CN112766677A - Modeling method and charging system for residential electric vehicle charging demand based on fuzzy comprehensive evaluation method - Google Patents

Modeling method and charging system for residential electric vehicle charging demand based on fuzzy comprehensive evaluation method Download PDF

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CN112766677A
CN112766677A CN202110025595.6A CN202110025595A CN112766677A CN 112766677 A CN112766677 A CN 112766677A CN 202110025595 A CN202110025595 A CN 202110025595A CN 112766677 A CN112766677 A CN 112766677A
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李军
蔡峥嵘
刘克天
邓晖
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Abstract

本发明涉及电动汽车充电技术领域，提供了一种基于模糊综合评价法的住宅区电动汽车充电需求的建模方法及充电系统，方法包括步骤：建立单台电动汽车动力电池的充电模型，采用蒙特卡洛法抽取每辆电动汽车的开始充电时间和日行驶里程，针对充电桩数量不足造成的排队等待问题以及车主结束充电服务后不能及时离开充电车位造成的充电设施浪费现象，利用模糊综合评价法判断车主的充电选择，得出每位车主的充电响应时间等。本发明能够充分考虑剩余荷电状态和排队等待时间对车主充电选择行为的影响情况，为配电网安全稳定运行以及充电设施合理布局提供了依据。

Figure 202110025595

The invention relates to the technical field of electric vehicle charging, and provides a modeling method and a charging system for the charging demand of electric vehicles in residential areas based on a fuzzy comprehensive evaluation method. The method includes the steps of: establishing a charging model for a power battery of a single electric vehicle; The Carlo method extracts the starting charging time and daily mileage of each electric vehicle, and uses the fuzzy comprehensive evaluation method to solve the problem of queuing and waiting caused by insufficient number of charging piles and the waste of charging facilities caused by the failure of car owners to leave the charging parking space in time after the charging service is completed. Determine the charging choice of the car owner, and get the charging response time of each car owner. The invention can fully consider the influence of the remaining state of charge and queuing waiting time on the vehicle owner's charging selection behavior, and provides a basis for the safe and stable operation of the distribution network and the rational layout of charging facilities.

Figure 202110025595

Description

Modeling method and charging system for residential electric vehicle charging demand based on fuzzy comprehensive evaluation method
Technical Field
The invention relates to the technical field of electric vehicle charging demand modeling, in particular to a residential area electric vehicle charging demand modeling method and a residential area electric vehicle charging system based on a fuzzy comprehensive evaluation method.
Background
In recent years, the electric vehicle, a clean energy vehicle, has been widely used due to the continuous emphasis on environmental problems and the great pressure caused by energy crisis. Limited by the current battery technology, people cannot avoid the problem of frequent charging of the power battery of the electric automobile, and the residential community is one of the primary places for charging selection of the owner of the automobile. However, the charging power of the electric automobile is high, the randomness is strong, and the future large-scale charging requirement of the electric automobile will certainly have great influence on the power distribution system of the old residential district. Therefore, the current fine modeling of the charging demand of the electric automobile in the residential area is an important precondition for safe and stable operation of a future power grid and reasonable layout of charging facilities.
At present, a lot of researchers have made a lot of researches on modeling the charging requirement of the electric automobile. In the 'statistical modeling method for electric vehicle charging power demand', the Tianli pavilion firstly puts forward three main aspects of influence on electric vehicle power demand of a power battery, charging facilities and user behaviors, then obtains probability distribution of last trip ending time and daily driving mileage according to statistical data of a fuel vehicle, and finally establishes a load curve for charging demand of a plurality of electric vehicles by a Monte Carlo simulation method. The document mainly performs load demand distribution on a time scale on electric vehicle charging loads of different application scales in one day, and fails to perform more detailed analysis modeling on randomness of user charging behaviors. Guo Innovation introduces the concept of residential area parking generation rate in the residential area charging load modeling analysis of electric vehicles to the parking behaviors of the electric vehicles, and establishes a residential area parking model. On the basis, the relation between the historical mileage of the electric automobile and the power consumption is researched based on the historical mileage distribution data, and a residual charge state model is established. And finally, giving the charging load distribution of the residential area. The document fails to deeply model a charging power model of the electric vehicle, performs constant power approximation processing on an actual charging process, simplifies the charging model, and can make prediction of large deviation on charging time of a user, thereby affecting accuracy of final charging demand prediction. Chenpeng mainly adopts charging modes with different efficiencies (fast and slow charging) aiming at three types of electric automobiles, namely private cars, buses and taxiers in the Monte Carlo method-based electric automobile charging load calculation, calculates the time distribution of charging demands based on the Monte Carlo method, and obtains a charging load curve. However, the document does not consider that the charging facility is insufficient, a queuing phenomenon occurs in a charging place of a residential area, and a part of car owners cannot leave a charging parking space in time after the charging is finished, so that the charging efficiency and the charging service quality are affected.
