Optimization method, device and nonvolatile storage medium for charging and discharging of electric vehicle

Abstract

The application discloses an electric vehicle charging and discharging optimization method and device and a nonvolatile storage medium. The method comprises the following steps: obtaining vehicle data and operation data of a target electric vehicle, wherein the vehicle data comprises at least one of the following: battery capacity of the target electric vehicle, charging and discharging power limit; inputting the vehicle data into a deterministic model to output 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 minimum cost of an aggregation system where the target electric vehicle is located as the target; inputting the vehicle data and the operation data into a hierarchical model to adjust the charging and discharging plan of the target electric vehicle in the hierarchical model and determine a target charging and discharging plan of the target electric vehicle. The application solves the technical problem that the current electric vehicle charging and discharging optimization method cannot effectively deal with the uncertainty of the driving mode of the electric vehicle and the uncertainty of the charging and discharging demand, thereby resulting in poor optimization effect.

Description

Technical Field

[0001] This invention relates to the field of power technology, and more specifically, to an optimized method, apparatus, and non-volatile storage medium for charging and discharging electric vehicles. Background Technology

[0002] Electric vehicle (EV) aggregation systems can provide grid stability by optimizing the controlled charging and recharging of large-scale EV fleets. In the rapidly evolving EV environment, the primary responsibility of aggregation systems is to optimize electric navigation / chasing operations 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, aggregation systems face two main obstacles when handling large-scale EV fleets: first, assessing the driving mode uncertainties of each EV while determining precise data on its internal and technical specifications; and second, selecting a robust computational scheme to practically optimize the actual EV fleet. Traditional EV aggregation optimization methods cannot effectively handle the uncertainties of EV driving modes, charging and discharging demands, and the stochastic arrival times at the aggregation system.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides an optimization method, apparatus, and non-volatile storage medium for charging and discharging electric vehicles, at least to solve the technical problem that current optimization methods for charging and discharging electric vehicles cannot effectively handle the uncertainty of electric vehicle driving modes and the uncertainty of charging and discharging requirements, resulting in poor optimization effects.

[0005] According to one aspect of the present invention, an optimization method for charging and discharging an electric vehicle is provided, 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 of the target electric vehicle and charging / discharging power limit; inputting the vehicle data into a deterministic model and outputting a charging / discharging plan corresponding to the target electric vehicle, wherein the deterministic model is used to adjust the charging / discharging plan of the target electric vehicle with the goal of minimizing the cost of the aggregation system to which the target electric vehicle belongs; inputting the vehicle data and operating data into a hierarchical model, adjusting the charging / discharging plan of the target electric vehicle in the hierarchical model, and determining a target charging / discharging plan for the target electric vehicle, wherein the hierarchical model includes an upper-level model and a lower-level model, the upper-level model is used to adjust the charging / discharging plan based on the uncertainty factors in the operating data with the aggregation system as the target, and the lower-level model is used to adjust the charging / discharging plan based on the uncertainty factors in the operating data with the goal of ensuring the target electric vehicle operates normally under worst-case conditions.

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

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

[0008] Optionally, before inputting vehicle data and operational data into the hierarchical model and adjusting the charging and discharging plan of the target electric vehicle in the hierarchical model, the process includes: setting a second constraint based on a deterministic model to obtain an upper-level model in the hierarchical model, wherein the second constraint characterizes the constraint on the target electric vehicle during operation; and constructing a lower-level model in the hierarchical model based on the constraints 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 includes at least one of the following: a transportation energy constraint for the electric vehicle, a power demand constraint for the electric vehicle, and a logical constraint, wherein the logical constraint characterizes that the transportation power of the electric vehicle is non-negative.

[0010] Optionally, the lower-level model includes a worst-case availability plan model and a worst-case vehicle-to-network interaction plan model.

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

[0012] Optionally, the worst-case vehicle-to-grid interaction plan model can be constructed using the following method: 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 scenario; and constructing the worst-case vehicle-to-grid interaction plan model based on the fourth objective function and the fourth constraint.

[0013] According to another aspect of the present invention, an optimization apparatus for charging and discharging electric vehicles is also provided, comprising: an acquisition module, configured to acquire vehicle data and operating data of a target electric vehicle, wherein the vehicle data includes at least one of the following: battery capacity of the target electric vehicle and charging / discharging power limit; an output module, configured to input the vehicle data into a deterministic model and output a charging / discharging plan corresponding to the target electric vehicle, wherein the deterministic model is used to adjust the charging / 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; and a determination module, configured to input the vehicle data into the deterministic model and output a charging / discharging plan corresponding to the target electric vehicle. 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 aggregate system in which the target electric vehicle is located. Vehicle data and 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. The hierarchical model includes an upper-level model and a lower-level model. The upper-level model is used to adjust the charging and discharging plan based on the uncertainty factors in the operation data with the aggregate system as the target, based on the deterministic model. The lower-level model is used to adjust the charging and discharging plan based on the uncertainty factors in the operation data with the goal of ensuring the target electric vehicle operates normally under the worst-case scenario.

