Electric vehicle charging method and device based on electricity-carbon coupling pricing, medium and equipment

Abstract

The invention discloses an electric vehicle charging method and device based on electricity-carbon coupling pricing, a medium and equipment, and relates to the technical field of electric vehicle charging optimization. The method comprises the following steps: simulating disordered charging load data of electric vehicles in a target power distribution network area by using a Monte Carlo method, and obtaining a prediction result of the total charging load amount of the electric vehicles in the target power distribution network area based on the disordered charging load data; dividing a charging load time period of the target power distribution network region according to the prediction result to obtain a plurality of load time periods; constructing a dynamic electricity-carbon coupling pricing model of the target power distribution network area according to the charging load prediction results corresponding to the plurality of load time periods and the actual charging load in combination with the carbon price of the target power distribution network area; and solving the dynamic electricity-carbon coupling pricing model by adopting a particle swarm algorithm to obtain the optimal dynamic electricity-carbon price of the target power distribution network region, and guiding the electric vehicle to charge by utilizing the optimal dynamic electricity-carbon price, so that the peak-valley difference of the power grid can be reduced, the operation stability of the power grid can be improved, and the charging cost of a user can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging optimization, and in particular to an electric vehicle charging method, device, medium and equipment based on electricity-carbon coupling pricing. Background Art

[0002] Electric vehicles (EVs) have attracted widespread attention due to their significant advantages in reducing greenhouse gas emissions and lowering driving costs. Connecting a large number of EV charging loads to the distribution network exacerbates peak-to-valley differences. Charging during peak hours results in excessively high charging costs for users, increasing the cost of EV use. Therefore, guiding EV charging in an orderly manner (charging during off-peak hours) is crucial for improving grid operational safety and reducing user charging costs. This approach primarily involves classifying electricity prices for different time periods and using these prices to guide users to charge their EVs in an orderly manner.

[0003] Existing strategies for guiding electric vehicle charging are primarily implemented through time-of-use static electricity pricing, which divides the peak electricity consumption periods of midday and evening into peak periods, the valley period of night into valley period, and the rest of the time into flat periods. While time-of-use static electricity pricing has a positive effect on peak-load shifting and valley-loading, it does not. However, since static electricity prices are fixed once they are defined, the basic load of residents is not static and can fluctuate with changes in different regions and climate conditions. This can easily conflict with the original range of the divisions, resulting in a mismatch between peak electricity prices and valley or flat loads. This, in turn, leads to excessively high charging costs for users and increased costs of using electric vehicles. Summary of the Invention

[0004] Based on this, it is necessary to provide electric vehicle charging methods, devices, media and equipment based on electric-carbon coupling pricing to address the technical problem that when existing technologies guide electric vehicle charging, electricity prices are at peak values ​​while loads are at valley or flat values, which in turn leads to excessively high charging costs for users and increased costs of using electric vehicles.

[0005] The present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides an electric vehicle charging method based on electricity-carbon coupled pricing, the method comprising:

[0007] Simulating the disordered charging load data of electric vehicles in the target distribution network area using the Monte Carlo method, and obtaining a prediction result of the total charging load of electric vehicles in the target distribution network area based on the disordered charging load data;

[0008] Dividing the charging load period of the target distribution network area within a day according to the prediction results to obtain multiple load periods; constructing a dynamic electricity-carbon coupling pricing model for the target distribution network area according to the charging load prediction results and actual charging loads corresponding to the multiple load periods, as well as the carbon price of the target distribution network area;

[0009] The dynamic electricity-carbon coupling pricing model is solved using a particle swarm algorithm to obtain the optimal dynamic electricity-carbon price in the target distribution network area, and the optimal dynamic electricity-carbon price is used to guide electric vehicles to charge.

[0010] Furthermore, the charging load period of the target distribution network area in one day is divided according to the prediction result to obtain multiple load periods, specifically including:

[0011] determining a maximum load and a minimum load from the prediction results;

[0012] According to the maximum load and the minimum load, the charging load period of the target distribution network area in one day is divided into multiple load periods.

