A residential area exchanges charging station charging control method and device and storage medium

Abstract

The application discloses a residential area alternating current charging station charging control method and device and a storage medium, belongs to the orderly charging technical field of electric vehicles, and comprises the following steps: acquiring user charging information and preprocessing; based on the preprocessed user charging information, an adaptive artificial bee colony algorithm is used to solve a constructed charging optimization model, and an optimal charging scheme of an electric vehicle is output; and the charging process of the electric vehicle is controlled according to the optimal charging scheme. The application can guarantee the convenience of user charging while reducing the charging cost of the user, greatly improves the load index of a transformer area, reduces the impact of disorderly charging on the power grid, realizes good grid access of the electric vehicle, and promotes the development of the electric vehicle.

Description

Technical Field

[0001] This invention relates to a charging control method, device, and storage medium for AC charging stations in residential areas, belonging to the field of orderly charging technology for electric vehicles. Background Technology

[0002] Driven by the "dual-carbon" policy, the development of electric vehicles is progressing rapidly. This rapid development has brought numerous challenges to the power distribution network. In the foreseeable future, charging at AC charging stations in residential areas will become the primary way for electric vehicles to replenish their energy. However, residents' charging behavior is highly correlated with their travel patterns. Related research indicates that disorderly charging of electric vehicles will further increase peak-valley differences, affect power quality, overload power supply equipment, and impact the power grid. Summary of the Invention

[0003] The purpose of this invention is to provide a charging control method, device, and storage medium for AC charging stations in residential areas, thereby reducing the impact of disorderly charging on the power grid.

[0004] To achieve the above objectives, the present invention employs the following technical solution:

[0005] In a first aspect, the present invention provides a charging control method for an AC charging station in a residential area, comprising:

[0006] Obtain and preprocess user charging information;

[0007] Based on the preprocessed user charging information, the constructed charging optimization model is solved by the adaptive artificial bee colony algorithm, and the optimal charging scheme for electric vehicles is output.

[0008] The charging process of the electric vehicle is controlled according to the optimal charging scheme.

[0009] In conjunction with the first aspect, further, the user charging information includes the start charging time, the expected end charging time, and the expected charging amount, and the preprocessing includes:

[0010] The following formulas convert the start charging time and the expected end charging time into the start charging time and end charging time, respectively:

[0011]

[0012] in, This is the charging start time for the kth electric vehicle. This represents the charging completion time of the k-th electric vehicle, where floor is the floor number and ceil is the ceil number. This is the time in minutes for the kth electric vehicle to begin charging. It is the expected charging end time of the kth electric vehicle, converted into minutes.

[0013] In conjunction with the first aspect, further, before solving the constructed charging optimization model, the following method is used to determine whether the charging control conditions are met:

[0014] The average charging power of the vehicle during the charging period is calculated based on the start charging time, the expected end charging time, and the expected charging amount. If the average charging power is less than a preset threshold, the charging control conditions are met, and the operation of the constructed charging optimization model is performed.

[0015] In conjunction with the first aspect, the overall objective function of the charging optimization model is further defined as follows:

[0016] minF=λ1F′1+λ2F′2

[0017] Where F is the overall objective function, λ1 and λ2 are weighting coefficients, and λ1+λ2=1, F′1 is the standardized result of the charging cost objective function, and F′2 is the standardized result of the transformer area load fluctuation objective function.

[0018]

[0019]

[0020] Among them, C k It's the charging cost, f k This refers to the load fluctuation rate of the distribution area. min indicates the minimum value, and max indicates the maximum value.

[0021] The expression for the objective function of charging costs is:

[0022]

[0023] Where F1 is the objective function for charging costs, It is the charging power vector of the k-th electric vehicle in time period t. It is the charging time vector of the k-th electric vehicle in time period t. Let be the charging price vector for the kth electric vehicle in time period t, and d be the number of charging time periods to be optimized.

[0024]

[0025] The expression for the objective function of the load fluctuation rate of the transformer area is:

[0026]

[0027] Where F2 is the objective function for the load fluctuation rate of the transformer area, and P sIt is the standard deviation of the load in the transformer area. This is the average load of the transformer substation.