Disclosure of Invention
The technical purpose is as follows: in order to overcome the defects in the prior art, the invention provides a residential electric vehicle charging demand modeling method based on fuzzy comprehensive evaluation, which fully considers the influence of the residual charge state and the queuing waiting time on the vehicle owner charging selection behavior, calculates a one-day charging load curve of a residential area by using a Monte Carlo simulation method, and provides a reliable basis for safe and stable operation of a future power distribution network and reasonable layout of charging facilities.
The technical scheme is as follows: in order to achieve the technical purpose, the invention adopts the following technical scheme:
a modeling method for residential electric vehicle charging demand based on a fuzzy comprehensive evaluation method is characterized by comprising the following steps:
s1, establishing a power battery charging model of the single electric automobile;
s2, establishing a probability model of the time of arrival at the residential area and the daily mileage of each electric automobile;
s3, establishing a charging selection model based on a fuzzy comprehensive evaluation method;
and S4, calculating the charging requirement of each vehicle owner based on the Monte Carlo method.
2. The modeling method of electric vehicle charging demand for residential areas based on the fuzzy comprehensive evaluation method as set forth in claim 1, wherein in said step S4, the charging demand calculation step based on the monte carlo method is as follows:
s4.1, setting the maximum number N of the charging piles in the residential area, and generating a charging pile mark matrix S convenient for confirming the use condition of the charging pile:
Figure BDA0002890211970000021
wherein n represents the nth charging pile, and t represents time and unit minute;
s4.2, setting the number of the electric automobiles returning to the residential area within one day to be M, and acquiring vehicle information of the jth electric automobile at the return moment, including the residual electric quantity of the battery;
judging the occupation condition of a charging pile when the jth electric automobile arrives at the residential area, wherein the occupation condition comprises the condition that the charging pile is used for charging service and the condition that the charging automobile finishes the charging service but does not leave the parking space; judging whether redundant idle charging piles exist or not;
s4.3, simulating the charging willingness of the jth electric vehicle owner by using a fuzzy comprehensive evaluation method according to the charging selection model in the step S3;
if the jth charging automobile owner is willing to charge, a charging pile providing a charging service is distributed for the jth charging automobile, the corresponding element of the charging pile mark matrix is set to be 1, and if the jth electric automobile owner gives up charging, the corresponding element of the charging pile mark matrix is kept to be 0;
s4.4, calculating the charging time length T of the jth electric automobileC，j：
Figure BDA0002890211970000031
Figure BDA0002890211970000032
Figure BDA0002890211970000033
In the formula: t is twait，jTime for vehicle owner waiting for charging, tch，jThe length of service time for which charging is to be performed,
Figure BDA0002890211970000034
time to start charging for electric vehicle, tarr，jTime to reach charging pile, SOC, for electric vehicletdIs a state of charge threshold, SOCarr，jFor the jth electric vehicle to reach the state of charge, SOC, of the charging pileexp，jDesired state of charge for jth electric vehicle, CbatThe battery capacity of the electric automobile is shown, eta is the charging efficiency, and lambda is the power decay time constant;
s4.5, simulating the behavior of the jth electric vehicle owner leaving the charging parking space by using a fuzzy comprehensive evaluation system according to the charging selection model in the step S3;
and if the jth charging car owner wants to approve moving the car at the moment of charging ending, setting the corresponding element of the charging pile mark matrix to be 0, otherwise, setting the corresponding element to be 1 until the next trip.
