Method and device for determining charging and discharging electricity price of electric vehicle

Abstract

The invention discloses a charging and discharging electricity price determination method and device for an electric vehicle. The method comprises the following steps: acquiring aggregated load data of an electric vehicle, aggregated data of flexible resources, power supply capacity data of a power grid and time-of-use electricity price data in a current time period; the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning are input into a pricing model to be processed, the electricity price in the current time period is obtained, the pricing model is a model constructed with the benefit maximization of a virtual power plant operator as the target under the condition that multiple constraint conditions are met, and the price of the electricity price in the current time period is obtained. The constraint condition is obtained by constraining a plurality of dimensions of factors influencing the pricing of the electricity price. According to the invention, the technical problem that the pricing result is not ideal because the pricing model used for pricing the power grid and the charging (replacing) power station generally only considers the electricity price and does not fully consider multiple factors influencing the electricity price in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile charging pricing, and in particular to a method and device for determining the charging and discharging electricity price of an electric vehicle. Background Art

[0002] With the rapid development of the electric vehicle (EV) industry, large-scale disordered charging behavior has caused an increasing impact on the power grid, especially during peak hours, when the charging load of the power grid is gradually overloaded, which may lead to unstable operation of the power system. In order to solve this problem, a reasonable and scientific electricity price pricing mechanism has become a hot topic of research. By formulating an effective electricity price strategy, electric vehicle owners can be encouraged to charge and discharge in an orderly manner, that is, charging during the low-load period of the power grid and discharging during the peak period (if the vehicle supports V2G technology), thereby effectively adjusting the peak-valley difference of the power grid load, ensuring the balance of power supply and power consumption, and improving the comprehensive utilization efficiency of electric energy.

[0003] At present, there are roughly two strategies for solving electric vehicle charging pricing:

[0004] 1. Orderly charging load model based on time-of-use electricity price: This model predicts the charging load of vehicles connected to the power grid by analyzing the relationship between electric vehicle charging power and time, and improves the vehicle's access behavior by formulating the optimal electricity price policy, making the load curve smooth and achieving the effect of "shifting peaks and filling valleys". This model mainly considers the interests of the power grid and charging (swapping) stations, and reduces the load pressure of the power grid by optimizing the daily load indicators.

[0005] 2. Two-stage optimization model based on maximizing charging revenue of charging stations: This model aims to improve the profitability of charging stations and reduce peak-valley differences by formulating reasonable electricity price strategies, while meeting the electricity demand of users and the capacity constraints of transformers. The first-stage optimization model considers the charging demand of each vehicle and the capacity constraints of transformers, with the goal of achieving maximum benefits for charging stations; the second-stage optimization model considers the constraints of battery power, charging time, and distribution transformer capacity, with the goal of reducing peak-valley differences.

[0006] However, the existing pricing models of power grids and charging (swapping) stations usually only consider the electricity price itself. The pricing method is relatively extensive, lacks dynamic adjustment capabilities, and does not fully consider real-time electricity price fluctuations and user satisfaction, and cannot meet the diverse needs of charging (swapping) stations and users.

[0007] The pricing models used for pricing power grids and charging (swapping) stations in the above-mentioned related technologies usually only consider the electricity price itself, without fully considering the various factors that affect the electricity price, resulting in unsatisfactory pricing results. No effective solution has been proposed yet. Summary of the invention

[0008] The embodiments of the present invention provide a method and device for determining the charging and discharging electricity price of an electric vehicle, so as to at least solve the technical problem that the pricing model used by the power grid and charging (swapping) station in the related art usually only considers the electricity price itself, and does not fully consider the various factors that affect the electricity price, resulting in unsatisfactory pricing results.

[0009] According to one aspect of an embodiment of the present invention, a method for determining charging and discharging electricity prices for electric vehicles is provided, comprising: obtaining aggregated load data of electric vehicles, aggregated data of flexibility resources, power supply capacity data of a power grid, and time-of-use electricity price data within a current time period, wherein the aggregated load data is the total charging load demand of all the electric vehicles connected to the power grid within the current time period, the flexibility resources are used to adjust the load of the power grid, and the time-of-use electricity price data is electricity price data within different time periods; performing data cleaning on the aggregated load data, the aggregated data, the power supply capacity data, and the time-of-use electricity price data; inputting the aggregated load data, the aggregated data, the power supply capacity data, and the time-of-use electricity price data after data cleaning into a pricing model for processing to obtain electricity pricing within the current time period, wherein the pricing model is a model constructed with the goal of maximizing the profit of a virtual power plant operator while satisfying multiple constraints, the virtual power plant operator is used to sell electric energy to the electric vehicles and purchase the electric energy from the power grid, and the constraints are constraints obtained by constraining factors of multiple dimensions that affect electricity pricing.

[0010] Optionally, before the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning are input into the pricing model for processing to obtain the electricity price pricing in the current time period, the method for determining the charging and discharging electricity price of the electric vehicle also includes: obtaining the electricity sales and purchase amounts of the virtual power plant operator in the current time period, wherein the electricity sales amount is the first total amount of electricity sold by the virtual power plant operator to the electric vehicle in the current time period, and the purchase amount is the second total amount of electricity purchased by the virtual power plant operator from the power grid in the current time period; determining the objective function based on the electricity sales and the purchase amount, wherein The objective function is a function that aims to maximize the profit of the virtual power plant operator; constraints are imposed on the supply and demand balance of the power grid, the capacity of the transformer, the charging demand and charging time of the electric vehicle, the fluctuation range of the electricity price, the battery charging status of the electric vehicle, and the satisfaction of the target object using the electric vehicle with the charging service, to obtain multiple constraints, wherein the constraints include: power grid supply and demand balance constraints, transformer capacity constraints, electric vehicle charging demand constraints, time window constraints, upper and lower limit constraints of electricity prices, battery charging status constraints, penalty factor constraints, and user satisfaction constraints; the pricing model is constructed according to the objective function and multiple constraints.

[0011] Optionally, determining the objective function based on the amount of electricity sold and the amount of electricity purchased includes: determining the objective function based on the amount of electricity sold and the amount of electricity purchased according to a first formula, wherein the first formula is: Π represents the total revenue of the virtual power plant operator, i represents the number of the electric vehicle, N represents the total number of electric vehicles, t represents the number of the time period, T represents the total number of the time period, represents the electricity selling price of the virtual power plant operator in the time period t, represents the electricity sales of the virtual power plant operator in the time period t, represents the electricity purchase cost of the virtual power plant operator in the time period t, represents the power sales of the virtual power plant operator in the time period t, f represents the penalty factor, D t represents the total power consumption of the power grid during the time period t, L t represents the total power supply of the power grid in the time period t, S i represents the user satisfaction of the target object using the i-th electric vehicle for the charging service, represents the charging price of the ith electric vehicle in the time period t, p baserepresents the base electricity price, α represents a quantitative parameter used to quantify the degree of decline in user satisfaction when the charging price deviates from the base electricity price, λ represents a weight parameter, and F i represents the penalty term of the i-th electric vehicle.

[0012] Optionally, constraining the supply and demand balance of the power grid, the capacity of the transformer, the charging demand and charging time of the electric vehicle, the fluctuation range of electricity prices, the charging state of the battery, and the satisfaction of the target object using the electric vehicle with the charging service to obtain a plurality of the constraint conditions, including: constraining the supply and demand balance of the power grid according to a second formula to obtain the power grid supply and demand balance constraint, wherein the second formula is: P i,t represents the charging power of the i-th electric vehicle in time period t; constraining the capacity of the transformer according to the third formula to obtain the transformer capacity constraint, wherein the third formula is: P max represents the maximum power supply capacity of the transformer; constraining the charging demand of the electric vehicle according to the fourth formula to obtain the charging demand constraint of the electric vehicle, wherein the fourth formula is: E i,t ≥E min , E i,t represents the charging amount of the ith electric vehicle in the time period t, E min represents the minimum charging requirement of the electric vehicle; constraining the charging time of the electric vehicle according to the fifth formula to obtain the time window constraint, wherein the fifth formula is: t start ≤t≤t end , t start and t end Respectively represent the time windows for charging start and charging end; constrain the fluctuation range of the electricity price according to the sixth formula to obtain the upper and lower limit constraints of the electricity price, wherein the sixth formula is: P min ≤P t ≤P max , P t is the electricity price in the time period t, P min represents the lower limit of the electricity price, P max represents the upper limit of the electricity price; constraining the battery charging state of the electric vehicle according to the seventh formula to obtain the battery charging state constraint, wherein the seventh formula is: 0≤SoC i,t ≤100%,SoC i,t represents the battery charging state of the ith electric vehicle in the time period t; the penalty factor is constrained according to the eighth formula to obtain the penalty factor constraint, wherein the eighth formula is: η represents the penalty factor, which is used to balance the supply and demand difference when the total power consumption of the power grid exceeds the total power supply; according to the ninth formula, the satisfaction of the target object with the charging service is constrained to obtain the user satisfaction constraint, wherein the ninth formula is: S i,t ≥S base -ε(P t -P base ), S i,t represents the user satisfaction of the target object using the i-th electric vehicle with the charging service in the time period t, ε represents the price sensitivity parameter, S base Indicates basic satisfaction.

