CN110232219B - Electric vehicle schedulable capacity verification method based on data mining - Google Patents

Electric vehicle schedulable capacity verification method based on data mining Download PDF

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CN110232219B
CN110232219B CN201910415261.2A CN201910415261A CN110232219B CN 110232219 B CN110232219 B CN 110232219B CN 201910415261 A CN201910415261 A CN 201910415261A CN 110232219 B CN110232219 B CN 110232219B
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王浩林
张勇军
叶琳浩
宋伟伟
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South China University of Technology SCUT
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Abstract

The invention provides a data mining-based method for checking the dispatchable capacity of an electric vehicle. The method comprises the following steps: firstly, establishing an automobile transportation trip chain concept, and establishing a mathematical model including first trip time, travel time, parking time, travel mileage and a spatial probability transfer matrix by performing data mining on a database, and forming an electric automobile transportation trip chain; then, on the basis of the automobile traffic behavior model, a charging behavior model is established; then, simulating the traffic behavior and the charging behavior of the automobile to obtain an automobile real-time SOC curve; deducing a schedulable SOC judgment threshold and an automobile schedulable time period under different power grid frequency modulation modes; and finally, calculating the schedulable capacity of the automobile. The invention provides a data mining-based electric vehicle schedulable capacity verification method, which can provide a basis for power grid peak and frequency modulation, and schedule the charging and discharging of an electric vehicle according to the schedulable capacity in the power grid scheduling frequency modulation.

Description

Electric vehicle schedulable capacity verification method based on data mining
Technical Field
The invention relates to the technical field of intelligent control of electric automobiles, in particular to a method for verifying schedulable capacity of an electric automobile.
Background
In recent years, electric vehicles powered by green energy have become popular due to the large demand for urban public transportation and the energy environmental problem. The electric automobile mainly operates in cities, can be regarded as an explicit load and a recessive power supply, and the uncertain charging and discharging behaviors of the electric automobile can bring great influence on the operation of an urban power grid. Therefore, the charge and discharge capacity evaluation of the electric automobile has important significance for future urban traffic networks and urban power grids.
In recent years, many methods have been proposed by related scholars to perform a lot of research on the charge and discharge scheduling of the electric vehicle. The schedulable capacity of the electric automobile is the electric energy which can meet various scheduling requirements in the aspect of a power grid on the premise of ensuring the travel requirements of a user. Whether the electric vehicle is scheduled or not is judged according to the real-time remaining power (SOC) of the electric vehicle. The grid can continue capacity scheduling only when the SOC-related constraints are met. Therefore, the premise of researching schedulable capacity is to accurately and scientifically simulate the traffic behavior of the electric automobile and further obtain the SOC curve of the electric automobile. Nowadays, most documents mostly give a certain probability distribution model when obtaining an SOC curve, and the method is too simplified to describe the power change situation in the driving process of the vehicle scientifically, and has a great influence on the subsequent result of schedulable capacity.
Disclosure of Invention
The invention aims to solve the defects of the schedulable capacity of the electric automobile, and provides a data mining-based schedulable capacity checking method for the electric automobile, so that the charging and discharging load of the electric automobile can provide reference and basis for power grid scheduling frequency modulation and the like.
The invention provides a method for checking schedulable capacity of an electric vehicle based on data mining, which comprises the following steps:
(1) Analyzing the automobile travel place data to obtain a main area of automobile travel, and taking the main area as the basis of subsequent traffic behavior analysis;
(2) Mining and analyzing data of the first trip time, the running time, the parking time, the running mileage and the space probability transition matrix of the automobile to form a one-day trip chain of the automobile and deduce a traffic behavior model of the automobile;
(3) And analyzing the automobile charging process and deducing a charging behavior model of the automobile.
(4) And simulating the traffic behavior and the charging behavior of the automobile by a Monte Carlo simulation method to obtain the SOC change curve of the automobile in one day.