Preferably, in step S3, the method for establishing a charging selection model based on a fuzzy comprehensive evaluation method includes the steps of:
s3.1, determining an evaluation index system of charging selection, wherein the index system comprises a selectable factor set U;
s3.2, selecting an evaluation set V and a membership function mu (x);
s3.3, determining an evaluation factor weight vector W;
and S3.4, calculating a fuzzy relation matrix B for the fuzzy evaluation comprehensive evaluation method.
Preferably, in step S3.1, the evaluation index system includes a selectable factor set U, as shown in formula (4):
U＝{u1，u2} (4)
wherein u is1Is an SOC index, u2Is a queuing time index;
in the step S3.2, the electric vehicle owner charging selection evaluation result is divided into 3 levels, and the evaluation set is constructed as follows:
V＝{v1，v2，v3} (5)
in the formula: v. of1V for high willingness to charge2For general purpose of charging, v3The willingness to charge is low;
in step S3.2, a membership function is selected, specifically as follows:
aiming at SOC, selecting a trapezoidal distribution membership function model, and taking a smaller trapezoidal distribution as v1Distribution function of (d):
Figure BDA0002890211970000041
the middle type is distributed in a trapezoidal way as v2Distribution function of (d):
Figure BDA0002890211970000042
v is a large trapezoid3Distribution function of (d):
Figure BDA0002890211970000043
in the formula: a. b, c and d are fuzzy demarcation points of 3 evaluation grades respectively;
aiming at the queuing time, a ridge type distribution function model is selected, and a smaller ridge distribution is used as v1Distribution function of (d):
Figure BDA0002890211970000044
intermediate ridge distribution as v2Distribution function of (d):
Figure BDA0002890211970000051
partial large ridge distribution as v3Distribution function of (d):
Figure BDA0002890211970000052
in the formula a1、a2、a3、a4Fuzzy demarcation points of 3 evaluation grades respectively.
Preferably, in step S3.3, the evaluation factor weight vector is determined, and the specific steps are as follows:
after the SOC of the electric automobile arriving at the residential area and the queuing waiting time are definitely taken as main factors influencing a charging decision, a judgment matrix for comparing the two factors is constructed:
Figure BDA0002890211970000053
wherein a isij(i, j ═ 1, 2) is a proportional scale showing the ratio of the degree of influence of the two factors on the vehicle owner charging choice;
determining a relative weight vector by adopting a root finding method, wherein the formula is as follows:
Figure BDA0002890211970000054
i, j and k are the number of factors in an evaluation index system respectively, and the number of the factors is 1 and 2;
and (5) carrying out consistency check to verify the reasonability of the weight:
Figure BDA0002890211970000061
in the formula: CI is a consistency index, λmaxJudging the maximum eigenvalue of the matrix;
Figure BDA0002890211970000062
in the formula: RI is a random consistency index; CR is an index of the consistency ratio, and if CR <0.10, the consistency of the judgment matrix is considered to be acceptable.
Preferably, in step S3.4, the fuzzy relation matrix B is calculated as follows:
Figure BDA0002890211970000063
rmnrepresenting the degree of membership of the object to be evaluated to a certain evaluation factor, bnAnd represents the membership degree of the evaluated object to the evaluation result as a whole.
Preferably, the method further comprises the steps of:
s4.6, obtaining the waiting time, the charging time and the parking time of each electric vehicle in the parking lot of the whole residential area, wherein the total charging power W (t) of all the electric vehicles in the residential area is as follows:
Figure BDA0002890211970000064
wherein, PC，j(t) represents the charging power of the jth electric vehicle.
Preferably, in step S1, the power battery charging process of the electric vehicle includes a constant current stage and a constant voltage stage, and the formula of the power battery charging model is as follows:
Figure BDA0002890211970000065
wherein, PCFor charging electric vehicles, PmaxFor constant charging power in the constant-current phase, TtdAt the end of the constant current phase, TmAt the end of the constant voltage phase, λ is the power decay time constant, and t represents time.
Preferably, in the step S2, the arrival time of the vehicle owner at the residential area follows a normal distribution:
Figure BDA0002890211970000071
wherein, musAnd σsRespectively is the mean value and standard deviation of the time of the random variable returning to the residential area; x represents the time when the owner arrives at the residential area, fs(x) Indicating the arrival of the ownerA probability density function of time obeying of the residential area;
the daily mileage of the car owner follows lognormal distribution:
Figure BDA0002890211970000072
wherein, muDAnd σDRespectively is the mean value and standard deviation of the daily driving mileage of the random variable; f. ofD(x) And the probability density function represents the obedience of the daily mileage of the vehicle owner.