[0028] The constraints of the charging optimization model include:

[0029] P min ≤p t ≤P max t = 1, 2, ..., d

[0030]

[0031] P T,r ≤αP M r∈A

[0032] It also includes constraining the vehicle's charging process to be continuous and to change the charging power only once at every hour.

[0033] Where, p t P is the charging power during time period t. min It is the minimum charging power, P max It is the maximum charging power, h t It is the charging time during period t, SOC e SOC0 and Q represent the user's desired state of charge and the vehicle's initial state of charge, respectively. Q represents the vehicle's battery capacity, and P represents the battery capacity. T,r Let P be the predicted total load at time r when the vehicle enters the transformer substation, α represent the maximum capacity margin multiple that the transformer can carry, and P is the total load at time r. M A is the transformer capacity, and A is the set of all moments included in the vehicle charging process.

[0034] In conjunction with the first aspect, further, the charging optimization model constructed by solving the adaptive artificial bee colony algorithm includes:

[0035] Obtain the initial honey source, and iterate through the following steps until the preset maximum number of iterations is reached. Output the honey source after the iterations are completed as the optimal charging scheme:

[0036] The hired bees corresponding to the nectar source search for nectar sources within the territory of the nectar source, calculate the amount of nectar in the nectar source, and update the nectar source using a greedy algorithm based on the amount of nectar.

[0037] The hired bee shares the nectar source and the amount of nectar in the nectar source with the follower bee. Each follower bee selects a nectar source by roulette based on the amount of nectar and searches for other nectar sources within that nectar source's territory. The nectar source is then updated using a greedy algorithm based on the amount of nectar.

[0038] If the preset conditions for scout bees to be dispatched are met, the nectar source will be updated by the scout bees;

[0039] The nectar source represents a feasible solution, and the amount of nectar represents the fitness of the feasible solution.

[0040] In conjunction with the first aspect, the search formula for the employed bees to search for nectar sources within the nectar source's territory is as follows:

[0041]

[0042] in, X is the i-th nectar source found by the hired bees. i It is the i-th nectar source, X j It is the j-th nectar source, c1 and c2 are adaptive normal decay coefficients, and c1 + c2 = 1. X is a random number in [0, 1]. best This is the current optimal solution;

[0043] c1 is calculated using the following formula:

[0044]

[0045] Where iter is the current iteration number, G is the preset maximum iteration number, and θ is 0.4433.

[0046] In conjunction with the first aspect, further, the method of selecting a nectar source using a roulette wheel approach and searching for other nectar sources within that source's region, and updating the nectar sources using a greedy algorithm based on the amount of nectar, includes:

[0047] The probability of winning a roulette wheel bet is calculated using the following formula, and then a roulette wheel bet is used to select a honey source:

[0048]

[0049] Where, p j It is the probability that the j-th honey source is selected, fit j SN is the fitness of the j-th nectar source, and SN is the number of nectar sources;

[0050] Follower bees search for nectar sources using the following formula:

[0051]

[0052] In conjunction with the first aspect, further, the preset condition for the scout bees to be dispatched is: the amount of nectar in the nectar source stagnates for a preset time.

[0053] The scout bees search for nectar sources using the following formula:

[0054] X Sc =X min +rand*(X max -X min )

[0055] Among them, XSc It was a scout bee that found the nectar source, X min X is the minimum value of rand. max It is the maximum value of rand, and rand is a random number in [0, 1].

[0056] Secondly, the present invention also provides a charging control device for an AC charging station in a residential area, comprising:

[0057] The information preprocessing module is configured to: acquire user charging information and perform preprocessing;

[0058] The module is configured to: solve the constructed charging optimization model based on the preprocessed user charging information using an adaptive artificial bee colony algorithm, and output the optimal charging scheme for electric vehicles;

[0059] The charging process of the electric vehicle is controlled according to the optimal charging scheme.

[0060] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the residential area AC charging station charging control method as described in any one of the first aspects.