[0013] Furthermore, a dynamic electricity-carbon coupling pricing model for the target distribution network area is constructed based on the charging load prediction results and actual charging loads corresponding to the multiple load periods, as well as the carbon price of the target distribution network area; specifically, the model includes:

[0014] Obtain the highest electricity price and the lowest electricity price of the target distribution network during the multiple load periods;

[0015] Based on the maximum electricity price, the minimum electricity price, the maximum load, the minimum load, and the carbon price of the target distribution network area, a mapping function for each load period in the price range is constructed through a membership function, and the charging cost of the target distribution network in the multiple load periods is calculated using the mapping function;

[0016] Constructing an objective function to characterize the grid load variance of the target distribution network area;

[0017] Based on the linear weighted sum method, the dynamic electricity-carbon coupling pricing model is constructed according to the objective function and the charging costs of the multiple load periods.

[0018] Furthermore, the constraints of the dynamic electricity-carbon coupling pricing model include power constraints, charging period constraints and total power constraints.

[0019] Furthermore, the dynamic electricity-carbon coupling pricing model is solved using a particle swarm algorithm to obtain the optimal dynamic electricity-carbon price in the target distribution network area, specifically including:

[0020] Construct a particle swarm containing electricity price parameters and carbon price parameters, where different populations in the particle swarm represent different groups of electricity prices and carbon prices;

[0021] Determining the fitness function value corresponding to each population in the particle swarm, and comparing the fitness function values ​​corresponding to each population to obtain a comparison result;

[0022] According to the comparison results, the individual optimal position, global optimal position and speed of each population are iteratively updated. When the number of iterations reaches a preset number, the iterative result of the particle swarm is output, and the optimal dynamic electricity-carbon price is obtained based on the iterative result.

[0023] In a second aspect, the present invention provides an electric vehicle charging device based on electricity-carbon coupled pricing, comprising:

[0024] a data acquisition module, configured to simulate the disordered charging load data of electric vehicles in a target distribution network area using a Monte Carlo method, and obtain a prediction result of the total charging load of electric vehicles in the target distribution network area based on the disordered charging load data;

[0025] A model construction module is used to divide the charging load period of the target distribution network area within a day according to the prediction results to obtain multiple load periods; and construct a dynamic electricity-carbon coupling pricing model for the target distribution network area based on the charging load prediction results and actual charging load corresponding to the multiple load periods, as well as the carbon price of the target distribution network area;

[0026] The charging guidance module is used to solve the dynamic electricity-carbon coupling pricing model using a particle swarm algorithm to obtain the optimal dynamic electricity-carbon price in the target distribution network area, and use the optimal dynamic electricity-carbon price to guide electric vehicles to charge.

[0027] The present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the electric vehicle charging method based on electricity-carbon coupling pricing is implemented.

[0028] The present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the electric vehicle charging method based on electricity-carbon coupled pricing is implemented.

[0029] At least one technical solution employed by the present invention can achieve the following beneficial effects: The present invention uses the Monte Carlo method to simulate the random charging load data of electric vehicles in a target distribution network area. Based on this random charging load data, a prediction result for the total charging load of electric vehicles in the target distribution network area is obtained. This prediction result is more accurate for the total charging load of electric vehicles in the target distribution network area, taking into account the nonlinear and highly random characteristics of electric vehicle charging load data. The target distribution network area is then divided into charging load periods within a day based on the prediction results to obtain multiple load periods. A dynamic electricity-carbon coupling pricing model for the target distribution network area is then constructed based on the charging load prediction results corresponding to each of the multiple load periods and the actual charging load, combined with the carbon price in the target distribution network area. This model can accurately and effectively guide load transfer based on different actual base load conditions, thereby improving efficiency. Finally, the dynamic electricity-carbon coupling pricing model is solved using a particle swarm algorithm to obtain the optimal electricity price for each period in the target distribution network area. The optimal electricity price and carbon price combination can be selected from multiple combinations. This optimal electricity price and carbon price combination is then used to guide users to charge their electric vehicles, effectively reducing the peak-to-valley difference in the power grid, improving the stability of power grid operation, and reducing user charging costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. 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:

[0031] Figure 1 A flow chart of the electric vehicle charging method based on electricity-carbon coupled pricing provided by the present invention;

[0032] Figure 2 The Monte Carlo simulation flow chart of electric vehicle charging load provided by the present invention;

[0033] Figure 3 The optimal dynamic electricity carbon price curve provided by the present invention;

[0034] Figure 4 Schematic diagram of the electric vehicle charging device based on electricity-carbon coupled pricing provided by the present invention;

[0035] Figure 5 A schematic diagram of a computer device for implementing an electric vehicle charging method based on electricity-carbon coupled pricing provided by the present invention. DETAILED DESCRIPTION

[0036] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] The server mentioned in the present invention can be a server installed on a business platform, or a device such as a desktop computer or laptop computer capable of executing the solution of the present invention. For ease of explanation, the following description will only use the server as the execution entity. The following, combined with the accompanying drawings, details the technical solutions provided by various embodiments of the present invention.

[0038] refer to Figure 1 The electric vehicle charging method based on electricity-carbon coupled pricing in the present invention specifically includes the following steps:

[0039] S10: using the Monte Carlo method to simulate the disordered charging load data of electric vehicles in the target distribution network area, and obtaining a prediction result of the total charging load of electric vehicles in the target distribution network area based on the disordered charging load data.

[0040] In this embodiment, the target distribution network area refers to the area where improved EV charging strategies are needed. Typically, when analyzing a single EV, its charging behavior is uncertain and difficult to predict, making it difficult to apply any probability distribution. However, as the number of EVs increases and their scope scales, charging behavior will follow a certain probability distribution and can be simulated stochastically. Monte Carlo simulation, combined with probability theory, performs statistical analysis on the variables in an event, fitting a probability distribution function. It then samples and generates approximate data that satisfies this distribution. Further analysis of this approximate data allows for accurate judgment of the event.

[0041] Assuming users charge their electric vehicles as soon as they return home, a Monte Carlo simulation reveals a chaotic charging load. Random sampling is then performed using the Monte Carlo simulation method to initialize parameters. These parameters are then used to calculate charging duration. Finally, the number of electric vehicles charging during each time period is added together and multiplied by the charging power to determine the charging load demand.

[0042] Specifically, refer to Figure 2 The specific steps of simulating the disordered charging load data of electric vehicles in the target distribution network area by using the Monte Carlo method and obtaining the prediction result of the total charging load of electric vehicles in the target distribution network area based on the disordered charging load data include:

[0043] Initialize the maximum total number of electric vehicles NEV in the target distribution network area.

[0044] Initialize the number of trams k to 0, that is, K=0. After entering the loop, k=k+1; randomly extract the mileage of the tram to obtain the power consumption of the tram, that is, the charging capacity of the tram; randomly extract the charging start time of the tram, divide the charging capacity by the unit charging power to obtain the charging time, and thus obtain the time series of the tram's charging power; determine whether the maximum total number of electric vehicles has been reached. If not, continue to execute k=k+1; if yes, accumulate to obtain the total tram load.

[0045] S20: Divide the charging load periods of the target distribution network area within a day according to the prediction results to obtain multiple load periods; construct a dynamic electricity-carbon coupling pricing model for the target distribution network area based on the charging load prediction results and actual charging load corresponding to the multiple load periods, combined with the carbon price of the target distribution network area.

[0046] In this embodiment, the charging load period of the target distribution network area in one day is divided according to the prediction results to obtain multiple load periods, specifically including:

[0047] Determine the maximum and minimum loads from the forecast results.