A residential electric vehicle charging system based on a fuzzy comprehensive evaluation method is characterized by comprising the following steps:
the monitoring data acquisition module is used for monitoring the entrance and exit conditions of the electric automobiles in the residential area and generating arrival time and departure time data, arrival charging pile time of each electric automobile, charging waiting time and charging duration data;
the database module is used for storing monitoring data and system data, wherein the system data comprises the number of charging piles of wind in a residential area, the number of electric vehicles in the residential area, the charge state threshold value of the electric vehicles and the battery capacity of the electric vehicles;
the data processing module executes the modeling method and is used for establishing a power battery charging model of a single electric vehicle according to system data and monitoring data, extracting the charging starting time and daily driving mileage of each electric vehicle by adopting a Monte Carlo method, establishing a probability model of the time of arriving at a residential area and the daily driving mileage of each electric vehicle, judging the charging selection of vehicle owners by utilizing a fuzzy comprehensive evaluation method, establishing a charging selection model based on the fuzzy comprehensive evaluation method, and analyzing to obtain the charging response time of each vehicle owner;
and the statistical analysis module is used for counting the daily charging behavior data of each vehicle owner, analyzing and obtaining the charging demand time distribution of each electric vehicle, and counting the daily load curve of the charging demand of the electric vehicles in the whole residential area.
Has the advantages that: due to the adoption of the technical scheme, the invention has the following technical effects:
the residential electric vehicle charging demand modeling method based on the fuzzy comprehensive judgment method provided by the invention considers the situation of insufficient charging facilities, and comprises two aspects, namely, early construction fails to consider the rapid increase of future electric vehicle reserves, and secondly, partial owners who finish charging service cannot leave a charging parking space in time to cause that the subsequent owners cannot carry out charging planning, and meanwhile, the charging demand modeling based on the charging willingness of the owners is carried out to carry out more refined modeling processing on the charging power of the electric vehicle, so that the residential charging demand is closer to the actual situation.
Drawings
FIG. 1 is a Monte Carlo flow chart of the present invention.
Detailed Description
As shown in fig. 1, the invention discloses a residential electric vehicle charging demand modeling method based on a fuzzy comprehensive judgment method.
Firstly, establishing a power battery model:
Figure BDA0002890211970000081
in the formula: pCFor charging electric vehicles, PmaxFor constant charging power in the constant-current phase, TtdAt the end of the constant current phase, TmAnd lambda is a power decay time constant at the end time of the constant voltage phase.
Secondly, establishing a probability model of the mileage of arriving at the residential area and traveling on the day:
the time of arrival of the owner at the residential area follows a normal distribution:
Figure BDA0002890211970000082
in the formula: mu.ssAnd σsRespectively, mean and standard deviation of the time for the vehicle owner to return to the residential area.
The daily mileage of the car owner follows lognormal distribution:
Figure BDA0002890211970000083
in the formula: mu.sDAnd σDThe average value and the standard deviation of the daily mileage of the electric automobile are respectively. f. ofD(x) And (3) representing a probability density function obeyed by the daily mileage of the vehicle owner, and establishing a data model of the daily mileage of the vehicle owner for extracting a random number obeying the probability density so as to accurately simulate the driving behavior of the vehicle owner.
Calculating the starting SOC of the jth electric automobile:
Figure BDA0002890211970000091
in the formula: d is the mileage on the day, dmThe maximum daily mileage.
Then, establishing a charging selection model based on a fuzzy comprehensive evaluation method:
when the owner judges the charging of the electric automobile which runs back to the residential area, the owner is restricted by various subjective and objective factors, and the subjective charging selection of the owner is reasonably judged by a fuzzy comprehensive evaluation method. The fuzzy comprehensive evaluation comprises the following specific steps:
(1) determining an evaluation index system of charging selection;
(2) selecting a comment set and a membership function;
(3) determining an evaluation factor weight vector;
(4) calculating a fuzzy relation matrix;
an evaluation index system of charging selection is determined, and the method comprises the following steps:
when the electric vehicle returns to the residence zone, the magnitude of the charge capacity (SOC) of the electric vehicle is directly related to whether the vehicle owner can continue the next journey, so the SOC is one of the factors influencing the charging selection of the vehicle owner. If the charging facility cannot meet all the car owners needing charging at a certain time, a queuing phenomenon occurs, and the length of the team is one of the factors directly influencing the charging selection of the car owners. The set of selectable factors is:
U＝{u1，u2}
in the formula u1Is an SOC index, u2Is a queuing time index.