(5) And deducing SOC thresholds and automobile schedulable time periods in the corresponding three scheduling modes according to the three frequency modulation modes of the power grid.
(6) And calculating to obtain the schedulable capacity of the automobile according to the SOC curve of the automobile and the schedulable SOC threshold.
In the method for checking the dispatchable capacity of the electric vehicle based on the data mining, the distribution proportion of the main areas of vehicle traveling is obtained by analyzing the data of the vehicle traveling places.
In the method for checking the schedulable capacity of the electric vehicle based on the data mining, the first trip time of the electric vehicle obtained by fitting the vehicle driving data meets the following formula (1) of multidimensional normal distribution:
Figure GDA0002146130970000021
in the formula, wherein a 1 ＝0.34,μ 1 ＝7.46,σ 1 ＝0.77；a 2 ＝0.66,μ 2 ＝9.20,σ 2 ＝2.75
Obtaining the logarithmic normal distribution of the electric automobile running time satisfying the following formula (2) by fitting the vehicle running data:
Figure GDA0002146130970000031
in the formula, t tr For the length of travel, μ tr ,σ tr The values for the corresponding expected and standard deviations of the origin and destination points are shown in the matrix, where the ith row represents the origin D i The j-th row represents the arrival place D j 。
Figure GDA0002146130970000032
The parking time of the electric automobile obtained by fitting the vehicle driving data meets the lognormal distribution of the following formula (3):
Figure GDA0002146130970000033
in the formula, t d For the duration of travel, mu td ,σ td Expectation and standard deviation for the respective parking locations;
the automobile basically can be regarded as constant-speed driving when running in a city, and the driving mileage and the driving time of the electric automobile obtained by fitting the vehicle driving data can be regarded as a linear relation.
d＝v(t tr )×t tr (3)
From the central limit law and the law of majority, it can be seen that the driving range satisfies the lognormal distribution of the formula (4) as well as the driving duration, and the expectation and the standard deviation mu thereof d (t tr ),σ d (t tr ) Should be equal to the period of travel time t tr To (3) is described. Now let t tr Dividing the time into 20 minutes into one window for data analysis to obtain mu in each time window d And σ d The value of (c).
Figure GDA0002146130970000034
The user can start the journey at any time and any place, and the destination can be any area of D1-D4. The selection of the destination is related to the starting time and the starting place of the user journey, so that the destination of the journey starting at the corresponding time and the starting place can be described by a plurality of space transition probability matrixes taking time as a section. I.e. one according to a certain time period t k Discretized k 4 x 4 matrix P k To describe the spatial transition situation within the time period. Matrix P k The ith row of (A) represents the departure place D i The jth column represents the arrival ground D j . k is the number of time intervals after discretization, P k Is the spatial transition probability matrix over the k period. For example, a day is 2 hoursA period, divided into k =12 periods. For example, P 10 A spatial probability transition matrix of 20-21 points.
Figure GDA0002146130970000041
Through the steps, a one-day travel chain of the electric automobile can be established, and a traffic behavior model of the automobile can be deduced. The charging behavior of the electric vehicle is analyzed.
After the automobile finishes each section of travel, whether the automobile is charged or not is determined according to the residual capacity, and the formula (5) is a criterion for charging the automobile.
(E×SOC i -d i ×k)≤0.4E (5)
In the formula, E is the capacity of the automobile battery, kW.h; d i The driving mileage of the ith journey is km; k is the power consumption per kilometer of the automobile, kW.h/km; SOC i And starting the electric quantity for the ith stroke.
SOC of i +1 th trip after charging is finished i+1 Comprises the following steps:
Figure GDA0002146130970000042
in the formula, η is the charging efficiency, and is 0.9.P is the slow charging or fast charging power, kW.
