A method, system, device, and medium for optimizing orderly charging strategies for electric vehicles.

Abstract

This invention relates to the field of data processing technology, and discloses a method, system, device, and medium for optimizing an orderly charging strategy for electric vehicles. The method includes: constructing a charging strategy objective function based on the charging needs of electric vehicle customers and the operating status of charging piles, with the goal of minimizing electricity purchase costs; constructing optimization constraints for the electricity purchase strategy; constructing and solving a charging strategy optimization model based on the objective function and constraints; and evaluating the expected loss of charging resource occupancy based on the solution results, and performing a convergence assessment. This invention effectively avoids the waste of charging pile resources caused by prolonged charging time, thereby achieving a synergistic improvement in user economy and charging pile operating efficiency, and has significant economic benefits and practical application value.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method, system, device, and medium for optimizing an orderly charging strategy for electric vehicles. Background Technology

[0002] With the gradual establishment of the electricity spot market, electric vehicle users and charging station operators face new challenges:

[0003] First, the traditional method of constant-power charging at maximum charging power cannot fully tap the potential of time-of-use pricing in the electricity spot market. The clearing price in the electricity spot market is affected by the supply and demand relationship of electricity at different times, leading to more drastic price fluctuations. Electric vehicle users can only choose times that meet their usage requirements and have relatively low electricity prices based on price trends. However, electricity prices are still fluctuating during charging, and constant-power charging cannot guarantee the lowest charging costs for electric vehicle users.

[0004] Secondly, changing the charging method to constant power charging at maximum charging power will inevitably lead to a longer charging time for individual electric vehicles, which is equivalent to occupying the charging resources of charging pile operators. How to coordinate the charging method to effectively make up for the loss of resources occupied by charging pile operators has also become an important factor in adapting to the electricity spot market.

[0005] Because the electricity spot market is still under rapid development and the electric vehicle industry is also booming, research on the above issues is insufficient and no relevant research results have been found yet. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by this invention is: how to achieve an optimal balance between minimizing users' electricity purchase costs and the efficiency of charging pile resource utilization in the context of the electricity spot market.

[0008] To address the aforementioned technical problems, this invention provides the following technical solution: an optimization method for orderly charging strategies of electric vehicles, comprising: constructing a dynamic cost optimization model under a multi-period electricity price response mechanism based on the collaborative analysis of electric vehicle user charging behavior data and charging pile dynamic operating parameters; employing a dynamic programming algorithm and a closed-loop feedback mechanism for charging resource occupancy risk prediction, and solving an asymmetric charging power allocation scheme that satisfies the spatiotemporal distribution characteristics of charging demand through interactive iteration of real-time electricity market price signals and charging pile status monitoring data; establishing a two-layer convergence criterion based on charging load transfer elasticity assessment, and implementing Pareto optimal boundary determination of the economic efficiency and resource utilization rate of the charging strategy by quantifying the dynamic game relationship between user economic benefits generated by charging strategy optimization and the opportunity cost of charging facility resource occupancy; and outputting the optimal orderly charging strategy that satisfies the dynamic balance of the system when the user cost reduction brought about by strategy optimization exceeds the charging pile service capacity reduction threshold.

[0009] As a preferred embodiment of the electric vehicle orderly charging strategy optimization method described in this invention, wherein:

[0010] The dynamic cost optimization model includes an objective function of minimizing electricity purchase costs, and the construction of constraints for optimizing the electricity purchase strategy. The objective function for optimizing electricity purchase costs is expressed as follows:

[0011] ,

[0012] in, This represents the electricity cost for the v-th electric vehicle; Indicates the number of charging periods; Indicates the duration of the charging period; This represents the time-of-use electricity price in the spot market during the t-th charging period, published by the market trading platform. This represents the charging power of the v-th electric vehicle during the t-th charging period.

[0013] As a preferred embodiment of the electric vehicle orderly charging strategy optimization method described in this invention, wherein:

[0014] The constraints for constructing the electricity purchase strategy optimization include charging power constraints and charging time constraints.

[0015] The charging power constraint includes ensuring that the charging power does not exceed the charging pile and electric vehicle charging power limit range, and that the charging power corresponds to the charging amount that meets the expected charging amount requirements of electric vehicle customers.

[0016] Establish charging time constraints, including ensuring 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 charging pile operator's resources.