[0014] According to another aspect of the present invention, a non-volatile storage medium is also 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 of the above-described electric vehicle charging and discharging optimization methods.

[0015] According to another aspect of the present invention, a computer device is also provided, the computer device including a processor for running a program, wherein the program executes any of the above-described optimized methods for charging and discharging electric vehicles.

[0016] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements any of the above-described optimized methods for charging and discharging electric vehicles.

[0017] In this embodiment of the invention, an optimization method for electric vehicle charging and discharging is employed. This involves acquiring vehicle data and operational data of the target electric vehicle, wherein the vehicle data includes at least one of the following: the battery capacity of the target electric vehicle and charging / discharging power limitations. The vehicle data is input into a deterministic model, which outputs a charging / discharging plan corresponding to the target electric vehicle. The deterministic model is used to adjust the charging / discharging plan of the target electric vehicle with the objective of minimizing the cost of the aggregated system to which the target electric vehicle belongs. The vehicle data and operational data are then input into a hierarchical model, where the charging / discharging plan of the target electric vehicle is adjusted to determine the target charging / discharging plan for the target electric vehicle. The hierarchical model includes an upper-level model and a lower-level model. The upper-level model is used to adjust the charging and discharging plan based on the uncertainties in the operating data and with the aggregation system as the target, based on the deterministic model. The lower-level model is used to adjust the charging and discharging plan based on the uncertainties in the operating data and with the target electric vehicle operating normally under the worst-case scenario as the target. This achieves the goal of optimizing the charging and discharging plan by combining the uncertainties in the operation of electric vehicles, thereby improving the technical effect of optimization. It also solves the technical problem that current electric vehicle charging and discharging optimization methods cannot effectively handle the uncertainty of electric vehicle driving mode and the uncertainty of charging and discharging demand, resulting in poor optimization effect. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0019] Figure 1 A hardware block diagram of a computer terminal for implementing an optimized method for charging and discharging electric vehicles is shown.

[0020] Figure 2 This is a flowchart illustrating an optimized method for charging and discharging electric vehicles according to an embodiment of the present invention.

[0021] Figure 3 This is a modeling flowchart of an optimized method for charging and discharging electric vehicles according to an optional embodiment of the present invention;

[0022] Figure 4 This is a schematic diagram of the upper and lower layer relationships in the hierarchical model of the electric vehicle charging and discharging optimization method provided by an optional embodiment of the present invention;

[0023] Figure 5 This is a structural block diagram of an optimized charging and discharging device for electric vehicles provided according to an embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort 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, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] First, some nouns or terms that appear in the description of the embodiments of this application shall be interpreted as follows:

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

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

[0029] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing an optimized method for charging and discharging electric vehicles is shown. Figure 1As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and 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 a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0030] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be implemented wholly or partially as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be wholly or partially integrated into any other element in the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as processor control (e.g., selection of a variable resistor termination 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 electric vehicle charging and discharging optimization method in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the electric vehicle charging and discharging optimization method of the aforementioned application. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

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

[0033] Figure 2 This is a flowchart illustrating an optimized method for charging and discharging electric vehicles according to an embodiment of the present invention. Figure 2As shown, the method includes the following steps:

[0034] Step S202: Obtain vehicle data and operating data of the target electric vehicle, wherein the vehicle data includes at least one of the following: battery capacity of the target electric vehicle and charging / discharging power limit.

[0035] In this step, vehicle data for the target electric vehicle is acquired. This data can include the battery capacity, maximum and minimum power during charging and discharging, and battery degradation costs. Operational data can include the battery status and output power of the target electric vehicle during operation, as well as the user's driving habits. All of this data will affect the charging and discharging of the target electric vehicle. While factors like battery capacity and charging / discharging power limitations can be considered deterministic factors, operational data, such as user driving preferences, are considered uncertain factors.

[0036] Step S204: Input vehicle data into the deterministic model and output the charging and discharging plan corresponding to the target electric vehicle. 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.