In the process from the beginning of the automobile running to the stopping of the automobile, the electric quantity is linearly reduced according to the power consumption k per kilometer, and the SOC in the running process is calculated as a formula (7). The automobile is considered to adopt a constant power charging mode from the beginning to the end of charging, the SOC shows a linear increasing trend until the end of charging, and the SOC in the charging process is calculated as a formula (8).
Figure GDA0002146130970000051
In the formula, SOC i The electric quantity at the beginning of the ith journey; k is power consumption per kilometer; d is a radical of i The ith driving mileage is km; e is the battery capacity kW.h;t tr the driving time is min.
Figure GDA0002146130970000052
In the formula, SOC i0 The electric quantity when the charging is started for the ith journey; SOC i+1 Is the starting power of the i +1 th trip, t c Is the charging duration.
Through the charging behavior analysis, a continuous and complete SOC curve of the automobile can be obtained.
Based on the above situation, the method firstly establishes an automobile traffic trip chain concept, establishes an electric automobile trip model by performing data mining on a database, simultaneously establishes an electric automobile charging model, simulates an automobile driving process to obtain a real-time SOC curve, then obtains an SOC judgment threshold under a schedulable condition, analyzes a schedulable time period and a scheduling mode of the automobile according to the SOC judgment threshold, and finally evaluates the schedulable capacity of the electric automobile.
Compared with the prior art, the invention has the following advantages and technical effects:
according to the invention, the schedulable capacity is calculated based on the SOC change curve of the electric vehicle. And obtaining a traffic behavior model of the electric automobile by a data analysis method, and deducing to obtain a complete and continuous SOC change curve by combining the charging behavior model. When the traffic behavior model is established, the automobile travel conditions of various places at various time intervals are considered, and a more accurate mathematical model is obtained through deduction, so that the randomness of the automobile travel in the actual life can be more fitted. According to the invention, corresponding SOC threshold value calculation methods and scheduling conditions are respectively given according to three frequency modulation modes of the power grid, and the maximum schedulable potential capacity under three conditions can be determined under the condition of ensuring the normal running of the automobile.
Drawings
FIG. 1 is an SOC variation curve of an electric vehicle discharge scheduling in an embodiment.
FIG. 2 is a flowchart illustrating a method for checking a dispatchable capacity of an electric vehicle according to an embodiment.
Fig. 3 shows schedulable capacities in three scheduling manners in the embodiment.
Fig. 4 shows the primary scheduling capacity of the four types of regions in the embodiment.
Fig. 5 is a general flow chart of a method for checking a dispatchable capacity of an electric vehicle based on data mining.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples, but the invention is not limited thereto, and it is to be noted that processes or symbols which are not described in detail below can be understood or implemented by those skilled in the art with reference to the prior art.
FIG. 5 is a general flow chart of a method for checking the dispatchable capacity of an electric vehicle based on data analysis. Fig. 2 is a flow further detailed in this embodiment, which includes the following steps:
(1) Analyzing the automobile travel location data to obtain a main area of automobile travel, and taking the main area as the basis of subsequent traffic behavior analysis;
(2) Mining and analyzing data of the first trip time, the driving time, the parking time, the driving mileage and the spatial probability transfer matrix of the automobile to form a one-day trip chain of the automobile and deduce a traffic behavior model of the automobile;
(3) And analyzing the automobile charging process and deducing a charging behavior model of the automobile.
(4) And simulating the traffic behavior and the charging behavior of the automobile by a Monte Carlo simulation method to obtain the SOC change curve of the automobile in one day.
(5) And deducing SOC thresholds and automobile schedulable time periods in the corresponding three scheduling modes according to the three frequency modulation modes of the power grid.
(6) And calculating to obtain the schedulable capacity of the automobile according to the SOC curve of the automobile and the schedulable SOC threshold.
In the method for checking the schedulable capacity of the electric vehicle based on the data mining, the distribution proportion of the main areas of the vehicle trip is obtained by analyzing the data of the vehicle trip location:
TABLE 1 electric vehicle ratio of travel destination
Figure GDA0002146130970000071
Four types of regions are denoted by D1, D2, D3, D4, respectively.
