Cell electric automobile orderly charging method and system

Abstract

The application discloses a kind of orderly charging method and system of cell electric vehicle, the method includes: by obtaining the first charging data of electric vehicle in target cell;First charging data includes waiting time, stay time, current power, charged amount;According to first charging data, ITOPSIS sorting is carried out to electric vehicle in target cell, and charging sorting is obtained;The electric vehicle of target cell is simulated charging control according to charging sorting, and the simulation charging process is evaluated by the ordered charging decision model of pre-established multi-objective optimization, and second charging data is obtained;According to second charging data, ITOPSIS sorting is carried out to electric vehicle in target cell, and the final charging sorting is obtained and charging control is carried out;The application can avoid that cell is easy to cause transformer load out of limit due to a large number of electric vehicles simultaneously peak charging, satisfy the charging demand of electric vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicle charging, in particular to a community electric vehicle orderly charging method and system. BACKGROUND

[0002] Electric vehicles reduce greenhouse gas emissions by improving energy conversion efficiency and reducing the use of fossil fuels. Compared with traditional fuel vehicles, electric vehicles also have the advantages of high energy utilization rate and comfort and quietness, and are widely considered as a key solution to reduce pollution and energy consumption.

[0003] At present, the number of electric vehicles is growing explosively, but the construction of charging facilities for electric vehicles is obviously lagging behind, such as too few charging piles and too low total power limit of supporting transformers in old communities. A large number of electric vehicles accessing residential areas in disorder will impact their operation, and the charging management of electric vehicles also faces a series of challenges. At present, the traditional electric vehicle disorderly charging method has problems such as charging pile congestion and increased power grid load fluctuation, which leads to transformer overload in communities due to a large number of electric vehicles charging at the same time during peak hours, and cannot meet the charging demand of electric vehicles. SUMMARY

[0004] In view of the problems existing in the prior art, the embodiments of the present application provide a community electric vehicle orderly charging method and system, which introduces an orderly charging strategy of electric vehicles based on two-stage ITOPSIS sorting, avoids charging congestion, improves charging efficiency and user experience, and reduces power grid load fluctuation, so that transformer overload in communities due to a large number of electric vehicles charging at the same time during peak hours can be avoided, and the charging demand of electric vehicles can be met.

[0005] In a first aspect, the embodiments of the present application provide a community electric vehicle orderly charging method, comprising:

[0006] obtaining first charging data of electric vehicles in a target community; wherein the first charging data includes waiting time, stay time, current power, and charged power;

[0007] performing ITOPSIS sorting on the electric vehicles in the target community according to the first charging data to obtain a charging order;

[0008] performing simulation charging control on the electric vehicles in the target community according to the charging order, and evaluating the simulation charging process through a pre-established multi-objective optimization orderly charging decision model to obtain second charging data; wherein the second charging data includes charging cost, charging time, charging demand satisfaction rate, and community power load fluctuation;

[0009] According to the second charging data, the electric vehicles in the target cell are sorted by ITOPSIS to obtain a final charging sequence, and the electric vehicles in the target cell are controlled according to the final charging sequence.

[0010] As an improvement of the above scheme, the sorting of the electric vehicles in the target cell according to the first charging data to obtain a charging sequence comprises:

[0011] The waiting time, the stay time, the current power and the charged power in the first charging data of each electric vehicle are taken as indexes to construct a multi-dimensional evaluation matrix.

[0012] The evaluation matrix is normalized.

[0013] According to the weight of each index, the normalized evaluation matrix is standardized to obtain a standardized weight matrix.

[0014] According to the standardized weight matrix, an absolute positive ideal solution and an absolute negative ideal solution are calculated.

[0015] According to the evaluation matrix, the absolute positive ideal solution and the absolute negative ideal solution, each electric vehicle is sorted to obtain the charging sequence.

[0016] As an improvement of the above scheme, the sorting of the electric vehicles in the target cell according to the evaluation matrix, the absolute positive ideal solution and the absolute negative ideal solution to obtain the charging sequence comprises:

[0017] The evaluation matrix, the absolute positive ideal solution and the absolute negative ideal solution are weighted and normalized.

[0018] According to the weighted and normalized absolute positive ideal solution and the evaluation matrix, the first Euclidean distance of each index of each electric vehicle to the weighted and normalized absolute positive ideal solution is calculated.

[0019] According to the weighted and normalized absolute negative ideal solution and the evaluation matrix, the second Euclidean distance of each index of each electric vehicle to the weighted and normalized absolute negative ideal solution is calculated.

[0020] According to the first Euclidean distance and the second Euclidean distance, the proximity of the index evaluation of each electric vehicle to the optimal value is calculated.

[0021] According to the proximity of the index evaluation of each electric vehicle to the optimal value, each electric vehicle is sorted to obtain the charging sequence.

[0022] As an improvement of the above scheme, the calculating absolute positive ideal solution and absolute negative ideal solution according to the normalized weight matrix comprises:

[0023] According to the normalized weight matrix, the positive ideal solution and the negative ideal solution are calculated;

[0024] The maximum value corresponding to each index is selected from the positive ideal solution to construct the absolute positive ideal solution;

[0025] The minimum value corresponding to each index is selected from the negative ideal solution to construct the absolute negative ideal solution.

[0026] As an improvement of the above scheme, the method further comprises the following process of constructing an ordered charging decision model:

[0027] All electric vehicles in the target cell are added to the ITOPSIS sorting sequence;

[0028] For each electric vehicle in the ITOPSIS sorting sequence, it is judged whether the electric vehicle is located in the target cell;

[0029] If not, the electric vehicle is removed from the ITOPSIS sorting sequence;

[0030] If yes, it is judged whether the electric vehicle is in a full charge state;

[0031] When the electric vehicle is in a full charge state, the electric vehicle is removed from the ITOPSIS sorting sequence;

[0032] When the electric vehicle is not in a full charge state, the first charging data of the electric vehicle is obtained.

[0033] As an improvement of the above scheme, the method further comprises the following process of constructing an ordered charging decision model:

[0034] According to the power load limit of the target cell, the number of available electric vehicle charging piles is determined;

[0035] According to the charging sequence and the number of available electric vehicle charging piles, the charging state of the electric vehicle is updated and charging simulation is performed until a preset simulation end condition is reached;

[0036] The simulation charging process is evaluated through the ordered charging decision model to obtain the second charging data.

[0037] As an improvement of the above scheme, the method further comprises the following process of constructing an ordered charging decision model:

[0038] establishing a first objective function aiming at minimizing charging cost;

[0039] establishing a second objective function aiming at maximizing charging demand satisfaction rate;

[0040] establishing a third objective function aiming at minimizing cell power load fluctuation;

[0041] establishing a fourth objective function aiming at minimizing charging time;

[0042] establishing a multi-objective optimization ordered charging decision model under preset constraints according to the first objective function, the second objective function, the third objective function and the fourth objective function;

[0043] The constraints include cell power load power limit constraint, electric vehicle charging state constraint, electric vehicle location constraint and electric vehicle power constraint.