[0037] In this step, vehicle data can be input into a deterministic model. This deterministic model adjusts the charging and discharging schedule based on deterministic factors within the target electric vehicle, aiming to minimize the overall cost of the aggregated system to which the target electric vehicle belongs. Specifically, the electric vehicle aggregated system is deterministically modeled, including an objective function and constraints. The fundamental objective of the electric vehicle aggregated system is to effectively achieve two goals: obtaining the optimal charging and discharging scheduling mode for each connected electric vehicle and appropriately satisfying their physical limitations, thereby minimizing the overall operating cost. This optimization model is constructed with the specific objectives of minimizing the energy purchase and sale cost, battery degradation cost, and default penalty cost.

[0038] Step S206: Input vehicle data and operation 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. The hierarchical model includes an upper-level model and a lower-level model. The upper-level model is used to adjust the charging and discharging plan based on the uncertainty factors in the operation data with the aggregation system as the target, based on the deterministic model. The lower-level model is used to adjust the charging and discharging plan based on the uncertainty factors in the operation data with the target electric vehicle operating normally under the worst-case scenario as the target.

[0039] In this step, vehicle data and operational data are input into a hierarchical model, which includes an upper-layer model and a lower-layer model. The upper-layer model can be a model of the upper-level aggregation system built based on a deterministic model. Unlike the deterministic model, the upper-layer model considers the uncertainties of electric vehicles during operation. The lower-layer model considers the specific availability and behavioral uncertainties of electric vehicles based on the uncertainties of the target electric vehicle, specifically taking into account worst-case charging and discharging states and sudden situations that reduce participation in grid interaction. The charging and discharging of electric vehicles is optimized based on the above model to obtain the target charging and discharging plan. The charging and discharging plan refers to the charging and discharging schedule and power level established for each electric vehicle in the electric vehicle (EV) aggregation system. This plan is the core part of the EV aggregation optimization process, aiming to balance grid load, maximize economic benefits, reduce battery degradation costs, and meet the charging and discharging needs of vehicle owners. In a smart grid environment, the charging and discharging plan can be dynamically adjusted to respond to real-time grid demand and market price fluctuations. For example, during periods of low grid load, plans might schedule electric vehicle charging to take advantage of cheaper electricity; during peak load periods, electric vehicles might be dispatched to discharge electricity back into the grid to alleviate peak pressure, while simultaneously generating revenue through participation in the electricity market. This hierarchical model enhances the efficiency and incentive for electric vehicles to participate in grid interaction and improves the optimization of charging and discharging plans.

[0040] Through the above steps, the goal of optimizing the charging and discharging plan by taking into account the uncertainties in the operation of electric vehicles can be achieved, thereby improving the technical effect of optimization. This solves the technical problem that current electric vehicle charging and discharging optimization methods cannot effectively handle the uncertainties of electric vehicle driving modes and charging and discharging needs, resulting in poor optimization effects.

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

[0042] Optionally, the deterministic model can define a first objective function based on the fundamental objectives of the electric vehicle aggregation system. The upper-level objective function aims to minimize the cost of the aggregation system. Then, first constraints are set, which can be constraints in the deterministic model, including power supply constraints, transmission capacity limits, electric vehicle battery state of charge constraints, single charge / discharge limits, battery capacity limits, and battery degradation cost constraints.

[0043] The fundamental goal of the electric vehicle aggregation system is to improve its overall profitability by effectively achieving two objectives: defining the optimal charging and discharging scheduling mode for each connected electric vehicle and appropriately satisfying their physical constraints. Therefore, an optimization model is constructed with the objective of minimizing overall operating costs, specifically including energy purchase and sales costs. Battery degradation cost Penalty cost for breach of contract The specific objective function is expressed as follows:

[0044]

[0045] in, c represents a set of decision variables. e,t This represents the charging power provided by the electric vehicle at time t; d e,t e represents the discharge power provided by the electric vehicle at time t. e,t This represents the state of charge of the battery in the electric vehicle at time t; p represents the electricity price at time t; 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 was purchased from the grid during that time period; otherwise, it means that electricity was sold. Costs associated with electric vehicle battery degradation; Fines levied per kilowatt-hour of electricity for non-compliance with certain operational requirements; e,t These are slack variables used for energy balance. First constraints are set based on the vehicle data of the target electric vehicle, such as aggregated system power supply constraints, transmission capacity limits, electric vehicle battery state of charge constraints, and single charge / discharge limit constraints. A deterministic model can be constructed based on the first objective function and the first constraints.

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

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

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

[0049]

[0050] Where, p t c represents the total amount of electricity provided by the aggregation system at time t. e,t This represents the charging power provided by the electric vehicle at time t; de,t This represents the discharge power provided by the electric vehicle at time t.