[0061] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0062] This invention provides a charging control method, device, and storage medium for AC charging stations in residential areas. By using an adaptive artificial bee colony algorithm to obtain the optimal charging scheme for electric vehicles, it can ensure charging convenience for users while reducing their charging costs, significantly improve the load index of the transformer area, reduce the impact of disorderly charging on the power grid, achieve good grid connection for electric vehicles, and promote the development of electric vehicles. Attached Figure Description

[0063] Figure 1 This is one of the flowcharts of a charging control method for an AC charging station in a residential area provided by an embodiment of the present invention;

[0064] Figure 2 This is the second flowchart of a charging control method for an AC charging station in a residential area provided by an embodiment of the present invention;

[0065] Figure 3 This is a schematic diagram comparing the load of different charging methods in the transformer substations provided in this embodiment of the invention.

[0066] Figure 4 This is a schematic diagram comparing the load indicators of charging stations for 100 electric vehicles according to an embodiment of the present invention.

[0067] Figure 5 This is a schematic diagram of user charging costs provided in an embodiment of the present invention. Detailed Implementation

[0068] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to illustrate the technical solution of the present invention more clearly, and should not be used to limit the scope of protection of the present invention.

[0069] Example 1

[0070] like Figure 1 As shown in the figure, this embodiment of the invention provides a charging control method for AC charging stations in residential areas, including the following steps:

[0071] S1. Obtain user charging information and perform preprocessing.

[0072] Wait for the kth electric vehicle to connect to the grid for charging, and determine basic data such as grid information, vehicle information, and algorithm parameters.

[0073] Set the parameters in Table 1:

[0074] Table 1 - Parameter Setting Table

[0075]

[0076] Unauthorized charging is calculated based on time-of-use pricing, as shown in Table 2:

[0077] Table 2 - Time-of-use Electricity Pricing Table

[0078]

[0079] One hundred electric vehicles were randomly selected for the experiment in Example 1. User charging information included the start charging time, expected end charging time, and expected charging amount. Preprocessing of the user charging information included:

[0080] Let the user charging information of the k-th electric vehicle be EV. k Then EV k There are two relationships between the start charging time and the expected end charging time: whether they are on the same day. The start charging time and expected end charging time in the user's charging information are converted to minutes. If the start charging time is less than the expected end charging time, they are on the same day; otherwise, they are not.

[0081] For ease of calculation, we assume that the charging start time and charging end time are both within one hour, at minutes 0, 15, 30, and 45. EVs whose charging start time is not at any of these times... k The next moment is taken as the charging start time, and the charging end time is not at the above moment for EVs. k If the previous moment is taken as the charging end time, then the charging start time for the electric vehicle is... and charging end time It can be represented as follows:

[0082]

[0083] in, This is the charging start time for the kth electric vehicle. This represents the charging completion time of the k-th electric vehicle, where floor is the floor number and ceil is the ceil number. This is the time in minutes for the kth electric vehicle to begin charging. It is the expected charging end time of the kth electric vehicle, converted into minutes.

[0084] Based on the above, EV k All moments included in the entire charging period can be represented as a set A, which can be specifically defined as follows:

[0085]

[0086] In EV k When customizing charging plans, to simplify algorithm optimization, EVs are divided into segments based on the hour. k During the charging period, assuming EV k If the number of charging periods to be optimized is d, then:

[0087]

[0088] S2. Based on the preprocessed user charging information, the constructed charging optimization model is solved using an adaptive artificial bee colony algorithm, and the optimal charging scheme for electric vehicles is output.

[0089] like Figure 2 As shown, before solving the constructed charging optimization model, the following method is used to determine whether the charging control conditions are met:

[0090] The average charging power of the vehicle during the charging period is calculated based on the start charging time, the expected end charging time, and the expected charging amount. If the average charging power is less than the preset threshold, the charging control conditions are met, and the operation of solving the constructed charging optimization model is performed; otherwise, disordered charging is directly performed.

[0091] This embodiment also establishes a dynamic electricity pricing strategy, where the electricity price refers to the electricity price for electric vehicle charging stations. The dynamic electricity pricing strategy includes:

[0092] This invention divides the transformer's operating range into four zones—valley, normal, peak, and extreme—based on its load factor. Each zone corresponds to a different electricity price level, and the electricity price for each time period is dynamically updated according to the transformer's load level. (EV)k When charging in parallel with the grid, calculate the EV k The average load during each period of charging is calculated, and then the EV is determined using the following two formulas. k The electricity price at different times during charging is used as a basis to optimize the vehicle charging process, thereby achieving the goal of supporting EVs in the area. k Optimization of charging load.

