Ordered charging strategy optimization method, system and equipment for electric vehicle and medium

Abstract

The invention relates to the technical field of data processing, and discloses an electric vehicle ordered charging strategy optimization method, system, device and medium, and the method comprises the steps: constructing a charging strategy objective function with the lowest electricity purchase charge as a target based on the charging demand of an electric vehicle customer and the operation state of a charging pile, and constructing an electricity purchase strategy optimization constraint condition; according to the objective function and the constraint condition, constructing a charging strategy optimization model and solving the charging strategy optimization model; and according to a solving result, evaluating expected loss of charging resource occupancy, and performing convergence evaluation. According to the invention, charging pile resource waste caused by charging duration prolonging is effectively avoided, so that collaborative improvement of user economy and charging pile operation efficiency is realized, and remarkable economic benefits and practical application values are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to an electric vehicle orderly charging strategy optimization method, system, device and medium. BACKGROUND

[0002] With the gradual establishment of the electricity spot market, electric vehicle users and charging pile operators face new challenges: First, the traditional constant power charging mode with maximum charging power cannot maximize the potential of time-of-use electricity prices in the electricity spot market. The electricity spot market clearing price is affected by the relationship between supply and demand in each period, and the electricity price changes more sharply. Electric vehicle users can only choose the period with relatively low electricity price that meets their use requirements as much as possible according to the price trend, but the electricity price is still in the process of change during charging, and constant power charging cannot guarantee the lowest charging cost for electric vehicle users; Second, when the constant power charging mode with maximum charging power is changed, it will inevitably cause the charging time of a single electric vehicle to be prolonged, which means that the charging resources of the charging pile operator are occupied. How to coordinate the charging mode to effectively compensate for the loss of resource occupation of the charging pile operator also becomes an important factor in adapting to the electricity spot market.

[0003] Since the electricity spot market is still in the process of rapid construction, and the electric vehicle industry is also in the stage of rapid development, the above problems have not been fully studied, and no relevant research results have been found. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is: how to balance the optimization between the minimum purchase cost of users and the benefit of resource occupation of charging piles in the environment of the electricity spot market.

[0006] To solve the above technical problems, the present application provides the following technical scheme: an electric vehicle orderly charging strategy optimization method, comprising: based on the cooperative analysis of electric vehicle user charging behavior data and charging pile dynamic operation parameters, a dynamic cost optimization model under a multi-period electricity price response mechanism is constructed; a closed-loop feedback mechanism of dynamic programming algorithm and charging resource occupation risk prediction is adopted, through the interaction and iteration of real-time electricity market price signals and charging pile state monitoring data, a non-symmetrical charging power distribution scheme meeting the space-time distribution characteristics of charging demand is solved; a double-layer convergence criterion based on charging load transfer elasticity evaluation is established, through quantifying the dynamic game relationship between the user economic benefits generated by the charging strategy optimization and the opportunity cost of the charging facility resource occupation, the economic-resource utilization rate Pareto optimal boundary judgment of the charging strategy is implemented, when the user cost reduction brought by the strategy optimization exceeds the service capacity damage threshold of the charging pile, the optimal orderly charging strategy meeting the system dynamic balance is output.

[0007] As a preferred scheme of the electric vehicle orderly charging strategy optimization method of the application, wherein:

[0008] The dynamic cost optimization model comprises that the optimization objective function is to minimize the electricity purchase cost, the electricity purchase strategy optimization constraint condition is constructed, and the electricity purchase cost optimization objective function is expressed as: , Wherein, represents the electricity purchase cost of the vth electric vehicle; represents the number of charging periods; represents the duration of the charging period; represents the time-of-use electricity price of the tth charging period power spot market, which is published by the market transaction platform; represents the charging power of the vth electric vehicle in the tth charging period.

