An electric vehicle orderly charging method and device, electronic equipment and storage medium

Abstract

The application provides an electric vehicle orderly charging method and device, electronic equipment and storage medium. First, the electric vehicle user's expected charging time is obtained, and the electric vehicle charging plan with the lowest charging cost is generated according to the expected charging time and the charging time-of-use electricity price data. Then, whether the electric vehicle user executes the electric vehicle charging plan is determined according to the current transformer total capacity and the user charging load, and the peak and valley time periods of the community power consumption are predicted by analyzing the historical daily load data of the community resident users and the community electric vehicle users, so that the load scheduling is performed and the remaining capacity of the community transformer is ensured. The application is based on the historical load data of the users, analyzes from multiple angles, reduces the average charging cost of the electric vehicle users, increases the load wave trough of the community, reduces the wave peak, reduces the peak-valley difference, improves the charging experience of the electric vehicle users, and ensures the stable operation of the community power grid system.

Description

Technical Field

[0001] This application relates to the field of power dispatching technology, and in particular to an orderly charging method, apparatus, electronic device, and storage medium for electric vehicles. Background Technology

[0002] As an infrastructure for supplementing the energy supply of electric vehicles (EVs), the use of charging stations in residential communities is facing increasingly prominent contradictions. On the one hand, the contradiction between the charging load of charging stations and the load of residents in the community is gradually increasing. When a large number of cars are charging at the same time, it can easily lead to a sharp increase in the load of the community, which in severe cases can cause transformer overload and thus pose a serious threat to the safe operation of the community power grid. On the other hand, the contradiction between the supporting needs and the current situation of charging facilities in older communities is constantly widening. The public transformers in older communities have disadvantages such as small capacity and high load rate, but due to site limitations, transformer renovation is not possible, making it difficult to deploy charging stations. Summary of the Invention

[0003] This invention provides an orderly charging method, device, electronic device, and storage medium for electric vehicles. By planning the charging of electric vehicle users according to the transformer capacity, it solves the problem of peak-on-peak electricity load in residential areas while ensuring the charging experience of electric vehicle users.

[0004] In a first aspect, the present invention provides a method for orderly charging of electric vehicles, comprising the following steps:

[0005] Obtain the estimated charging time for electric vehicle users;

[0006] Based on the estimated charging time and time-of-use electricity price data, an electric vehicle charging plan is generated, which indicates the lowest charging cost when the charging behavior is completed.

[0007] The system obtains the current total transformer capacity and user charging load. If the user charging load is less than 60% of the total transformer capacity, the electric vehicle charging plan is executed. If the user charging load is between 60% and 80% of the total transformer capacity, the current electric vehicle charging power is reduced and charging is performed within the time period of the electric vehicle charging plan. If the user charging load is greater than 80% of the total transformer capacity, the electric vehicle charging plan is stopped.

[0008] Furthermore, before obtaining the current total transformer capacity, the following steps are also included:

[0009] Based on the historical daily load data of the community users, the peak and valley states of the community users load at different times of the day are predicted using the Elman neural network model.

[0010] Based on historical load data of electric vehicle users in the community, the peak and valley load status of electric vehicle users at different times of the day is predicted using the Elman neural network model.

[0011] Load scheduling is performed based on the peak and valley load status of the community users and the electric vehicle users.

[0012] Furthermore, before performing load scheduling, the method includes the following steps:

[0013] Based on the historical load data of electric vehicle users in the community, the charging behavior characteristics of electric vehicle users in the community are obtained through K-means classification.

[0014] The electric vehicle users in the community are classified according to their charging behavior characteristics.

[0015] Load scheduling is performed based on the peak and valley load status of the community users and the classification of the community's electric vehicle users.

[0016] Furthermore, before generating an electric vehicle charging plan, the following methodological steps are also included:

[0017] The remaining available capacity of the community transformer is obtained by subtracting the real-time power load of the community and the reserved power buffer load of the community from the total capacity of the community transformer.