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

[0052]

[0053] Among them, P S This indicates the maximum capacity that the aggregation system is allowed to transmit when connected to the power grid.

[0054] The state of charge constraint of an electric vehicle battery characterizes the change in battery charge between two adjacent time points.

[0055]

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

[0057] Single charge / discharge limit constraint, which is the maximum power limit for a single charge / discharge operation of an electric vehicle.

[0058]

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

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

[0061]

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

[0063] Electric vehicle battery degradation cost constraint refers to the degradation cost caused by the reduction in battery life due to each charge and discharge operation of an electric vehicle battery.

[0064]

[0065] in, This represents the total charging cost of an electric vehicle; This indicates the available battery power of the electric vehicle.

[0066] As an optional embodiment, before inputting vehicle data and 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: setting a second constraint based on a deterministic model to obtain an upper-level model in the hierarchical model, wherein the second constraint represents the constraint on the target electric vehicle during operation; and constructing a lower-level model in the hierarchical model based on the constraints 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, it is also necessary to assess the uncertainties of electric vehicles, such as driving mode uncertainty, charging and discharging demand uncertainty, and random arrival times at the aggregation system. These uncertainties can be assessed by setting up a hierarchical model. A hierarchical optimization model can be constructed based on a deterministic model by introducing uncertainties. The upper-level optimization model mainly considers grid stability and economic benefits, while the lower-level optimization model considers the specific availability and behavioral uncertainties of electric vehicles. In the upper-level optimization, the aggregation system needs to determine the optimal charging and discharging schedule 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-level model will guide the specific charging and discharging operations of electric vehicles, ensuring that the entire system achieves optimal performance in terms of economy and stability. In the lower-level optimization, the model needs to consider the worst-case availability of electric vehicles, ensuring that the entire system can still operate normally even if some electric vehicles are unavailable or charging and discharging demand is reduced. The lower-level model generates specific charging and discharging plans based on the upper-level scheduling plan and the specific circumstances of each electric vehicle (such as battery status, user demand, etc.).

[0068] Specifically, the second objective function can be to initiate vehicle-to-network interaction functions based on a deterministic model, and to execute behaviors that increase operating costs:

[0069]

[0070] in, It represents a set of decision variables; compared to a deterministic model, a e,t Indicates whether an electric vehicle can be charged and discharged, s e,t This represents the slack variable introduced for stable charging of electric vehicles, meaning the amount of electricity in default, expressed in kWh. The second constraint can include constraints on electric vehicle transportation energy, electricity demand adjustment constraints, logical constraints, and availability scheduling constraints.

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

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

[0073] Electric vehicle transportation energy constraints mean that the energy required by an electric vehicle in each time period must be within the optimal range and should correspond to the expected power obtained by the electric vehicle each day.

[0074]

[0075] Among them, T e,t The total power required for electric vehicle transportation This is the expected energy required to drive an electric vehicle every day.

[0076] The electricity demand adjustment constraint was originally based on an average distribution across all hours. Due to the presence of randomness, the electricity demand adjustment is now calculated based on the maximum stored energy, as shown below:

[0077]

[0078] Among them, a e,t This indicates whether the electric vehicle can be charged and discharged; a value of 1 indicates it can, and a value of 0 indicates it cannot. E represents the maximum amount of electricity that an electric vehicle can store. -e This indicates the minimum amount of electricity that an electric vehicle can store.

[0079] The logical constraint, namely the non-negative total power of the electric vehicle, is as follows:

[0080]

[0081] Availability scheduling constraints, which define the energy supply to the electric vehicle battery in the worst-case scenario, are as follows:

[0082]

[0083] in, This represents the total power supplied by the electric vehicle under worst-case scenario. a e,t The underlying parameters, provided by the lower-level model, provide availability information for electric vehicles. The following two constraint formulas influence the participation of electric vehicles in grid dispatch from the perspectives of availability and interactivity, respectively.

[0084] Figure 3 This is a modeling flowchart of an optimization method for charging and discharging electric vehicles according to an optional embodiment of the present invention, such as... Figure 3 As shown, we first model the deterministic model of the electric vehicle aggregation system, then build the upper-level model in the hierarchical model based on the deterministic model, which is the uncertainty model of the aggregation system, and then build the lower-level model in the hierarchical model, which is the schedulable and interactive modeling based on electric vehicles.

[0085] As an optional implementation, the lower-level model includes a worst-case availability plan model and a worst-case vehicle-to-network interaction plan model.