[0013] Optionally, the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning are input into the pricing model for processing to obtain the electricity price pricing in the current time period, including: after the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning are input into the pricing model, the objective function in the pricing model is iteratively solved by a solver until the iteration termination condition is met and the candidate solution currently output is determined to be the optimal solution, wherein the iteration termination condition is at least one of the following: the number of iterative calculations reaches the maximum number of iterations and the candidate solution meets the accuracy requirements; the optimal solution is determined to be the electricity price pricing.

[0014] Optionally, after the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning are input into the pricing model, the objective function in the pricing model is iteratively solved using a solver until the candidate solution currently outputted is determined to be the optimal solution when the iteration termination condition is met, including: after the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning are input into the pricing model, whether the objective function and the multiple constraints in the pricing model contain nonlinear terms, and a judgment result is obtained; when the judgment result indicates that the objective function and / or the constraints contain the nonlinear terms, the nonlinear terms are linearized to integrate the objective function and the multiple constraints into a linear optimization problem; the linear optimization problem is iteratively solved using the solver until the candidate solution currently outputted is determined to be the optimal solution when the iteration termination condition is met.

[0015] Optionally, the method for determining the charging and discharging electricity price of the electric vehicle also includes: in the process of using the pricing model to determine the electricity price, evaluating the pricing model according to a predetermined period to obtain an evaluation result, wherein the evaluation result is the result of evaluating whether the pricing result of the pricing model meets the pricing requirements; adaptively adjusting the pricing model according to the evaluation result to optimize the pricing model.

[0016] According to another aspect of an embodiment of the present invention, a device for determining charging and discharging electricity prices for electric vehicles is also provided, comprising: a first acquisition unit, for acquiring aggregated load data of electric vehicles, aggregated data of flexible resources, power supply capacity data of a power grid and time-of-use electricity price data in a current time period, wherein the aggregated load data is the total charging load demand of all the electric vehicles connected to the power grid in the current time period, the flexible resources are used to adjust the load of the power grid, and the time-of-use electricity price data is electricity price data in different time periods; a cleaning unit, for performing data cleaning on the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data; a second acquisition unit, for inputting the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning into a pricing model for processing to obtain electricity pricing in the current time period, wherein the pricing model is a model constructed with the goal of maximizing the profit of a virtual power plant operator while satisfying multiple constraints, the virtual power plant operator is used to sell electric energy to the electric vehicles and purchase the electric energy from the power grid, and the constraints are constraints obtained by constraining factors of multiple dimensions that affect electricity pricing.

[0017] Optionally, the device for determining the charging and discharging electricity price of the electric vehicle also includes: a third acquisition unit, which is used to obtain the amount of electricity sold and purchased by the virtual power plant operator in the current time period before inputting the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning into the pricing model for processing to obtain the electricity price pricing in the current time period, wherein the amount of electricity sold is the first total amount of electricity sold by the virtual power plant operator to the electric vehicle in the current time period, and the amount of electricity purchased is the second total amount of electricity purchased by the virtual power plant operator from the power grid in the current time period; a determination unit, which is used to determine the objective function based on the amount of electricity sold and the amount of electricity purchased, wherein , the objective function is a function aimed at maximizing the profit of the virtual power plant operator; the fourth acquisition unit is used to constrain the supply and demand balance of the power grid, the capacity of the transformer, the charging demand and charging time of the electric vehicle, the fluctuation range of the electricity price, the battery charging status of the electric vehicle, and the satisfaction of the target object using the electric vehicle with the charging service, so as to obtain multiple constraints, wherein the constraints include: power grid supply and demand balance constraints, transformer capacity constraints, electric vehicle charging demand constraints, time window constraints, upper and lower limit constraints of electricity prices, battery charging status constraints, penalty factor constraints, and user satisfaction constraints; a construction unit is used to construct the pricing model according to the objective function and multiple constraints.

[0018] Optionally, the determination unit includes: a first determination module, configured to determine the objective function according to a first formula based on the power sales amount and the power purchase amount, wherein the first formula is: Π represents the total revenue of the virtual power plant operator, i represents the number of the electric vehicle, N represents the total number of electric vehicles, t represents the number of the time period, T represents the total number of the time period, represents the electricity selling price of the virtual power plant operator in the time period t, represents the electricity sales of the virtual power plant operator in the time period t, represents the electricity purchase cost of the virtual power plant operator in the time period t, represents the power sales of the virtual power plant operator in the time period t, f represents the penalty factor, D t represents the total power consumption of the power grid during the time period t, L t represents the total power supply of the power grid in the time period t, S i represents the user satisfaction of the target object using the i-th electric vehicle for the charging service, represents the charging price of the ith electric vehicle in the time period t, p baserepresents the base electricity price, α represents a quantitative parameter used to quantify the degree of decline in user satisfaction when the charging price deviates from the base electricity price, λ represents a weight parameter, and F i represents the penalty term of the i-th electric vehicle.

[0019] Optionally, the fourth acquisition unit includes: a first acquisition module, configured to constrain the supply and demand balance of the power grid according to a second formula to obtain the power grid supply and demand balance constraint, wherein the second formula is: P i,t represents the charging power of the i-th electric vehicle in time period t; a second acquisition module is used to constrain the capacity of the transformer according to a third formula to obtain a transformer capacity constraint, wherein the third formula is: P max represents the maximum power supply capacity of the transformer; a third acquisition module, used to constrain the charging demand of the electric vehicle according to the fourth formula to obtain the charging demand constraint of the electric vehicle, wherein the fourth formula is: E i,t ≥E min , E i,t represents the charging amount of the ith electric vehicle in the time period t, E min represents the minimum charging requirement of the electric vehicle; a fourth acquisition module, used to constrain the charging time of the electric vehicle according to the fifth formula to obtain the time window constraint, wherein the fifth formula is: t start ≤t≤t end , t start and t end Respectively represent the time windows for charging start and charging end; a fifth acquisition module, used to constrain the fluctuation range of the electricity price according to the sixth formula to obtain the upper and lower limit constraints of the electricity price, wherein the sixth formula is: P min ≤P t ≤P max , P t is the electricity price in the time period t, P min represents the lower limit of the electricity price, P max represents the upper limit of the electricity price; a sixth acquisition module, used to constrain the battery charging state of the electric vehicle according to the seventh formula to obtain the battery charging state constraint, wherein the seventh formula is: 0≤SoC i,t ≤100%,SoC i,t represents the battery charging state of the i-th electric vehicle in the time period t; a seventh acquisition module, used to constrain the penalty factor according to the eighth formula to obtain the penalty factor constraint, wherein the eighth formula is: η represents the penalty factor, which is used to balance the supply and demand difference when the total power consumption of the power grid exceeds the total power supply; an eighth acquisition module is used to constrain the satisfaction of the target object with the charging service according to the ninth formula to obtain the user satisfaction constraint, wherein the ninth formula is: S i,t ≥S base -ε(P t -P base ), S i,t represents the user satisfaction of the target object using the i-th electric vehicle with the charging service in the time period t, ε represents the price sensitivity parameter, S base Indicates basic satisfaction.

[0020] Optionally, the second acquisition unit includes: a second determination module, which is used to iteratively solve the objective function in the pricing model using a solver after the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning are input into the pricing model, until the currently output candidate solution is determined to be the optimal solution when the iteration termination condition is met, wherein the iteration termination condition is at least one of the following: the number of iterative calculations reaches the maximum number of iterations and the candidate solution meets the accuracy requirements; a third determination module, which is used to determine that the optimal solution is the electricity price pricing.