[0018] The range of charging pile output power is calculated based on the remaining available capacity of the community transformer and the number of charging piles currently in charging status.

[0019] Based on the output power range, the charging power in the electric vehicle charging plan is determined.

[0020] The present invention also provides an orderly charging device for electric vehicles, comprising:

[0021] The charging time acquisition module is used to acquire the estimated charging time for electric vehicle users;

[0022] The charging plan generation module is used to generate an electric vehicle charging plan based on the estimated charging time and the time-of-use electricity price data. The electric vehicle charging plan indicates the lowest charging cost when the charging behavior is completed.

[0023] The charging planning execution module is used to obtain the current total transformer capacity and user charging load. If the user charging load is less than 60% of the total transformer capacity, the electric vehicle charging plan is executed. If the user charging load is between 60% and 80% of the total transformer capacity, the current electric vehicle charging power is reduced and charging is performed within the time period of the electric vehicle charging plan. If the user charging load is greater than 80% of the total transformer capacity, the electric vehicle charging plan is stopped.

[0024] Furthermore, it also includes:

[0025] The user load peak and valley prediction module is used to predict the peak and valley status of user load in the community at different times of the day based on the historical daily load data of users in the community and through the Elman neural network model.

[0026] The electric vehicle user load peak and valley prediction module is used to predict the peak and valley status of electric vehicle users at different times of the day based on the historical load data of electric vehicle users in the community and through the Elman neural network model.

[0027] The first load scheduling module is used to perform load scheduling based on the peak and valley load status of the community users and the peak and valley load status of the electric vehicle users.

[0028] Furthermore, it also includes:

[0029] The behavior feature acquisition module is used to acquire the charging behavior features of electric vehicle users in the community based on the historical load data of electric vehicle users in the community and by K-means classification.

[0030] The community user classification module is used to classify electric vehicle users in the community based on their charging behavior characteristics.

[0031] The second load scheduling module is used to perform load scheduling based on the peak and valley load status of the users in the community and the classification of the electric vehicle users in the community.

[0032] Furthermore, it also includes:

[0033] The transformer available capacity acquisition module is used to acquire the remaining available capacity of the community transformer. The remaining available capacity of the community transformer is obtained by subtracting the real-time power load of the community and the reserved power buffer load of the community from the total capacity of the community transformer.

[0034] The charging pile power calculation module is used to calculate the range of the charging pile output power based on the remaining available capacity of the community transformer and the number of charging piles currently in charging status.

[0035] The charging pile power determination module is used to determine the charging power in the electric vehicle charging plan based on the output power range.

[0036] Thirdly, the present invention provides an electronic device, comprising:

[0037] At least one memory and at least one processor;

[0038] The memory is used to store one or more programs;

[0039] When the one or more programs are executed by the at least one processor, the at least one processor implements the steps of an orderly charging method for an electric vehicle as described in the first aspect.

[0040] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of an orderly charging method for an electric vehicle as described in the first aspect.

[0041] This invention obtains the estimated charging time of electric vehicle (EV) users and generates the EV charging plan with the lowest charging cost based on the estimated charging time and time-of-use electricity price data. It then determines whether EV users should execute the EV charging plan based on the current total transformer capacity and user charging load. Furthermore, by analyzing the historical daily load data of community users and their EV users, it predicts peak and off-peak electricity consumption periods for load scheduling, ensuring the remaining capacity of the community transformer. It also categorizes EV users based on their historical daily load data and intervenes in the EV charging plan according to the different charging behavior characteristics of each user type. Simultaneously, when EV charging is executed, the output power of the charging pile is calculated based on the real-time remaining transformer capacity. This invention, based on historical user data and analyzed from multiple perspectives, reduces the average charging cost for EV users, increases load troughs, reduces peak loads, and decreases the peak-to-valley difference, thus improving the charging experience for EV users and ensuring the stable operation of the community power grid system.

