CN109918798B - Electric vehicle charging mode optimization method based on charging power level - Google Patents
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Abstract
The method for optimizing the charging mode of the electric automobile based on the charging power level comprises the steps of analyzing the charging behavior of the electric automobile according to the charging record of 100 electric automobiles in a certain city for one week; analyzing the connection between the charging mode and the charging power level of the electric automobile; establishing an electric vehicle charging load model; establishing a double objective function of minimum total equipment investment and minimum peak-valley difference of charging load of the electric automobile charging pile; setting constraint conditions of the double objective functions; solving the double objective function. The invention considers the optimization of the charging mode of the electric automobile with the charging power level. Firstly, research and analysis are carried out on the characteristics of strong randomness, high synchronous rate and the like of unordered charging behaviors of the electric automobile, and three characteristic quantities of the charging starting time, the connection time and the charging electric quantity of the electric automobile are considered. Meanwhile, the influence of disordered charging of the electric automobile is also researched, and the proportion of different charging power levels is 4:5: and 1, the requirements of users can be met, the equipment investment can be reduced, and the peak-valley difference of unordered charging load of the electric automobile can be reduced.
Description
Technical Field
The invention belongs to the technical field of power demand response of power systems, and particularly relates to an electric vehicle charging mode optimization method based on charging power level.
Background
About half of the petroleum resources in our country are consumed in the traffic field, and a huge amount of greenhouse gases are generated. To cope with the increasingly serious environmental and resource problems, governments around the world are actively popularizing electric vehicles (Electrical Vehicle, EV). In addition to adopting a series of economic benefit policies for the production and sales of the fuel-off-the-shelf fuel-gas vehicles, various countries including China have set up an agenda for the schedule of the fuel-off-the-shelf fuel-gas vehicles.
The application field of electric automobiles is gradually introduced into the electric power system due to the advantages of energy conservation and environmental protection, and the energy of the electric automobiles mainly comes from a power grid, so that the electric automobiles are developed on a large scale and cannot be supported by the electric power system. However, the random charging behavior of the electric automobile has randomness and simultaneity in time and space, and the random charging of a large number of electric automobiles can influence the load, operation and planning of the power distribution network. For example, the electric automobile is accessed to the network in a large scale, the type of the charging equipment of the electric automobile is distributed unreasonably, and the cruising ability of the charging equipment is insufficient; the electric automobile users charge in disorder during the peak period of the power grid load, so that the capacity of the power grid is insufficient, and the like. Meanwhile, the electric automobile can also be used as mobile energy storage, and has wide application prospects in the aspects of peak clipping and valley filling, providing auxiliary service of an electric power system, cooperatively absorbing new energy and the like.
The charging behavior characteristic quantity (charging start time, connection time length, charging electric quantity) of the electric automobile has a certain influence on the configuration of the charging power level. Meanwhile, the unreasonable configuration of the charging power level may not meet the requirements of users, and may also generate higher equipment investment and increase the peak-valley difference of the charging load.
Disclosure of Invention
The invention provides an electric vehicle charging mode optimization method based on charging power level, which optimizes the electric vehicle charging mode by analyzing the charging behavior of an electric vehicle and considering the charging power level.
The technical scheme adopted by the invention is as follows:
the electric vehicle charging mode optimization method based on the charging power level comprises the following steps:
step one: according to the charging record of 100 electric vehicles in a certain city for a week, the charging behavior of the electric vehicles is analyzed:
(1) Analyzing the charging starting time to obtain the probability density function as follows:
wherein f s (t) represents a probability density function of a charge start time, t represents a vehicle charge start time, μ s Representing the expected value, sigma, of the probability density function s Representing the variance of the probability density function.
And (3) performing curve fitting on the charging start time data of 100 electric vehicles for one week by using MATLAB, wherein the fitted function accords with normal distribution, the expected value of the MATLAB fitted normal distribution function is 0.48, and the variance is 0.22.
(2) The charging connection time is analyzed, so that the distribution rule of the charging connection time of the electric automobile is generally in poisson distribution.
(3) And (3) analyzing the charge quantity to obtain the probability density function of the charge quantity as follows:
wherein f c (x) A probability density function representing the charge quantity, x represents the vehicle charge starting time, and the value range of x is 0 to 45kWh and mu c Representing the expected value, sigma, of the probability density function c Representing the variance of the probability density function.