[0044] As an improvement of the above scheme, the method further comprises:

[0045] updating the weights of the indexes, re-performing ITOPSIS sorting and simulation charging control on the electric vehicles in the target cell, and evaluating the simulation charging process through the ordered charging decision model to obtain new second charging data;

[0046] iterating all combinations of the weights of the indexes to obtain a plurality of second charging data under different index weight combinations.

[0047] As an improvement of the above scheme, the ITOPSIS sorting on the electric vehicles in the target cell according to the second charging data to obtain a final charging order, and the charging control on the electric vehicles in the target cell according to the final charging order, comprises:

[0048] performing weighted summation on charging cost, charging time, charging demand satisfaction rate and cell power load fluctuation in the plurality of second charging data under different index weight combinations to obtain a comprehensive score of each second charging data;

[0049] sorting each second charging data according to the comprehensive score thereof to determine the best weight of each index;

[0050] re-performing ITOPSIS sorting on the electric vehicles in the target cell according to the best weight of each index to obtain a final charging order.

[0051] In a second aspect, an embodiment of the present application provides a cell electric vehicle ordered charging system, comprising:

[0052] The first data acquisition module is configured to acquire first charging data of the electric vehicles in the target cell, wherein the first charging data comprises waiting time, staying time, current power, and charged power.

[0053] The first sorting module is configured to perform ITOPSIS sorting on the electric vehicles in the target cell according to the first charging data, and obtain a charging sequence.

[0054] The charging simulation module is configured to perform simulation charging control on the electric vehicles in the target cell according to the charging sequence, and evaluate the simulation charging process by using a pre-established multi-objective optimization ordered charging decision model, and obtain second charging data, wherein the second charging data comprises charging cost, charging time, charging demand satisfaction rate, and cell power load fluctuation.

[0055] The second sorting module is configured to perform ITOPSIS sorting on the electric vehicles in the target cell according to the second charging data, and obtain a final charging sequence, and perform charging control on the electric vehicles in the target cell according to the final charging sequence.

[0056] Compared with the prior art, the embodiment of the application provides a cell electric vehicle ordered charging method and system, which acquires first charging data of electric vehicles in a target cell, wherein the first charging data comprises waiting time, staying time, current power, and charged power, performs ITOPSIS sorting on the electric vehicles in the target cell according to the first charging data, and obtains a charging sequence, performs simulation charging control on the electric vehicles in the target cell according to the charging sequence, and evaluates the simulation charging process by using a pre-established multi-objective optimization ordered charging decision model, and obtains second charging data, wherein the second charging data comprises charging cost, charging time, charging demand satisfaction rate, and cell power load fluctuation, performs ITOPSIS sorting on the electric vehicles in the target cell according to the second charging data, and obtains a final charging sequence, and performs charging control on the electric vehicles in the target cell according to the final charging sequence. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the present application, the drawings used in the embodiments will be briefly introduced as follows. Obviously, the drawings described in the following embodiments are only some of the embodiments of the present application, and all other drawings obtained by those of ordinary skill in the art without creative effort based on these drawings also belong to the protection scope of the present application.

[0058] Figure 1 is a flow chart of a cell electric vehicle orderly charging method provided by an embodiment of the present application;

[0059] Figure 2 is a schematic diagram of an electric vehicle travel distribution curve provided by an embodiment of the present application;

[0060] Figure 3 is an electric vehicle electric quantity travel scatter diagram provided by an embodiment of the present application;

[0061] Figure 4 is a schematic diagram of a cell load curve provided by an embodiment of the present application;

[0062] Figure 5 is a flow chart of two-stage ITOPSIS weight optimization provided by an embodiment of the present application;

[0063] Figure 6 is a second schematic diagram of the influence of different weight sizes on charging satisfaction provided by an embodiment of the present application;

[0064] Figure 7 is a first schematic diagram of the influence of different weight sizes on charging satisfaction provided by an embodiment of the present application;

[0065] Figure 8 is a structural block diagram of a cell electric vehicle orderly charging system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0066] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort belong to the protection scope of the present application.

[0067] It can be understood that the various numbers involved in the embodiments of the present application are only distinguished for convenience of description, and are not used to limit the scope of the present application. The size of the serial number of each process does not mean the execution order, and the execution order of each process should be determined according to its function and inherent logic.

[0068] Please refer to Figure 1 , Figure 1is a flow chart of a community electric vehicle orderly charging method provided by an embodiment of the present application. The community electric vehicle orderly charging method specifically comprises:

[0069] S11: acquiring first charging data of an electric vehicle in a target community; wherein the first charging data comprises waiting time, staying time, current power, and charged power;

[0070] Specifically, S11: acquiring first charging data of an electric vehicle in a target community comprises:

[0071] adding all electric vehicles of the target community to an ITOPSIS sorting sequence;

[0072] for each electric vehicle in the ITOPSIS sorting sequence, judging whether the electric vehicle is located in the target community;

[0073] if not, removing the electric vehicle from the ITOPSIS sorting sequence;

[0074] if yes, judging whether the electric vehicle is in a full power state;

[0075] when the electric vehicle is in the full power state, removing the electric vehicle from the ITOPSIS sorting sequence;

[0076] when the electric vehicle is not in the full power state, acquiring first charging data of the electric vehicle.

[0077] For example, the target community has 200 households, each of which has one car, and the penetration rate of electric vehicles is 50%, i.e. the number of electric vehicles is 100. Add the 100 electric vehicles to the ITOPSIS (Technique for Order Preference by Similarity to an Ideal Solution, ideal solution method for short) sorting sequence, and real-time statistics of the state of the 100 electric vehicles. When a certain electric vehicle is not in the target community, i.e. the state of going out, the electric vehicle is removed from the ITOPSIS sorting sequence, and the electric vehicle will not participate in the charging sorting management of the target community. If the electric vehicle is in the target community, it is further determined whether the electric vehicle is in the full charge state. If yes, the electric vehicle is removed from the ITOPSIS sorting sequence, and the full electric vehicle does not need to be charged, and the electric vehicle will not participate in the charging sorting management of the target community. If not, it is determined that the electric vehicle participates in the charging sorting management of the target community, and the waiting time (i.e. the charging waiting time) of the electric vehicle participating in the charging sorting management of the target community in the ITOPSIS sorting sequence, the stay time (i.e. the stay time in the target community), the current power, and the charged power are obtained as the first charging data.

[0078] S12: performing ITOPSIS sorting on the electric vehicles in the target community according to the first charging data to obtain a charging sequence;

[0079] In the embodiment of the application, the electric vehicles participating in the charging sorting management of the target community in the ITOPSIS sorting sequence are taken as objects, and the waiting time, the stay time, the current power, and the charged power are taken as indexes. The waiting time and the stay time are taken as positive indexes, and the current power and the charged power are taken as reverse indexes, i.e. the longer the waiting time and the stay time, and the less the current power and the charged power, the higher the queuing priority. The electric vehicles participating in the charging sorting management of the target community in the ITOPSIS sorting sequence are subjected to one-stage ITOPSIS sorting to obtain the initial charging sequence of the electric vehicles participating in the charging sorting management.