In the method for checking the schedulable capacity of the electric vehicle based on the data mining, the first trip time of the electric vehicle obtained by fitting the vehicle driving data meets the following formula (1) of multidimensional normal distribution:
Figure GDA0002146130970000072
in the formula, wherein a 1 ＝0.34,μ 1 ＝7.46,σ 1 ＝0.77；a 2 ＝0.66,μ 2 ＝9.20,σ 2 ＝2.75
Obtaining the logarithmic normal distribution of the electric automobile running time satisfying the following formula (2) by fitting the vehicle running data:
Figure GDA0002146130970000073
in the formula, t tr For the length of travel, μ tr ,σ tr For the corresponding expected and standard deviations of the origin-destination point, take the values in the following matrix, where the ith row represents the origin D i The j-th row represents the arrival place D j 。
Figure GDA0002146130970000074
The parking time of the electric automobile obtained by fitting the vehicle driving data meets the lognormal distribution of the following formula (3):
Figure GDA0002146130970000075
in the formula, t d In order to set the travel time period,μ td ,σ td the expectation and standard deviation for the respective parking spot;
TABLE 2 logarithmic distribution parameters of parking duration
Figure GDA0002146130970000081
The automobile basically can be regarded as constant-speed driving when driving in a city, and the driving mileage and the driving duration of the electric automobile can be obtained by fitting the driving data of the automobile and can be regarded as a linear relation.
d＝v(t tr )×t tr (3)
It can be understood from the central limit law and the law of large numbers that the driving range satisfies the lognormal distribution of the formula (4) as well as the driving time, and the expectation and the standard deviation mu thereof d (t tr ),σ d (t tr ) Should be equal to the period of travel time t tr To (3). Now let t tr Dividing the time into 20 minutes into one window for data analysis to obtain mu in each time window d And σ d The specific values of the parameters are shown in Table 3.
Figure GDA0002146130970000082
TABLE 3 logarithmic mileage distribution parameters
Figure GDA0002146130970000083
The user can start the journey at any time and any place, and the destination can be any area of D1-D4. The selection of the destination is related to the starting time and the starting place of the user journey, so that the destination of the journey starting at the corresponding time and the starting place can be described by a plurality of space transition probability matrixes taking time as a section. I.e. one according to a certain time period t k Discretized k 4 x 4 matrix P k To describe the spatial transition situation within the time period. Matrix P k The ith row of (A) represents the departure place D i J th row generationTable arrival place D j . k is the number of time intervals after discretization, P k Is the spatial transition probability matrix over the k period. For example, a day is divided into k =12 periods by 2 hours as one period. For example, P 10 A spatial probability transition matrix of 20-21 points.
Figure GDA0002146130970000091
Through the steps, a one-day travel chain of the electric automobile can be established, and a traffic behavior model of the automobile can be deduced. The charging behavior of the electric vehicle is analyzed.
All spatial probability transition matrices are as follows.
Figure GDA0002146130970000092
Figure GDA0002146130970000093
Figure GDA0002146130970000094
Figure GDA0002146130970000101
Figure GDA0002146130970000102
Figure GDA0002146130970000103
After each section of travel of the automobile is finished, whether the automobile is charged or not is determined according to the residual capacity, and the formula (5) is a criterion for charging the automobile.
(E×SOC i -d i ×k)≤0.4E (5)
In the formula, E is the capacity of the automobile battery, kW.h; d is a radical of i The driving mileage of the ith journey is km; k is the power consumption per kilometer of the automobile, kW.h/km; SOC i And starting the electric quantity for the ith stroke.
SOC of i +1 th trip after charging i+1 Comprises the following steps:
Figure GDA0002146130970000104
in the formula, η is charging efficiency, and is 0.9.P is slow charging or fast charging power, kW.