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating the steps of an orderly charging method for an electric vehicle in an exemplary embodiment.

[0044] Figure 2 Here is a logic block diagram of an orderly charging method for an electric vehicle provided in an exemplary embodiment;

[0045] Figure 3 Elman neural network topology diagram for an orderly charging method for electric vehicles provided in an exemplary embodiment;

[0046] Figure 4 A graph showing the predicted average daily load change of residents in a residential community for an orderly charging method for electric vehicles provided in an exemplary embodiment;

[0047] Figure 5 A graph showing the predicted average daily load change of electric vehicle users in a residential community for an orderly charging method for electric vehicles provided in an exemplary embodiment;

[0048] Figure 6 This is a schematic diagram of the K-means algorithm flow for an orderly charging method for electric vehicles provided in an exemplary embodiment;

[0049] Figure 7 This is a cluster center diagram of electric vehicle users in a residential community, provided in an exemplary embodiment of an orderly charging method for electric vehicles.

[0050] Figure 8 A line graph comparing an ordered charging method for electric vehicles with a conventional disordered charging method in an exemplary embodiment;

[0051] Figure 9 This is a schematic diagram of a module for an electric vehicle orderly charging device provided in an exemplary embodiment;

[0052] Figure 10 This is a schematic diagram of an electronic device provided in one exemplary embodiment. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0054] It should be understood that the described embodiments are merely some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of the embodiments of this application.

[0055] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the embodiments of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0056] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0057] Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0058] To address the problems raised in the background, current research has focused on orderly EV charging from various perspectives, including user-side charging costs, distribution network operation, and V2G technology, depending on the research focus. However, existing studies only roughly extract information from load data without further data mining. They focus solely on how EV users can engage in orderly electricity consumption, neglecting to consider EV user charging habits. This results in incomplete consideration of charging influencing factors, overly simplistic optimization objectives, and an unfriendly charging experience.

[0059] Based on the above concepts and background technology, this application provides a method for orderly charging of electric vehicles by data mining of data on the electricity load of residents and charging piles in a community, and by considering multiple factors. Figure 1 As shown, the specific methods and steps include the following:

[0060] S201: Obtain the estimated charging time for electric vehicle users.

[0061] The estimated charging time is obtained from the charging pile in a pre-charging state. Specifically, it is calculated by obtaining the user's required charging amount and the current charging power of the charging pile. The charging power of the charging pile can be planned in real time based on the current community load capacity or predictions based on the user's historical load data. In some other examples, electric vehicle users can also set a permitted charging time period, and the charging pile will plan charging within the user-set time period.

[0062] S202: Based on the estimated charging time and time-of-use electricity price data, generate an electric vehicle charging plan, wherein the electric vehicle charging plan indicates that the charging cost is lowest when the charging behavior is completed.

[0063] Peak-valley electricity pricing, also known as time-of-use pricing, is an electricity pricing system that calculates electricity costs separately for peak and off-peak electricity consumption. Peak electricity consumption generally refers to electricity consumption when there are more concentrated electricity users and the power supply is tight, such as during the daytime, and the charges are higher. Off-peak electricity consumption generally refers to electricity consumption when there are fewer electricity users and the power supply is more abundant, such as at night, and the charges are lower.

[0064] In a specific application scenario, as shown in the table below, which presents time-of-use electricity price data for electric vehicle users, it can be seen that the lowest cost for electric vehicle users to charge their vehicles is during the period from 22:00 to 08:00.

[0065] Time period Time-of-use electricity price for charging (RMB / kW·h) 08:00-22:00 0.588 22:00-08:00 0.308

[0066] In one specific embodiment, the charging plan should prioritize users charging during periods with lower time-of-use (TOU) rates. In a preferred embodiment, if the low TOU range covers all user-defined charging periods, charging should be completed within those periods; if the low TOU range only partially covers user-defined charging periods, charging should be completed, as much as possible, within the period where user-defined charging periods overlap with low TOU periods, based on actual conditions.