[0080] S13: performing simulation charging control on the electric vehicles of the target community according to the charging sequence, and evaluating the simulation charging process through a pre-established multi-objective optimization ordered charging decision model to obtain second charging data; wherein the second charging data includes charging cost, charging time, charging demand satisfaction rate, and community power load fluctuation.

[0081] Based on the charging sequence obtained above, the charging simulation of the corresponding electric vehicles is performed in the order of the charging sequence, and then the simulation charging process is evaluated by using the ordered charging decision model based on multi-objective optimization, so as to estimate the charging cost, charging time, charging demand satisfaction rate and community power load fluctuation of each electric vehicle as the second charging data.

[0082] S14: According to the second charging data, the electric vehicles in the target community are sorted by ITOPSIS, and the final charging sequence is obtained, and the electric vehicles in the target community are charged according to the final charging sequence.

[0083] The embodiment of the application estimates the charging cost, charging time, charging demand satisfaction rate and community power load fluctuation of each electric vehicle based on the simulation charging of the one-stage ITOPSIS sorting sequence, and further sorts the electric vehicles participating in the charging sequence management of the target community in the ITOPSIS sorting sequence by two-stage ITOPSIS sorting, to obtain the final charging sequence.

[0084] The embodiment of the application introduces the two-stage ITOPSIS sorting based electric vehicle ordered charging strategy, avoids charging congestion, improves charging efficiency and user experience, reduces power grid load fluctuation, so that the transformer load limit can be avoided due to simultaneous peak charging of a large number of electric vehicles in the community, and the charging demand of electric vehicles can be met.

[0085] In an optional embodiment, the method further includes the following ordered charging decision model construction process:

[0086] First, analyze the electric vehicle travel time distribution and community load distribution of the target community, as follows:

[0087] Taking the morning peak period of 7:00-9:00 and the evening peak period of 17:00-19:00 as an example, the travel time of electric vehicles is divided into two stages of travel distribution and return distribution, wherein the travel distribution law conforms to the double exponential function distribution, and the return distribution law conforms to the Gaussian distribution, based on which the travel distribution probability density function and the return distribution probability density function of the electric vehicles are established.

[0088] The travel distribution probability density function is:

[0089] (1) ;

[0090] The return distribution probability density function is:

[0091] (2) ;

[0092] Wherein, x represents the time variable, , b, c, d represent the fitting parameters of the travel distribution probability density function, , represent the mean and standard deviation of the Gaussian distribution.

[0093] By fitting the travel time distribution of the morning peak period 7:00-9:00 and the evening peak period 17:00-19:00 with the travel distribution probability density function and the return distribution probability density function, the parameters of the travel distribution probability density of the electric vehicle , , , ; the parameters of the return distribution probability density = 5, . In order to meet the randomness of electric vehicle travel, the travel distribution of the electric vehicle is superimposed with Gaussian distribution with parameters , , and the return distribution is superimposed with Gaussian distribution with parameters , , to simulate the random travel of electric vehicles in the whole day. The fitted electric vehicle travel distribution curve is shown in Figure 2 .

[0094] Assuming that the electric vehicle travels with a random interval of , the annual average driving distance of a private car is 14332km, and the daily travel distance is 40.8km, due to traffic congestion and fluctuations in user travel willingness, it is assumed that the daily travel distance of the user obeys the Gaussian distribution , where , . Assuming that the endurance of the electric vehicle is 500 kilometers, the electric quantity of the electric vehicle after returning is:

[0095] (3) ;

[0096] Assuming that there are 100 electric vehicles participating in the simulation, the electric quantity scatter plot of the electric vehicle after simulation is shown in Figure 3 . Assuming that the rated capacity of the target cell is 1200kW, and the number of electric vehicles is 100, each electric vehicle is equipped with a charging pile, that is, 100 electric vehicles correspond to 100 charging piles. The daily load curve of the cell is obtained by using the Monte Carlo simulation method, as shown in Figure 4 .

[0097] Based on the above scenario, a compromise optimization is made between meeting the user charging demand and normal power supply of the cell, and a sequential charging decision model is established with the multi-objective optimization of user charging cost, charging demand satisfaction rate, charging waiting time and cell power load fluctuation as the goal.

[0098] Specifically, a first objective function is established with the minimum charging cost as the goal ,

[0099] (4) ;

[0100] Wherein is the charging cost and service fee of the ith vehicle at time t, is the charging state of the ith vehicle at time t, is not charging, is charging, and num is the number of electric vehicles.

[0101] A second objective function is established with the maximum charging demand satisfaction rate as the goal ;

[0102] (5) ;

[0103] Wherein, is the total number of times of charging of electric vehicles in the target cell, is the number of times of charging satisfaction of electric vehicles in the target cell greater than 0.9, wherein the charging satisfaction of the ith vehicle and The calculation method is as follows:

[0104] (6) ;

[0105] (7) ;

[0106] (8) ;

[0107] Wherein, is the actual charging capacity of the ith vehicle, is the charging capacity of the ith vehicle at the beginning of charging, is the user expected charging capacity of the ith vehicle, and f i is the charging satisfaction rate statistical times of the ith vehicle.

[0108] A third objective function is established with the minimum cell power load fluctuation as the goal ;

[0109] (9) ;

[0110] Wherein, is the total electricity load of the target cell at time t, is the average electricity load of the target cell.

[0111] establishes a fourth objective function with the shortest charging time as the target ;

[0112] (10);

[0113] wherein, is the total charging time of the ith vehicle.

[0114] According to the first objective function, the second objective function, the third objective function, and the fourth objective function, an ordered charging decision model of multi-objective optimization under a preset constraint condition is established.

[0115] The constraint condition includes a cell electricity load power limit constraint, an electric vehicle charging state constraint, an electric vehicle location constraint, and an electric vehicle power constraint.

[0116] Based on the above-mentioned four objective functions, an ordered charging decision model of multi-objective optimization can be constructed, as follows:

[0117] (11);

[0118] The constraint condition includes:

[0119] (1) Cell electricity load power limit constraint:

[0120] (12);

[0121] wherein, is the upper limit of the target cell electricity load power; is the charging power of the ith vehicle at time t; is the background load of the target cell, and the cell electricity load power limit constraint indicates that the sum of the background load of the target cell and the total charging power of the electric vehicles cannot exceed the upper limit of the target cell electricity load power.

[0122] (2) Electric vehicle charging state constraint:

[0123] (13);

[0124] The electric vehicle charging state constraint indicates that the charging state of the ith vehicle at time t can only be not charging or charging.

[0125] (3) Electric vehicle location constraint:

[0126] (14);

[0127] wherein, is the position of the i-th vehicle at time t, 0 represents not in the target cell, and 1 represents in the target cell; the electric vehicle position constraint represents that the position of the i-th vehicle at time t can only be not in the target cell or in the target cell, and the electric vehicle can only charge in the target cell.