[0086] Optionally, the worst-case availability planning model and the worst-case vehicle-grid interaction planning model are two key components of the electric vehicle aggregation system optimization strategy under the consideration of uncertainty. They respectively address the uncertainty of electric vehicle availability and the uncertainty of electric vehicle interaction with the grid to ensure the robustness and economy of the optimization plan.

[0087] The worst-case availability planning model addresses the uncertainty surrounding electric vehicle (EV) availability by identifying and resolving the worst-case scenario where EV charging demand cannot be met. The model aims to ensure EV charging needs are met even in the worst-case scenario by reducing the amount of electricity stored in EV batteries throughout the day, while minimizing the overall operating costs of the aggregation system. The worst-case vehicle-to-grid interaction planning model focuses on handling sudden reductions in EV-grid interaction. Specifically, it addresses how to develop the optimal charging / discharging schedule to minimize cost increases caused by decreased EV availability when EV participation decreases. This model aims to set the EV availability state to 0 (unable to participate in charging / discharging tasks) while considering the EV's daily energy needs, and optimize charging and discharging operations to adapt to this worst-case scenario. By combining these two models, the EV aggregation system can better cope with various uncertainties, including fluctuations in EV charging / discharging demand and changes in willingness to participate in grid interaction, thereby improving the overall system's operational efficiency and economic benefits.

[0088] As an optional embodiment, the worst-case availability planning model is constructed using the following method: 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 per hour of the target electric vehicle and the limit on the number of available electric vehicles in the aggregation system; and constructing the worst-case availability planning model based on the third objective function and the third constraint.

[0089] Alternatively, the worst-case availability planning model is the scenario where the aggregated system cannot accommodate electric vehicles or meet their daily energy needs, with parameter Λ eConsider a set of worst-case availability plans where the charging power obtained by electric vehicles from the aggregation system is very low and insufficient to charge the electric vehicle batteries within a specified time period or a specified optimization time range. Given an uncertain set of availability where the availability of electric vehicles is unknown, these worst-case sets can be transformed into feasible plans by applying the following optimization constraints:

[0090]

[0091] The third objective function aims to reduce the amount of electricity stored in an electric vehicle battery throughout the day. It is calculated by determining the difference between the injected / recovered electricity and the actual power output efficiency during charging or discharging. a′ represents the total power supplied by the electric vehicle in the worst-case scenario. e,t H is the decision variable, representing whether electric vehicles are available; e ζ represents the minimum number of time periods during which electric vehicle e needs to remain in an available state during the optimization process. e B′ -e,t , The dual variables are introduced to constrain the system. The three third constraints represent the minimum available electric power per hour, the maximum and minimum available electric vehicle quantity limits, and the logical constraints on whether an electric vehicle is available. The values ​​given in parentheses after the colon are the dual variables.

[0092] As an optional embodiment, the worst-case vehicle-to-grid interaction plan model is constructed using the following method: 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 scenario; and constructing the worst-case vehicle-to-grid interaction plan model based on the fourth objective function and the fourth constraint.

[0093] Optionally, worst-case vehicle-to-grid (V2G) interaction planning is modeled and solved. Within the optimization timeframe, when the availability of electric vehicles (EVs) is uncertain, the aforementioned methods are insufficient. To handle such contingencies, a charging / discharging scheduling configuration plan for the aggregation system is enforced. This plan must be optimal and intelligent enough to handle worst-case EV availability scheduling, primarily when the number of EVs available for V2G operation decreases or EV interaction with the market diminishes. Due to these optimal load scheduling configuration plans, although the aggregation system's role in the market remains limited, it can significantly manage the demand for both the grid and EVs, where the fourth objective function is expressed as:

[0094]

[0095] The fourth constraint is as follows:

[0096]

[0097] Compared to the formula in the worst-case availability planning model, the two are structurally identical 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 electric vehicles to 0 when they perform charging or discharging tasks according to the scheduling plan of the aggregation system, and this operation takes into account the daily energy required for driving.

[0098] Figure 4 This is a schematic diagram of the upper and lower layer relationships in the hierarchical model of the electric vehicle charging and discharging optimization method provided by an optional embodiment of the present invention, as shown below. Figure 4 As shown, where c e,t This represents the charging power provided by the electric vehicle at time t; d e,t This represents the discharge power provided by the electric vehicle at time t. For the total power supplied by the electric vehicle in the worst-case scenario, a e,t This indicates whether the electric vehicle can be charged and discharged. a e,t As the underlying parameters, it provides availability information for electric vehicles. Vehicle data and operational data are input into the hierarchical model, where the upper-level model first processes the data to adjust the charging and discharging schedule, and then... e,t d e,t It can be passed into the lower-level model, specifically into the worst-case availability plan model and the worst-case vehicle-to-network interaction plan model respectively. The worst-case availability plan model will then obtain... It is passed into the upper-level model for further adjustment. The worst-case scenario car network interaction plan model will obtain a e,t The data is then fed into the upper-level model for further adjustments until the final charging and discharging plan is obtained.

