchical model and adjusting the charging and discharging plan of the target electric vehicle in the hierarchical model, the method includes: Based on the deterministic model, a second constraint condition is set to obtain an upper model in the hierarchical model, wherein the second constraint condition represents a constraint on the target electric vehicle during operation; According to the constraints of the aggregation system, with the goal of minimizing the impact of the target electric vehicle on the aggregation system, a lower model in the hierarchical model is constructed.

5. The method according to claim 4, characterized in that The second constraint condition includes at least one of the following: The transportation energy constraint condition of the electric vehicle, the power demand constraint condition of the electric vehicle and the logical constraint condition, wherein the logical constraint condition represents that the transportation power of the electric vehicle is non-negative.

6. The method according to any one of claims 1 to 5, characterized in that: The lower-level model includes a worst availability planning model and a worst vehicle-grid interaction planning model.

7. The method according to claim 6, characterized in that The worst availability planning model is constructed using the following method, including: Setting a third objective function according to the amount of electricity stored in the target electric vehicle within the target duration; Setting a third constraint condition according to the minimum available power of the target electric vehicle per hour and the limit on the number of available electric vehicles in the aggregation system; The worst availability planning model is constructed according to the third objective function and the third constraint condition.

8. The method according to claim 6, characterized in that The worst vehicle-grid interaction planning model is constructed using the following method, including: setting a fourth objective function according to the influence of the target electric vehicle on the aggregation system; Setting a fourth constraint condition according to the battery energy supply of the target electric vehicle in the worst case; According to the fourth objective function and the fourth constraint condition, the worst vehicle-grid interaction planning model is constructed.

9. An optimization device for charging and discharging of electric vehicles, characterized in that: include: An acquisition module, used to acquire vehicle data and operation data of a target electric vehicle, wherein the vehicle data includes at least one of the following: battery capacity and charge and discharge power limits of the target electric vehicle; An output module, used to input the vehicle data into a deterministic model, and output a charge and discharge plan corresponding to the target electric vehicle, wherein the deterministic model is used to adjust the charge and discharge plan of the target electric vehicle with the goal of minimizing the cost of the aggregation system where the target electric vehicle is located; A determination module is used to input the vehicle data and the operating data into a hierarchical model, adjust the charging and discharging plan of the target electric vehicle in the hierarchical model, and determine the target charging and discharging plan of the target electric vehicle, wherein the hierarchical model includes an upper model and a lower model, the upper model is used to adjust the charging and discharging plan based on the deterministic model according to the uncertain factors in the operating data with the aggregation system as the target, and the lower model is used to adjust the charging and discharging plan based on the uncertain factors in the operating data with the target electric vehicle operating normally under the worst case as the target.

10. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the electric vehicle charging and discharging optimization method as described in any one of claims 1 to 8.

11. A computer device, characterized in that: include: Memory and processor, The memory stores a computer program; The processor is used to execute the computer program stored in the memory, and when the computer program is running, the processor executes the electric vehicle charging and discharging optimization method as described in any one of claims 1 to 8.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for optimizing charging and discharging of an electric vehicle as claimed in any one of claims 1 to 8 is implemented.

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