[0017] As a preferred embodiment of the electric vehicle orderly charging strategy optimization method described in this invention, the closed-loop feedback mechanism includes: establishing a charging pile cluster status observation matrix, collecting charging facility utilization rate and waiting vehicle queue length in real time, generating dynamic electricity price influencing factors through a sliding time window prediction algorithm, and triggering charging power reallocation using an event-driven dynamic programming algorithm.

[0018] The charging strategy with the goal of minimizing electricity purchase costs is obtained by using Cplex (solver).

[0019] As a preferred embodiment of the electric vehicle orderly charging strategy optimization method described in this invention, the solution for the asymmetric charging power allocation scheme that satisfies the spatiotemporal distribution characteristics of charging demand includes calculating the optimization strategy delay, wherein the optimization strategy delay is the difference between the charging time after adopting the charging strategy optimization method and the traditional constant power charging time at maximum charging power.

[0020] The synchronous charging frequency is defined as the ratio of the number of electric vehicles charging at a charging pile during the corresponding charging time of the optimized charging strategy to the product of the number of charging periods and the time interval.

[0021] The expected loss of charging resource occupancy is calculated as the product of the charging frequency during the same period, the delay of the optimization strategy, and the average charging service fee revenue of the charging pile operator during the charging of electric vehicles.

[0022] As a preferred embodiment of the electric vehicle orderly charging strategy optimization method of the present invention, the two-layer convergence criterion includes a convergence condition in which the decrease in electricity purchase cost generated by the optimization strategy is greater than the expected loss of charging resource occupation.

[0023] As a preferred embodiment of the electric vehicle orderly charging strategy optimization method of the present invention, the Pareto optimal boundary determination of the economic-resource utilization rate of the charging strategy includes: if the convergence condition is met, then the convergence requirement is met, indicating that the optimized charging strategy meets the convergence condition, and the charging strategy is output.

[0024] If the convergence condition is not met, the time limit for expected loss of charging pile operator resources will be reduced.

[0025] Another objective of this invention is to provide an optimized system for orderly charging strategies of electric vehicles.

[0026] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an electric vehicle orderly charging strategy optimization system, characterized in that it includes,

[0027] The model module is constructed based on the charging needs of electric vehicle customers and the operating status of charging piles. The objective function of the charging strategy with the goal of minimizing the electricity purchase cost is constructed, and the optimization constraints of the electricity purchase strategy are constructed.

[0028] The solution module constructs and solves a charging strategy optimization model based on the objective function and constraints.

[0029] The convergence module establishes a two-layer convergence criterion based on the elasticity assessment of charging load transfer, implements the Pareto optimal boundary determination of the economics and resource utilization of the charging strategy, and outputs the optimal ordered charging strategy that satisfies the dynamic balance of the system.

[0030] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the electric vehicle orderly charging strategy optimization method.

[0031] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the above-described method for optimizing an orderly charging strategy for electric vehicles.

[0032] The beneficial effects of this invention are as follows: By constructing a charging strategy objective function aimed at minimizing electricity purchase costs and introducing multiple constraints on charging power and time, an ordered charging optimization model is established and solved, enabling the minimization of user costs under time-of-use pricing. Simultaneously, by evaluating the expected loss of charging resource occupancy caused by the optimization strategy and introducing a convergence judgment mechanism, the waste of charging pile resources due to extended charging time is effectively avoided. This achieves a synergistic improvement in user economy and charging pile operational efficiency, demonstrating significant economic benefits and practical application value. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 The above is an overall flowchart of an electric vehicle orderly charging strategy optimization method provided in one embodiment of the present invention.

[0035] Figure 2 The time-of-use electricity price curve is provided as an embodiment of the present invention for an electric vehicle orderly charging strategy optimization method.

[0036] Figure 3The present invention provides a system scheme flowchart for an electric vehicle orderly charging strategy optimization system according to an embodiment of the present invention. Detailed Implementation

[0037] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0038] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for optimizing an orderly charging strategy for electric vehicles is provided, comprising:

[0039] S1: Based on the collaborative analysis of electric vehicle user charging behavior data and charging pile dynamic operating parameters, a dynamic cost optimization model under a multi-period electricity price response mechanism is constructed.

[0040] It should be noted that the purpose of this implementation is to construct a charging strategy objective function with the goal of minimizing electricity purchase costs, which will serve as an important factor in the charging optimization strategy and achieve an overall reduction in electricity purchase costs.

[0041] The objective function is to minimize the electricity purchase cost, and the objective function for optimizing the electricity purchase cost is expressed as follows:

[0042] ,

[0043] in, This represents the electricity cost for the v-th electric vehicle. Indicates the number of charging periods. Indicates the duration of the charging period; This represents the time-of-use electricity price in the spot market for the t-th charging period, published by the market trading platform. This represents the charging power during the t-th charging period.