[0099] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

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

[0101] According to embodiments of the present invention, an electric vehicle charging and discharging optimization apparatus for implementing the above-described electric vehicle charging and discharging optimization method is also provided. Figure 5 This is a structural block diagram of an optimized charging and discharging device for electric vehicles provided 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 will be described below.

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

[0103] The output module 54, connected to the acquisition module 52, is used to input vehicle data into the deterministic model and output the charging and discharging plan corresponding to the target electric vehicle. 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.

[0104] The determination module 56, connected to the output module 54, is used to input vehicle data and operating data into the hierarchical model. In the hierarchical model, the charging and discharging plan of the target electric vehicle is adjusted to determine the target charging and discharging plan of the target electric vehicle. The hierarchical model includes an upper-level model and a lower-level model. The upper-level model is used to adjust the charging and discharging plan based on the uncertainty factors in the operating data and with the aggregation system as the target. The lower-level model is used to adjust the charging and discharging plan based on the uncertainty factors in the operating data and with the target electric vehicle operating normally under the worst-case scenario as the target.

[0105] It should be noted that the acquisition module 52, output module 54, and determination module 56 mentioned above correspond to steps S202 to S206 in the embodiments. Multiple modules implement the same instances and application scenarios as their corresponding steps, but are not limited to the content disclosed in the above embodiments. It should also be noted that the above modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

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

[0107] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the electric vehicle charging and discharging optimization method and apparatus in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned electric vehicle charging and discharging optimization method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0108] The processor can invoke information and application programs stored in the memory via a transmission device to perform the following steps: acquiring vehicle data and operational data of the target electric vehicle, wherein the vehicle data includes at least one of the following: battery capacity and charging / discharging power limits of the target electric vehicle; inputting the vehicle data into a deterministic model and outputting a charging / discharging plan corresponding to the target electric vehicle, wherein the deterministic model is used to adjust the charging / discharging plan of the target electric vehicle with the goal of minimizing the cost of the aggregation system to which the target electric vehicle belongs; inputting the vehicle data and operational data into a hierarchical model, adjusting the charging / discharging plan of the target electric vehicle in the hierarchical model, and determining the target charging / discharging plan of the target electric vehicle, wherein the hierarchical model includes an upper-level model and a lower-level model, the upper-level model is used to adjust the charging / discharging plan based on the uncertainties in the operational data with the aggregation system as the target, and the lower-level model is used to adjust the charging / discharging plan based on the uncertainties in the operational data with the goal of ensuring the target electric vehicle operates normally under worst-case conditions.

[0109] Optionally, the processor may also execute program code for 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 based on the deterministic factors corresponding to the electric vehicle, wherein the deterministic factors characterize factors related to the electric vehicle itself that will not change during vehicle operation; and constructing a deterministic model based on the first objective function and the first constraint.

[0110] Optionally, the processor may also execute program code that includes the following steps: the first constraint in the deterministic model includes at least one of the following: a power constraint for a single charge-discharge cycle of the target electric vehicle, a battery capacity constraint for the target electric vehicle, a constraint for the state of charge of the electric vehicle battery, and a transmission capacity constraint 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 processor may perform the following steps: based on the deterministic model, set a second constraint condition to obtain the upper-level model in the hierarchical model, wherein the second constraint condition represents the constraint on the target electric vehicle during operation; and construct the lower-level model in the hierarchical model based on the constraints of the aggregation system and with the goal of minimizing the impact of the target electric vehicle on the aggregation system.

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

[0113] Optionally, the processor may also execute program code that includes the following steps: the lower-level model includes a worst-case availability plan model and a worst-case vehicle-to-everything (V2X) interaction plan model.

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

[0115] Optionally, the processor may also execute program code that performs the following steps: constructing a worst-case vehicle-to-grid interaction plan 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 scenario; and constructing a worst-case vehicle-to-grid interaction plan model based on the fourth objective function and the fourth constraint.