[0021] Optionally, the second determination module includes: an acquisition submodule, which is used to determine whether the objective function and multiple constraints in the pricing model contain nonlinear terms after the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning are input into the pricing model, and obtain a judgment result; a processing submodule, which is used to linearize the nonlinear terms when the judgment result indicates that the objective function and / or the constraints contain nonlinear terms, so as to integrate the objective function and the multiple constraints into a linear optimization problem; a determination submodule, which is used to use the solver to iteratively solve the linear optimization problem until the iteration termination condition is met and the candidate solution currently output is determined to be the optimal solution.

[0022] Optionally, the device for determining the charging and discharging electricity price of the electric vehicle also includes: a fifth acquisition unit, which is used to evaluate the pricing model according to a predetermined period in the process of using the pricing model to determine the electricity price, and obtain an evaluation result, wherein the evaluation result is the result of evaluating whether the pricing result of the pricing model meets the pricing requirements; an optimization unit, which is used to adaptively adjust the pricing model according to the evaluation result to optimize the pricing model.

[0023] According to another aspect of an embodiment of the present invention, a system for determining charging and discharging electricity prices of an electric vehicle is provided. The system for determining charging and discharging electricity prices of an electric vehicle uses any one of the above-mentioned methods for determining charging and discharging electricity prices of an electric vehicle.

[0024] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, wherein the computer-readable storage medium includes a stored program, wherein the program executes any one of the above-mentioned methods for determining charging and discharging electricity prices of an electric vehicle.

[0025] According to another aspect of an embodiment of the present invention, a processor is further provided, the processor being used to run a program, wherein when the program is run, any one of the above-mentioned methods for determining charging and discharging electricity prices of an electric vehicle is executed.

[0026] According to another aspect of an embodiment of the present invention, a computer program product is provided, comprising computer instructions, wherein when the computer instructions are executed by a processor, any one of the above-mentioned methods for determining charging and discharging electricity prices of an electric vehicle is executed.

[0027] In an embodiment of the present invention, aggregated load data of electric vehicles, aggregated data of flexibility resources, power supply capacity data of the power grid and time-of-use electricity price data in the current time period are obtained, wherein the aggregated load data is the total charging load demand of all electric vehicles connected to the power grid in the current time period, the flexibility resources are used to adjust the load of the power grid, and the time-of-use electricity price data is the electricity price data in different time periods; data cleaning is performed on the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data; the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data after data cleaning are input into the pricing model for processing to obtain the electricity price in the current time period, wherein the pricing model is a model constructed with the goal of maximizing the profit of the virtual power plant operator while satisfying multiple constraints, the virtual power plant operator is used to sell electricity to electric vehicles and purchase electricity from the power grid, and the constraints are constraints obtained by constraining factors in multiple dimensions that affect electricity price pricing. Through the above technical scheme, the purpose of pricing the charging and discharging prices of electric vehicles is achieved by utilizing a pricing model constructed by considering multiple factors. The technical effect of building and optimizing a pricing model by considering multiple uncertain factors in the pricing process to improve the flexibility of the pricing model is achieved. The pricing model's ability to respond quickly to real-time grid status and user behavior changes is improved, and the revenue of virtual power plant operators is increased. This solves the technical problem that the pricing models used for pricing power grids and charging (swapping) stations in related technologies usually only consider the electricity price itself, and do not fully consider the multiple factors that affect the electricity price, resulting in unsatisfactory pricing results. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] 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:

[0029] Figure 1 It is a hardware structure block diagram of a mobile terminal of a method for determining charging and discharging electricity prices of an electric vehicle according to an embodiment of the present invention;

[0030] Figure 2 is a flow chart of a method for determining charging and discharging electricity prices of an electric vehicle according to an embodiment of the present invention;

[0031] Figure 3 is a flow chart of an optional method for determining charging and discharging electricity prices of an electric vehicle according to an embodiment of the present invention;

[0032] Figure 4 is a schematic diagram of electricity price pricing according to an embodiment of the present invention;

[0033] Figure 5 is a schematic diagram of total revenue of a virtual power plant operator according to an embodiment of the present invention;

[0034] Figure 6 2 is a schematic diagram of a device for determining charging and discharging electricity prices of an electric vehicle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] 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.

[0036] 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.

[0037] As introduced in the background technology, the pricing models used in the pricing of power grids and charging (swapping) stations in the related art usually only consider the electricity price itself, without fully considering the various factors that affect the electricity price, resulting in unsatisfactory pricing results. In view of the above defects, a method and device for determining the charging and discharging electricity price of an electric vehicle are provided in an embodiment of the present invention.

[0038] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0039] The method embodiments provided in the embodiments of the present invention can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal of a method for determining charging and discharging electricity prices of an electric vehicle according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. 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 mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0040] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining the charging and discharging electricity price of an electric vehicle in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. 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 102, and these remote memories can be connected to the mobile 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 a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0041] According to an embodiment of the present invention, a method embodiment of a method for determining charging and discharging electricity prices of 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.

[0042] Figure 2 is a flow chart of a method for determining charging and discharging electricity prices for an electric vehicle according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0043] Step S202, obtain the aggregated load data of electric vehicles, the aggregated data of flexibility resources, the power supply capacity data of the power grid and the time-of-use electricity price data in the current time period, wherein the aggregated load data is the total charging load demand of all electric vehicles connected to the power grid in the current time period, the flexibility resources are used to adjust the load of the power grid, and the time-of-use electricity price data is the electricity price data in different time periods.

[0044] Combine the following Figure 3 The above embodiments of the present invention are described in detail. Figure 3 is a flow chart of an optional method for determining charging and discharging electricity prices of an electric vehicle according to an embodiment of the present invention; Figure 3As shown, the aggregated load data of electric vehicles, the aggregated data of flexibility resources, the power supply capacity data of the power grid and the time-of-use electricity price data can be directly obtained through the data interface. Based on these data, a more reasonable electricity price can be formulated using the pricing model.

[0045] Specifically, the aggregated load data of electric vehicles reflects the impact of charging and discharging of electric vehicles on the grid load in different time periods, which can help evaluate the peak and valley changes in the grid load and provide a basis for formulating electricity price strategies with dynamic adjustment capabilities; flexibility resources, such as energy storage systems and demand response, can provide the ability to regulate the grid load. The load data of flexibility resources helps to evaluate and quantify the scale and capacity of flexibility resources available to virtual power plant operators, which is crucial for supply and demand balance, cost-benefit analysis and risk management in optimization models; the power supply capacity data of the power grid includes the maximum power supply capacity of the power grid and the capacity limit of the transformer, etc., to ensure that the power supply capacity constraints in the electricity price optimization model are met, which is crucial for evaluating the carrying capacity of the power grid and formulating safe and reliable electricity price strategies; time-of-use electricity price data provides a benchmark for market electricity prices, helps to determine the rationality of electricity price strategies, and can be used to evaluate and compare electricity price differences in different time periods, as well as the impact on user behavior and grid load.

[0046] Step S204, performing data cleaning on the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data.

[0047] In this embodiment, data such as aggregated load data of electric vehicles, aggregated data of flexibility resources, power supply capacity data of the power grid, and time-of-use electricity price data can be cleaned and preprocessed to ensure the accuracy of the input data of the pricing model.

[0048] Step S206, input the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data after data cleaning into the pricing model for processing to obtain the electricity price in the current time period, wherein the pricing model is a model constructed with the goal of maximizing the profit of the virtual power plant operator while satisfying multiple constraints. The virtual power plant operator is used to sell electricity to electric vehicles and purchase electricity from the power grid, and the constraints are constraints obtained by constraining factors in multiple dimensions that affect electricity price pricing.

[0049] As above Figure 3 As shown, in this embodiment, the cleaned aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data can be input into the pricing model for processing to obtain the corresponding electricity price. Here, the electricity price refers to the price at which the virtual power plant operator sells electricity to electric vehicles. It can also be understood that the pricing model outputs the pricing strategy of the virtual power plant operator for the sale of electricity to electric vehicles, and then pricing is performed according to the pricing strategy.

[0050] According to the above embodiment of the present invention, before the above step S206, that is, before the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data after data cleaning are input into the pricing model for processing to obtain the electricity price pricing in the current time period, the method for determining the charging and discharging electricity price of the electric vehicle also includes: obtaining the power sales and purchase amounts of the virtual power plant operator in the current time period, wherein the power sales amount is the first total amount of electricity sold by the virtual power plant operator to the electric vehicle in the current time period, and the purchase amount is the second total amount of electricity purchased by the virtual power plant operator from the power grid in the current time period; determining the target based on the power sales and purchase amounts Function, where the objective function is a function that aims to maximize the profit of the virtual power plant operator; constraints are imposed on the supply and demand balance of the power grid, the capacity of the transformer, the charging demand and charging time of electric vehicles, the fluctuation range of electricity prices, the battery charging status of electric vehicles, and the satisfaction of the target objects using electric vehicles with the charging service, and multiple constraints are obtained, where the constraints include: power grid supply and demand balance constraints, transformer capacity constraints, electric vehicle charging demand constraints, time window constraints, upper and lower limits of electricity prices, battery charging status constraints, penalty factor constraints, and user satisfaction constraints; a pricing model is constructed based on the objective function and multiple constraints.