[0009] As a preferred scheme of the electric vehicle orderly charging strategy optimization method of the application, wherein: The construction of the electricity purchase strategy optimization constraint condition comprises that the electricity purchase strategy optimization constraint comprises a charging power constraint and a charging time constraint; The construction of the charging power constraint comprises that the charging power cannot exceed the charging pile and the charging power limit range of the electric vehicle, and the charging power corresponding to the charging capacity meets the expected charging capacity requirement of the electric vehicle customer; The construction of the charging time constraint comprises that the charging time does not exceed the expected charging time limit of the customer and the charging time does not exceed the expected loss of resource occupation of the charging pile operator.

[0010] As a preferred scheme of the electric vehicle orderly charging strategy optimization method of the application, wherein: the closed-loop feedback mechanism comprises establishing a charging pile cluster state observation matrix, collecting the charging facility utilization rate and the length of the queue of vehicles to be charged in real time, generating a dynamic electricity price influence factor through a sliding time window prediction algorithm, and triggering charging power redistribution by using an event-driven dynamic programming algorithm. The Cplex (solver) is used to solve the charging strategy with the optimization objective of minimizing the electricity purchase cost.

[0011] As a preferred scheme of the electric vehicle orderly charging strategy optimization method of the application, wherein: the asymmetric charging power distribution scheme satisfying the space-time distribution characteristics of the charging demand comprises calculating an optimization strategy delay, which is the difference between the charging time after using the charging strategy optimization method and the constant power charging time with the maximum charging power of the traditional method. The same period charging frequency is calculated, which is the ratio of the number of electric vehicles charged in the charging time corresponding to the optimization charging strategy to the product of the number of charging periods and the time interval. The expected loss of charging resource occupation is the product of the charging frequency, the optimization strategy delay and the average charging service fee income of the charging pile operator during charging.

[0012] As a preferred solution of the electric vehicle orderly charging strategy optimization method, the double-layer convergence criterion includes that the convergence condition is that the electricity purchase fee reduction value generated by the optimization strategy is greater than the expected loss of charging resource occupation.

[0013] As a preferred solution of the electric vehicle orderly charging strategy optimization method, the economic-resource utilization Pareto optimal boundary determination of the implemented charging strategy includes that if the convergence condition is met, the convergence requirement is met, indicating that the optimized charging strategy meets the convergence condition, and the charging strategy is output. If the convergence condition is not met, the expected loss of charging pile operator resource occupation time limit is reduced.

[0014] Another object of the present application is to provide an electric vehicle orderly charging strategy optimization system.

[0015] To solve the above technical problems, the present application provides the following technical solutions: an electric vehicle orderly charging strategy optimization system, characterized by comprising, The model construction module constructs a charging strategy objective function with the lowest electricity purchase fee as the target based on the charging demand of the electric vehicle customer and the operating state of the charging pile, and constructs the electricity purchase strategy optimization constraint condition. The solving module constructs a charging strategy optimization model and solves it according to the target function and the constraint condition. The convergence module establishes a double-layer convergence criterion based on charging load transfer elasticity evaluation, determines the economic-resource utilization Pareto optimal boundary of the implemented charging strategy, and outputs the optimal orderly charging strategy that meets the system dynamic balance.

[0016] The present application provides a computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the electric vehicle orderly charging strategy optimization method when executing the computer program.

[0017] The present application provides a computer readable storage medium having a computer program stored thereon, characterized in that the computer program is executed by a processor to implement the steps of the electric vehicle orderly charging strategy optimization method.

[0018] The beneficial effects of the present application: by constructing the charging strategy objective function with the lowest electricity purchase cost as the target, and introducing the multiple constraints of charging power and time, the ordered charging optimization model is established and solved, which can realize the cost minimization of the user side under the time-of-use electricity price. At the same time, by evaluating the expected loss of charging resource occupation caused by the optimization strategy, the convergence judgment mechanism is introduced, which effectively avoids the waste of charging pile resources caused by the extension of charging time, so as to realize the coordinated improvement of user economy and charging pile operation efficiency, and has significant economic benefits and practical application value. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Figure 1 The overall flowchart of an electric vehicle ordered charging strategy optimization method provided by an embodiment of the present application.