[0128] (4) Electric vehicle power constraint:

[0129] (15);

[0130] wherein, is the remaining power of the i-th vehicle at time t; the electric vehicle power constraint represents that the remaining power of the i-th vehicle at time t ranges between 0 and 1.

[0131] In an optional embodiment, the ITOPSIS sorting of the electric vehicles in the target cell according to the first charging data to obtain the charging sequence comprises:

[0132] a multi-dimensional evaluation matrix is constructed by taking the waiting time, the staying time, the current power and the charged power in the first charging data of each electric vehicle as indexes;

[0133] the evaluation matrix is normalized;

[0134] the normalized evaluation matrix is standardized according to the weight of each index to obtain a standardized weight matrix;

[0135] the absolute positive ideal solution and the absolute negative ideal solution are calculated according to the standardized weight matrix;

[0136] each electric vehicle is sorted according to the evaluation matrix, the absolute positive ideal solution and the absolute negative ideal solution to obtain the charging sequence.

[0137] Specifically, the sorting of each electric vehicle according to the evaluation matrix, the absolute positive ideal solution and the absolute negative ideal solution to obtain the charging sequence comprises:

[0138] the evaluation matrix, the absolute positive ideal solution and the absolute negative ideal solution are weighted and normalized;

[0139] the first Euclidean distance of each index of each electric vehicle to the weighted and normalized absolute positive ideal solution is calculated according to the weighted and normalized absolute positive ideal solution and the evaluation matrix;

[0140] According to the weighted normalized absolute negative ideal solution and the evaluation matrix, a second Euclidean distance of each index of each electric vehicle to the weighted normalized absolute negative ideal solution is calculated.

[0141] According to the first Euclidean distance and the second Euclidean distance, the closeness of the index evaluation of each electric vehicle to the optimal value is calculated.

[0142] According to the closeness of the index evaluation of each electric vehicle to the optimal value, each electric vehicle is sorted to obtain the charging sequence.

[0143] Specifically, the calculation of the absolute positive ideal solution and the absolute negative ideal solution according to the standardized weight matrix comprises:

[0144] According to the standardized weight matrix, the positive ideal solution and the negative ideal solution are calculated.

[0145] Selecting the maximum value corresponding to each index from the positive ideal solution to construct the absolute positive ideal solution.

[0146] Selecting the minimum value corresponding to each index from the negative ideal solution to construct the absolute negative ideal solution.

[0147] In the embodiment of the application, the ITOPSIS sequence is performed based on the first charging data of the electric vehicles participating in the charging sequence management of the target cell in the ITOPSIS sequence obtained in the above step S11, and the specific process is as follows:

[0148] The electric vehicles participating in the charging sequence management of the target cell in the ITOPSIS sequence are taken as objects, and the waiting time, the staying time, the current power and the charged power are taken as indexes; wherein the waiting time and the staying time are taken as positive indexes, and the current power and the charged power are taken as reverse indexes, a multi-dimensional evaluation matrix is established, and the specific process is as follows:

[0149] (16) ;

[0150] Wherein, n represents the number of objects (i.e. the number of electric vehicles), p represents the number of indexes, x np represents the pth index of the nth index, for example, n=100, p=4.

[0151] The evaluation matrix is normalized to unify the dimensions of each index, and the value is normalized to the size between [0, 1], so as to eliminate the influence of the dimension, and the normalized evaluation matrix is as follows:

[0152] (17) ;

[0153] (18) ;

[0154] wherein i = 1, 2,..., n; j = 1, 2,..., p.

[0155] The normalized weight matrix is calculated as follows:

[0156] (17);

[0157] (20)

[0158] (21);

[0159] wherein wj is the weight corresponding to the index j, and by adjusting the weight of the index, the ordered charging effect under different decisions can be realized. is the proportion of the weight corresponding to the index j to the total weight of all indexes. The optimal vector (i.e. positive ideal solution) and the worst vector (i.e. negative ideal solution) are respectively constructed by the optimal value and the worst value of each index.

[0160] The optimal vector (i.e. positive ideal solution) and the worst vector (i.e. negative ideal solution) are respectively constructed by the optimal value and the worst value of each index.

[0161] (22);

[0162] (23);

[0163] wherein wj is the weight corresponding to the index j, and by adjusting the weight of the index, the ordered charging effect under different decisions can be realized. , .

[0164] The Euclidean distance between the index evaluation of each electric vehicle and the positive ideal solution and the negative ideal solution is calculated.

[0165] wherein the Euclidean distance between the index evaluation of the electric vehicle and the positive ideal solution is:

[0166] (24);

[0167] The Euclidean distance between the index evaluation of the electric vehicle and the negative ideal solution is:

[0168] (25);

[0169] The closeness of the index evaluation of each electric vehicle to the optimal value is calculated as follows:

[0170] (26);

[0171] The closeness of the index evaluation of each electric vehicle to the optimal value is calculated as follows: ​​​​​The greater, the closer the i-th electric vehicle is to the optimal value level, and the higher the ranking is, and the ranked charging ranking of the electric vehicle is formed after ranking.

[0172] Further, considering that the positive ideal solution and the negative ideal solution of the ITOPSIS ranking method will change with the increase or decrease of objects, thereby causing the change of the decision environment, and ultimately leading to the change of the ranking of the objects before and after the increase or decrease of the objects, the reverse sequence phenomenon may occur. For example, for n objects, the result after adopting the ITOPSIS ranking method is that the i-th object is better than the k-th object, and after increasing or decreasing several objects (the decreased objects do not include the i-th object and the k-th object), the result after ranking is that the k-th object is better than the i-th object, which is obviously not reasonable.

[0173] For example, in the ordered charging of electric vehicles, it is assumed that there are five electric vehicles EV1, EV2, EV3, EV4 and EV5, and the weight vector is Based on the above ITOPSIS ranking, the charging ranking shown in the following table is obtained.

[0174]

[0175] Based on the above table, the ITOPSIS ranking reverse sequence problem after adding one electric vehicle EV6 is analyzed. Based on the above calculation, before adding EV6, the charging ranking of the five electric vehicles is EV2> EV5> EV4> EV3> EV1, and after adding EV6, the charging ranking is EV2> EV4> EV5> EV3> EV6> EV1. It is not difficult to find that before EV6 is added, EV5> EV4, and after EV6 is added, EV4> EV5, which is obviously contradictory to the actual situation. The fundamental reason is that the addition of EV6 changes the negative ideal solution. The negative ideal solution of the original evaluation matrix is: The Euclidean distance of each object from the negative ideal solution is: When is added, the negative ideal solution becomes: The Euclidean distance of each object from the negative ideal solution becomes: The change of the Euclidean distance causes the ranking error. The relative positive ideal point and the relative negative ideal point are closely related to the number of objects and the decision attributes of the objects, and cannot avoid the change of the relative ideal point caused by the change of the number of objects. The embodiment of the present application proposes an absolute positive and negative ideal point method to optimize and improve the above ITOPSIS ranking process, which is as follows:

[0176] Let the absolute positive ideal solution and the absolute negative ideal solution of the ITOPSIS ranking problem be and respectively satisfy the following conditions:

[0177] (1) .