Selecting a comment set:
the method comprises the following steps of dividing an electric vehicle owner charging selection evaluation result into 3 grades, wherein a constructed evaluation set is as follows:
V＝{υ1，υ2，υ3}
in the formula: upsilon is1Has high willingness to charge, upsilon2V is a general desire to charge3The willingness to charge is low.
Selecting a membership function:
aiming at SOC, selecting a trapezoidal distribution membership function model, and taking smaller trapezoidal distribution as upsilon1Distribution function of (d):
Figure BDA0002890211970000092
the middle type is in trapezoidal distribution as upsilon2Distribution function of (d):
Figure BDA0002890211970000101
slightly large trapezoidal distribution as upsilon3Distribution function of (d):
Figure BDA0002890211970000102
in the formula: a. b, c and d are fuzzy demarcation points of 3 evaluation grades respectively.
Aiming at queuing time, a ridge type distribution function model is selected, and smaller ridge distribution is used as upsilon1Distribution function of (d):
Figure BDA0002890211970000103
the middle ridge distribution as upsilon1Distribution function of (d):
Figure BDA0002890211970000104
partial large ridge distribution as upsilon1Distribution function of (d):
Figure BDA0002890211970000105
in the formula a1、a2、a3、a4Fuzzy demarcation points of 3 evaluation grades respectively.
Determining an evaluation factor weight vector:
after the SOC of the electric automobile arriving at the residential area and the queuing waiting time are definitely taken as main factors influencing a charging decision, a judgment matrix for comparing the two factors is constructed:
Figure BDA0002890211970000106
in the formula: a isij(i, j ═ 1, 2) is a proportional scale showing the ratio of the degree of influence of the two factors on the vehicle owner charging selection. Measuring the scale a by a ratio of 1-9ijSpecifically, the examples are shown in Table 1. Both Ci and Cj in table 1 are evaluation factors, referred to herein as electric vehicle SOC and queue waiting time to arrive at the residential area.
TABLE 1
Figure BDA0002890211970000111
Wherein Ci and Cj are the electric automobile SOC and the queue waiting time of the electric automobile arriving at the residential district respectively
Determining a relative weight vector by adopting a root finding method, wherein the formula is as follows:
Figure BDA0002890211970000112
and (5) carrying out consistency check to verify the reasonability of the weight:
Figure BDA0002890211970000113
in the formula: CI is a consistency index, λmaxThe maximum eigenvalue of the decision matrix.
Figure BDA0002890211970000114
In the formula: RI is a random consistency index, and is specifically shown in table 2.
TABLE 2
Figure BDA0002890211970000115
CR is an index of the consistency ratio, and if CR <0.10, the consistency of the judgment matrix is considered to be acceptable.
Calculating a fuzzy relation matrix, and multiplying the weight vector by the judgment matrix to obtain:
Figure BDA0002890211970000121
in the formula: r ismnRepresenting the degree of membership of the object to be evaluated to a certain evaluation factor, bnAnd represents the membership degree of the evaluated object to the evaluation result as a whole.