[0021] Figure 2 The time-of-use electricity price curve diagram of an electric vehicle ordered charging strategy optimization method provided by an embodiment of the present application.

[0022] Figure 3 The system scheme flowchart of an electric vehicle ordered charging strategy optimization system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0024] Embodiment 1, refer to Figure 1 For an embodiment of the present application, an electric vehicle ordered charging strategy optimization method is provided, which comprises: S1: based on the cooperative analysis of electric vehicle user charging behavior data and charging pile dynamic operation parameters, a dynamic cost optimization model under the multi-time period electricity price response mechanism is constructed.

[0025] It should be noted that the implementation purpose is to construct the charging strategy objective function with the lowest electricity purchase cost as the target, which is an important factor of the charging optimization strategy, and realizes the overall electricity purchase cost reduction.

[0026] The optimization objective function is to minimize the electricity purchase cost, and the electricity purchase cost optimization objective function is represented as: , wherein, represents the electricity purchase cost of the vth electric vehicle, represents the number of charging periods, represents the duration of the charging period; represents the time-of-use electricity price of the tth charging period power spot market, published by the market trading platform, represents the charging power in the tth charging period.

[0027] The electricity purchase strategy optimization constraints include charging power constraints and charging time constraints.

[0028] It should be noted that the implementation aims to build electricity purchase strategy optimization constraints as constraint items of the charging optimization strategy to meet the operation requirements.

[0029] The charging power constraints include that the charging power cannot exceed the charging pile and the electric vehicle charging power limit range, and the charging power corresponding to the charging capacity should meet the customer's expected charging capacity requirements, represented as: , , wherein, represents the charging power limit determined by the electric vehicle itself, represents the charging power limit determined by the charging pile device itself, represents the expected charging capacity of the vth electric vehicle customer, represents the number of charging periods.

[0030] The charging time constraints include that the charging time cannot exceed the customer's expected charging time limit, i.e., the optimization of charging power cannot result in excessively long charging time, which in turn affects the normal use of customers. Also, the charging time cannot exceed the expected loss of charging pile operator resource occupation, represented as: , , wherein, represents the customer's expected charging time limit, set by the electric vehicle charging user, represents the number of charging periods, represents the expected loss of charging pile operator resource occupation time limit, represents the duration of the charging period.

[0031] In summary, by double power constraint (user equipment end + charging pile end) to avoid overload risk, while ensuring user charging capacity demand rigid satisfaction; Introducing the expected loss of resource occupation time limit, while reducing the cost of electricity purchase, prevent excessive extension of charging time leading to user loss or operator revenue loss; Time-of-use pricing mechanism design makes the model dynamic response to power market fluctuations, laying the foundation for subsequent real-time optimization.

[0032] S2: Adopting dynamic programming algorithm and charging resource occupation risk prediction closed-loop feedback mechanism, through the interaction iteration of real-time electricity price signal and charging pile state monitoring data, solving the asymmetric charging power allocation scheme that meets the spatial and temporal distribution characteristics of charging demand.

[0033] The charging strategy optimization model takes the lowest electricity purchase cost as the optimization objective, and considers the charging power and charging time constraints, which is represented as: , Among them, The electricity purchase cost of the vth electric vehicle is represented as v, and the Cplex solver is used to solve the charging strategy with the lowest electricity purchase cost as the optimization objective.

[0034] In summary, by dynamic programming algorithm to deal with the space-time imbalance of charging demand, generate "asymmetric allocation scheme" (such as concentrated charging in low valley period and reduced power in peak period), which significantly improves the grid load rate; Charging resource occupation risk prediction module real-time evaluation pile group state (such as congestion probability), avoid local resource overload leading to optimization failure; Using Cplex solver to process high-dimensional nonlinear constraints, while ensuring global optimal solution, meet the real-time calculation demand (second-level response) of large-scale electric vehicle group control.