[0044] The constraints for optimizing the electricity purchase strategy include charging power constraints and charging time constraints.

[0045] It should be noted that the purpose of this implementation is to construct constraints for optimizing the electricity purchase strategy, which will serve as constraints for the charging optimization strategy to meet operational requirements.

[0046] The charging power constraint includes ensuring that the charging power does not exceed the charging power limits of the charging pile and electric vehicle, and that the charging power corresponding to the charging amount should meet the customer's expected charging amount requirements, expressed as:

[0047] ,

[0048] ,

[0049] in, This indicates the charging power limit determined by the electric vehicle itself. This indicates the charging power limit determined by the charging station's own settings. This represents the expected charging amount for the v-th electric vehicle customer. This indicates the number of charging periods.

[0050] Establish charging time constraints, including ensuring that charging time does not exceed the customer's expected charging time limit; that is, optimizing charging power should not lead to excessively long charging times, thereby affecting normal customer use. Additionally, charging time should not exceed the charging station operator's expected resource utilization loss, expressed as:

[0051] ,

[0052] ,

[0053] in, This indicates the customer's expected charging time limit, which is set by the electric vehicle charging user. Indicates the number of charging periods. This indicates the time limit for expected resource loss for charging pile operators. Indicates the duration of the charging period.

[0054] In summary, overload risks are avoided by using dual power constraints (user equipment end + charging pile end) while ensuring that users' charging demand is rigidly met; the introduction of resource occupation expected loss time limit reduces electricity purchase costs while preventing user churn or operator revenue loss due to excessively extended charging time; the time-of-use pricing mechanism design enables the model to dynamically respond to electricity market fluctuations, laying the foundation for subsequent real-time optimization.

[0055] S2: Employing a dynamic programming algorithm and a closed-loop feedback mechanism for predicting charging resource occupancy risks, an asymmetric charging power allocation scheme that satisfies the spatiotemporal distribution characteristics of charging demand is solved through the interactive iteration of real-time electricity price signals from the electricity market and charging pile status monitoring data.

[0056] The charging strategy optimization model aims to minimize electricity purchase costs and considers constraints on charging power and charging time, and is expressed as follows:

[0057] ,

[0058] in, Let v represent the electricity cost of the v-th electric vehicle. The charging strategy with the goal of minimizing the electricity cost is obtained by using the existing Cplex solver.

[0059] In summary, dynamic programming algorithms are used to address the spatiotemporal imbalance of charging demand and generate "asymmetric allocation schemes" (such as concentrated charging during off-peak hours and reduced power during peak hours), significantly improving the grid load factor. The charging resource occupancy risk prediction module assesses the status of charging pile groups in real time (such as congestion probability) to avoid optimization failure caused by local resource overload. The Cplex solver is used to handle high-dimensional nonlinear constraints, ensuring the global optimal solution while meeting the real-time computing requirements (second-level response) of large-scale electric vehicle group control.

[0060] S3: Establish a two-layer convergence criterion based on the elasticity assessment of charging load transfer. By quantifying the dynamic game relationship between the user economic benefits generated by the optimization of charging strategy and the opportunity cost of charging facility resource occupation, implement the Pareto optimal boundary determination of the economics-resource utilization rate of the charging strategy. When the user cost reduction brought about by the strategy optimization exceeds the threshold of the charging pile service capacity loss, output the optimal ordered charging strategy that satisfies the dynamic balance of the system.

[0061] It should be noted that the purpose of this implementation is to assess the potential economic losses caused by optimizing electric vehicle charging strategies based on the charging time. The assessment approach involves calculating the expected economic losses due to the extended charging time resulting from optimized charging strategies, based on the number of electric vehicles charging during historical periods.

[0062] The time difference between the optimized charging strategy and the traditional constant power charging time at maximum charging power is calculated and expressed as:

[0063] ,

[0064] in, Indicates the delay of the optimization strategy. Indicates the number of charging periods. Indicates the duration of the charging period. This represents the expected charging amount for the v-th electric vehicle customer. This indicates the charging power limit determined by the electric vehicle itself. The ratio of the customer's expected charging amount to the maximum charging power.

[0065] The statistical charging frequency during the same period is expressed as:

[0066] ,

[0067] in, Indicates the charging frequency during the same period. This represents the number of electric vehicles charged by charging piles during the same historical period under the optimized charging strategy. If the evaluation object is a charging station, it represents the average number of electric vehicles charged by each charging pile.