[0048] According to the maximum load and the minimum load, the charging load period of the target distribution network area within a day is divided into multiple load periods.

[0049] Specifically, the expression for dividing the charging load period in the target distribution network area within a day is:

[0050]

[0051] Among them, ΔL represents the load segment interval, L max , L min are the highest and lowest loads in the day-ahead base load forecast, respectively. H is the number of segments, which is determined by the grid load and computational efficiency. In this paper, H is taken as 24.

[0052] In this embodiment, a dynamic electricity-carbon coupling pricing model for the target distribution network area is constructed based on the charging load forecast results and actual charging load corresponding to multiple load periods, combined with the carbon price in the target distribution network area. Specifically, the model includes:

[0053] Obtain the highest and lowest electricity prices of the target distribution network during multiple load periods.

[0054] Based on the highest electricity price, lowest electricity price, highest load, lowest load and carbon price in the target distribution network area, a mapping function of each load period in the price range is constructed through the membership function, and the mapping function is used to calculate the charging cost of the target distribution network in multiple load periods.

[0055] In this embodiment, based on the maximum electricity price, minimum electricity price, maximum load, minimum load, and carbon price of the target distribution network area, the mapping function of each load period in the price range is constructed through the membership function as follows:

[0056]

[0057] Q i =μ*ΔL+Q min +β*C;

[0058] Among them, Q* is the mapping of each load period in the price range, Q max and Q min They are the peak value (maximum value) and valley value (minimum value) of the original time-of-use electricity price. i is the charging price in the i-th period, C is the carbon price, μ represents the index coefficient of the load segment, which is used to determine the electricity price range corresponding to the current load, and β is the carbon price coefficient, which is taken as 1 in this paper.

[0059] Construct an objective function to characterize the grid load variance in the target distribution network area.

[0060] In this embodiment, the smaller the grid load variance is, the smaller the grid load fluctuation is, which is more conducive to the stable operation of the grid system. The objective function of the grid load variance can be expressed as:

[0061]

[0062] P i =P i,c +P i,v ;

[0063] Among them, P i is the total load in the ith period of a day; P i，c is the charging load of electric vehicles in the i-th period of a day; P i，v is the base load in the i-th period of a day; P is the expected total load mean; N is the total number of periods during the valley hour; and D is the objective function of optimization.

[0064] In this embodiment, assuming that user satisfaction is positively correlated with the total charging cost, the total cost of user charging should be considered in the optimization process. The total cost S t The calculation expression is:

[0065]

[0066] Among them, P i，c is the charging load of electric vehicles in the i-th period of a day, N is the total number of valley time periods, Q i is the charging unit price in the i-th period.

[0067] Based on the linear weighted sum method, a dynamic electricity-carbon coupling pricing model is constructed according to the objective function and the charging costs of multiple load periods.

[0068] In this embodiment, based on the linear weighted sum method, the objective function D and P are i，c Perform normalization processing as shown below:

[0069]

[0070] Among them, S is the multi-objective optimization function of the vehicle, D max 、 They are single objective functions D and S respectively t The maximum value of λ k，1 ,λ k，2 They are single objective functions D and S respectively t Optimization weights.

[0071] S30: Use the particle swarm algorithm to solve the dynamic electricity-carbon coupling pricing model to obtain the optimal dynamic electricity-carbon price in the target distribution network area, and use the optimal dynamic electricity-carbon price to guide electric vehicles to charge.

[0072] In one or more embodiments of the present invention, the electricity-carbon price refers to both the electricity price and the carbon price. This embodiment utilizes a particle swarm optimization algorithm to solve a dynamic electricity-carbon coupled pricing model. This algorithm can derive the optimal dynamic electricity-carbon price for the target distribution network region at various time periods from a variety of electricity-carbon pricing strategies. This optimal dynamic electricity-carbon price is then used to guide electric vehicle charging, effectively reducing peak-to-valley variations in the power grid and improving grid operational stability.