And (3) performing curve fitting on the charge capacity data of 100 electric vehicles for one week by using MATLAB, wherein the fitted function accords with normal distribution, the expected value of the MATLAB fitted normal distribution function is 14.23, and the variance is 8.
Step two: and analyzing the relation between the charging mode and the charging power level of the electric automobile:
the charging peak period of the electric automobile is 8:00-18:00, and the peak value is reached at 12:00 noon. Electric vehicles often have the charging requirement of going to a charging station only in the commute peak period, so that more vehicles are charged in the peak period, and the charging load can bring a certain influence to a power grid. The connection time of the electric automobile is different, and the influence of the electric automobile on the power grid is also different. The connection duration is mainly distributed between 0 and 15 hours, and less than 15 hours. Therefore, EV users use less slow charge charging, and most use faster charge. The electric automobile has a charge capacity of 7-19 kWh, and a small part of the charge capacity is 0-7 kWh and a small part of the charge capacity is more than 19kWh. The three characteristic quantities determine the charging modes of the electric automobile and influence charging facilities corresponding to the charging modes.
In order to provide charging and discharging services for EV users, charging equipment configured by a certain charging station mainly comprises slow charging piles and faster charging piles, and a certain number of fast charging piles are configured in an auxiliary mode. It is now assumed that the charging station has three charging modes of ac 1, ac 2 and dc charging. The EV user can select three charging and discharging modes of ac 1 level, ac 2 level and dc charging, and the relationship of the three modes is shown in fig. 4. The alternating current 1 level is in a slow charging mode, the alternating current 2 level is in a faster charging mode, and the direct current charging is in a high-power fast charging mode. The power and cost of each charging device are shown in table 1.
Table 1 power and cost table for each charging device
| |
|
Direct current | |
| Power (kW) | 1.4-1.9 | 7.7-25.6 | 40-100 |
| Cost per equipment (Yuan) | 3000 | 15000 | 500000 |
The direct current charging has high power and high cost, the alternating current charging has low cost and low power, and different configurations of the direct current and alternating current charging equipment can generate larger equipment cost and cause larger charging load peak-valley difference to the power grid.
Step three: establishing an electric vehicle charging load model:
24 hours a day was divided into 96 time instants, one time instant every 15 minutes. The i-th period total charge load is the sum of all the vehicle loads charged at that time. i=1, 2,..96. And (3) assuming that the user charging is not controlled by the power grid, carrying out disordered charging, and linearizing the charging duration to be taken as the charging load at the starting moment. The charging start time of 100 data is summarized to each period of 24 hours according to the charging start time.
Wherein: m is the number of charged automobiles at each moment; x is X i The number of the electric vehicles at the ith moment; m is the total amount of the electric automobile.
Wherein:
the average charging power of the alternating-current 1-level electric automobile. />
P 1max The maximum power of the alternating current 1-level charging equipment; p (P) 1min The power minimum value of the alternating-current 1-level charging equipment;
as in Table 1, P 1max 1.9, P 1min 1.4, substituting formula (4) to obtain
1.65.
Wherein:
and switching on the average charging power of the 2-level electric automobile. P (P) 2max The maximum power of the alternating current 2-level charging equipment; p (P) 2min The power minimum value of the alternating current 2-level charging equipment; as in Table 1, P 2max 25.6, P 2min Substitution of 7.7 into equation (5) gives +.>
16.65.
Wherein:
the average charging power of the direct-current charging electric automobile. P (P) 3max Is the maximum power value of the direct current charging equipment; p (P) 3min The power minimum value of the direct current charging equipment; as in Table 1, P 3max 100, P 3min 40 is substituted into formula (6) to obtain +.>
70.
Charge load:
wherein: p is the sum of power when the power is distributed according to the proportion of the requirement of the problem; p (P) i The i-th time period total charging load; x is x i ，y i ，z i The electric vehicle comprises an ith alternating current 1 level, an alternating current 2 level and a direct current charging electric vehicle.
X i The number of the electric vehicles at the ith moment;
average power for an ac level 1 charging device; />
Average power for an ac level 2 charging device; />
Is the average power of the dc charging device.