[0067] S203: Obtain the current total transformer capacity and user charging load. If the user charging load is less than 60% of the total transformer capacity, execute the electric vehicle charging plan. If the user charging load is between 60% and 80% of the total transformer capacity, reduce the current electric vehicle charging power and charge within the time period of the electric vehicle charging plan. If the user charging load is greater than 80% of the total transformer capacity, stop executing the electric vehicle charging plan.

[0068] In one specific embodiment, if the charging power of an electric vehicle is reduced or charging needs to be suspended, the user should be notified via SMS, WeChat mini-program notification, or other means.

[0069] In a specific embodiment, such as Figure 2 As shown, users can selectively join any of the charging plans proposed by the orderly charging method for electric vehicles described in this application embodiment. Electric vehicle users who join the charging plan will execute the charging plan according to the method described in step S203; while electric vehicle users who do not join the charging plan will be judged based on whether the total load of the community's electricity consumption exceeds 60% of the transformer capacity. If the total load does not exceed 60% of the capacity, the user can use the charging plan immediately; otherwise, they must comply with the charging plan. The distribution network load data is updated in real time. The charging plan ends when charging is complete; otherwise, the charging plan should continue to cycle.

[0070] In a preferred embodiment, to ensure that the remaining total capacity of the transformer is sufficient, the following steps are also included:

[0071] Based on the historical daily load data of the community users, the peak and valley states of the community users load at different times of the day are predicted using the Elman neural network model.

[0072] Based on historical load data of electric vehicle users in the community, the peak and valley load status of electric vehicle users at different times of the day is predicted using the Elman neural network model.

[0073] Load scheduling is performed based on the peak and valley load status of the community users and the electric vehicle users.

[0074] Specifically, the basic structure of the Elman neural network constructed in the embodiments of this application is as follows: Figure 3 As shown, an Elman neural network consists of four layers: an input layer, hidden layers, a continuation layer, and an output layer. The nonlinear state-space expression of an Elman neural network is:

[0075] y(k)=g(w 3 x(k))

[0076] x(k)=f(w 1 x c (k)+w 2 (u(k-1)))

[0077] x c (k)=x(k-1)

[0078] Where y is the m-dimensional output node vector, x is the n-dimensional intermediate layer node unit vector, u is the r-dimensional input vector, and x c Let w be an n-dimensional feedback state vector. 3 w represents the connection weights from the intermediate layer to the output layer. 2 w represents the connection weights from the input layer to the intermediate layer. 1denoted as the connection weights from the receiving layer to the intermediate layer, g(*) is the transfer function of the output neuron, is the linear combination of the outputs of the intermediate layer, and f(*) is the transfer function of the intermediate neuron.

[0079] Simultaneously, the BP algorithm is introduced for weight correction, and the sum of squared errors function is used as the learning index function:

[0080]

[0081] In a specific application scenario, this embodiment of the application predicts the hourly load of users in a cell, using real cell data from 61 days in November and December as the sample data. The data from the first 60 days is used as the network training sample, with the load for every 3 days used as the input vector and the load for the 4th day used as the target vector, resulting in 15 sets of training samples.

[0082] The initial weights and thresholds of the Elman network are optimized using a genetic algorithm, and a fitness function is introduced:

[0083]

[0084] Among them, y p For the predicted value, y t The value represents the true value; the smaller the F-value, the higher the predictive power.

[0085] Calculate and obtain the predicted curve of average daily load change for residential users (t-SRT_M) as follows: Figure 4 As shown, the predicted curve indicates that the residential user load reaches its lowest point around 3 PM and peaks around 9 PM. t-SRT_M satisfies the following relationship:

[0086] y SRT_M = -0.01112x 5 +0.6364x 4 -13.22x 3 +120.8x 2 -399.6x+1007

[0087] Among them, absolute error and F SRT_M =0.03459, therefore it can be judged that the accuracy of the average daily load of residential users obtained through the embodiments of this application is relatively high.