Charging demand calculations based on monte carlo. The method comprises the following steps:
1. the number N of the maximum charging piles in the residential area is set, and a charging pile mark matrix S convenient for confirming the use condition of the charging pile is generated:
Figure BDA0002890211970000122
2. setting the number of the electric automobiles returning to the community within one day as M;
3. acquiring all traffic information of the jth quantity of electric automobiles at the return moment, including the residual electric quantity of batteries and queuing waiting time;
4. and judging the occupation condition of the charging pile when the jth electric automobile arrives at the residential area, wherein the occupation condition comprises the condition that the charging pile is used for charging service and the condition that the charging automobile finishes the charging service but does not leave the parking space. Judging whether redundant idle charging piles exist or not;
5. simulating the charging willingness of the jth electric vehicle owner by using a fuzzy comprehensive evaluation method;
6. if the jth charging automobile owner is willing to charge, setting the corresponding element of the charging pile zone bit matrix for providing the charging service to the jth charging automobile owner to be 1, and if the jth electric automobile owner abandons the charging, keeping the corresponding element of the charging pile zone bit matrix to be 0;
7. calculating the charging time length T of the jth electric automobileC，j；
Figure BDA0002890211970000123
Figure BDA0002890211970000131
Figure BDA0002890211970000132
In the formula: t is twait，jTime for vehicle owner waiting for charging, tch，jThe length of service time for which charging is to be performed,
Figure BDA0002890211970000133
time to start charging for electric vehicle, tarr，jTime to reach charging pile, SOC, for electric vehicletdIs a state of charge threshold, SOCarr，jFor the ith electric vehicle to reach the state of charge, SOC of the charging pileexp，jDesired state of charge for jth electric vehicle, CbatFor the battery capacity of the electric vehicle, different vehicles have different electricityThe pool capacity considers the respective battery capacity of different brand cars access charging piles, and the access charging piles can be identified and acquired by the charging piles. Eta is the charging efficiency, and lambda is the power decay time constant;
8. simulating the behavior of the jth electric automobile owner leaving the charging parking space by using a fuzzy comprehensive evaluation system;
9. if the jth charging automobile owner is willing to agree to move the automobile at the moment of charging ending, setting the corresponding element of the flag bit matrix to be 0, otherwise, setting the element to be 1 until the next trip;
10. and obtaining the waiting time, the charging time and the parking time of each electric automobile in the parking lot of the whole residential area. The charging total power w (t) of all electric vehicles in the residential area is shown in fig. 1 in a specific flow;
Figure BDA0002890211970000134
PC，j(t) represents the charging power of the jth electric vehicle.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (10)

1. A modeling method for residential electric vehicle charging demand based on a fuzzy comprehensive evaluation method is characterized by comprising the following steps:
s1, establishing a power battery charging model of the single electric automobile;
s2, establishing a probability model of the time of arrival at the residential area and the daily mileage of each electric automobile;
s3, establishing a charging selection model based on a fuzzy comprehensive evaluation method;
and S4, calculating the charging requirement of each vehicle owner based on the Monte Carlo method.
2. The modeling method of electric vehicle charging demand for residential areas based on the fuzzy comprehensive evaluation method as set forth in claim 1, wherein in said step S4, the charging demand calculation step based on the monte carlo method is as follows:
s4.1, setting the maximum number N of the charging piles in the residential area, and generating a charging pile mark matrix S convenient for confirming the use condition of the charging pile:
Figure FDA0002890211960000011
wherein n represents the nth charging pile, and t represents time and unit minute;
s4.2, setting the number of the electric automobiles returning to the residential area within one day to be M, and acquiring vehicle information of the jth electric automobile at the return moment, including the residual electric quantity of the battery;
judging the occupation condition of a charging pile when the jth electric automobile arrives at the residential area, wherein the occupation condition comprises the condition that the charging pile is used for charging service and the condition that the charging automobile finishes the charging service but does not leave the parking space; judging whether redundant idle charging piles exist or not;
s4.3, simulating the charging willingness of the jth electric vehicle owner by using a fuzzy comprehensive evaluation method according to the charging selection model in the step S3;
if the jth charging automobile owner is willing to charge, a charging pile providing a charging service is distributed for the jth charging automobile, the corresponding element of the charging pile mark matrix is set to be 1, and if the jth electric automobile owner gives up charging, the corresponding element of the charging pile mark matrix is kept to be 0;
s4.4, calculating the charging time length T of the jth electric automobileC，j：
Figure FDA0002890211960000012
Figure FDA0002890211960000021
Figure FDA0002890211960000022
In the formula: t is twait，jTime for vehicle owner waiting for charging, tch，jThe length of service time for which charging is to be performed,
Figure FDA0002890211960000023
time to start charging for electric vehicle, tarr，jTime to reach charging pile, SOC, for electric vehicletdIs a state of charge threshold, SOCarr，jFor the jth electric vehicle to reach the state of charge, SOC, of the charging pileexp，jDesired state of charge for jth electric vehicle, CbatThe battery capacity of the electric automobile is shown, eta is the charging efficiency, and lambda is the power decay time constant;
s4.5, simulating the behavior of the jth electric vehicle owner leaving the charging parking space by using a fuzzy comprehensive evaluation system according to the charging selection model in the step S3;
and if the jth charging car owner wants to approve moving the car at the moment of charging ending, setting the corresponding element of the charging pile mark matrix to be 0, otherwise, setting the corresponding element to be 1 until the next trip.