In the process from the start of the automobile to the stop of the automobile, the electric quantity is linearly reduced according to the power consumption k per kilometer, and the SOC in the driving process is calculated as a formula (7). During the process from the beginning to the end of charging, the vehicle is considered to adopt a constant power charging mode, the SOC shows a linear increasing trend until the end of charging, and the SOC during charging is calculated as formula (8).
Figure GDA0002146130970000111
In the formula, SOC i The electric quantity at the beginning of the ith journey; k is power consumption per kilometer; d is a radical of i The ith driving mileage is km; e is the battery capacity kW.h; t is t tr Min is the driving time.
Figure GDA0002146130970000112
In the formula, SOC i0 The electric quantity when the charging is started for the ith journey; SOC (system on chip) i+1 Is the initial electric quantity of the i +1 th trip, t c Is the charging period.
Through the charging behavior analysis, a continuous and complete SOC curve of the automobile can be obtained.
In the method for checking the schedulable capacity of the electric vehicle based on the data mining, when the vehicle is in an idle state of network access, namely the SOC curve is kept horizontal, the electric vehicle has the basic condition of capacity scheduling. Meanwhile, since the charge/discharge scheduling of the vehicle must be performed without affecting the next trip, a more detailed SOC determination constraint should be set for this interval.
(1) Discharge scheduling SOC threshold calculation
FIG. 1 shows a SOC variation curve of an electric vehicle discharge scheduling. When the automobile is in an idle state and the SOC value is high, the automobile has the possibility of discharging and dispatching at the moment. However, the discharging schedule should ensure that the remaining capacity of the automobile after the discharging is finished does not affect the next trip, for example, the SOC value at the beginning of each trip should be not less than 0.2, and the SOC threshold value calculation formula of the discharging schedule is (9-11).
Figure GDA0002146130970000113
In the formula, SOC 0 i+1 The electric quantity at the end of the ith journey scheduling is obtained; SOC (system on chip) iend Responding to the electric quantity after discharge scheduling after the automobile is connected to the network for the ith journey; SOC lim Taking 0.2; p is the charging power of the power grid, kW; t is t c Charging time from the end of automobile discharging to the off-grid time, min; e is the battery capacity kW.h.
Figure GDA0002146130970000114
In the formula, SOC i 0 The electric quantity at the beginning of the ith journey scheduling; p is the automobile discharge power, kW; t is t d Time, min, is scheduled.
The network access electric quantity of the automobile should be larger than a discharge threshold value SOC dis_i The discharge scheduling constraint conditions are as follows:
Figure GDA0002146130970000121
(2) Charge scheduling SOC threshold calculation
When the electric automobile is in an idle state of network access, the electric automobile has a potential of charge scheduling as long as the electric quantity is not fully charged, so that the SOC threshold of the charge scheduling is derived from the following formula (12):
Figure GDA0002146130970000122
in the formula, SOC 0 i+1 The electric quantity at the end of the ith journey scheduling is obtained; t is t d Time, min, is scheduled.
The network access electric quantity of the automobile should be less than the charging threshold value SOC charge_i The constraint conditions of the charging schedule are as follows:
Figure GDA0002146130970000123
in the method for checking the schedulable capacity of the electric vehicle based on the data mining, the method is influenced by three frequency modulation modes of a power grid, the scheduling time of the electric vehicle in different scheduling modes is different, and the scheduling time is shown in table 3:
TABLE 3 scheduling times
Figure GDA0002146130970000124
If the network access idle time of the automobile is not shorter than the schedulable time, t in the formula d Taking values according to the table, otherwise, no scheduling condition exists.
In the method for checking the schedulable capacity of the electric vehicle based on the data mining, the schedulable capacity of the electric vehicle is calculated to meet the following requirements.
For simplicity of analysis, the charging and discharging process of the electric vehicle is regarded as constant power charging and discharging (p).