[0088] Similarly, the calculated predicted curve for the average daily load change of electric vehicle users (t-SCP_M) is as follows: Figure 5 As shown, the predicted curve indicates that the residential user load peaks around 1:00 AM and troughs around 10:00 AM. t-SCP_M satisfies:

[0089] y SCP_M =0.002736x5 -0.157x 4 +3.219x 3 -26.64x 2 +54.32x+204.8

[0090] Among them, absolute error and F SCP_M =0.04721, therefore it can be judged that the accuracy of the average daily load of electric vehicle users obtained through the embodiments of this application is relatively high.

[0091] By predicting the load of residential users and electric vehicle users in the above embodiments, load scheduling can be carried out in advance before the arrival of load peaks and valleys, so as to ensure sufficient remaining capacity of transformers, reduce the operating pressure of community transformers, and ensure the safe operation of the community power grid system.

[0092] In a preferred embodiment, the orderly charging method for electric vehicles described in this application can also be promoted by classifying electric vehicle users, specifically including the following steps:

[0093] Based on the historical load data of electric vehicle users in the community, the charging behavior characteristics of electric vehicle users in the community are obtained through K-means classification.

[0094] The electric vehicle users in the community are classified according to their charging behavior characteristics.

[0095] Load scheduling is performed based on the peak and valley load status of the community users and the classification of the community's electric vehicle users.

[0096] Specifically, the general flow of the K-means algorithm described in the embodiments of this application is as follows: Figure 6 As shown, the correct selection of the k value in the above process is crucial to the final clustering result. This embodiment uses the silhouette coefficient method to select the k value. The silhouette coefficient (SC) is an indicator for evaluating clustering effectiveness, with a value range of [-1, 1]. The closer the value is to 1, the better the clustering effect. Silhouette coefficient s i satisfy:

[0097] s i =(b i -a i ) / max{a i ,b i}

[0098] Among them, a i Let be the average distance between samples i and other samples within the same cluster. If there is only one sample i in the cluster, then let s be the average distance between samples i and other samples within the cluster. i =0; b i b is the minimum average distance between i and other clusters. i =min{bi1 ,b i2 ,…,b ik}. All s i The average value is the final metric for evaluating the clustering results.

[0099] The K-means algorithm flow is described in conjunction with the application scenarios of the embodiments of this application, namely:

[0100] First, data preprocessing was performed to remove the load from ordinary residential users, filter users of charging piles in the community (electric vehicle users), and exclude users of charging piles that are rarely used to avoid affecting the final analysis results. K-means classification was then applied to all charging pile users in the community, and each user was analyzed on a 24-hour time axis.

[0101] Then, the k value was selected. When classifying electric vehicle users, considering the actual situation, k was set to 3-9, and their SC values ​​were calculated respectively. The results are shown in the table below:

[0102] Profile coefficient values ​​under different k values

[0103]

[0104] The comparison of SC values ​​for each k value in the table above shows that SC reaches its maximum value when k is 5. That is, the best clustering effect is achieved when k = 5.

[0105] Based on the above calculation and comparison results, the total number of EV users is divided into 5 user categories (I, II, III, IV, V) for clustering, and the cluster centers are obtained as follows: Figure 7 As shown.

[0106] Based on the charging behavior characteristics of various types of electric vehicle users, the following load control principles can be determined: For Class I users with relatively even charging times and Class III users who charge less frequently, there will be little impact on load peaks and valleys, and normal charging is permitted. For Class II and IV users, attention should be paid to whether their peak charging time coincides with the peak load time of the community. For Class II users who tend to charge in the early morning, since the community load is at its lowest during this period, no intervention is necessary. For Class IV users, whose charging period coincides with the rising load of the community, control should be implemented based on the actual load situation. For Class V users, the first charging peak occurs at 10:00 AM, when the community load is at its lowest, so no intervention is necessary. The second charging peak occurs at 4:00 PM and is even higher, so control should be implemented based on the transformer capacity and the total load of the community. The aforementioned intervention refers to suggesting that electric vehicle users participate in the orderly charging method planning for electric vehicles provided in this application embodiment. If an electric vehicle user confirms that they will not participate in the charging planning, community load scheduling should be carried out in advance based on the charging behavior characteristics of the electric vehicle user.