3. The method for modeling a residential electric vehicle charging demand based on the fuzzy comprehensive evaluation method according to any one of claims 1 or 2, wherein the step S3 of establishing a charging selection model based on the fuzzy comprehensive evaluation method comprises the steps of:
s3.1, determining an evaluation index system of charging selection, wherein the index system comprises a selectable factor set U;
s3.2, selecting an evaluation set V and a membership function mu (x);
s3.3, determining an evaluation factor weight vector W;
and S3.4, calculating a fuzzy relation matrix B for the fuzzy evaluation comprehensive evaluation method.
4. The modeling method of residential electric vehicle charging demand based on fuzzy comprehensive evaluation method as claimed in claim 3, characterized in that:
in step S3.1, the evaluation index system includes a selectable factor set U, as shown in formula (4):
U＝{u1，u2} (4)
wherein u is1Is an SOC index, u2Is a queuing time index;
in the step S3.2, the electric vehicle owner charging selection evaluation result is divided into 3 levels, and the evaluation set V is constructed as follows:
V＝{v1，v2，v3} (5)
in the formula: v. of1V for high willingness to charge2For general purpose of charging, v3The willingness to charge is low;
in step S3.2, a membership function μ (x) is selected, specifically as follows:
aiming at SOC, selecting a trapezoidal distribution membership function model, and taking a smaller trapezoidal distribution as v1Distribution function of (d):
Figure FDA0002890211960000031
the middle type is distributed in a trapezoidal way as v2Distribution function of (d):
Figure FDA0002890211960000032
v is a large trapezoid3Distribution function of (d):
Figure FDA0002890211960000033
in the formula: a. b, c and d are fuzzy demarcation points of 3 evaluation grades respectively;
aiming at the queuing time, a ridge type distribution function model is selectedThe distribution of type and minor ridge is v1Distribution function of (d):
Figure FDA0002890211960000034
intermediate ridge distribution as v2Distribution function of (d):
Figure FDA0002890211960000041
partial large ridge distribution as v3Distribution function of (d):
Figure FDA0002890211960000042
in the formula a1、a2、a3、a4Fuzzy demarcation points of 3 evaluation grades respectively.
5. The modeling method for residential electric vehicle charging demand based on fuzzy comprehensive evaluation method according to claim 4, characterized in that in step S3.3, an evaluation factor weight vector is determined, and the specific steps are as follows:
after the SOC of the electric automobile arriving at the residential area and the queuing waiting time are definitely taken as main factors influencing a charging decision, a judgment matrix for comparing the two factors is constructed:
Figure FDA0002890211960000043
wherein a isij(i, j ═ 1, 2) is a proportional scale showing the ratio of the degree of influence of the two factors on the vehicle owner charging choice;
determining a relative weight vector by adopting a root finding method, wherein the formula is as follows:
Figure FDA0002890211960000044
i, j and k are the number of factors in an evaluation index system respectively, and the number of the factors is 1 and 2;
and (5) carrying out consistency check to verify the reasonability of the weight:
Figure FDA0002890211960000051
in the formula: CI is a consistency index, λmaxJudging the maximum eigenvalue of the matrix;
Figure FDA0002890211960000052
in the formula: RI is a random consistency index; CR is an index of the consistency ratio, and if CR is less than 0.10, it is considered that the consistency of the judgment matrix is acceptable.
6. The modeling method for residential electric vehicle charging demand based on fuzzy comprehensive evaluation method according to claim 6, characterized in that in step S3.4, the fuzzy relation matrix B is calculated as follows:
Figure FDA0002890211960000053
rmnrepresenting the degree of membership of the object to be evaluated to a certain evaluation factor, bnAnd represents the membership degree of the evaluated object to the evaluation result as a whole.