[0178] (2) In the limited range of decision object attributes, for any electric vehicle there are and .

[0179] In the normalization of the decision matrix, the change of the reference standard is another important reason for the reverse order. Therefore, the embodiment of the present application constructs the reference standard of each decision attribute based on the absolute positive and negative ideal solution . Let ; wherein , is the reliability of the absolute positive and negative ideal solution of the decision maker, which can be set subjectively by the decision maker.

[0180] Weighted normalized decision data (including the positive and negative ideal solutions calculated above, and the evaluation matrix), let , , , ; wherein is the attribute value of the index in the evaluation matrix, the weighted and normalized absolute positive ideal solution is , and the absolute negative ideal solution is .

[0181] Calculate the first Euclidean distance of each index to the weighted and normalized absolute positive ideal solution:

[0182] (27);

[0183] Calculate the second Euclidean distance of each index to the weighted and normalized absolute negative ideal solution:

[0184] (28);

[0185] Calculate the closeness of the index evaluation of each electric vehicle to the optimal value :

[0186] (29);

[0187] According to the closeness of the index evaluation of each electric vehicle to the optimal value , the sorting is performed, at this time, if new objects n+1, n+2,..., n+n' are added, n' represents the number of newly added objects (i.e. the number of newly added electric vehicles); through the above steps, the first Euclidean distance of each index to the absolute positive ideal solution and the second Euclidean distance to the absolute negative ideal solution Under the premise that the Euclidean distance meets the uniqueness and invariance within the effective decision range (i.e. the index and the index weight remain unchanged), when the number of electric vehicles increases or decreases, the absolute ideal point will not change, and therefore the ranking error phenomenon will not occur.

[0188] The embodiment of the application introduces the absolute positive and negative ideal solution in the ITOPSIS ranking to calculate the closeness of the index evaluation and the optimal value of each electric vehicle, and then ranks the electric vehicles, which can avoid the ranking error phenomenon caused by the increase or decrease of the number of electric vehicles, and improve the rationality of the electric vehicle ranking.

[0189] In an optional embodiment, the electric vehicles in the target cell are simulated for charging control according to the charging ranking, and the simulation charging process is evaluated through a pre-established ordered charging decision model of multi-objective optimization to obtain second charging data, including:

[0190] According to the electricity load limit of the target cell, the number of available electric vehicle charging piles is determined;

[0191] According to the charging ranking and the number of available electric vehicle charging piles, the charging state of the electric vehicles is updated and charging simulation is performed until a preset simulation end condition is reached;

[0192] The simulation charging process is evaluated through the ordered charging decision model to obtain second charging data.

[0193] In an optional embodiment, the method further includes:

[0194] The weights of each index are updated, and the electric vehicles in the target cell are re-ranked by ITOPSIS and simulated for charging control, and the simulation charging process is evaluated through the ordered charging decision model to obtain new second charging data;

[0195] All combinations of the weights of each index are traversed to obtain a plurality of second charging data under different index weight combinations.

[0196] In an optional embodiment, the ITOPSIS ranking of the electric vehicles in the target cell according to the second charging data to obtain a final charging ranking, and the charging control of the electric vehicles in the target cell according to the final charging ranking, includes:

[0197] The charging cost, charging time, charging demand satisfaction rate, and cell electricity load fluctuation in the plurality of second charging data under different index weight combinations are weighted and summed to obtain a comprehensive score of each second charging data;

[0198] The second charging data is sorted according to the comprehensive score of each, and the best weight of each index is determined;

[0199] According to the best weight of each index, the electric vehicles in the target cell are re-ITOPSIS sorted to obtain the final charging order.

[0200] Exemplarily, after the above-mentioned one-stage ITOPSIS sorting is completed, charging simulation is performed. The specific process includes:

[0201] Step a: first initialize the basic data, including the data of each electric vehicle in the target cell (such as whether to go out, go out time, return time, cruising range, battery capacity, current power, etc.), the number of electric vehicles and charging piles, the background load of the cell, the power of the charging pile, the time-of-use electricity price, the simulation step and time, etc. Initial parameter setting.

[0202] Step b: update the state of each electric vehicle in the system, including whether the electric vehicle is in the cell, if not, mark 0, update the remaining power, and remove it from the ITOPSIS sorting queue; if it is in the cell, it is judged whether it is full or not, if it is full, it is removed from the ITOPSIS sorting queue, if it is not full, the waiting time, stay time, current power and charged power are updated according to its state.

[0203] Step c: ITOPSIS sorting (i.e. one-stage ITOPSIS sorting) is performed on the evaluation matrix of the waiting time, stay time, current power and charged power of the electric vehicle, the charging order of each electric vehicle is calculated, and the specific calculation process is described above, which is not repeated here. At the same time, the number of available electric vehicle charging piles is updated according to the load limit of the cell, and the calculation method is as follows:

[0204] (30);

[0205] Wherein, is the number of available electric vehicle charging piles, is the rated power of the charging pile, is the charging power of the i-th electric vehicle at time t, is the upper limit of the load power of the cell.

[0206] Step d: update the charging state of the electric vehicle according to the charging order of the electric vehicle and the number of available electric vehicle charging piles, if the charging order of the electric vehicle is less than , enter the charging state, otherwise do not enter the charging state.

[0207] Step e: judge whether the simulation is finished, if the simulation days reach the set simulation days, the round of simulation is finished, jump to step f; if the simulation days are not reached, judge whether the simulation time reaches 24h, if reached, the simulation time is reset to 0, the simulation date +1; if not reached, update the state information of each electric vehicle (i.e. waiting time, stay time, current power and charged power), the simulation time increases by simulation step length, the simulation jumps to step b.

[0208] Step f: the simulation is finished, the second charging data of the electric vehicle is counted, including charging cost, charging demand satisfaction rate, charging time, and community power load fluctuation.

[0209] Step g: in the charging sorting of electric vehicles using one-stage ITOPSIS, the weight vector including waiting time, stay time, current power and charged power needs to be manually set. In order to achieve the optimization goal of minimum charging cost, maximum charging demand satisfaction rate, minimum community power load fluctuation and shortest charging time, a reasonable index weight is needed. Based on this, through the two-stage ITOPSIS index weight optimization method, the collaborative optimization of multiple objectives is ensured, specifically as follows:

[0210] The ITOPSIS sorting calculation of electric vehicle charging is called one-stage ITOPSIS, the input index is waiting time, stay time, current power and charged power, and the weight of the four indexes of one-stage ITOPSIS is . The weight of waiting time, stay time, current power and charged power is represented by

[0211] The combination of weight is traversed, and the weight of all indexes is searched in all combination methods in the range of [0, 1], and the traversal step length is set to 0.01, for example, a total of 5050 times of traversal are needed. For each weight combination, the above one-stage ITOPSIS sorting method is used to calculate the charging sorting, and then the second charging data under each weight combination is counted, including charging cost, charging demand satisfaction rate, charging time, community power load fluctuation, community power load curve, waiting time, and community load variance.