[0051] In this embodiment, an objective function including multiple dimensions such as revenue, cost, user satisfaction and grid stability can be established with the goal of maximizing the interests of the virtual power plant operator, and a series of constraints can be defined, such as grid supply and demand balance constraints, transformer capacity constraints, electric vehicle charging demand constraints, time window constraints, upper and lower limits of electricity prices, battery charging status constraints, penalty factor constraints, and user satisfaction constraints, so as to construct a pricing model; when determining the decision variables (i.e., constraints), taking user satisfaction into consideration can take into account the user's wishes when pricing, so as to formulate a more reasonable pricing strategy with the goal of maximizing the interests of the virtual power plant operator on the premise of satisfying the user's wishes as much as possible.

[0052] In an optional embodiment of the present invention, the satisfaction of electric vehicle users during charging can be considered from the following aspects: 1) Charging convenience: users can easily and quickly find available charging piles for charging; 2) Charging speed: the charging speed must meet the needs of users and cannot be too slow to affect users' daily use; 3) Charging cost: the charging cost is within the acceptable range for users and will not bring excessive financial burden to users; 4) Charging safety: the charging process is safe and will not cause damage to electric vehicles or users; 5) Service quality: the service level provided by the charging station, including service attitude, facility maintenance, etc.; 6) Environmental factors: the comfort of the charging environment, such as whether there are sunshade, rain shelter facilities, etc.; 7) Charging time flexibility: users can choose the charging time according to their own schedule without restriction; 8) Electricity price acceptance: users' sensitivity and acceptability to changes in electricity prices; 9) Charging reliability: the reliability of charging facilities, including the normal operation of charging piles and the stability of power supply.

[0053] In a specific embodiment of the present invention, determining the objective function based on the amount of electricity sold and the amount of electricity purchased includes: determining the objective function based on the amount of electricity sold and the amount of electricity purchased according to a first formula, wherein the first formula is: Π represents the total revenue of the virtual power plant operator, i represents the number of electric vehicles, N represents the total number of electric vehicles, t represents the number of time periods, T represents the total number of time periods, represents the electricity price sold by the virtual power plant operator in time period t, represents the electricity sales of the virtual power plant operator in time period t, represents the electricity purchase cost of the virtual power plant operator in time period t, represents the electricity sales of the virtual power plant operator in time period t, f represents the penalty factor, and D t Indicates the total power consumption of the power grid in time period t, L t It represents the total power supply of the power grid in time period t, S i represents the user satisfaction of the target object using the i-th electric vehicle for the charging service, represents the charging price of the ith electric vehicle in time period t, p base represents the benchmark electricity price, α represents a quantitative parameter used to quantify the degree of decline in user satisfaction when the charging price deviates from the benchmark electricity price, λ represents a weight parameter, and F i represents the penalty term for the i-th electric car.

[0054] Specifically, as above Figure 3 As shown in the figure, when establishing the objective function, the goal is to maximize the interests of the virtual power plant operator, while also ensuring the stability of the power grid and meeting the charging needs of electric vehicle users. The objective function can be expressed as: Where Π represents the total revenue of the virtual power plant operator, i represents the number of electric vehicles, N represents the total number of electric vehicles, t represents the number of the time period, and T represents the total number of time periods. represents the electricity price sold by the virtual power plant operator in time period t, represents the electricity sales of the virtual power plant operator in time period t, represents the electricity purchase cost of the virtual power plant operator in time period t, represents the electricity sales of the virtual power plant operator in time period t, f represents the penalty factor, and D t Indicates the total power consumption of the power grid in time period t, L t It represents the total power supply of the power grid in time period t, S i represents the user satisfaction of the target object using the i-th electric vehicle for the charging service, represents the charging price of the ith electric vehicle in time period t, p base represents the benchmark electricity price, α represents a quantitative parameter used to quantify the degree of decline in user satisfaction when the charging price deviates from the benchmark electricity price, λ represents a weight parameter, and F i represents the penalty term for the i-th electric car.

[0055] The penalty term here is a penalty mechanism for the negative impact that electric vehicles may have on the stability of the power grid during the charging or discharging process. This penalty term usually includes the following aspects: 1) Penalty for electricity prices deviating from the benchmark electricity price: When the electricity price deviates from the standard electricity price in the market or the electricity price level that users are accustomed to, user satisfaction will be affected. The penalty term will quantify the extent to which user satisfaction decreases when the price deviates from the benchmark electricity price; 2) Penalty for user satisfaction: User satisfaction is taken into account in the electricity price setting model. If unreasonable electricity price settings lead to reduced user satisfaction, the model will use penalty terms to reduce the occurrence of this situation. The penalty for user satisfaction can be implemented through user satisfaction constraints to ensure that electricity prices do not deviate from user expectations. Too far; 3) Penalty for violation of grid stability and market rules: If the charging or discharging behavior of electric vehicles violates the grid stability requirements or market rules, such as exceeding the capacity limit of the transformer or charging during an unallowed time period, the model will use penalty items to limit this behavior to ensure the safe operation of the grid; 4) Penalty for supply and demand differences: The penalty factor is used to balance the supply and demand differences. When the demand exceeds the supply, the penalty item will be used to reduce the supply and demand imbalance; 5) Penalty for electric vehicle charging demand: If the charging demand of electric vehicles exceeds the power supply capacity of the grid or violates the operating rules of the charging station, the model will use penalty items to reduce this behavior to ensure the stable operation of the grid and the normal operation of the charging station.

[0056] In another specific embodiment of the present invention, constraints are imposed on the supply and demand balance of the power grid, the capacity of the transformer, the charging demand and charging time of the electric vehicle, the fluctuation range of the electricity price, the charging state of the battery, and the satisfaction of the target object using the electric vehicle with the charging service to obtain multiple constraint conditions, including: constraining the supply and demand balance of the power grid according to the second formula to obtain the power grid supply and demand balance constraint, wherein the second formula is: P i,t represents the charging power of the i-th electric vehicle in time period t; the capacity of the transformer is constrained according to the third formula to obtain the transformer capacity constraint, where the third formula is: P max represents the maximum power supply capacity of the transformer; the charging demand of the electric vehicle is constrained according to the fourth formula to obtain the charging demand constraint of the electric vehicle, where the fourth formula is: E i,t ≥E min , E i,t represents the charging amount of the ith electric vehicle in time period t, E min represents the minimum charging requirement of the electric vehicle; the charging time of the electric vehicle is constrained according to the fifth formula to obtain the time window constraint, where the fifth formula is: t start ≤t≤t end , t start and t end Respectively represent the time windows for charging start and charging end; according to the sixth formula, the fluctuation range of the electricity price is constrained to obtain the upper and lower limit constraints of the electricity price, where the sixth formula is: min ≤P t ≤P max , P t is the electricity price in time period t, P min represents the lower limit of electricity price, P max represents the upper limit of the electricity price; according to the seventh formula, the battery charging state of the electric vehicle is constrained to obtain the battery charging state constraint, wherein the seventh formula is: 0≤SoC i,t ≤100%,SoC i,t represents the battery charging state of the ith electric vehicle in time period t; the penalty factor is constrained according to the eighth formula to obtain the penalty factor constraint, where the eighth formula is: η represents a penalty factor, which is used to balance the difference between supply and demand when the total power consumption of the power grid exceeds the total power supply; according to the ninth formula, the satisfaction of the target object with the charging service is constrained to obtain the user satisfaction constraint, where the ninth formula is: S i,t ≥S base -ε(P t -P base ), S i,trepresents the user satisfaction of the target object using the i-th electric vehicle for the charging service in time period t, ε represents the price sensitivity parameter, S base Indicates basic satisfaction.