[0116] This invention provides an optimization method for charging and discharging electric vehicles. The method involves acquiring vehicle data and operational data of a target electric vehicle, wherein the vehicle data includes at least one of the following: battery capacity and charging / discharging power limits of the target electric vehicle. The vehicle data is input into a deterministic model, which outputs a charging / discharging plan corresponding to the target electric vehicle. The deterministic model is used to adjust the charging / discharging plan of the target electric vehicle with the objective of minimizing the cost of the aggregated system to which the target electric vehicle belongs. The vehicle data and operational data are then input into a hierarchical model, where the charging / discharging plan of the target electric vehicle is adjusted to determine the target charging / discharging plan for the target electric vehicle. The hierarchical model includes an upper-layer model and a lower-layer model. The upper-layer model is used to adjust the charging and discharging plan based on the uncertainties in the operating data and with the aggregation system as the target, based on the deterministic model. The lower-layer model is used to adjust the charging and discharging plan based on the uncertainties in the operating data and with the target electric vehicle operating normally under the worst-case scenario as the target. This achieves the goal of optimizing the charging and discharging plan by combining the uncertainties in the operation of electric vehicles, thereby improving the technical effect of optimization. It also solves the technical problem that current electric vehicle charging and discharging optimization methods cannot effectively handle the uncertainty of electric vehicle driving mode and the uncertainty of charging and discharging demand, resulting in poor optimization effect.

[0117] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0118] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the optimized method for charging and discharging electric vehicles provided in the above embodiments.

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

[0120] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: acquiring 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 / discharging power limits of the target electric vehicle; inputting the vehicle data into a deterministic model and outputting a charging / discharging plan corresponding to the target electric vehicle, wherein the deterministic model is used to adjust the charging / 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; inputting the vehicle data and operating data into a hierarchical model, adjusting the charging / discharging plan of the target electric vehicle in the hierarchical model, and determining the target charging / discharging plan of the target electric vehicle, wherein the hierarchical model includes an upper-level model and a lower-level model, the upper-level model is used to adjust the charging / discharging plan based on the uncertainty factors in the operating data with the aggregation system as the target, and the lower-level model is used to adjust the charging / discharging plan based on the uncertainty factors in the operating data with the goal of ensuring the target electric vehicle operates normally under the worst-case scenario.

[0121] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: before inputting vehicle data into the deterministic model and outputting the 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 based on deterministic factors corresponding to the electric vehicle, wherein the deterministic factors characterize factors related to the electric vehicle itself that will not change during vehicle operation; and constructing a deterministic model based on the first objective function and the first constraint.

[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 for a single charge-discharge cycle of the target electric vehicle, a battery capacity limit for the target electric vehicle, a limit for the state of charge of the electric vehicle battery, and a transmission capacity limit for the target electric vehicle.

[0123] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing 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 following steps are included: based on the deterministic model, setting a second constraint condition to obtain an upper-level model in the hierarchical model, wherein the second constraint condition characterizes the constraint on the target electric vehicle during operation; and constructing a lower-level model in the hierarchical model with the objective of minimizing the impact of the target electric vehicle on the aggregation system, according to the constraints of 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 code for performing the following steps: the lower-level model includes a worst-case availability plan model and a worst-case vehicle-to-network interaction plan model.

[0126] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: constructing a worst-case availability plan 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 per hour of the target electric vehicle and the limit on the number of available electric vehicles in the aggregation system; and constructing a worst-case availability plan 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 performing the following steps: constructing a worst-case vehicle-to-grid interaction plan 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 scenario; and constructing a worst-case vehicle-to-grid interaction plan model based on the fourth objective function and the fourth constraint.

[0128] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: acquire 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 / discharging power limits of the target electric vehicle; input the vehicle data into a deterministic model and output a charging / discharging plan corresponding to the target electric vehicle, wherein the deterministic model is used to adjust the charging / discharging plan of the target electric vehicle with the goal of minimizing the cost of the aggregation system to which the target electric vehicle belongs; input the vehicle data and operating data into a hierarchical model, adjust the charging / discharging plan of the target electric vehicle in the hierarchical model, and determine the target charging / discharging plan of the target electric vehicle, wherein the hierarchical model includes an upper-level model and a lower-level model, the upper-level model is used to adjust the charging / discharging plan based on the uncertainty factors in the operating data with the aggregation system as the target, and the lower-level model is used to adjust the charging / discharging plan based on the uncertainty factors in the operating data with the goal of ensuring the target electric vehicle operates normally under the worst-case scenario.