[0212] For the weight in the mth traversal, and satisfy: , m=1, 2,..., 1050. For each weight of traversal, 30-day one-stage ITOPSIS sorting of electric vehicle charging is carried out, and the specific sorting and simulation process is referred to the above steps c-f, and the weight The system calculates charging costs, charging demand fulfillment rates, community electricity load fluctuations, and charging time. A total of 5050 sets of data with different weighting combinations can be obtained, providing data on charging costs, charging demand fulfillment rates, community electricity load fluctuations, and charging time.

[0213] Step i: In the two-stage ITOPSIS calculation, it is necessary to calculate multiple groups with different weight combinations. The evaluation comprehensively considers charging costs, charging demand fulfillment rate, residential electricity load fluctuations, and charging time. The weights of these factors can be set according to actual needs; in this embodiment, they are set as follows: The weights for charging cost, charging demand fulfillment rate, residential electricity load fluctuation, and charging time are 0.1, 0.5, 0.2, and 0.2, respectively. A weighted sum is calculated for each group of charging cost, charging demand fulfillment rate, residential electricity load fluctuation, and charging time. The weighted sums are then sorted in descending order, and the weights corresponding to the highest-ranking weight combination are selected. That is, the optimal weight, such as Figure 5 As shown.

[0214] Step j: Using the optimal weights obtained in step i, reorder them using the one-stage ITOPSIS sorting in step c to obtain the final charging sort.

[0215] By comparing disordered charging, traditional TOPSIS sorting method charging, and the two-stage ITOPSIS sorting method charging of the present invention.

[0216] (1) Comparison of simulation results considering different numbers of vehicles and load limits. Considering charging and load scenarios, two cases were set: one with a load limit of 1000kW and the other without a load limit. The number of vehicles in the community was set to three cases: 100 vehicles (low load), 200 vehicles (medium load), and 300 vehicles (high load). The charging differences under each scenario were compared, and the results are shown in the table below.

[0217]

[0218] Without limiting the load, as the number of vehicles in the community increases, the increase in charging cost is also linear; the charging satisfaction decreases as the number of vehicles increases, such as when the number of vehicles increases from 100 to 200, the charging satisfaction is almost unchanged, and when it increases to 300, due to the insufficient number of charging piles, the charging satisfaction decreases significantly; the average waiting time increases significantly as the number of vehicles in the community increases; the load fluctuation variance changes is not obvious. In the case of limiting the load, as the number of vehicles in the community increases, the changes of charging cost and charging satisfaction are similar to the former, the average waiting time reaches a peak when the number of vehicles reaches 200, and the number of vehicles is 300 The former is flat; the load fluctuation variance decreases due to the increase in the number of vehicles, which fills the load valley.

[0219] Comparing the two cases of limiting the load and not limiting the load, it can be found that in the case of limiting the load, the charging cost spent is lower than that in the case of not limiting the load, because in the case of limiting the load, the proportion of charging in the low valley period is significantly improved; the charging satisfaction is similar to that in the case of not limiting the load, but in the case of 300 vehicles (high load) Limiting the load, the charging satisfaction is significantly lower; and the load fluctuation variance is significantly reduced compared with not limiting the load, indicating that not limiting the load is more suitable for high load conditions, but will significantly increase the risk of transformer and line damage; The average waiting time is significantly smaller than that of not limiting the load, because in the case of limiting the load, the power at the load peak is transferred to the load valley, achieving the purpose of peak load shifting.

[0220] Overall, in the case of medium and low load, limiting the load can effectively reduce the charging cost while ensuring the charging satisfaction of electric vehicles, and significantly reduce the load fluctuation variance, improving the stability of the power grid.

[0221] (2) Comparison of simulation results considering different charging sorting strategies. The charging vehicles in the embodiment of the application are selected to be 200 to ensure a certain load pressure, and a load limit of 1000kW is set. On this basis, the sorting strategies of unordered charging, traditional TOPSIS sorting method charging and ITOPSIS sorting method charging of the embodiment of the application are compared and analyzed, and the charging results under different sorting strategies are compared as shown in the following table:

[0222]

[0223] It can be seen that the satisfaction rate of disordered charging is significantly lower than that of ordered charging. This is because electric vehicles with low remaining battery power cannot be prioritized for charging, and load limitations cause charging congestion. The charging cost of disordered charging is slightly higher than that of ordered charging. Moreover, the average waiting time of disordered charging is slightly shorter than that of ordered charging, but since the rate of residents driving at night is very low, the impact on user charging satisfaction is negligible. The load fluctuation variance of disordered charging is significantly higher than that of ordered charging. In summary, the ordered charging strategy is significantly better than disordered charging. Comparing the traditional TOPSIS and the ITOPSIS of this invention, it can be seen that the charging satisfaction rate of ITOPSIS is higher than that of TOPSIS, and the load fluctuation variance and charging cost are lower than those of TOPSIS. The average waiting time is similar for both. Therefore, it can be considered that the ITOPSIS ranking method is better than the traditional TOPSIS ranking method.

[0224] (3) Weight sensitivity analysis: In this embodiment of the invention, the weight of a certain indicator is fixed, and the weights of other indicators are adjusted to adjust the weights. The impact of different weights on charging satisfaction and load fluctuation variance is analyzed. As the weight ratio of the waiting time indicator increases, charging satisfaction decreases accordingly; when ( When simplified to weighted indicators based on battery level, a higher weight for dwell time correlates with higher charging satisfaction. Figure 6 As shown, when ( When the weighting of indicators (based on battery level) is simplified, a higher dwell time weight initially leads to a higher charging satisfaction level, followed by a decrease. Conversely, as the battery level weight increases, charging satisfaction also increases, then remains relatively stable. Figure 7 The results conform to basic principles. For example, considering user charging satisfaction as the primary factor, the optimal weight is selected. By flexibly setting the weights of various indicators, the orderly charging of electric vehicles can better meet actual needs and improve the rationality of the weights.

[0225] Compared to existing technologies, this invention, based on the TOPSIS ranking method, solves the problem of incorrect ranking in traditional TOPSIS by proposing absolute positive and negative ideal points. Simultaneously, it employs a two-stage ITOPSIS weight vector optimization method to achieve optimal collaborative optimization across multiple objectives. This minimizes competition among electric vehicles, avoids charging congestion, and, in conjunction with grid load constraints, shifts the charging load of electric vehicles from peak to off-peak hours, thereby reducing charging costs and improving the efficiency and stability of the entire community's power grid. This invention, while considering user charging costs and average waiting time, improves user charging satisfaction, reduces peak load in the community, smooths out load fluctuations, and increases load stability.