[0057] Specifically, as above Figure 3 As shown, the following constraints can be established: 1) Power grid supply and demand balance constraints: Among them, P i,t represents the charging power of the i-th electric vehicle in time period t; 2) Transformer capacity constraint: Among them, P max represents the maximum power supply capacity of the transformer; 3) Electric vehicle charging demand constraint: E i,t ≥E min , where E i,t represents the charging amount of the ith electric vehicle in time period t, E min Represents the minimum charging requirement of electric vehicles; 4) Time window constraint: t start ≤t≤t end , where t start and t end Respectively represent the time window for charging start and charging end; 5) Upper and lower limits of electricity price: P min ≤P t ≤P max , where P t is the electricity price in time period t, P min represents the lower limit of electricity price, P max Indicates the upper limit of the electricity price; 6) Battery charging state constraint: 0≤SoC i,t ≤100%, of which SoC i,t represents the battery charging state of the i-th electric vehicle in time period t; 7) Penalty factor constraint: Where η represents the penalty factor, which is used to balance the difference between supply and demand when the total power consumption of the power grid exceeds the total power supply; 8) User satisfaction constraint: S i,t ≥S base -ε(P t -P base ), S i,t represents the user satisfaction of the target object using the i-th electric vehicle for the charging service in time period t, ε represents the price sensitivity parameter, S base Indicates basic satisfaction.

[0058] According to the above embodiment of the present invention, in the above step S206, the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data after data cleaning are input into the pricing model for processing to obtain the electricity price pricing in the current time period, including: after the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data after data cleaning are input into the pricing model, the objective function in the pricing model is iteratively solved by the solver until the iteration termination condition is met and the candidate solution currently output is determined to be the optimal solution, wherein the iteration termination condition is at least one of the following: the number of iterative calculations reaches the maximum number of iterations and the candidate solution meets the accuracy requirements; the optimal solution is determined to be the electricity price pricing.

[0059] Specifically, after the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning are input into the pricing model, the pricing model can be iteratively solved using a solver (which may include but is not limited to: a CPLEX solver) so that when the iteration termination condition is reached, the currently solved candidate solution is used as the optimal solution to obtain the optimal electricity price.

[0060] The iteration termination conditions here can be considered from the following aspects: 1) Convergence criterion: the solution meets certain accuracy requirements, that is, the difference between the solutions after several consecutive iterations is less than the preset tolerance (for example, the change in electricity price or income is less than 0.01 yuan); 2) Stability: the stability of the model output, that is, the fluctuation range of the results of several consecutive iterations is within an acceptable range, indicating that the model has stabilized; 3) Number of iterations: reaching the preset maximum number of iterations, which is a conservative termination condition used to prevent infinite loops; 4) Running time: the solution process reaches the preset time limit. If the solution time is too long, the iteration may be considered to be terminated, especially when the improvement of further iterations is very small; 5) Algorithm performance: If the algorithm performance degrades during the iteration process, such as a sharp increase in solution time or numerical instability , you may consider ending the iteration; 6) User intervention: The user determines whether the current solution meets the application requirements based on actual needs or experience, and decides whether to end the iteration; 7) Constraint satisfaction: Whether all constraints are met. If the satisfaction reaches an acceptable level, you can consider ending the iteration; 8) Improvement of the objective function value: If the improvement of the objective function value is very small, or there is almost no improvement in several consecutive iterations, this may mean that the optimal solution is already close or the cost of further improvement is too high; 9) Resource constraints: The limitation of computing resources (such as memory, processor time). When the resource usage reaches the upper limit, you may need to end the iteration; 10) Actual application requirements: According to the requirements of the actual application scenario, if the current solution can already meet the needs of the actual application, you can choose to end the iteration.

[0061] In an optional embodiment of the present invention, after the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data after data cleaning are input into the pricing model, the objective function in the pricing model is iteratively solved by a solver until the candidate solution currently outputted is determined to be the optimal solution when the iteration termination condition is met, including: after the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data after data cleaning are input into the pricing model, whether the objective function and multiple constraints in the pricing model contain nonlinear terms, and a judgment result is obtained; when the judgment result indicates that the objective function and / or the constraints contain nonlinear terms, the nonlinear terms are linearized to integrate the objective function and the multiple constraints into a linear optimization problem; the linear optimization problem is iteratively solved by a solver until the candidate solution currently outputted is determined to be the optimal solution when the iteration termination condition is met.

[0062] Specifically, the objective function and constraints in the pricing model can be integrated, and the possible nonlinear terms therein can be linearized to form an optimization problem, which is usually a linear programming (LP) problem. The CPLEX solver can then be used to solve the set optimization problem to find the optimal solution. After the solution is completed, the result is a set of recommended values ​​for electricity prices and charging volumes. These recommended values ​​need to be analyzed to ensure that they meet all constraints and can effectively achieve the goal of maximizing the operator's interests.

[0063] According to the above embodiment of the present invention, the method for determining the charging and discharging electricity price of the electric vehicle also includes: in the process of using the pricing model to determine the electricity price, evaluating the pricing model according to a predetermined period to obtain an evaluation result, wherein the evaluation result is the result of evaluating whether the pricing result of the pricing model meets the pricing requirements; adaptively adjusting the pricing model according to the evaluation result to optimize the pricing model.

[0064] In this embodiment, since the grid load and user behavior are dynamically changing, the pricing model may need to be regularly updated and adjusted to adapt to new data, and the final electricity price pricing plan must be monitored and evaluated when implemented in actual operations to ensure its effectiveness and adaptability; in addition, the pricing model can also be regularly evaluated to continuously optimize the pricing model and ensure its accuracy.

[0065] It can be seen from the above that, through the technical solution provided by the above embodiment of the present invention, the aggregated load data of electric vehicles in the current time period, the aggregated data of flexibility resources, the power supply capacity data of the power grid and the time-of-use electricity price data are obtained, wherein the aggregated load data is the total charging load demand of all electric vehicles connected to the power grid in the current time period, the flexibility resources are used to adjust the load of the power grid, and the time-of-use electricity price data is the electricity price data in different time periods; the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data are cleaned; the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning are input into the pricing model for processing to obtain the electricity demand in the current time period. Price pricing, where the pricing model is a model built with the goal of maximizing the revenue of the virtual power plant operator under the condition of satisfying multiple constraints. The virtual power plant operator is used to sell electricity to electric vehicles and purchase electricity from the power grid. The constraints are constraints on factors in multiple dimensions that affect electricity price pricing. The purpose of pricing the charging and discharging prices of electric vehicles by utilizing a pricing model built by considering multiple factors is achieved. The technical effect of building and optimizing the pricing model by considering multiple uncertain factors in the pricing process to improve the flexibility of the pricing model is achieved, and the pricing model's ability to respond quickly to real-time grid status and user behavior changes is improved, thereby increasing the revenue of the virtual power plant operator. 。

[0066] Therefore, the technical solution provided by the above-mentioned embodiment of the present invention solves the technical problem that the pricing model used in the relevant technology for pricing power grids and charging (swapping) stations usually only considers the electricity price itself, does not fully consider the various factors affecting the electricity price, and leads to unsatisfactory pricing results.

[0067] Combine the following Figure 4 and Figure 5 The above embodiments of the present invention are described in detail. Figure 4 is a schematic diagram of electricity price pricing according to an embodiment of the present invention; Figure 5 is a schematic diagram of the total revenue of a virtual power plant operator according to an embodiment of the present invention; Figure 4 As shown, it can be seen that electricity price pricing changes dynamically in real time, and the cumulative total revenue of the virtual power plant operator also shows an upward trend. To a certain extent, this illustrates that the pricing model provided by the above embodiment of the present invention can fully consider the dynamic changes of grid load and user charging behavior, so that the pricing model can adjust electricity price pricing in time to better adapt to these changes.

[0068] The technical solution provided by the above-mentioned embodiments of the present invention also has the following technical effects: 1) Through an orderly charging and discharging pricing model, the load curve of the power grid can be effectively smoothed, the peak-valley difference of the power grid can be reduced (it can be simply understood as raising the electricity price during peak hours to reduce the user's willingness to charge, and lowering the electricity price during low hours to increase the user's willingness to charge, thereby reducing the peak-valley difference of the power grid), and improving the overall operation efficiency and stability of the power grid; 2) Taking into account the operating costs and benefits of virtual power plant operators, by optimizing the electricity price strategy, it helps to improve the economic benefits of the operator, thereby attracting more investment and promoting the development of charging infrastructure; 3) Through a reasonable pricing strategy, the model aims to achieve a win-win situation among power grid operators, charging station operators and electric vehicle users, balances the interests of all parties, and helps to build a harmonious power ecosystem; 4) The electricity price can be dynamically adjusted according to the real-time status and forecast data of the power grid, making the power grid operation more flexible and adaptable, and better able to cope with various emergencies and market changes; 5) The operation efficiency and safety of the power grid are improved, and the sustainable development of the electric vehicle industry is promoted.