7. The modeling method of residential electric vehicle charging demand based on fuzzy comprehensive evaluation method according to claim 2, characterized by further comprising the steps of:
s4.6, obtaining the waiting time, the charging time and the parking time of each electric vehicle in the parking lot of the whole residential area, wherein the total charging power W (t) of all the electric vehicles in the residential area is as follows:
Figure FDA0002890211960000054
wherein, PC,j(t) represents the charging power of the jth electric vehicle.
8. The modeling method for electric vehicle charging demand in residential areas based on fuzzy comprehensive evaluation method of any one of claim 1, wherein in step S1, the power battery charging process of the electric vehicle includes a constant current stage and a constant voltage stage, and the formula of the power battery charging model is as follows:
Figure FDA0002890211960000061
wherein, PCFor charging electric vehicles, PmaxFor constant charging power in the constant-current phase, TtdAt the end of the constant current phase, TmAt the end of the constant voltage phase, λ is the power decay time constant, and t represents time.
9. The modeling method for electric vehicle charging demand of residential area based on fuzzy comprehensive evaluation method as claimed in claim 1, wherein in said step S2, the arrival time of vehicle owner at residential area follows normal distribution:
Figure FDA0002890211960000062
wherein, musAnd σsRespectively is the mean value and standard deviation of the time of the random variable returning to the residential area; x represents the time when the owner arrives at the residential area, fs(x) A probability density function representing a time obedient of the vehicle owner's arrival at the residential area;
the daily mileage of the car owner follows lognormal distribution:
Figure FDA0002890211960000063
wherein, muDAnd σDRespectively is the mean value and standard deviation of the daily driving mileage of the random variable; f. ofD(x) And the probability density function represents the obedience of the daily mileage of the vehicle owner.
10. A residential electric vehicle charging system based on a fuzzy comprehensive evaluation method is characterized by comprising the following steps:
the monitoring data acquisition module is used for monitoring the entrance and exit conditions of the electric automobiles in the residential area and generating arrival time and departure time data, arrival charging pile time of each electric automobile, charging waiting time and charging duration data;
the database module is used for storing monitoring data and system data, wherein the system data comprises the number of charging piles of wind in a residential area, the number of electric vehicles in the residential area, the charge state threshold value of the electric vehicles and the battery capacity of the electric vehicles;
the data processing module executes the modeling method of any one of claims 1 to 9, and is used for establishing a power battery charging model of a single electric vehicle according to system data and monitoring data, extracting the charging starting time and daily driving mileage of each electric vehicle by adopting a Monte Carlo method, establishing a probability model of the time of arrival at a residential area and the daily driving mileage of each electric vehicle, judging the charging selection of the vehicle owner by using a fuzzy comprehensive evaluation method, establishing a charging selection model based on the fuzzy comprehensive evaluation method, and analyzing to obtain the charging response time of each vehicle owner;
and the statistical analysis module is used for counting the daily charging behavior data of each vehicle owner, analyzing and obtaining the charging demand time distribution of each electric vehicle, and counting the daily load curve of the charging demand of the electric vehicles in the whole residential area.
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Cited By (4)

* Cited by examiner, † Cited by third party
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CN113269372A (en) * 2021-06-23 2021-08-17 华北电力大学 Cluster electric vehicle schedulable capacity prediction evaluation method considering user will
CN113386611A (en) * 2021-06-22 2021-09-14 南方电网数字电网研究院有限公司 Charging and discharging control method and device, computer equipment and storage medium
CN113627741A (en) * 2021-07-20 2021-11-09 国网湖南省电力有限公司 Comprehensive evaluation method and device for operation state of charging pile electric energy metering system
CN117429303A (en) * 2023-10-10 2024-01-23 北京理工大学前沿技术研究院 Electric automobile battery replacement method, system and equipment based on Internet of things

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113386611A (en) * 2021-06-22 2021-09-14 南方电网数字电网研究院有限公司 Charging and discharging control method and device, computer equipment and storage medium
CN113269372A (en) * 2021-06-23 2021-08-17 华北电力大学 Cluster electric vehicle schedulable capacity prediction evaluation method considering user will
CN113627741A (en) * 2021-07-20 2021-11-09 国网湖南省电力有限公司 Comprehensive evaluation method and device for operation state of charging pile electric energy metering system
CN113627741B (en) * 2021-07-20 2023-12-12 国网湖南省电力有限公司 Comprehensive evaluation method and device for operation state of charging pile electric energy metering system
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