[0107] In a preferred embodiment, the charging power of the charging pile can also be planned based on the remaining available capacity of the transformer, specifically including the following steps:

[0108] The remaining available capacity of the community transformer is obtained by subtracting the real-time power load of the community and the reserved power buffer load of the community from the total capacity of the community transformer.

[0109] The range of charging pile output power is calculated based on the remaining available capacity of the community transformer and the number of charging piles currently in charging status.

[0110] Based on the output power range, the charging power in the electric vehicle charging plan is determined.

[0111] Specifically, based on the available charging power P of the charging station a The user's charging power demand P rn and the remaining available capacity P of the transformer r This determines the operating status of the charging station, controls its charging power, and enables dynamic adjustment for charging station users. The specific formula is as follows:

[0112] Remaining available capacity P in the community r satisfy:

[0113] P r =P ∑ -P s -P y

[0114] Among them, P ∑ P represents the total capacity of the transformer in the community. s P represents the real-time electricity load of the residential area. y Reserve a power buffer load for the community.

[0115] Total reserved starting power P of charging piles in standby mode d∑ satisfy:

[0116] P d∑ =m1×P d

[0117] Where m1 represents the number of charging stations in standby mode, and P d Reserved starting power for standby charging piles.

[0118] The adjustable power P of the charging station while it is charging up satisfy:

[0119]

[0120] Where m2 represents the number of charging stations in the charging state, and P cnP is the rated power of the charging pile. cr This represents the actual output power of the charging station.

[0121] The available charging power P is calculated using the above formula. r With reserved starting power P d∑ The difference in power that the charging station can adjust upwards is:

[0122]

[0123] The overload warning trigger condition is met as follows:

[0124] P r <0, K=0

[0125] Where K represents the number of charging stations with unrestricted charging power.

[0126] Overload alarm reset conditions (increase hysteresis value):

[0127] P ol =M×P d +(M×P cn )×10%

[0128] Where M represents the total number of charging stations.

[0129] Therefore, when the charging pile switches from standby to charging, the adjustable power p of the charging pile is controlled according to the remaining available capacity of the transformer. up This ensures that overload warnings are not triggered. Based on this premise, the charging power in the described orderly charging method for electric vehicles is planned according to the charging needs of electric vehicle users.

[0130] In a specific test environment, such as Figure 8 As shown, Figure 8 A comparison is made between the disordered charging load curve and the ordered charging method curve described in the embodiments of this application. After planning the access of electric vehicle users in the community, when the load reaches approximately P... ∑ When the load reaches 60% (at which point the average daily load is 1000 × 60% / 447 = 1342.3 W), orderly charging begins according to the described orderly charging method. Under the orderly charging method for electric vehicles described in this application, the average charging cost for electric vehicle users is reduced, the load trough is increased, the load peak is reduced, and the peak-to-valley difference is reduced, which not only improves the charging experience for electric vehicle users but also ensures the stable operation of the community power grid system.

[0131] This application also provides an electric vehicle orderly charging device 300, such as... Figure 9 As shown, it includes:

[0132] The charging time acquisition module is used to acquire the estimated charging time for electric vehicle users;

[0133] The charging plan generation module is used to generate an electric vehicle charging plan based on the estimated charging time and the time-of-use electricity price data. The electric vehicle charging plan indicates the lowest charging cost when the charging behavior is completed.