[0069] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0070] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment 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 this understanding, the technical solution of the present application, 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 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 application.

[0071] According to an embodiment of the present invention, there is also provided a device for determining charging and discharging electricity prices of an electric vehicle for implementing the above-mentioned method for determining charging and discharging electricity prices of an electric vehicle. Figure 6 is a schematic diagram of a device for determining charging and discharging electricity prices for an electric vehicle according to an embodiment of the present invention. Figure 6As shown, the device includes: a first acquisition unit 61, a cleaning unit 63 and a second acquisition unit 65. The device for determining the charging and discharging electricity price of an electric vehicle is described in detail below.

[0072] The first acquisition unit 61 is used to obtain the aggregated load data of electric vehicles, the aggregated data of flexibility resources, the power supply capacity data of the power grid and the time-of-use electricity price data in the current time period, wherein the aggregated load data is the total charging load demand of all electric vehicles connected to the power grid in the current time period, the flexibility resources are used to adjust the load of the power grid, and the time-of-use electricity price data is the electricity price data in different time periods.

[0073] The cleaning unit 63 is used to clean the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data.

[0074] The second acquisition unit 65 is used to input the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data after data cleaning into the pricing model for processing to obtain the electricity price in the current time period, wherein the pricing model is a model constructed with the goal of maximizing the profit of the virtual power plant operator while satisfying multiple constraints. The virtual power plant operator sells electricity to electric vehicles and purchases electricity from the power grid. The constraints are constraints obtained by constraining factors in multiple dimensions that affect electricity price pricing.

[0075] It should be noted here that the above-mentioned first acquisition unit 61, cleaning unit 63 and second acquisition unit 65 correspond to steps S202 to S206 in the above-mentioned embodiment, and the three units and the corresponding steps implement the same instances and application scenarios, but are not limited to the contents disclosed in the above-mentioned embodiment.

[0076] As can be seen from the above, in the scheme recorded in the above-mentioned embodiment of the present invention, the first acquisition unit can be used to obtain the aggregated load data of electric vehicles in the current time period, the aggregated data of flexibility resources, the power supply capacity data of the power grid and the time-of-use electricity price data, wherein the aggregated load data is the total charging load demand of all electric vehicles connected to the power grid in the current time period, the flexibility resources are used to adjust the load of the power grid, and the time-of-use electricity price data is the electricity price data in different time periods; then the cleaning unit is used to clean the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data; finally, the second acquisition unit is used to input the cleaned aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data into the pricing model for processing , and obtain the electricity price in the current time period, where the pricing model is a model constructed with the goal of maximizing the profit of the virtual power plant operator while satisfying multiple constraints. The virtual power plant operator is used to sell electricity to electric vehicles and purchase electricity from the power grid. The constraints are constraints on factors in multiple dimensions that affect electricity price pricing. The purpose of pricing the charging and discharging prices of electric vehicles by utilizing a pricing model constructed by considering multiple factors is achieved, and the technical effect of constructing and optimizing the pricing model by considering multiple uncertain factors in the pricing process to improve the flexibility of the pricing model is achieved, which improves the pricing model's ability to respond quickly to real-time grid status and changes in user behavior, and increases the profits of virtual power plant operators.

[0077] Therefore, the technical solution provided by the above-mentioned embodiment of the present invention solves the technical problem that the pricing model used in the relevant technology for pricing power grids and charging (swapping) stations usually only considers the electricity price itself, does not fully consider the various factors affecting the electricity price, and leads to unsatisfactory pricing results.

[0078] Optionally, the device for determining the charging and discharging electricity price of the electric vehicle also includes: a third acquisition unit, which is used to obtain the power sales and purchase amounts of the virtual power plant operator in the current time period before inputting the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data after data cleaning into the pricing model for processing to obtain the electricity price pricing in the current time period, wherein the power sales amount is the first total amount of electricity sold by the virtual power plant operator to the electric vehicle in the current time period, and the power purchase amount is the second total amount of electricity purchased by the virtual power plant operator from the power grid in the current time period; a determination unit, which is used to determine the objective function based on the power sales amount and the power purchase amount, wherein the objective function is A function with the goal of maximizing the profit of the virtual power plant operator; a fourth acquisition unit, used to constrain the supply and demand balance of the power grid, the capacity of the transformer, the charging demand and charging time of electric vehicles, the fluctuation range of electricity prices, the battery charging status of electric vehicles, and the satisfaction of the target objects using electric vehicles with the charging service, so as to obtain multiple constraints, among which the constraints include: power grid supply and demand balance constraints, transformer capacity constraints, electric vehicle charging demand constraints, time window constraints, upper and lower limits of electricity prices, battery charging status constraints, penalty factor constraints, and user satisfaction constraints; a construction unit, used to construct a pricing model according to the objective function and multiple constraints.

[0079] Optionally, the determination unit includes: a first determination module, configured to determine the objective function according to a first formula based on the amount of electricity sold and the amount of electricity purchased, wherein the first formula is: Π represents the total revenue of the virtual power plant operator, i represents the number of electric vehicles, N represents the total number of electric vehicles, t represents the number of time periods, T represents the total number of time periods, represents the electricity price sold by the virtual power plant operator in time period t, represents the electricity sales of the virtual power plant operator in time period t, represents the electricity purchase cost of the virtual power plant operator in time period t, represents the electricity sales of the virtual power plant operator in time period t, f represents the penalty factor, and D t Indicates the total power consumption of the power grid in time period t, L t It represents the total power supply of the power grid in time period t, S i represents the user satisfaction of the target object using the i-th electric vehicle for the charging service, represents the charging price of the ith electric vehicle in time period t, p base represents the benchmark electricity price, α represents a quantitative parameter used to quantify the degree of decline in user satisfaction when the charging price deviates from the benchmark electricity price, λ represents a weight parameter, and F i represents the penalty term for the i-th electric car.

[0080] Optionally, the fourth acquisition unit includes: a first acquisition module, configured to constrain the supply and demand balance of the power grid according to a second formula to obtain the supply and demand balance constraint of the power grid, wherein the second formula is: P i,t represents the charging power of the i-th electric vehicle in the time period t; the second acquisition module is used to constrain the capacity of the transformer according to the third formula to obtain the transformer capacity constraint, wherein the third formula is: P max represents the maximum power supply capacity of the transformer; a third acquisition module is used to constrain the charging demand of the electric vehicle according to the fourth formula to obtain the charging demand constraint of the electric vehicle, wherein the fourth formula is: E i,t ≥E min , E i,t represents the charging amount of the ith electric vehicle in time period t, E min represents the minimum charging requirement of the electric vehicle; a fourth acquisition module is used to constrain the charging time of the electric vehicle according to the fifth formula to obtain a time window constraint, wherein the fifth formula is: t start ≤t≤t end , t start and t end Respectively represent the time windows for charging start and charging end; a fifth acquisition module, used to constrain the fluctuation range of the electricity price according to the sixth formula to obtain the upper and lower limit constraints of the electricity price, wherein the sixth formula is: min ≤P t ≤P max , P t is the electricity price in time period t, P min represents the lower limit of electricity price, P max Indicates the upper limit of the electricity price; a sixth acquisition module, used to constrain the battery charging state of the electric vehicle according to the seventh formula to obtain the battery charging state constraint, wherein the seventh formula is: 0≤SoC i,t ≤100%,SoC i,t represents the battery charging state of the ith electric vehicle in the time period t; a seventh acquisition module is used to constrain the penalty factor according to the eighth formula to obtain the penalty factor constraint, wherein the eighth formula is: η represents a penalty factor, which is used to balance the difference between supply and demand when the total power consumption of the power grid exceeds the total power supply; the eighth acquisition module is used to constrain the satisfaction of the target object with the charging service according to the ninth formula to obtain the user satisfaction constraint, wherein the ninth formula is: S i,t ≥S base -ε(P t -P base ), S i,trepresents the user satisfaction of the target object using the i-th electric vehicle for the charging service in time period t, ε represents the price sensitivity parameter, S base Indicates basic satisfaction.