[0226] Please refer to Figure 8 , Figure 8 The embodiment of the present application provides a structural block diagram of a community electric vehicle orderly charging system, and the community electric vehicle orderly charging system comprises:

[0227] A first data acquisition module 11 is used for acquiring first charging data of electric vehicles in a target community; wherein the first charging data comprises waiting time, staying time, current power and charged power;

[0228] A first sorting module 12 is used for performing ITOPSIS sorting on the electric vehicles in the target community according to the first charging data, so as to obtain charging sorting;

[0229] A charging simulation module 13 is used for performing simulation charging control on the electric vehicles in the target community according to the charging sorting, and performing evaluation on the simulation charging process through a pre-established multi-target optimization orderly charging decision model, so as to obtain second charging data; wherein the second charging data comprises charging cost, charging time, charging demand satisfaction rate and community power load fluctuation;

[0230] A second sorting module 14 is used for performing ITOPSIS sorting on the electric vehicles in the target community according to the second charging data, so as to obtain final charging sorting, and performing charging control on the electric vehicles in the target community according to the final charging sorting.

[0231] In an optional embodiment, the first sorting module 12 comprises:

[0232] An evaluation matrix construction unit is used for constructing a multi-dimensional evaluation matrix by taking the waiting time, staying time, current power and charged power in the first charging data of each electric vehicle as indexes;

[0233] A normalization processing unit is used for performing normalization processing on the evaluation matrix;

[0234] A standardization processing unit is used for performing standardization processing on the normalized evaluation matrix according to the weight of each index, so as to obtain a standardization weight matrix;

[0235] An ideal solution calculation unit is used for calculating absolute positive ideal solutions and absolute negative ideal solutions according to the standardization weight matrix;

[0236] A first charging sorting unit is used for sorting each electric vehicle according to the evaluation matrix, the absolute positive ideal solutions and the absolute negative ideal solutions, so as to obtain the charging sorting.

[0237] In an optional embodiment, the first charging sorting unit comprises:

[0238] The weighted normalization processing sub-unit is configured to perform weighted normalization processing on the evaluation matrix, the absolute positive ideal solution, and the absolute negative ideal solution.

[0239] The first Euclidean distance calculation sub-unit is configured to calculate a first Euclidean distance between each index of each electric vehicle and the weighted normalized absolute positive ideal solution according to the weighted normalized absolute positive ideal solution and the evaluation matrix.

[0240] The second Euclidean distance calculation sub-unit is configured to calculate a second Euclidean distance between each index of each electric vehicle and the weighted normalized absolute negative ideal solution according to the weighted normalized absolute negative ideal solution and the evaluation matrix.

[0241] The closeness calculation sub-unit is configured to calculate the closeness between the index evaluation of each electric vehicle and the optimal value according to the first Euclidean distance and the second Euclidean distance.

[0242] The vehicle charging ranking sub-unit is configured to rank each electric vehicle according to the closeness between the index evaluation of each electric vehicle and the optimal value, to obtain the charging ranking.

[0243] In an optional embodiment, the ideal solution calculation unit comprises:

[0244] The positive and negative ideal solution calculation sub-unit is configured to calculate a positive ideal solution and a negative ideal solution according to the standardized weight matrix.

[0245] The absolute positive ideal solution construction sub-unit is configured to select the maximum value corresponding to each index from the positive ideal solution, to construct an absolute positive ideal solution.

[0246] The absolute negative ideal solution construction sub-unit is configured to select the minimum value corresponding to each index from the negative ideal solution, to construct an absolute negative ideal solution.

[0247] In an optional embodiment, the first data acquisition module 11 comprises:

[0248] The sequence construction unit is configured to add all electric vehicles in the target cell to the ITOPSIS ranking sequence.

[0249] The first judgment unit is configured to judge whether each electric vehicle in the ITOPSIS ranking sequence is located in the target cell.

[0250] The first removal unit is configured to remove the electric vehicle from the ITOPSIS ranking sequence if the judgment result is negative.

[0251] The second judgment unit is configured to judge whether the electric vehicle is in a full charge state if the judgment result is positive.

[0252] a second removal unit configured to remove the electric vehicle from the ITOPSIS ordering sequence when the electric vehicle is in a full charge state;

[0253] a charging data acquisition unit configured to acquire first charging data of the electric vehicle when the electric vehicle is not in a full charge state.

[0254] In an optional embodiment, the charging simulation module 13 comprises:

[0255] a charging pile number determination unit configured to determine a number of available electric vehicle charging piles according to a power load limit of the target cell;

[0256] a vehicle charging simulation unit configured to update a charging state of the electric vehicle and perform charging simulation according to the charging ordering and the number of available electric vehicle charging piles until a preset simulation end condition is reached;

[0257] a charging evaluation unit configured to evaluate the simulated charging process by using the ordered charging decision model to obtain second charging data.

[0258] In an optional embodiment, the system further comprises:

[0259] a first objective function construction module configured to establish a first objective function with a minimum charging cost as a target;

[0260] a second objective function construction module configured to establish a second objective function with a maximum charging demand satisfaction rate as a target;

[0261] a third objective function construction module configured to establish a third objective function with a minimum cell power load fluctuation as a target;

[0262] a fourth objective function construction module configured to establish a fourth objective function with a minimum charging time as a target;

[0263] an ordered charging decision model construction module configured to establish an ordered charging decision model of multi-objective optimization under a preset constraint condition according to the first objective function, the second objective function, the third objective function, and the fourth objective function;

[0264] The constraint condition comprises a cell power load power limit constraint, an electric vehicle charging state constraint, an electric vehicle location constraint, and an electric vehicle power constraint.

[0265] In an optional embodiment, the system further comprises:

[0266] The weight updating module is configured to update the weight of each index, re-perform ITOPSIS sorting and simulation charging control on the electric vehicles in the target cell, and evaluate the simulation charging process through the ordered charging decision model to obtain new second charging data.

[0267] The second charging data acquisition module is configured to traverse all combination manners of the weight of each index, and obtain a plurality of second charging data under different index weight combination manners.

[0268] In an optional embodiment, the second sorting module 14 comprises:

[0269] The comprehensive evaluation unit is configured to perform weighted summation on the charging cost, charging time, charging demand satisfaction rate and cell power load fluctuation in the plurality of second charging data under different index weight combination manners, and obtain a comprehensive score of each second charging data.

[0270] The optimal weight determination unit is configured to sort each second charging data according to the respective comprehensive score, and determine the optimal weight of each index.

[0271] The second charging sorting unit is configured to re-perform ITOPSIS sorting on the electric vehicles in the target cell according to the optimal weight of each index, and obtain a final charging order.

[0272] It should be noted that the working process of each module in the cell electric vehicle ordered charging system described in the embodiments of the present application can refer to the working process of the cell electric vehicle ordered charging method described in the above embodiments, and the technical effects achieved are the same as those of the cell electric vehicle ordered charging method described in the above embodiments, which will not be repeated here.

[0273] The above is the preferred embodiment of the present application. It should be noted that for ordinary skilled persons in the technical field, various improvements and refinements can be made without departing from the principles of the present application, and these improvements and refinements are also considered within the protection scope of the present application.