[0035] S3: Establish a double-layer convergence criterion based on charging load transfer elasticity evaluation, by quantifying the dynamic game relationship between user economic benefits generated by charging strategy optimization and opportunity cost of charging facility resource occupation, implement the economic-resource utilization Pareto optimal boundary judgment of charging strategy, when the user cost reduction brought by strategy optimization exceeds the service capacity damage threshold of charging pile, output the optimal orderly charging strategy that meets the dynamic balance of the system.

[0036] It should be noted that the purpose of implementation is to evaluate the economic loss that may be caused by the optimization strategy according to the charging time of electric vehicles. The evaluation idea is to measure the expected economic loss caused by the time extension caused by the optimization of charging strategy according to the number of electric vehicles charging at the same period in history.

[0037] The delay of optimization strategy is the difference between the charging time after adopting the charging strategy optimization method and the traditional constant power charging time with maximum charging power, which is represented as: , wherein, denotes the optimization strategy delay, denotes the number of charging periods, denotes the duration of the charging period, denotes the expected charging amount of the vth electric vehicle customer, denotes the charging power limit determined by the electric vehicle itself setting, is the ratio of the customer's expected charging amount to the maximum charging power.

[0038] The statistical contemporaneous charging frequency is represented as: , wherein, denotes the contemporaneous charging frequency, denotes the number of electric vehicles charged by the historical contemporaneous charging pile within the charging time corresponding to the optimization charging strategy, if the evaluation object is a charging station, it is the average number of electric vehicles charged corresponding to each charging pile.

[0039] The expected loss of charging resource occupation is calculated and represented as: , wherein, denotes the expected loss of charging resource occupation, denotes the contemporaneous charging frequency, denotes the average charging service fee income of the charging pile operator during the charging of the electric vehicle, is the optimization strategy delay.

[0040] It should be noted that the purpose of judging the convergence condition is to compare the change in electricity purchase cost generated by the optimization strategy with the expected loss of charging resource occupation, and to adjust the charging pile operator's resource occupation expected loss time limit for scenarios that do not meet the convergence condition.

[0041] The convergence condition is that the decrease in electricity purchase cost generated by the optimization strategy is greater than the charging pile service capacity loss threshold, i.e., the expected loss of charging resource occupation, which can be represented as: , wherein, denotes the expected loss of charging resource occupation, denotes the electricity purchase cost of the vth electric vehicle, is the electricity purchase cost under the traditional maximum charging power constant power mode, which can be represented as: , wherein, denotes the time-of-use electricity price of the power spot market in the charging period t, denotes the number of charging times under the traditional maximum charging power constant power mode, denotes the duration of the charging period, represents a charging power limit determined by the electric vehicle itself setting.

[0042] If the convergence condition is met, the convergence requirement is met, indicating that the optimized charging strategy meets the convergence condition, and the charging strategy is output.

[0043] If the convergence condition is not met, the charging pile operator resource occupation expected loss time limit is reduced.

[0044] In summary, through the economic-resource utilization Pareto boundary determination, the single pursuit of reducing electricity purchase is avoided, and the operator's interests are sacrificed (such as excessive time extension leading to a decrease in charging pile turnover rate); the charging load transfer elasticity concept (user tolerance to charging time extension and sensitivity to economic benefits) is introduced, making the strategy more in line with real user behavior; when the user cost reduction does not exceed the service capacity loss threshold, the resource occupation time limit is automatically reduced and re-optimized, ensuring that the strategy always converges within the system's bearable boundary.

[0045] Embodiment 2, refer to Figure 3 As an embodiment of the present application, the embodiment provides an electric vehicle orderly charging strategy optimization system, comprising a model construction module, a solving module and a convergence module.