When the electric automobile is in an idle state of network access, namely the SOC curve is kept horizontal, the following conditions are met: SOC dis_i ≤SOC i (t)≤1；
Then discharge the scheduling capacity P discharge (t)＝p。
When the electric automobile is inThe net idle state, i.e. the SOC curve remains horizontal, satisfies: SOC i (t)≤SOC charge_i ≤1；
Then the charge scheduling capacity P charge (t)＝p。
And when the SOC of the electric automobile does not meet the conditions or the network access idle time is longer than the schedulable time, the charging and discharging scheduling potential is 0.
P dis (t)＝0；P charge (t)＝0。
The sum of the schedulable capacities of all electric vehicles in the power grid meets the formula (14):
Figure GDA0002146130970000131
fig. 3 shows schedulable capacities of three scheduling modes of 30 ten thousand electric vehicles in the embodiment, wherein the cases one, two and three represent the primary, secondary and tertiary scheduling modes respectively. Fig. 4 shows the primary scheduling capacity of four types of regions of 30 ten thousand electric vehicles, and scheduling can be performed according to the scheduling capacity in power grid scheduling frequency modulation.
As can be seen from fig. 3, the schedulable potential capacity of the automobile decreases with the increase of the scheduling time, and in the primary scheduling and the secondary scheduling with shorter scheduling time, the electric quantity consumed by the previous driving of the automobile has a strong scheduling potential in the short-time parking. The time of the three times of scheduling is as long as 120 minutes, the network access idle time meeting the three times of scheduling and the parking time are more than 120 minutes, the long-time parking is generally the rest parking at night, the electric quantity of the automobile is fully charged to a higher level, the potential of the automobile charging scheduling is very limited, and therefore the curve value in the step (a) is very small.
The longer the dispatching time is, the more time the automobile has to be charged after being discharged, so the calculated discharging SOC threshold is lower, and a large number of automobiles in the power grid have the discharging dispatching potential, so that in (b), the curve value of the case three is maintained at a higher level. In general, under any scheduling condition, the discharge schedulable potential capacity in the power grid is high and does not obviously respond to the change of the scheduling time, and the charge schedulable potential capacity is high in the short scheduling time and obviously responds to the change of the scheduling time.
1. The secondary charging schedulable potential capacity gradually increases to a maximum value at night because the electric quantity of the car is at a lower level at night after one day of driving. The discharge schedulable potential capacity is gradually increased from early morning to morning because the vehicle is fully charged during this time period to start the trip, and the charge is at a higher level. In general, the whole-day discharging schedulable potential capacity can basically cover the charging schedulable potential capacity, and the remainder value supplements other loads for the power grid, which brings great benefits to the operation of the power grid.
According to the simulation of the travel behaviors of the electric automobile, the automobile has four types of regions as destinations according to relevant travel characteristics, and the charging behaviors of the automobile in different regions are different. Therefore, the schedulable potential capacities in different regions are also different, and the primary schedulable potential capacity curves of the four types of regions are obtained and can be used for analyzing the regional differences of the schedulable potential capacities.
As can be seen from fig. 4, the change rules of the charge and discharge schedulable potential capacity of the electric vehicle in the four regions are similar with time, the charge and discharge potential capacity is concentrated in the relevant time period when the vehicle is in the region, and the capacity time periods in different regions are greatly different from each other. The scheduling potential of the living area is large mainly in the time period from late night to early morning, and the discharge capacity of the automobile can be used for the rest loads of the power grid as electric energy. The working area has large discharge potential in the daytime, extra power can be provided for a power grid to be used for other loads, the discharge loads of the working area are intensively scheduled, so that power resources can be fully utilized, and the efficiency is improved. The charging and discharging potential of the commercial and leisure areas is concentrated in the afternoon and evening hours, and the discharge capacity of the hours can be scheduled to compensate for the large load demands of the commercial and leisure areas. In conclusion, the targeted power grid dispatching is carried out in different areas in different time intervals, the potential of the invisible power supply of the shared automobile can be greatly explored, and meanwhile, the operation stability and flexibility of the power grid are improved.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents and are intended to be included in the scope of the present invention.