Claims

1. A method for orderly charging of electric vehicles in a residential community, characterized in that, include: Acquire the first charging data of electric vehicles in the target community; wherein the first charging data includes waiting time, dwell time, current battery level, and amount of charge already received; Based on the first charging data, the electric vehicles in the target cell are sorted using ITOPSIS to obtain the charging ranking; The electric vehicles in the target community are subjected to simulated charging control according to the charging order, and the simulated charging process is evaluated by a pre-established multi-objective optimized ordered charging decision model to obtain second charging data; wherein, the second charging data includes charging cost, charging time, charging demand satisfaction rate, and community electricity load fluctuation. Based on the second charging data, the electric vehicles in the target cell are sorted using ITOPSIS to obtain the final charging sort, and the charging of the electric vehicles in the target cell is controlled according to the final charging sort.

2. The method for orderly charging of electric vehicles in a residential community as described in claim 1, characterized in that, The step of sorting electric vehicles in the target cell using ITOPSIS based on the first charging data to obtain a charging ranking includes: A multi-dimensional evaluation matrix is ​​constructed using the waiting time, dwell time, current battery level, and charged amount in the first charging data of each electric vehicle as indicators. The evaluation matrix is ​​then normalized. Based on the weights of each indicator, the normalized evaluation matrix is ​​standardized to obtain a standardized weight matrix. Calculate the absolute positive ideal solution and the absolute negative ideal solution based on the standardized weight matrix. The electric vehicles are sorted according to the evaluation matrix, the absolute positive ideal solution, and the absolute negative ideal solution to obtain the charging sorting.

3. The method for orderly charging of electric vehicles in a residential community as described in claim 2, characterized in that, The step of sorting the electric vehicles according to the evaluation matrix, the absolute positive ideal solution, and the absolute negative ideal solution to obtain the charging ranking includes: The evaluation matrix, the absolute positive ideal solution, and the absolute negative ideal solution are subjected to weighted normalization. Based on the weighted normalized absolute positive ideal solution and the evaluation matrix, calculate the first Euclidean distance from each index of each electric vehicle to the weighted normalized absolute positive ideal solution; Based on the weighted normalized absolute negative ideal solution and the evaluation matrix, calculate the second Euclidean distance from each index of each electric vehicle to the weighted normalized absolute negative ideal solution; Based on the first Euclidean distance and the second Euclidean distance, calculate the degree of closeness between the evaluation index of each electric vehicle and the optimal value; The electric vehicles are ranked according to how close their performance indicators are to the optimal values, thus obtaining the charging ranking.

4. The method for orderly charging of electric vehicles in a residential community as described in claim 3, characterized in that, The step of calculating the absolute positive ideal solution and the absolute negative ideal solution based on the standardized weight matrix includes: Calculate the positive ideal solution and the negative ideal solution based on the standardized weight matrix. Select the maximum value corresponding to each index from the positive ideal solution to construct the absolute positive ideal solution; The minimum value corresponding to each index is selected from the negative ideal solution to construct the absolute negative ideal solution.

5. The method for orderly charging of electric vehicles in a residential community as described in claim 1, characterized in that, The acquisition of the first charging data of electric vehicles in the target community includes: Add all electric vehicles in the target cell to the ITOPSIS sorting sequence; For each electric vehicle in the ITOPSIS sorting sequence, determine whether the electric vehicle is located within the target cell; If not, remove the electric vehicle from the ITOPSIS sorting sequence; If so, determine whether the electric vehicle is fully charged; When the electric vehicle is fully charged, remove the electric vehicle from the ITOPSIS sorting sequence; When the electric vehicle is not fully charged, the first charging data of the electric vehicle is acquired.

6. The method for orderly charging of electric vehicles in a residential community as described in claim 2, characterized in that, The electric vehicles in the target community are subjected to simulated charging control according to the charging sequence, and the simulated charging process is evaluated by a pre-established multi-objective optimized ordered charging decision model to obtain second charging data, including: Based on the electricity load limitations of the target community, determine the number of available electric vehicle charging stations; Based on the charging order and the number of available electric vehicle charging stations, the charging status of the electric vehicle is updated and a charging simulation is performed until the preset simulation end condition is met. The simulated charging process is evaluated using the ordered charging decision model to obtain the second charging data.

7. The method for orderly charging of electric vehicles in a residential community as described in claim 1, characterized in that, The method also includes the following process for constructing an ordered charging decision model: Establish a first objective function that aims to minimize charging costs; Establish a second objective function with the goal of maximizing the charging demand satisfaction rate; Establish a third objective function with the goal of minimizing the fluctuation of the community's electricity load; Establish a fourth objective function with the goal of minimizing charging time; Based on the first objective function, the second objective function, the third objective function, and the fourth objective function, a multi-objective optimization ordered charging decision model is established under preset constraints. The constraints include the power load limit constraint of the community, the charging status constraint of electric vehicles, the location constraint of electric vehicles, and the power consumption constraint of electric vehicles.

8. The method for orderly charging of electric vehicles in a residential community as described in claim 6, characterized in that, The method further includes: The weights of each indicator are updated, and the electric vehicles in the target cell are re-sorted by ITOPSIS and the simulated charging control is performed. The simulated charging process is evaluated through the ordered charging decision model to obtain new second charging data. Iterate through all combinations of weights for each indicator to obtain multiple second charging data under different combinations of indicator weights.

9. The method for orderly charging of electric vehicles in a residential community as described in claim 8, characterized in that, The step of sorting electric vehicles in the target cell using ITOPSIS based on the second charging data to obtain a final charging order, and then controlling the charging of electric vehicles in the target cell according to the final charging order, includes: The charging cost, charging time, charging demand satisfaction rate, and community electricity load fluctuation in multiple second charging data under different indicator weight combinations are weighted and summed to obtain the comprehensive score of each second charging data. The second charging data are sorted according to their respective comprehensive scores to determine the optimal weight of each indicator; Based on the optimal weights of each indicator, the electric vehicles in the target cell are re-sorted using ITOPSIS to obtain the final charging ranking.

10. A community-based orderly charging system for electric vehicles, characterized in that, include: The first data acquisition module is used to acquire the first charging data of electric vehicles in the target community; wherein, the first charging data includes waiting time, dwell time, current battery level, and amount of charge already received; The first sorting module is used to sort the electric vehicles in the target cell using ITOPSIS based on the first charging data to obtain a charging sort. The charging simulation module is used to simulate charging control of electric vehicles in the target community according to the charging sequence, and evaluate the simulated charging process through a pre-established multi-objective optimized ordered charging decision model to obtain second charging data; wherein, the second charging data includes charging cost, charging time, charging demand satisfaction rate, and community electricity load fluctuation. The second sorting module is used to sort the electric vehicles in the target cell using ITOPSIS based on the second charging data to obtain the final charging sort, and to control the charging of the electric vehicles in the target cell according to the final charging sort.

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