[0046] The model construction module constructs a charging strategy objective function with the lowest electricity purchase as the target based on the charging demand of electric vehicle customers and the operating state of charging piles, and constructs the electricity purchase strategy optimization constraint condition.

[0047] The solving module constructs a charging strategy optimization model and solves it according to the target function and constraint condition.

[0048] The convergence module establishes a double-layer convergence criterion based on charging load transfer elasticity evaluation, implements economic-resource utilization Pareto optimal boundary determination of charging strategy, and outputs the optimal orderly charging strategy that meets the system dynamic balance.

[0049] The embodiment also provides an electronic device suitable for the case of an electric vehicle orderly charging strategy optimization method, comprising a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize an electric vehicle orderly charging strategy optimization method as proposed in the above embodiment.

[0050] The embodiment also provides a storage medium having a computer program stored thereon, which is executed by a processor to realize an electric vehicle orderly charging strategy optimization method as proposed in the above embodiment.

[0051] The storage medium provided in the embodiment belongs to the same inventive concept as the method for optimizing an orderly charging strategy of an electric vehicle provided in the above embodiment, and the technical details not described in detail in the embodiment can be seen from the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0052] Through the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary general hardware, and of course can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk or an optical disk, etc., including a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0053] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application Embodiment 3, refer to Figure 2 For an embodiment of the present application, a method, system, device and medium for optimizing an orderly charging strategy of an electric vehicle are provided, and in order to verify the beneficial effects of the present application, scientific demonstration is carried out through simulation experiments.

[0054] As Figure 2 shown, it is the time-of-use price of a certain charging station power spot market. Taking an electric vehicle charging before 08:00 as an example, the maximum charging power of the charging station is 10 kW, and the electric vehicle charging demand is 30 kWh.

[0055] According to the constant power charging strategy based on the maximum charging power, the charging station will charge the electric vehicle at a power of 10 kW, and it is expected to be fully charged at 11:00, and the charging cost is 9.63 yuan.

[0056] With the method, the optimal charging time period is 10:00-13:00, and the corresponding charging cost is 6.80 yuan. However, if only the minimum charging cost is considered, the electric vehicle may occupy the charging station parking space for a long time, affecting the charging of other electric vehicles. Therefore, the expected loss of charging resource occupation is evaluated by the method of step four. After evaluation, the convergence requirement is met by considering the expected loss of occupation.

[0057] Table 1 compares the constant power charging strategy based on the maximum charging power and the charging strategy of the method. It can be seen that the method proposed in the application will reduce the charging cost by 29.40% for the case.

[0058] Table 1 Comparison of constant power charging strategy based on maximum charging power and charging strategy of the method and charging cost , It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, which should be covered by the scope of the claims of the present application.

Claims

1. A method for optimizing an orderly charging strategy for electric vehicles, characterized in that: include: Based on the collaborative analysis of electric vehicle user charging behavior data and the dynamic operating parameters of charging piles, a dynamic cost optimization model under the multi-period electricity price response mechanism is constructed; Using a dynamic programming algorithm and a closed-loop feedback mechanism for charging resource occupancy risk prediction, the system iterates interactively between real-time electricity price signals from the power market and charging pile status monitoring data to develop an asymmetric charging power allocation solution that meets the spatiotemporal distribution characteristics of charging demand. A two-layer convergence criterion based on the charging load transfer elasticity evaluation is established. By quantifying the dynamic game relationship between the user economic benefits generated by charging strategy optimization and the opportunity cost of charging facility resource occupation, the Pareto optimal boundary judgment of the economic efficiency and resource utilization of the charging strategy is implemented. When the user cost reduction brought about by strategy optimization exceeds the preset charging pile service capacity impairment threshold, the optimal orderly charging strategy that satisfies the dynamic balance of the system is output.