Claims (6)

1. A method for checking schedulable capacity of an electric vehicle based on data mining is characterized by comprising the following steps:
(1) Analyzing the automobile trip location data to obtain a main area of automobile trip;
(2) Mining and analyzing data of the first trip time, the running time, the parking time, the running mileage and the space probability transfer matrix of the automobile to form a one-day trip chain of the automobile and establish a traffic behavior model of the automobile;
(3) Analyzing the automobile charging process, and establishing a charging behavior model of the automobile, which specifically comprises the following steps:
after the automobile finishes each section of travel, whether the automobile is charged or not is determined according to the residual capacity, the formula (5) is a criterion for charging the automobile,
(E×SOC i -d i ×k)≤0.4E
in the formula, E is the capacity of the automobile battery, kW.h; d i The driving mileage of the ith journey is km; k is the power consumption per kilometer of the automobile, kW.h/km; SOC i Starting electric quantity for the ith stroke;
SOC of i +1 th trip after charging is finished i+1 Comprises the following steps:
Figure FDA0003883468610000011
in the formula, eta is charging efficiency, 0.9 is taken, P is slow charging or fast charging power, and unit kW is obtained; t is t c Is the charging time;
in the process that the automobile starts to run to stop, the electric quantity linearly reduces according to the power consumption k per kilometer, and the SOC in the running process is calculated as follows:
Figure FDA0003883468610000012
in the formula, SOC (t) is the electric quantity at any time t, SOC i The electric quantity at the beginning of the ith journey; k is power consumption per kilometer; d i The unit of the ith driving mileage is km; e is the battery capacity kW.h; t is t tr The running time is in unit of min;
the method is characterized in that a constant-power charging mode is adopted in the process from the beginning to the end of charging of an automobile, the SOC shows a linear increasing trend until the end of charging, and specific numerical values of the SOC in the charging process are calculated as follows:
Figure FDA0003883468610000021
in the formula, SOC i0 The electric quantity when the charging is started for the ith journey; SOC (system on chip) i+1 The starting electric quantity of the (i + 1) th journey;
(4) Simulating the traffic behavior and the charging behavior of the automobile by a Monte Carlo simulation method to obtain a one-day SOC change curve of the automobile;
(5) According to three frequency modulation modes of a power grid, deducing to obtain SOC thresholds and automobile schedulable time periods under the corresponding three scheduling modes;
(6) And deducing to obtain the schedulable capacity of the automobile according to the SOC curve of the automobile and the schedulable SOC threshold, and checking the charging and discharging capacity of the electric automobile in different frequency modulation modes of the power grid.
2. The electric vehicle schedulable capacity verification method based on data mining of claim 1, wherein:
when the automobile trip destination is selected, one is used according to the set time period t according to the difference between the time period of the automobile trip time and the starting place k Discretized k 4 x 4 matrix P k To describe the spatial transition situation within the time period; matrix P k The ith row of (A) represents the departure place D i The jth column represents the arrival ground D j (ii) a k is the number of time intervals after discretization, P k Is in the k periodThe spatial transition probability matrix of (2).