[0073] based on Figure 1The electric vehicle charging method based on electricity-carbon coupled pricing is shown. Using the Monte Carlo method to simulate the random charging load data of electric vehicles in a target distribution network area, a total electric vehicle charging load forecast for the target distribution network area is obtained based on this random charging load data. This method can better reflect the nonlinear and highly random characteristics of electric vehicle charging load data and produce a more accurate forecast of the total electric vehicle charging load for the target distribution network area. Based on the predicted results, the charging load period within the target distribution network area is divided into multiple load periods. Then, based on the charging load forecasts corresponding to each of these load periods and the actual charging load, combined with the carbon price in the target distribution network area, a dynamic electricity-carbon coupled pricing model for the target distribution network area is constructed. This model can accurately and effectively guide load shifting based on different base load conditions, thereby improving efficiency. Finally, the dynamic electricity-carbon coupled pricing model is solved using a particle swarm optimization algorithm to obtain the optimal electricity-carbon price for each period in the target distribution network area. The optimal electricity-carbon price combination can be selected from multiple combinations. This optimal electricity-carbon price combination is then used to guide users to charge their electric vehicles in an orderly manner, effectively reducing the peak-to-valley difference in the power grid, improving power grid operation stability, and reducing user charging costs.

[0074] When applying the electric vehicle charging method based on electric-carbon coupling pricing provided by the present invention, it is not necessary to Figure 1 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0075] In addition, in one or more embodiments of the present invention, a particle swarm optimization algorithm is used to solve the dynamic electricity-carbon coupling pricing model to obtain the optimal dynamic electricity-carbon price in the target distribution network area, specifically including:

[0076] A particle swarm containing electricity price parameters and carbon price parameters is constructed, and different populations in the particle swarm represent different groups of electricity prices and carbon prices.

[0077] In this embodiment, the particle swarm parameters are initialized, and the upper and lower limits of the electricity price and the carbon price are set. Each population represents a set of electricity price and carbon price.

[0078] Determine the fitness function value corresponding to each population in the particle swarm, and compare the fitness function values ​​corresponding to each population to obtain a comparison result.

[0079] In this embodiment, the optimal position P of the individual particle is updated by comparing the fitness function values. best , the global optimal position G best And particle velocity Vi, particle velocity and position update formula are as follows:

[0080]

[0081] in, is the updated particle velocity, w is the inertia weight, c1 is the self-learning factor, c2 is the social learning factor, is the updated particle position.

[0082] According to the comparison results, the individual optimal position, global optimal position and speed of each population are iteratively updated. When the number of iterations reaches the preset number, the iterative results of the particle swarm are output, and the optimal dynamic electricity-carbon price is obtained based on the iterative results.

[0083] This embodiment uses a particle swarm algorithm to solve the dynamic electricity-carbon coupling pricing model to obtain the optimal electricity price in the target distribution network area in each time period. It can select the optimal combination of electricity price and carbon price from a variety of combinations of electricity price and carbon price, making the formulation of electricity price and carbon price in each load period more scientific.

[0084] Specifically, the typical daily load data of a certain area's power grid is used as the basic data. There are 1,000 households within the scope of this local power grid, with a certain number of electric vehicles. The rated power of the power transformer within this scope remains unchanged, and the specifications of the power batteries of electric vehicles are approximately the same, with a battery capacity of 35kWh, a slow charging power of 3.5kW, a battery charging efficiency of 0.85, and a battery energy conversion efficiency of 0.90. One day is divided into 1440 periods, each period is 1 minute, and 100 cycles of simulation are performed. The average value of the results is used to simulate the charging status of electric vehicles in this environment. The simulation running environment is the software Matlab2023b, and the initial setting number of groups is 300. Solve the optimal dynamic electricity carbon price as follows: Figure 3 The comparison of load peak-valley differences under different strategies is shown in Table 1:

[0085] Table 1 - Comparison of load peak-valley differences under different strategies

[0086]

[0087] Table 1 shows that the multi-period dynamic electricity pricing strategy reduced peak load by 28.5%, peak-to-valley variation by 58%, and the standard deviation of load fluctuation by 67.8% compared to the unordered charging strategy. The user charging cost under unordered charging was 6,143.67 yuan, while the multi-period dynamic electricity pricing strategy reduced the cost to 4,304.96 yuan, a 28.39% reduction. Therefore, the multi-period dynamic electricity-carbon pricing strategy effectively reduced the peak-to-valley variation of the power grid, improved the stability of power grid operation, and reduced user charging costs.

[0088] Furthermore, in one or more embodiments of the present invention, the constraints of the dynamic electricity-carbon coupling pricing model include power constraints, charging period constraints, and total power constraints.

[0089] In this embodiment, the power constraint relationship is:

[0090] P min ≤P t ≤P max ;

[0091] Among them, P min 、P max They are the minimum power and maximum power of the load curve respectively, and the maximum power depends on the maximum power capacity of the charging station.

[0092] The relationship between the charging period constraints is:

[0093] T s ≤T≤T e ;

[0094] Among them, T s is the start time of regulation, T e This is the end period of regulation.

[0095] The relationship between the total power constraint is:

[0096]

[0097] Among them, N g is the total number of electric vehicles that need to be charged, is the total charging time required for vehicle i.

[0098] This embodiment scheme imposes power constraints, charging period constraints and total power constraints on the dynamic electricity-carbon coupling pricing model, which can prevent the output results of the dynamic electricity-carbon coupling pricing model from deviating from the actual situation, making the output results of the dynamic electricity-carbon coupling pricing model more real and credible.

[0099] The above is an electric vehicle charging method based on electricity-carbon coupling pricing provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding electric vehicle charging device based on electricity-carbon coupling pricing, such as Figure 4 Shown, including:

[0100] The data acquisition module is used to simulate the disordered charging load data of electric vehicles in the target distribution network area using the Monte Carlo method, and obtain the prediction result of the total charging load of electric vehicles in the target distribution network area based on the disordered charging load data.

[0101] The model construction module is used to divide the charging load periods within the target distribution network area within a day according to the prediction results to obtain multiple load periods; based on the charging load prediction results and actual charging load corresponding to multiple load periods, combined with the carbon price of the target distribution network area, a dynamic electricity-carbon coupling pricing model for the target distribution network area is constructed.

[0102] The charging guidance module is used to solve the dynamic electricity-carbon coupling pricing model using a particle swarm algorithm to obtain the optimal dynamic electricity-carbon price in the target distribution network area, and use the optimal dynamic electricity-carbon price to guide electric vehicles to charge.

[0103] Regarding the specific definition of the electric vehicle charging device based on electricity-carbon coupled pricing, please refer to the definition of the electric vehicle charging method based on electricity-carbon coupled pricing above, and will not be repeated here. The various modules in the electric vehicle charging device based on electricity-carbon coupled pricing can be implemented in whole or in part through software, hardware, and a combination thereof. The modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0104] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the Figure 1 Provided is an electric vehicle charging method based on electricity-carbon coupling pricing.

[0105] The present invention also provides Figure 5 The structural diagram of the computer equipment shown in FIG. Figure 5 As shown, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the Figure 1 Provided is an electric vehicle charging method based on electricity-carbon coupling pricing.