[0093]

[0094]

[0095] P represents the average power of the transformer between two integer points in the t-th time period; M For transformer capacity; β t e represents the load factor. t This is the electricity price for the t-th time period.

[0096] A charging optimization model is established with the charging power of electric vehicles per time period as the control variable, and the objective functions being to minimize user charging costs and the charging load fluctuation rate of the electric vehicle access area. This model includes:

[0097] (1) Objective function

[0098] To protect user interests and incentivize more users to participate in orderly charging, the minimum charging cost for each electric vehicle is taken as one of the objective functions when formulating an orderly charging plan. Let the charging power vector of the electric vehicle be P. k Charging time vector H k and charging price vector E k It is expressed as follows:

[0099]

[0100]

[0101]

[0102] The user's charging cost can be represented as follows:

[0103]

[0104] To mitigate the impact of charging load on the power grid and optimize power grid load indicators, this invention uses minimizing the fluctuation rate of distribution area load as another objective function, specifically expressed as follows:

[0105]

[0106]

[0107]

[0108] Where: P s The standard deviation of the load in the transformer area; P represents the average load of the transformer area. T,r P represents the predicted total load at time r when electric vehicles connect to the transformer substation; n is the number of elements in set A. T,r The specific formula is as follows:

[0109]

[0110] Where: P B,r The base load of the transformer area at time r during the charging of electric vehicles; The planned charging load of the first k-1 vehicles at time r during the charging of electric vehicles; The planned charging load for the electric vehicle at time r. By P k It was transformed.

[0111] Standardizing multiple objectives and introducing weights to transform them into a single objective can further simplify the optimization process.

[0112]

[0113]

[0114] minF=λ1F′1+λ2F′2

[0115] Where F′1 and F′2 are the standardized versions of F1 and F2, respectively; λ1 and λ2 are weighting coefficients and λ1+λ2=1.

[0116] (2) Constraints

[0117] ① Charging power constraint

[0118] P min ≤p t ≤P max t = 1, 2, ..., d

[0119] Where P min and P max This indicates the minimum and maximum allowed charging power.

[0120] ② User's expected total charging amount constraint

[0121]

[0122] SOC eSOC0 and SOC0 represent the user's desired state of charge and the vehicle's initial state of charge, respectively; Q represents the battery capacity of the electric vehicle.

[0123] ③ Transformer capacity constraints

[0124] PT ,r ≤αP M r∈A

[0125] α represents the maximum capacity margin multiple that the transformer can carry, which is usually taken as 0.7 to 0.8.

[0126] ④ Charging continuity constraints and charging stability constraints

[0127] Frequent starts and stops and constant power changes during electric vehicle charging can affect the lifespan of the battery and charging station. Therefore, the charging process of electric vehicles is constrained to be continuous and the charging power is changed only once at each hour.

[0128] The adaptive artificial bee colony algorithm in this embodiment is formed by introducing an adaptive normal decay coefficient on the basis of the traditional artificial bee colony algorithm.

[0129] The constructed charging optimization model is solved using an adaptive artificial bee colony algorithm, including:

[0130] Obtain the initial honey source, and iterate through the following steps until the preset maximum number of iterations is reached. Output the honey source after the iterations are completed as the optimal charging scheme:

[0131] The hired bees corresponding to the nectar source search for nectar sources within the territory of the nectar source, calculate the amount of nectar in the nectar source, and update the nectar source using a greedy algorithm based on the amount of nectar.

[0132] The hired bee shares the nectar source and the amount of nectar in the nectar source with the follower bee. Each follower bee selects a nectar source by roulette based on the amount of nectar and searches for other nectar sources within that nectar source's territory. The nectar source is then updated using a greedy algorithm based on the amount of nectar.

[0133] If the preset conditions for scout bees to be dispatched are met, the nectar source will be updated by the scout bees;

[0134] The nectar source represents a feasible solution, and the amount of nectar represents the fitness of the feasible solution.

[0135] Artificial bee colony algorithms divide bee colonies into hired bees X Em Follow Bee X OL And the reconnaissance bee X Sc There is only one hired bee for each nectar source.