[0081] Optionally, the second acquisition unit includes: a second determination module, which is used to iteratively solve the objective function in the pricing model using a solver after inputting the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data after data cleaning into the pricing model, until the iteration termination condition is met and the candidate solution currently output is determined to be the optimal solution, wherein the iteration termination condition is at least one of the following: the number of iterative calculations reaches the maximum number of iterations and the candidate solution meets the accuracy requirements; a third determination module, which is used to determine that the optimal solution is electricity price pricing.

[0082] Optionally, the second determination module includes: an acquisition submodule, which is used to determine whether the objective function and multiple constraints in the pricing model contain nonlinear terms after the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data after data cleaning are input into the pricing model, and obtain a judgment result; a processing submodule, which is used to linearize the nonlinear terms when the judgment result indicates that the objective function and / or the constraints contain nonlinear terms, so as to integrate the objective function and multiple constraints into a linear optimization problem; a determination submodule, which is used to use a solver to iteratively solve the linear optimization problem until the currently output candidate solution is determined to be the optimal solution when the iteration termination condition is met.

[0083] Optionally, the device for determining the charging and discharging electricity price of the electric vehicle also includes: a fifth acquisition unit, which is used to evaluate the pricing model according to a predetermined period in the process of using the pricing model to determine the electricity price, and obtain an evaluation result, wherein the evaluation result is the result of evaluating whether the pricing result of the pricing model meets the pricing requirements; an optimization unit, which is used to adaptively adjust the pricing model according to the evaluation result to optimize the pricing model.

[0084] According to another aspect of an embodiment of the present invention, a system for determining charging and discharging electricity prices of an electric vehicle is further provided. The system for determining charging and discharging electricity prices of an electric vehicle uses any of the above-mentioned methods for determining charging and discharging electricity prices of an electric vehicle.

[0085] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, the computer-readable storage medium comprising a stored program, wherein the program executes any one of the above-mentioned methods for determining charging and discharging electricity prices of an electric vehicle.

[0086] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the communication devices in a communication device group.

[0087] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for executing the following steps: obtaining aggregated load data of electric vehicles, aggregated data of flexibility resources, power supply capacity data of the power grid and time-of-use electricity price data in the current time period, wherein the aggregated load data is the total charging load demand of all electric vehicles connected to the power grid in the current time period, the flexibility resources are used to adjust the load of the power grid, and the time-of-use electricity price data is the electricity price data in different time periods; performing data cleaning on the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data; inputting the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data after data cleaning into the pricing model for processing to obtain the electricity price in the current time period, wherein the pricing model is a model constructed with the goal of maximizing the profit of the virtual power plant operator while satisfying multiple constraints, and the virtual power plant operator is used to sell electricity to electric vehicles and purchase electricity from the power grid, and the constraints are constraints obtained by constraining factors in multiple dimensions that affect electricity price pricing.

[0088] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: obtaining the amount of electricity sold and purchased by the virtual power plant operator in the current time period, wherein the amount of electricity sold is the first total amount of electricity sold by the virtual power plant operator to the electric vehicle in the current time period, and the amount of electricity purchased is the second total amount of electricity purchased by the virtual power plant operator from the power grid in the current time period; determining an objective function based on the amount of electricity sold and the amount of electricity purchased, wherein the objective function is a function that aims to maximize the profit of the virtual power plant operator; constraining the supply and demand balance of the power grid, the capacity of the transformer, the charging demand and charging time of the electric vehicle, the fluctuation range of the electricity price, the battery charging status of the electric vehicle, and the satisfaction of the target object using the electric vehicle with the charging service to obtain multiple constraints, wherein the constraints include: power grid supply and demand balance constraints, transformer capacity constraints, electric vehicle charging demand constraints, time window constraints, upper and lower limit constraints on electricity prices, battery charging status constraints, penalty factor constraints, and user satisfaction constraints; and constructing a pricing model based on the objective function and multiple constraints.

[0089] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: determining an objective function according to a first formula based on the amount of electricity sold and the amount of electricity purchased, wherein the first formula is: Π represents the total revenue of the virtual power plant operator, i represents the number of electric vehicles, N represents the total number of electric vehicles, t represents the number of time periods, T represents the total number of time periods, represents the electricity price sold by the virtual power plant operator in time period t, represents the electricity sales of the virtual power plant operator in time period t, represents the electricity purchase cost of the virtual power plant operator in time period t, represents the electricity sales of the virtual power plant operator in time period t, f represents the penalty factor, and D t Indicates the total power consumption of the power grid in time period t, L t It represents the total power supply of the power grid in time period t, S i represents the user satisfaction of the target object using the i-th electric vehicle for the charging service, represents the charging price of the ith electric vehicle in time period t, p base represents the benchmark electricity price, α represents a quantitative parameter used to quantify the degree of decline in user satisfaction when the charging price deviates from the benchmark electricity price, λ represents a weight parameter, and F i represents the penalty term for the i-th electric car.

[0090] Optionally, in this embodiment, the computer-readable storage medium is configured to store a program code for executing the following steps: constraining the supply and demand balance of the power grid according to a second formula to obtain a power grid supply and demand balance constraint, wherein the second formula is: P i,t represents the charging power of the i-th electric vehicle in time period t; the capacity of the transformer is constrained according to the third formula to obtain the transformer capacity constraint, where the third formula is: P max represents the maximum power supply capacity of the transformer; the charging demand of the electric vehicle is constrained according to the fourth formula to obtain the charging demand constraint of the electric vehicle, where the fourth formula is: E i,t ≥E min , E i,t represents the charging amount of the ith electric vehicle in time period t, E min represents the minimum charging requirement of the electric vehicle; the charging time of the electric vehicle is constrained according to the fifth formula to obtain the time window constraint, where the fifth formula is: t start ≤t≤t end , t start and t end Respectively represent the time windows for charging start and charging end; according to the sixth formula, the fluctuation range of the electricity price is constrained to obtain the upper and lower limit constraints of the electricity price, where the sixth formula is: min ≤P t ≤P max , P t is the electricity price in time period t, P min represents the lower limit of electricity price, P max represents the upper limit of the electricity price; according to the seventh formula, the battery charging state of the electric vehicle is constrained to obtain the battery charging state constraint, wherein the seventh formula is: 0≤SoC i,t≤100%,SoC i,t represents the battery charging state of the ith electric vehicle in time period t; the penalty factor is constrained according to the eighth formula to obtain the penalty factor constraint, where the eighth formula is: η represents a penalty factor, which is used to balance the difference between supply and demand when the total power consumption of the power grid exceeds the total power supply; according to the ninth formula, the satisfaction of the target object with the charging service is constrained to obtain the user satisfaction constraint, where the ninth formula is: S i,t ≥S base -ε(P t -P base ), S i,t represents the user satisfaction of the target object using the i-th electric vehicle for the charging service in time period t, ε represents the price sensitivity parameter, S base Indicates basic satisfaction.

[0091] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: after the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data after data cleaning are input into the pricing model, the objective function in the pricing model is iteratively solved by a solver until the iteration termination condition is met and the candidate solution currently output is determined to be the optimal solution, wherein the iteration termination condition is at least one of the following: the number of iterative calculations reaches the maximum number of iterations and the candidate solution meets the accuracy requirements; the optimal solution is determined to be electricity price pricing.

[0092] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: after inputting the aggregated load data, aggregated data, power supply capacity data and time-of-use electricity price data after data cleaning into the pricing model, judging whether the objective function and multiple constraints in the pricing model contain nonlinear terms, and obtaining a judgment result; when the judgment result indicates that the objective function and / or constraints contain nonlinear terms, linearizing the nonlinear terms to integrate the objective function and multiple constraints into a linear optimization problem; using a solver to iteratively solve the linear optimization problem until the currently output candidate solution is determined to be the optimal solution when the iteration termination condition is met.

[0093] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: in the process of determining electricity price using a pricing model, evaluating the pricing model according to a predetermined period to obtain an evaluation result, wherein the evaluation result is a result of evaluating whether the pricing result of the pricing model meets the pricing requirements; and adaptively adjusting the pricing model according to the evaluation result to optimize the pricing model.

[0094] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein when the program is run, any one of the above-mentioned methods for determining charging and discharging electricity prices of an electric vehicle is executed.

[0095] According to another aspect of an embodiment of the present invention, a computer program product is also provided, comprising computer instructions, and when the computer instructions are executed by a processor, any one of the above-mentioned methods for determining charging and discharging electricity prices of an electric vehicle is executed.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 computer-readable 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, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a 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.