[0068] The estimated loss due to charging resource occupancy is expressed as follows:

[0069] ,

[0070] in, This indicates the expected loss due to the use of charging resources. Indicates the charging frequency during the same period. This represents the average revenue generated by charging station operators from charging service fees during the charging of electric vehicles. To optimize strategy latency.

[0071] It should be noted that the purpose of implementing the convergence criteria is to compare the changes in electricity purchase costs resulting from the optimization strategy with the expected loss of charging resource occupancy, and to adjust the time limit for the expected loss of charging pile operator resource occupancy in scenarios that do not meet the convergence criteria.

[0072] The convergence condition is that the reduction in electricity purchase costs resulting from the optimization strategy is greater than the threshold for the depletion of charging pile service capacity, i.e., the expected loss of charging resource occupancy, which can be expressed as:

[0073] ,

[0074] in, This indicates the expected loss due to the use of charging resources. This represents the electricity cost for the v-th electric vehicle. The electricity purchase cost under the traditional maximum charging power constant power mode can be expressed as:

[0075] ,

[0076] in, This indicates the time-of-use electricity price in the spot market during the charging period t. This indicates the charging time in the traditional maximum charging power constant power mode. Indicates the duration of the charging period. This indicates the charging power limit determined by the electric vehicle itself.

[0077] If the convergence condition is met, then the convergence requirement is satisfied, indicating that the optimized charging strategy meets the convergence condition, and the charging strategy is output.

[0078] If the convergence condition is not met, the time limit for expected loss of charging pile operator resources will be reduced.

[0079] In summary, by using the Pareto boundary of economic efficiency and resource utilization, we avoid sacrificing operator interests by solely pursuing lower electricity purchase costs (such as excessively extending charging time leading to a decrease in charging pile turnover). We introduce the concept of charging load shift elasticity (users' tolerance for extended charging time and their sensitivity to economic benefits) to make the strategy more closely reflect real user behavior. When the reduction in user costs does not exceed the service capacity reduction threshold, we automatically reduce the resource occupancy time limit and re-optimize, ensuring that the strategy always converges within the system's acceptable boundaries.

[0080] Example 2, refer to Figure 3 This is one embodiment of the present invention, which provides an electric vehicle orderly charging strategy optimization system, including a model building module, a solution module and a convergence module.

[0081] A model module is constructed based on the charging needs of electric vehicle customers and the operating status of charging piles. An objective function for a charging strategy with the goal of minimizing electricity purchase costs is constructed, along with constraints for optimizing the electricity purchase strategy.

[0082] The solution module constructs and solves the charging strategy optimization model based on the objective function and constraints.

[0083] The convergence module establishes a two-layer convergence criterion based on the elasticity assessment of charging load transfer, implements the Pareto optimal boundary determination of the economics and resource utilization of the charging strategy, and outputs the optimal ordered charging strategy that satisfies the dynamic balance of the system.

[0084] This embodiment also provides an electronic device applicable to 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 implement the electric vehicle orderly charging strategy optimization method proposed in the above embodiment.

[0085] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements an electric vehicle orderly charging strategy optimization method as proposed in the above embodiments.

[0086] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for optimizing an orderly charging strategy for electric vehicles proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0087] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0088] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0089] Example 3, referring to Figure 2 As an embodiment of the present invention, a method, system, device and medium for optimizing the orderly charging strategy of electric vehicles are provided. In order to verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.

[0090] like Figure 2 The image shows the time-of-use electricity price in the spot market for a certain charging station. Taking an electric vehicle charging at 08:00 as an example, the charging station's maximum charging power is 10 kW, and the electric vehicle's charging demand is 30 kWh.

[0091] Following the constant power charging strategy based on maximum charging power, the charging station will charge the electric vehicle at a power of 10 kilowatts, and it is expected to be fully charged at 11:00. The charging cost is 9.63 yuan.

[0092] This method optimizes charging by minimizing costs within the permitted charging time for electric vehicles. The optimal charging period is found to be 10:00-13:00, with a corresponding charging cost of 6.80 yuan. However, considering only the minimum charging cost might lead to electric vehicles occupying charging station parking spaces for extended periods, affecting other electric vehicles' charging. Therefore, step four of this method assesses the expected loss due to charging resource occupancy. After considering the expected loss, the convergence requirement is met.