[0136] Hired bees use the following formula to search for nectar sources within their territory:

[0137]

[0138] in, X is the i-th nectar source found by the hired bees. i It is the i-th nectar source, X j It is the j-th nectar source, c1 and c2 are adaptive normal decay coefficients, and c1 + c2 = 1. X is a random number in [0, 1]. best This is the current optimal solution;

[0139] x1 is calculated using the following formula:

[0140]

[0141] Where iter is the current iteration number, G is the preset maximum iteration number, and θ is 0.4433.

[0142] The probability of winning a roulette wheel bet is calculated using the following formula, and then a roulette wheel bet is used to select a honey source:

[0143]

[0144] Where, p j It is the probability that the j-th honey source is selected, fit j SN is the fitness of the j-th nectar source, and SN is the number of nectar sources;

[0145] Follower bees search for nectar sources using the following formula:

[0146]

[0147] The preset condition for scout bees to be dispatched is: the amount of nectar in the nectar source stagnates for a preset time.

[0148] The scout bees search for nectar sources using the following formula:

[0149] X Sc =X min +rand*(X max -X min )

[0150] Among them, X Sc It was a scout bee that found the nectar source, X min X is the minimum value of rand. max It is the maximum value of rand, and rand is a random number in [0, 1].

[0151] S3. Control the charging process of the electric vehicle according to the optimal charging scheme.

[0152] Since the start time of EV charging in residential areas is highly correlated with users' travel patterns, mainly concentrated in the evening, for ease of calculation, the 24-hour simulation starts at 12:00 noon on the current day and ends at 12:00 noon the following day. The random simulation uses the method provided in this embodiment to simulate the overall charging situation of 100 electric vehicles at charging stations within a day; the results are attached. Figure 3 and attached Figure 4 As shown, while ensuring user charging convenience, the charging cost for users is reduced, and the load index of the distribution area is significantly improved, achieving a win-win situation for both the power grid and users. A random simulation using the method provided in this embodiment shows the overall situation of 50 / 100 / 150 electric vehicles charging at the charging station within a day, as follows: Figure 5 As shown, the charging cost of electric vehicles is significantly lower with dynamic electricity pricing plus orderly charging than with time-of-use pricing plus disordered charging.

[0153] Example 2

[0154] This invention also provides a charging control device for an AC charging station in a residential area, comprising:

[0155] The information preprocessing module is configured to: acquire user charging information and perform preprocessing;

[0156] The module is configured to: solve the constructed charging optimization model based on the preprocessed user charging information using an adaptive artificial bee colony algorithm, and output the optimal charging scheme for electric vehicles;

[0157] The charging process of the electric vehicle is controlled according to the optimal charging scheme.

[0158] Example 3

[0159] This invention also provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the charging control method for AC charging stations in residential areas as provided in Embodiment 1.

[0160] Obtain user charging information and preprocess it;

[0161] Based on the preprocessed user charging information, the constructed charging optimization model is solved by the adaptive artificial bee colony algorithm, and the optimal charging scheme for electric vehicles is output.

[0162] The charging process of the electric vehicle is controlled according to the optimal charging scheme.