[0134] The charging planning execution module is used to obtain the current total transformer capacity and user charging load. If the user charging load is less than 60% of the total transformer capacity, the electric vehicle charging plan is executed. If the user charging load is between 60% and 80% of the total transformer capacity, the current electric vehicle charging power is reduced and charging is performed within the time period of the electric vehicle charging plan. If the user charging load is greater than 80% of the total transformer capacity, the electric vehicle charging plan is stopped.

[0135] In one exemplary example, the electric vehicle orderly charging device 300 further includes:

[0136] The user load peak and valley prediction module is used to predict the peak and valley status of user load in the community at different times of the day based on the historical daily load data of users in the community and through the Elman neural network model.

[0137] The electric vehicle user load peak and valley prediction module is used to predict the peak and valley status of electric vehicle users at different times of the day based on the historical load data of electric vehicle users in the community and through the Elman neural network model.

[0138] The first load scheduling module is used to perform load scheduling based on the peak and valley load status of the community users and the peak and valley load status of the electric vehicle users.

[0139] In one exemplary example, the electric vehicle orderly charging device 300 further includes:

[0140] The behavior feature acquisition module is used to acquire the charging behavior features of electric vehicle users in the community based on the historical load data of electric vehicle users in the community and by K-means classification.

[0141] The community user classification module is used to classify electric vehicle users in the community based on their charging behavior characteristics.

[0142] The second load scheduling module is used to perform load scheduling based on the peak and valley load status of the users in the community and the classification of the electric vehicle users in the community.

[0143] In one exemplary example, the electric vehicle orderly charging device 300 further includes:

[0144] The transformer available capacity acquisition module is used to acquire the remaining available capacity of the community transformer. The remaining available capacity of the community transformer is obtained by subtracting the real-time power load of the community and the reserved power buffer load of the community from the total capacity of the community transformer.

[0145] The charging pile power calculation module is used to calculate the range of the charging pile output power based on the remaining available capacity of the community transformer and the number of charging piles currently in charging status.

[0146] The charging pile power determination module is used to determine the charging power in the electric vehicle charging plan based on the output power range.

[0147] It should be noted that the electric vehicle orderly charging device and the electric vehicle orderly charging method provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0148] like Figure 10 As shown, Figure 10 This is a structural block diagram of an electronic device according to an exemplary embodiment of this application.

[0149] The electronic device includes a processor 910 and a memory 920. The main control chip may contain one or more processors 910. Figure 10 Taking a processor 910 as an example, the main control chip can contain one or more memory modules 920. Figure 10 Take a memory chip 920 as an example.

[0150] The memory 920, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the electric vehicle orderly charging method program described in any embodiment of this application, and the program instructions / modules corresponding to the electric vehicle orderly charging method described in any embodiment of this application. The memory 920 may mainly include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created according to the use of the device, etc. Furthermore, the memory 920 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 920 may further include memory remotely located relative to the processor 910, and these remote memories can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0151] The processor 910 executes various functional applications and data processing of the device by running software programs, instructions and modules stored in the memory 920, thereby implementing an orderly charging method for electric vehicles as described in any of the above embodiments.

[0152] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements an orderly charging method for electric vehicles as described in any of the above embodiments.

[0153] This invention can take the form of a computer program product implemented on one or more storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing program code. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0154] It should be understood that the embodiments of this application are not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from their scope. The scope of the embodiments of this application is limited only by the appended claims.

[0155] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the embodiments of this application, and these all fall within the protection scope of the embodiments of this application.