2. The method for optimizing an orderly charging strategy for an electric vehicle according to claim 1, wherein: The dynamic cost optimization model includes the optimization objective function of minimizing the electricity purchase fee, constructing the optimization constraint conditions of the electricity purchase strategy, and the electricity purchase fee optimization objective function is expressed as: ， in, represents the electricity purchase cost of the v-th electric vehicle, represents the number of charging periods, t is the index of the charging period, Indicates the duration of the charging period, represents the time-of-use electricity price in the electricity spot market during the tth charging period, which is published by the market trading platform. It represents the charging power of the vth electric vehicle in the tth charging period.

3. The method for optimizing an orderly charging strategy for an electric vehicle according to claim 2, wherein: The said constructing of the power purchase strategy optimization constraint condition includes that the power purchase strategy optimization constraint includes charging power constraint and charging time constraint; Constructing charging power constraints includes that the charging power cannot exceed the charging pile and the electric vehicle charging power limit range, and the charging power corresponding to the charging amount meets the expected charging amount requirements of electric vehicle customers; Establish charging time constraints, including that the charging time does not exceed the customer's expected charging time limit and that the charging time does not exceed the expected loss of resource usage by the charging pile operator.

4. The method for optimizing an orderly charging strategy for an electric vehicle according to claim 3, wherein: The closed-loop feedback mechanism includes establishing a charging pile cluster status observation matrix, collecting charging facility utilization and waiting vehicle queue length in real time, generating dynamic electricity price impact factors through a sliding time window prediction algorithm, and using an event-driven dynamic programming algorithm to trigger charging power redistribution; Cplex is used to solve the charging strategy with the lowest electricity purchase price as the optimization goal.

5. The method for optimizing an orderly charging strategy for an electric vehicle according to claim 4, wherein: The method of solving the asymmetric charging power allocation scheme that meets the spatiotemporal distribution characteristics of charging demand includes measuring the optimization strategy delay, where the optimization strategy delay is the difference between the charging time after using the charging strategy optimization method and the traditional charging time at a maximum charging power constant power; Counting the charging frequency during the same period, where the charging frequency during the same period is the ratio of the number of electric vehicles charged by the charging piles during the charging time corresponding to the optimized charging strategy during the same period in history to the product of the number of charging periods and the time interval; Calculate the expected loss of charging resource occupation, which is the product of the charging frequency, optimization strategy delay and the average charging service fee income of the charging pile operator when the electric vehicle is charging during the same period.

6. The method for optimizing an orderly charging strategy for an electric vehicle according to claim 5, wherein: The double-layer convergence criterion includes that the convergence condition is that the reduction in electricity purchase cost generated by the optimization strategy is greater than the expected loss of charging resource occupation.

7. The method for optimizing an orderly charging strategy for an electric vehicle according to claim 6, wherein: The economic efficiency-resource utilization Pareto optimal boundary determination of the charging strategy includes: if the convergence condition is met, the convergence requirement is met, indicating that the optimized charging strategy meets the convergence condition, and outputting the charging strategy; If the convergence conditions are not met, the expected loss time limit of the charging pile operator's resource occupation will be reduced.

8. An electric vehicle orderly charging strategy optimization system, applying an electric vehicle orderly charging strategy optimization method according to any one of claims 1 to 7, characterized in that: include: Build a model module to construct a charging strategy objective function with the goal of minimizing electricity purchase costs based on the charging needs of electric vehicle customers and the operating status of charging piles, and establish optimization constraints for the electricity purchase strategy; A solution module, which constructs and solves a charging strategy optimization model based on the objective function and constraints; The convergence module establishes a two-layer convergence criterion based on the charging load transfer elasticity evaluation, implements the Pareto optimal boundary judgment of the economy-resource utilization of the charging strategy, and outputs the optimal orderly charging strategy that meets the dynamic balance of the system.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for optimizing an orderly charging strategy for an electric vehicle according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing an orderly charging strategy for an electric vehicle according to any one of claims 1 to 7 are implemented.

Images