Step four: establishing a double objective function of minimum total equipment investment and minimum peak-valley difference of charging load of the electric automobile charging pile:
minF＝100×(k 1 x i +k 2 y i +k 3 z i )
wherein: k (k) 1 、k 2 、k 3 The equipment cost of the electric automobile is respectively AC 1 level, AC 2 level and DC charging; x is x i ，y i ，z i The electric vehicle comprises an ith alternating current 1 level, an alternating current 2 level and a direct current charging electric vehicle.
minΔP＝P maxi -P mini (9)
Wherein: p (P) maxi Charging a maximum value of the load for the ith power class ratio; p (P) mini The minimum value of the load is charged for the ith power class ratio.
Step five: setting constraint conditions of a double objective function:
(1) Different power class duty cycle constraints of electric vehicles:
x+y+z＝1 (10)
wherein: and x, v and z are respectively the percentages of the electric vehicles charged by the alternating current 1 level, the alternating current 2 level and the direct current.
(2) Expected charge capacity constraint:
P≥P s (11)
wherein: p (P) s The user demand is the expected charging electric quantity obtained in the charging connection time; p power sum of different charging power class ratios.
(3) Peak-to-valley difference constraint:
ΔP≤min{ΔP 1 ，ΔP 2 ，ΔP 3 ，...} (12)
wherein: Δp is the peak-valley difference; ΔP 1 ，ΔP 2 ，ΔP 3 Peak-to-valley differences for different power class duty cycles.
Step six: solving a double objective function:
and (3) changing the values of x, y and z to realize different proportion distribution through model arrangement and constraint condition excavation. And then observing power load curves at different ratio values to obtain an optimal solution capable of realizing three targets. As shown in FIG. 5, the green curves are 10%, 40%, 50% (1:4:5); the red curves are 40%, 30% (4:3:3); the blue curves are 40%, 50%, 10% (4:5:1). The three different charge power levels were then compared in different ratios as shown in table 2:
TABLE 2 comparison of total equipment investment and peak to valley difference
| Ac level 1: ac level 2: direct current | Total equipment investment F (Yuan) | Peak valley difference (kW) |
| 1∶4∶5 | 25630000 | 213310 |
| 4∶3∶3 | 15570000 | 135940 |
| 5∶4∶1 | 5750000 | 73874 |
It follows that: when the ratio of different charging power levels is 4:5:1, the requirements of users can be met, the equipment investment can be reduced, and the peak-valley difference of unordered charging load of the electric automobile can be reduced.
The method for optimizing the charging mode of the electric automobile based on the charging power level has the following beneficial effects:
1) And the optimal distribution ratio of different charging power levels is obtained through MATLAB drawing comparison, so that the method has the effects of reducing equipment investment and reducing unordered charging peak-valley difference of the electric automobile.
2) The invention is suitable for distributing charging piles with different charging power levels and optimizing the charging mode of the electric automobile. The invention considers the optimization of the charging mode of the electric automobile with the charging power level. Firstly, research and analysis are carried out on the characteristics of strong randomness, high synchronous rate and the like of unordered charging behaviors of the electric automobile, and three characteristic quantities of the charging starting time, the connection time and the charging electric quantity of the electric automobile are considered. Meanwhile, the influence of disordered charging of the electric automobile is studied, and when the ratio of different charging power levels is 4:5:1, the requirements of users can be met, the equipment investment can be reduced, and the peak-valley difference of disordered charging load of the electric automobile is reduced.
3) The invention reasonably distributes the charging device type and the charging power of the electric automobile on the premise of not influencing the use of the electric automobile user, and has important significance for expanding the electricity market of the electric terminal, improving the power supply and demand balance and the load efficiency of the electric equipment, improving the load characteristic of the power grid, reducing the peak regulation cost caused by maintaining the low-load operation of the power grid, and the like.
Drawings
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
fig. 1 is a charge start time characteristic diagram of the present invention.
Fig. 2 is a graph showing the characteristics of the connection time length of the present invention.
Fig. 3 is a charge capacity characteristic diagram of the present invention.
Fig. 4 is a diagram showing a relationship between EV users and charging devices according to the present invention.
FIG. 5 is a graph of peak-to-valley difference in accordance with the present invention.