Claims

1. A method for orderly charging of electric vehicles, characterized in that, The method includes the following steps: Obtain the estimated charging time for electric vehicle users; Based on the estimated charging time and time-of-use electricity price data, an electric vehicle charging plan is generated, which indicates the lowest charging cost when the charging behavior is completed. The system obtains the current total transformer capacity and user charging load. If the user charging load is less than 60% of the total transformer capacity, the electric vehicle charging plan is executed. If the user charging load is between 60% and 80% of the total transformer capacity, the current electric vehicle charging power is reduced and charging is performed within the time period of the electric vehicle charging plan. If the user charging load is greater than 80% of the total transformer capacity, the electric vehicle charging plan is stopped. Before obtaining the current total transformer capacity, the following steps are also included: Based on the historical daily load data of the community users, the peak and valley states of the community users load at different times of the day are predicted using the Elman neural network model. Based on historical load data of electric vehicle users in the community, the peak and valley load status of electric vehicle users at different times of the day is predicted using the Elman neural network model. Load scheduling is performed based on the peak and valley load status of the community users and the electric vehicle users; Before performing load scheduling, the following method steps are also included: Based on the historical load data of electric vehicle users in the community, the charging behavior characteristics of electric vehicle users in the community are obtained through K-means classification. The electric vehicle users in the community are classified according to their charging behavior characteristics. Load scheduling is performed based on the peak and valley load status of the users in the community and the classification of electric vehicle users in the community. Before generating an electric vehicle charging plan, the following steps are also included: The remaining available capacity of the community transformer is obtained by subtracting the real-time power load of the community and the reserved power buffer load of the community from the total capacity of the community transformer. The range of charging pile output power is calculated based on the remaining available capacity of the community transformer and the number of charging piles currently in charging status. Based on the output power range, the charging power in the electric vehicle charging plan is determined.

2. An orderly charging device for electric vehicles, characterized in that, include: The charging time acquisition module is used to acquire the estimated charging time for electric vehicle users; The charging plan generation module is used to generate an electric vehicle charging plan based on the estimated charging time and the time-of-use electricity price data. The electric vehicle charging plan indicates the lowest charging cost when the charging behavior is completed. The charging planning execution module is used to obtain the current total transformer capacity and user charging load. If the user charging load is less than 60% of the total transformer capacity, the electric vehicle charging plan is executed. If the user charging load is between 60% and 80% of the total transformer capacity, the current electric vehicle charging power is reduced and charging is performed within the time period of the electric vehicle charging plan. If the user charging load is greater than 80% of the total transformer capacity, the electric vehicle charging plan is stopped.

3. The electric vehicle orderly charging device according to claim 2, characterized in that, Also includes: The user load peak and valley prediction module is used to predict the peak and valley status of user load in the community at different times of the day based on the historical daily load data of users in the community and through the Elman neural network model. The electric vehicle user load peak and valley prediction module is used to predict the peak and valley status of electric vehicle users at different times of the day based on the historical load data of electric vehicle users in the community and through the Elman neural network model. The first load scheduling module is used to perform load scheduling based on the peak and valley load status of the community users and the peak and valley load status of the electric vehicle users.

4. The electric vehicle orderly charging device according to claim 3, characterized in that, Also includes: The behavior feature acquisition module is used to acquire the charging behavior features of electric vehicle users in the community based on the historical load data of electric vehicle users in the community and by K-means classification. The community user classification module is used to classify electric vehicle users in the community based on their charging behavior characteristics. The second load scheduling module is used to perform load scheduling based on the peak and valley load status of the users in the community and the classification of the electric vehicle users in the community.

5. The electric vehicle orderly charging device according to claim 4, characterized in that, Also includes: The transformer available capacity acquisition module is used to acquire the remaining available capacity of the community transformer. The remaining available capacity of the community transformer is obtained by subtracting the real-time power load of the community and the reserved power buffer load of the community from the total capacity of the community transformer. The charging pile power calculation module is used to calculate the range of charging pile output power based on the remaining available capacity of the community transformer and the number of charging piles currently in charging status. The charging pile power determination module is used to determine the charging power in the electric vehicle charging plan based on the output power range.

6. An electronic device, characterized in that, include: At least one memory and at least one processor; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the at least one processor implements the steps of the orderly charging method for an electric vehicle as described in claim 1.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the orderly charging method for an electric vehicle as described in claim 1.

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