[0163] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0164] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0165] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0166] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0167] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A charging control method for an AC charging station in a residential area, characterized in that, include: Obtain and preprocess user charging information; Based on the preprocessed user charging information, the constructed charging optimization model is solved by the adaptive artificial bee colony algorithm, and the optimal charging scheme for electric vehicles is output. The charging process of the electric vehicle is controlled according to the optimal charging scheme. The user charging information includes the start charging time, expected end charging time, and expected charging amount. The preprocessing includes: The following formulas convert the start charging time and the expected end charging time into the start charging time and end charging time, respectively: ； in, It is the first The charging start time for an electric vehicle. It is the first The charging end time of an electric vehicle. floor To round down, ceil To round up, It is the first The time it takes for an electric vehicle to start charging, converted to minutes. It is the first The expected end-of-charge time for a vehicle, converted to minutes; The overall objective function of the charging optimization model is: ； in, It is the overall objective function. and It is a weighting coefficient, and , This is the standardized result of the objective function for charging costs. It is the standardized result of the objective function for the load fluctuation rate of the distribution area; ； ； in, It's the charging fee. It is the load fluctuation rate of the distribution area. This indicates taking the minimum value. This indicates taking the maximum value; The expression for the objective function of charging costs is: ； in, It is the objective function for charging costs. yes Time period The charging power vector of an electric vehicle. yes Time period The charging time vector of an electric vehicle yes Time period The charging electricity price vector for electric vehicles. This represents the number of charging periods to be optimized. ； The expression for the objective function of the load fluctuation rate of the transformer area is: ； in, It is the objective function for the load fluctuation rate of the distribution area. It is the standard deviation of the load in the transformer area. This is the average load of the transformer substation. The constraints of the charging optimization model include: ； ； ； It also includes constraining the vehicle's charging process to be continuous and to change the charging power only once at every hour. in, yes Charging power during the period This is the minimum charging power. That is the maximum charging power. yes Charging time during the period and These represent the user's desired state of charge and the vehicle's initial state of charge, respectively. Indicates the vehicle's battery capacity. When a vehicle enters the transformer area r The predicted total load at each time point. This indicates the maximum capacity margin multiple that the transformer can carry. It refers to the transformer capacity. It is the collection of all moments included in the vehicle charging process; The charging optimization model constructed by solving the adaptive artificial bee colony algorithm includes: Obtain the initial honey source, and iterate through the following steps until the preset maximum number of iterations is reached. Output the honey source after the iterations are completed as the optimal charging scheme: The hired bees corresponding to the nectar source search for nectar sources within the territory of the nectar source, calculate the amount of nectar in the nectar source, and update the nectar source using a greedy algorithm based on the amount of nectar. The hired bee shares the nectar source and the amount of nectar in the nectar source with the follower bee. Each follower bee selects a nectar source by roulette based on the amount of nectar and searches for other nectar sources within that nectar source's territory. The nectar source is then updated using a greedy algorithm based on the amount of nectar. If the preset conditions for scout bees to be dispatched are met, scout bees will search for nectar sources. The nectar source represents a feasible solution, and the amount of nectar represents the fitness of the feasible solution; The search formula for hired bees to search for nectar sources within the territory of a nectar source is as follows: ； in, It is the first search after the hired bee A honey source It is the first A honey source It is the first A honey source and It is an adaptive normal decay coefficient, and , It is a random number in [0, 1]. This is the current optimal solution; It is calculated using the following formula: ； in, It is the current iteration number. This is the preset maximum number of iterations. It is 0.4433.

2. The charging control method for AC charging stations in residential areas according to claim 1, characterized in that, Before solving the constructed charging optimization model, the following method is used to determine whether the charging control conditions are met: The average charging power of the vehicle during the charging period is calculated based on the start charging time, the expected end charging time, and the expected charging amount. If the average charging power is less than a preset threshold, the charging control conditions are met, and the operation of the constructed charging optimization model is performed.

3. The charging control method for AC charging stations in residential areas according to claim 1, characterized in that, The process involves selecting a nectar source using a roulette wheel method, searching for other nectar sources within that source's region, and updating the nectar sources using a greedy algorithm based on nectar availability. This includes: The probability of winning a roulette wheel bet is calculated using the following formula, and then a roulette wheel bet is used to select a honey source: ； in, It is the first The probability of a honey source being selected. It is the first The adaptability of each nectar source It refers to the quantity of nectar sources; Follower bees search for nectar sources using the following formula: 。 4. The charging control method for AC charging stations in residential areas according to claim 1, characterized in that, The preset condition for the scout bees to be dispatched is that the amount of nectar in the nectar source stagnates for a preset time. The scout bees search for nectar sources using the following formula: ； in, X Sc The nectar source was found by scout bees. X min yes rand The minimum value, X max yes rand The maximum value, rand It is a random number in [0, 1].

5. A residential AC charging station charging control device based on the charging control method for residential AC charging stations according to any one of claims 1-4, characterized in that, include: The information preprocessing module is configured to: acquire user charging information and perform preprocessing; The optimal charging scheme generation module is configured to: based on the preprocessed user charging information, solve the constructed charging optimization model through an adaptive artificial bee colony algorithm, and output the optimal charging scheme for electric vehicles; The charging control module is configured to control the charging process of the electric vehicle according to the optimal charging scheme.

6. 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 charging control method for AC charging stations in residential areas as described in any one of claims 1-4.

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