[0129] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0130] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer 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. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

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

[0133] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0134] If the integrated unit is implemented as 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, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0135] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An optimized method for charging and discharging electric vehicles, characterized in that, include: Obtain vehicle data and operating data of the target electric vehicle, wherein the vehicle data includes at least one of the following: battery capacity of the target electric vehicle and charging / discharging power limit; The vehicle data is input into a deterministic model, and the charging and discharging plan corresponding to the target electric vehicle is output. 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. The vehicle data and the operational data are input into a hierarchical model. 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. The hierarchical model includes an upper-level model and a lower-level model. The upper-level model is used to adjust the charging and discharging plan based on the uncertainties in the operational data, with the goal of maximizing the economic benefits of the aggregation system. The lower-level model is used to adjust the charging and discharging plan based on the uncertainties in the operational data, with the goal of ensuring the target electric vehicle operates normally under the worst-case scenario. The worst-case scenario represents the situation where the target electric vehicle cannot participate in the charging and discharging task. The step of inputting the vehicle data and the operating data into the hierarchical model, and adjusting the charging and discharging plan of the target electric vehicle in the hierarchical model to determine the target charging and discharging plan of the target electric vehicle includes: The vehicle data and the operation data are first input into the upper-level model for processing to adjust the charging and discharging plan, thereby obtaining the charging power and discharging power of the electric vehicle. By inputting the charging power and the discharging power into the lower-level model, the total power supplied by the electric vehicle in the worst case and the result of whether the electric vehicle can be charged and discharged are obtained. The total power supply of the electric vehicle under the worst-case scenario and the results of whether the electric vehicle can be charged and discharged are fed into the upper-level model for further adjustment to obtain the target charging and discharging plan.

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 following steps are included: Based on the cost of the aggregation system, a first objective function is set; Based on the deterministic factors corresponding to electric vehicles, a first constraint condition is set, wherein the deterministic factors characterize factors that are related to the electric vehicle itself and will not change during vehicle operation; The deterministic model is constructed based on the first objective function and the first constraint.

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 limitation for a single charge-discharge cycle of the target electric vehicle, a battery capacity limitation for the target electric vehicle, a state of charge limitation for the electric vehicle battery, and a transmission capacity limitation for the target electric vehicle.

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

5. The method according to claim 4, characterized in that, The second constraint includes at least one of the following: a transportation energy constraint for the electric vehicle, a power demand constraint for the electric vehicle, and a logical constraint, wherein the logical constraint indicates 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 the worst-case availability plan model and the worst-case vehicle-to-network interaction plan model.

7. The method according to claim 6, characterized in that, The worst-case availability plan model is constructed using the following method: A third objective function is set based on the amount of electricity stored in the target electric vehicle within the target duration; A third constraint is set based on the minimum available power per hour of the target electric vehicle and the limit on the number of available electric vehicles in the aggregation system; Based on the third objective function and the third constraint, the worst-case availability plan model is constructed.

8. The method according to claim 6, characterized in that, The worst-case vehicle-to-everything (V2X) interaction plan model is constructed using the following method: A fourth objective function is set based on the impact of the target electric vehicle on the aggregation system; A fourth constraint is set based on the battery energy supply of the target electric vehicle under the worst-case scenario; Based on the fourth objective function and the fourth constraint, the worst-case vehicle-to-network interaction plan model is constructed.

9. An optimized charging and discharging device for electric vehicles, characterized in that, include: The acquisition module is used to acquire 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 / discharging power limit of the target electric vehicle; The output module is used to input the vehicle data into the deterministic model and output the charging and discharging plan corresponding to the target electric vehicle. 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 is used to input the vehicle data and the operating data into a hierarchical model, and to adjust the charging and discharging plan of the target electric vehicle in the hierarchical model to determine the target charging and discharging plan of the target electric vehicle. The hierarchical model includes an upper-level model and a lower-level model. The upper-level model is used to adjust the charging and discharging plan based on the uncertainty factors in the operating data with the goal of maximizing the economic benefits of the aggregation system. The lower-level model is used to adjust the charging and discharging plan based on the uncertainty factors in the operating data with the goal of ensuring the target electric vehicle operates normally under the worst-case scenario. The worst-case scenario represents the situation where the target electric vehicle cannot participate in the charging and discharging task. The determining module is further configured to first input the vehicle data and the operating data into the upper-level model for processing to adjust the charging and discharging plan, thereby obtaining the charging power and discharging power of the electric vehicle; input the charging power and the discharging power into the lower-level model to obtain the total supply power of the electric vehicle under the worst-case scenario and the result of whether the electric vehicle can charge and discharge; and input the total supply power of the electric vehicle under the worst-case scenario and the result of whether the electric vehicle can charge and discharge to the upper-level model for further adjustment to obtain the target charging and discharging plan.

10. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to perform the optimized method for charging and discharging an electric vehicle as described in any one of claims 1 to 8.

11. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the optimized method for charging and discharging an electric vehicle 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 the processor, it implements the optimized method for charging and discharging electric vehicles according to any one of claims 1 to 8.

Images