3. The data mining-based schedulable capacity check method for electric vehicles of claim 2, wherein: with t k The interval of =2 hours forms a spatial probability transition matrix of k =12 periods of a day; according to the matrix, the automobile selects a destination from the corresponding time place;
if P 10 A spatial probability transition matrix of 20-21 points;
Figure FDA0003883468610000031
4. the data mining-based schedulable capacity check method of an electric vehicle according to claim 2, wherein the traffic behavior model of the vehicle established in step (2) is as follows:
the automobile first trip time meets the following multidimensional normal distribution:
Figure FDA0003883468610000032
in the formula, wherein a 1 ＝0.34,μ 1 ＝7.46,σ 1 ＝0.77；a 2 ＝0.66,μ 2 ＝9.20,σ 2 ＝2.75；
The automobile driving time length meets the following log-normal distribution:
Figure FDA0003883468610000033
in the formula, t tr For the length of travel, t d For the length of the parking time, mu tr 、σ tr For the corresponding expected and standard deviations of the origin-destination point, take the values in the following matrix, where the ith row represents the origin D i The j-th row represents the arrival place D j ；
Figure FDA0003883468610000034
The parking time of the automobile meets the lognormal distribution of the following formula:
Figure FDA0003883468610000035
in the formula, mu td 、σ td The expectation and standard deviation for the respective parking spot;
the automobile mileage meets the lognormal distribution in a certain time window, and the automobile mileage is divided into a window in 20 minutes for data analysis:
Figure FDA0003883468610000041
wherein d is the mileage, mu d ,σ d The expected and standard deviation of the driving range within each time window.
5. The data mining-based schedulable capacity check method of an electric vehicle of claim 1, wherein step (5) comprises:
when the SOC curve of the automobile is horizontal, the automobile is in an idle state when the automobile is connected to the network; setting three scheduling modes according to three frequency modulation modes of a power grid, wherein the scheduling time is respectively 15 minutes, 30 minutes and 120 minutes;
the method for calculating the discharge scheduling SOC threshold criterion comprises the following steps:
Figure FDA0003883468610000042
in the formula, SOC 0 i+1 The electric quantity at the end of the ith journey scheduling; SOC iend Responding to the electric quantity after discharge scheduling after the automobile is connected to the network for the ith journey; SOC lim Taking 0.2; p is the charging power of the power gridkW；t c The charging time from the end of automobile discharging to the off-grid time is in unit of min; e is the battery capacity kW.h;
Figure FDA0003883468610000043
in the formula, SOC i 0 The electric quantity at the beginning of the ith journey scheduling; p is the automobile discharge power, kW; t is t d Scheduling time in units of min;
the network access electric quantity of the automobile should be larger than the discharging threshold value SOC dis_i The discharge scheduling constraint conditions are as follows:
Figure FDA0003883468610000044
the charge scheduling SOC threshold criterion is determined as follows:
when the electric automobile is in an idle state of network access, the electric automobile has a potential of charge scheduling as long as the electric quantity is not fully charged, so that the SOC threshold of the charge scheduling is obtained by the following formula:
Figure FDA0003883468610000051
in the formula, SOC 0 i+1 The electric quantity at the end of the ith journey scheduling; t is t d Scheduling time, min; SOC charge_i Represents a charging threshold;
the network access electric quantity of the automobile should be less than the charging threshold value SOC charge_i The constraint conditions of the charging schedule are as follows:
Figure FDA0003883468610000052
6. the method for checking schedulable capacity of electric vehicle based on data mining according to claim 1, wherein step (6) comprises:
the electric automobile charging and discharging process is regarded as constant power p charging and discharging, and the electric automobile schedulability evaluation process is as follows:
when the electric automobile is in an idle state of network access, namely the SOC curve keeps a level, the following conditions are met: SOC (system on chip) dis_i ≤SOC i (t)≤1；
Discharge the scheduling capacity P discharge (t)＝p；
When the electric automobile is in an idle state of network access, namely the SOC curve keeps a level, the following conditions are met: SOC i (t)≤SOC charge_i ≤1，SOC charge_i Represents a charging threshold;
then the charge scheduling capacity P charge (t)＝p；
When the SOC of the electric automobile does not meet the conditions or the network access idle time is longer than the schedulable time, the charging and discharging scheduling potential is 0;
P dis (t)＝0；P charge (t)＝0；
for a power grid, the schedulable capacity of a single electric vehicle is negligible, and the schedulable charge-discharge capacity is equal to the sum of the schedulable capacities of the electric vehicles in the distribution network:
Figure FDA0003883468610000061
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