[0106] Those skilled in the art will appreciate that all or part of the processes in the embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the various methods described. Among them, any reference to memory, storage, database or other media used in the various embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0107] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. The electric vehicle charging method based on electricity-carbon coupling pricing is characterized by: include: Simulating the disordered charging load data of electric vehicles in the target distribution network area using the Monte Carlo method, and obtaining a prediction result of the total charging load of electric vehicles in the target distribution network area based on the disordered charging load data; Dividing the target distribution network area into charging load periods within a day according to the prediction results to obtain multiple load periods; Constructing a dynamic electricity-carbon coupling pricing model for the target distribution network area based on the charging load prediction results and actual charging loads corresponding to the multiple load time periods, and the carbon price of the target distribution network area; The dynamic electricity-carbon coupling pricing model is solved using a particle swarm algorithm to obtain the optimal dynamic electricity-carbon price in the target distribution network area, and the optimal dynamic electricity-carbon price is used to guide electric vehicles to charge.

2. The electric vehicle charging method based on electricity-carbon coupled pricing according to claim 1, characterized in that: The target distribution network area is divided into charging load periods within a day according to the prediction results to obtain multiple load periods, specifically including: determining a maximum load and a minimum load from the prediction results; According to the maximum load and the minimum load, the charging load period of the target distribution network area in one day is divided into multiple load periods.

3. The electric vehicle charging method based on electricity-carbon coupled pricing as claimed in claim 2, characterized in that: Based on the charging load prediction results and actual charging loads corresponding to the multiple load periods, and the carbon price of the target distribution network area, a dynamic electricity-carbon coupling pricing model for the target distribution network area is constructed; specifically, the model includes: Obtain the highest electricity price and the lowest electricity price of the target distribution network during the multiple load periods; Based on the maximum electricity price, the minimum electricity price, the maximum load, the minimum load, and the carbon price of the target distribution network area, a mapping function for each load period in the price range is constructed through a membership function, and the charging cost of the target distribution network in the multiple load periods is calculated using the mapping function; Constructing an objective function to characterize the grid load variance of the target distribution network area; Based on the linear weighted sum method, the dynamic electricity-carbon coupling pricing model is constructed according to the objective function and the charging costs of the multiple load periods.

4. The electric vehicle charging method based on electricity-carbon coupled pricing as claimed in claim 3, characterized in that: The constraints of the dynamic electricity-carbon coupling pricing model include power constraints, charging period constraints and total power constraints.

5. The electric vehicle charging method based on electricity-carbon coupled pricing according to claim 1, characterized in that: The particle swarm algorithm is used to solve the dynamic electricity-carbon coupling pricing model to obtain the optimal dynamic electricity-carbon price in the target distribution network area, specifically including: Construct a particle swarm containing electricity price parameters and carbon price parameters, where different populations in the particle swarm represent different groups of electricity prices and carbon prices; Determining the fitness function value corresponding to each population in the particle swarm, and comparing the fitness function values ​​corresponding to each population to obtain a comparison result; According to the comparison results, the individual optimal position, global optimal position and speed of each population are iteratively updated. When the number of iterations reaches a preset number, the iterative result of the particle swarm is output, and the optimal dynamic electricity-carbon price is obtained according to the iterative result.

6. The electric vehicle charging device based on electricity-carbon coupling pricing is characterized by: include: A data acquisition module is used to simulate the disordered charging load data of electric vehicles in the target distribution network area using the Monte Carlo method, and obtain a prediction result of the total charging load of electric vehicles in the target distribution network area based on the disordered charging load data; A model building module, configured to divide the charging load period of the target distribution network area within a day according to the prediction result to obtain multiple load periods; Constructing a dynamic electricity-carbon coupling pricing model for the target distribution network area based on the charging load prediction results and actual charging loads corresponding to the multiple load time periods, and the carbon price of the target distribution network area; The charging guidance module is used to solve the dynamic electricity-carbon coupling pricing model using a particle swarm algorithm to obtain the optimal dynamic electricity-carbon price in the target distribution network area, and use the optimal dynamic electricity-carbon price to guide electric vehicles to charge.

7. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the electric vehicle charging method based on electricity-carbon coupling pricing according to any one of claims 1 to 5 is implemented.

8. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the electric vehicle charging method based on electricity-carbon coupling pricing as claimed in any one of claims 1 to 5 is implemented.

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