[0093] Table 1 compares the constant power charging strategy based on maximum charging power with the charging strategy of this method. It can be seen that using the method proposed in this invention will reduce the charging cost by 29.40% in this case.

[0094] Table 1. Comparison of charging costs between the constant power charging strategy based on maximum charging power and the proposed method.

[0095] ,

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

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 charging pile dynamic operating parameters, a dynamic cost optimization model under a multi-period electricity price response mechanism is constructed. By employing a dynamic programming algorithm and a closed-loop feedback mechanism for predicting charging resource occupancy risks, and through the interactive iteration of real-time electricity price signals from the electricity market and charging pile status monitoring data, an asymmetric charging power allocation scheme that satisfies the spatiotemporal distribution characteristics of charging demand is solved. A two-layer convergence criterion based on the elasticity assessment of charging load transfer is established. By quantifying the dynamic game relationship between the user economic benefits generated by the optimization of charging strategy and the opportunity cost of charging facility resource occupation, the Pareto optimal boundary determination of the economic efficiency and resource utilization rate of the charging strategy is implemented. When the user cost reduction brought about by the optimization of the strategy exceeds the preset charging pile service capacity reduction threshold, the optimal ordered charging strategy that satisfies the dynamic balance of the system is output. The dynamic cost optimization model includes an objective function of minimizing electricity purchase costs, and the construction of constraints for optimizing the electricity purchase strategy. The objective function for optimizing electricity purchase costs is expressed as follows: in, This represents the electricity cost for the v-th electric vehicle. This represents the number of charging periods, where t is the index of the charging period. Indicates the duration of the charging period. This represents the time-of-use electricity price in the spot market for the t-th charging period, published by the market trading platform. This represents the charging power of the v-th electric vehicle during the t-th charging period; The constraints for constructing the electricity purchase strategy optimization include charging power constraints and charging time constraints. The charging power constraint includes ensuring that the charging power does not exceed the charging pile and electric vehicle charging power limit range, and that the charging power corresponds to the charging amount that meets the expected charging amount requirements of electric vehicle customers. Establish charging time constraints, including ensuring 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 charging pile operator's resources.

2. The method for optimizing an orderly charging strategy for electric vehicles as described in claim 1, characterized in that: 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 influencing factors through a sliding time window prediction algorithm, and triggering charging power reallocation by using an event-driven dynamic programming algorithm. The charging strategy with the goal of minimizing electricity purchase cost is obtained by using Cplex to solve the problem.

3. The method for optimizing an orderly charging strategy for electric vehicles as described in claim 2, characterized in that: The solution for the asymmetric charging power allocation scheme that satisfies the spatiotemporal distribution characteristics of charging demand includes calculating the optimization strategy delay, wherein the optimization strategy delay is the difference between the charging time after adopting the charging strategy optimization method and the traditional constant power charging time at maximum charging power. The synchronous charging frequency is calculated as the ratio of the number of electric vehicles charging at a charging pile during the corresponding charging time of the optimized charging strategy to the product of the number of charging periods and the time interval. The expected loss of charging resource occupancy is calculated as the product of the charging frequency during the same period, the delay of the optimization strategy, and the average charging service fee revenue of the charging pile operator during the charging of electric vehicles.

4. The method for optimizing an orderly charging strategy for electric vehicles as described in claim 3, characterized in that: The two-layer convergence criterion includes a convergence condition where the decrease in electricity purchase cost resulting from the optimization strategy is greater than the expected loss from charging resource occupancy.

5. The method for optimizing an orderly charging strategy for electric vehicles as described in claim 4, characterized in that: The Pareto optimal boundary determination of the economic-resource utilization rate of the charging strategy includes: if the convergence condition is met, then 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 time limit for expected loss of charging pile operator resources will be reduced.

6. An electric vehicle orderly charging strategy optimization system, employing the electric vehicle orderly charging strategy optimization method as described in any one of claims 1 to 5, characterized in that, include: The model module is constructed based on the charging needs of electric vehicle customers and the operating status of charging piles. The objective function of the charging strategy with the goal of minimizing the electricity purchase cost is constructed, and the optimization constraints of the electricity purchase strategy are constructed. The solution module 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 elasticity assessment of charging load transfer, implements the Pareto optimal boundary determination of the economics and resource utilization of the charging strategy, and outputs the optimal ordered charging strategy that satisfies the dynamic balance of the system.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the electric vehicle orderly charging strategy optimization method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the electric vehicle orderly charging strategy optimization method according to any one of claims 1 to 5.

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