[0102] 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 determining the charging and discharging electricity price of an electric vehicle, characterized in that: include: Obtaining aggregated load data of electric vehicles, aggregated data of flexibility resources, power supply capacity data of the power grid and time-of-use electricity price data in the current time period, wherein the aggregated load data is the total charging load demand of all the electric vehicles connected to the power grid in the current time period, the flexibility resources are used to adjust the load of the power grid, and the time-of-use electricity price data is the electricity price data in different time periods; Performing data cleaning on the aggregated load data, the aggregated data, the power supply capacity data, and the time-of-use electricity price data; The aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning are input into the pricing model for processing to obtain the electricity price pricing in the current time period, wherein the pricing model is a model constructed with the goal of maximizing the profit of the virtual power plant operator while satisfying multiple constraints. The virtual power plant operator is used to sell electric energy to the electric vehicle and purchase the electric energy from the power grid, and the constraints are constraints obtained by constraining factors in multiple dimensions that affect electricity price pricing.

2. The method for determining the charging and discharging electricity price of an electric vehicle according to claim 1, characterized in that: Before inputting the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning into the pricing model for processing to obtain the electricity price in the current time period, the method further includes: Obtaining the amount of electricity sold and the amount of electricity purchased by the virtual power plant operator in the current time period, wherein the amount of electricity sold is a first total amount of electricity sold by the virtual power plant operator to the electric vehicle in the current time period, and the amount of electricity purchased is a second total amount of electricity purchased by the virtual power plant operator from the power grid in the current time period; Determine an objective function based on the power sales amount and the power purchase amount, wherein the objective function is a function aimed at maximizing the profit of the virtual power plant operator; Constraints are imposed on the supply and demand balance of the power grid, the capacity of the transformer, the charging demand and charging time of the electric vehicle, the fluctuation range of the electricity price, the battery charging state of the electric vehicle, and the satisfaction of the target object using the electric vehicle with the charging service to obtain a plurality of the constraint conditions, wherein the constraint conditions include: power grid supply and demand balance constraint, transformer capacity constraint, electric vehicle charging demand constraint, time window constraint, upper and lower limit constraints of electricity price, battery charging state constraint, penalty factor constraint, and user satisfaction constraint; The pricing model is constructed according to the objective function and the plurality of constraints.

3. The method for determining the charging and discharging electricity price of an electric vehicle according to claim 2, characterized in that: Determining an objective function based on the power sales and the power purchase includes: The objective function is determined according to a first formula based on the power sales and the power purchase, wherein the first formula is: Π represents the total revenue of the virtual power plant operator, i represents the number of the electric vehicle, N represents the total number of electric vehicles, t represents the number of the time period, T represents the total number of the time period, represents the electricity selling price of the virtual power plant operator in the time period t, represents the electricity sales of the virtual power plant operator in the time period t, represents the electricity purchase cost of the virtual power plant operator in the time period t, represents the power sales of the virtual power plant operator in the time period t, f represents the penalty factor, D t represents the total power consumption of the power grid during the time period t, L t represents the total power supply of the power grid in the time period t, S i represents the user satisfaction of the target object using the i-th electric vehicle for the charging service, represents the charging price of the ith electric vehicle in the time period t, p base represents the base electricity price, α represents a quantitative parameter used to quantify the degree of decline in user satisfaction when the charging price deviates from the base electricity price, λ represents a weight parameter, and F i represents the penalty term of the i-th electric vehicle.

4. The method for determining the charging and discharging electricity price of an electric vehicle according to claim 2, characterized in that: Constraints are imposed on the supply and demand balance of the power grid, the capacity of the transformer, the charging demand and charging time of the electric vehicle, the fluctuation range of the electricity price, the charging state of the battery, and the satisfaction of the target object using the electric vehicle with the charging service, to obtain a plurality of the constraint conditions, including: The supply and demand balance of the power grid is constrained according to a second formula to obtain the power grid supply and demand balance constraint, wherein the second formula is: P i,t represents the charging power of the i-th electric vehicle in time period t; The capacity of the transformer is constrained according to a third formula to obtain a transformer capacity constraint, wherein the third formula is: P max Indicates the maximum power supply capacity of the transformer; The charging demand of the electric vehicle is constrained according to the fourth formula to obtain the charging demand constraint of the electric vehicle, wherein the fourth formula is: E i,t ≥E min , E i,t represents the charging amount of the ith electric vehicle in the time period t, E min represents the minimum charging requirement of the electric vehicle; The charging time of the electric vehicle is constrained according to the fifth formula to obtain the time window constraint, wherein the fifth formula is: t start ≤t≤t end , t start and t end Respectively represent the time windows for charging start and charging end; According to the sixth formula, the fluctuation range of the electricity price is constrained to obtain the upper and lower limit constraints of the electricity price, wherein the sixth formula is: min ≤P t ≤P max , P t is the electricity price in the time period t, P min represents the lower limit of the electricity price, P max represents the upper limit of the electricity price; The battery charging state of the electric vehicle is constrained according to the seventh formula to obtain the battery charging state constraint, wherein the seventh formula is: 0≤SoC i,t ≤100%,SoC i,t represents the battery charging state of the i-th electric vehicle in the time period t; The penalty factor is constrained according to the eighth formula to obtain the penalty factor constraint, wherein the eighth formula is: η represents the penalty factor, which is used to balance the supply and demand difference when the total power consumption of the power grid exceeds the total power supply; According to the ninth formula, the target object's satisfaction with the charging service is constrained to obtain the user satisfaction constraint, wherein the ninth formula is: S i,t ≥S base -ε(P t -P base ), S i,t represents the user satisfaction of the target object using the i-th electric vehicle with the charging service in the time period t, ε represents the price sensitivity parameter, S base Indicates basic satisfaction.

5. The method for determining the charging and discharging electricity price of an electric vehicle according to claim 1, characterized in that: Inputting the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning into the pricing model for processing to obtain the electricity price in the current time period, including: After the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning are input into the pricing model, the objective function in the pricing model is iteratively solved by a solver until the candidate solution currently outputted is determined to be the optimal solution when an iteration termination condition is met, wherein the iteration termination condition is at least one of the following: the number of iterative calculations reaches the maximum number of iterations, and the candidate solution meets the accuracy requirement; The optimal solution is determined as the electricity price.

6. The method for determining the charging and discharging electricity price of an electric vehicle according to claim 5, characterized in that: After the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning are input into the pricing model, the objective function in the pricing model is iteratively solved by a solver until the candidate solution currently outputted is determined to be the optimal solution when an iteration termination condition is met, including: After the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning are input into the pricing model, it is determined whether the objective function and the plurality of constraint conditions in the pricing model contain nonlinear terms to obtain a determination result; When the judgment result indicates that the objective function and / or the constraint condition contain the nonlinear term, linearizing the nonlinear term to integrate the objective function and the plurality of constraint conditions into a linear optimization problem; The linear optimization problem is iteratively solved by using the solver until the iteration termination condition is satisfied and the candidate solution currently outputted is determined to be the optimal solution.

7. The method for determining the charging and discharging electricity price of an electric vehicle according to any one of claims 1 to 6, characterized in that: Also includes: In the process of determining the electricity price using the pricing model, the pricing model is evaluated according to a predetermined period to obtain an evaluation result, wherein the evaluation result is a result of evaluating whether the pricing result of the pricing model meets the pricing requirements; The pricing model is adaptively adjusted according to the evaluation result to optimize the pricing model.

8. A device for determining charging and discharging electricity prices of electric vehicles, characterized in that: include: A first acquisition unit is used to acquire aggregated load data of electric vehicles, aggregated data of flexibility resources, power supply capacity data of the power grid and time-of-use electricity price data in a current time period, wherein the aggregated load data is the total charging load demand of all the electric vehicles connected to the power grid in the current time period, the flexibility resources are used to adjust the load of the power grid, and the time-of-use electricity price data is the electricity price data in different time periods; a cleaning unit, configured to clean the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data; The second acquisition unit is used to input the aggregated load data, the aggregated data, the power supply capacity data and the time-of-use electricity price data after data cleaning into the pricing model for processing to obtain the electricity price in the current time period, wherein the pricing model is a model constructed with the goal of maximizing the profit of the virtual power plant operator while satisfying multiple constraints. The virtual power plant operator is used to sell electric energy to the electric vehicle and purchase the electric energy from the power grid, and the constraints are constraints obtained by constraining factors in multiple dimensions that affect electricity price pricing.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method for determining the charging and discharging electricity price of an electric vehicle according to any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by the processor, the method for determining the charging and discharging electricity price of an electric vehicle according to any one of claims 1 to 7 is performed.

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