Detailed Description
The electric vehicle charging mode optimization method based on the charging power level comprises the following steps:
step one: according to the charging record of 100 electric vehicles in a certain city for a week, the charging behavior of the electric vehicles is analyzed:
(1) And analyzing the charging starting time, carrying out statistical analysis on sample data of the charging starting time of the electric automobile, and dividing 24 hours a day into 96 time periods, wherein each time period is 15 minutes. Taking the charging starting time period as an abscissa, taking the ordinate as the charging vehicle number duty ratio corresponding to the charging starting time, and then using MATLAB as a frequency distribution histogram and a fitting probability density function to obtain the figure 1. As can be seen from fig. 1, the distribution rule of the charging start time of the electric vehicle generally shows a normal distribution, and the probability density function is:
wherein f s (t) represents a probability density function of a charge start time, t represents a vehicle charge start time, μ s The expected value representing the probability density function is 0.48, σ s The variance representing the probability density function is 0.22.
As can be seen from fig. 1, the user charges the vehicle between 8:00 and 24:00, and the vehicle reaches a peak at 12:00 noon during a peak charge period of one day between 8:00 and 18:00. And less between 0:00 and 8:00 and 18:00 and 24:00. The reasons for the above phenomena are: 8:00 most people have or are ready to go to work and begin looking for a charging point to charge after arriving at the site. Most people choose to charge the electric car during the working hours. Whereas 18:00 most people have or are ready to go home. Few people go out in the morning and at night, and the charging is less.
(2) And analyzing the charging connection time length, performing statistical analysis on sample data of the connection time length of the electric automobile, taking the connection time length (h) as an abscissa, taking the ordinate as the charging vehicle number duty ratio corresponding to the connection time, and then using MATLAB as a frequency distribution histogram and a fitting probability density function to obtain a graph 2. As can be seen from fig. 2, the distribution rule of the connection duration of the electric automobile generally shows poisson distribution, and the connection duration is mainly distributed between 0 and 15 hours, and less than 15 hours.
(3) And analyzing the charging electric quantity, performing statistical analysis on sample data of the charging electric quantity of the electric automobile, taking the charging electric quantity (kWh) as an abscissa, taking the ordinate as the charging vehicle number duty ratio corresponding to the charging electric quantity, and then using MATLAB as a frequency distribution histogram and a fitting probability density function to obtain a graph 3. As can be seen from fig. 3, the distribution rule of the charging power of the electric vehicle generally shows a normal distribution, and the probability density function of the available charging power is:
wherein f c (x) Probability density function representing charge quantity, x represents vehicle charge starting time, and x is a value range
0 to 45kWh, mu c The expected value representing the probability density function is 14.23, σ c The variance representing the probability density function is 8.
As can be seen from fig. 3, the amount of charge is mostly 7 to 19kWh, and thus it can be presumed that most people use the electric vehicle for a short time, and the amount of charge is small because of low power consumption. And the short distance and long distance use is less, so the charging quantity is little and the frequency is less.
Step two: and analyzing the relation between the charging mode and the charging power level of the electric automobile:
the charging peak period of the electric automobile is 8:00-18:00, and the peak value is reached at 12:00 noon. Electric vehicles often have the charging requirement of going to a charging station only in the commute peak period, so that more vehicles are charged in the peak period, and the charging load can bring a certain influence to a power grid. The connection time of the electric automobile is different, and the influence of the electric automobile on the power grid is also different. The connection duration is mainly distributed between 0 and 15 hours, and less than 15 hours. Therefore, EV users use less slow charge charging, and most use faster charge. The electric automobile has a charge capacity of 7-19 kWh, and a small part of the charge capacity is 0-7 kWh and a small part of the charge capacity is more than 19kWh. The three characteristic quantities determine the charging modes of the electric automobile and influence charging facilities corresponding to the charging modes.
In order to provide charging and discharging services for EV users, charging equipment configured by a certain charging station mainly comprises slow charging piles and faster charging piles, and a certain number of fast charging piles are configured in an auxiliary mode. It is now assumed that the charging station has three charging modes of ac 1, ac 2 and dc charging. The EV user can select three charging and discharging modes of ac 1 level, ac 2 level and dc charging, and the relationship of the three modes is shown in fig. 4. The alternating current 1 level is in a slow charging mode, the alternating current 2 level is in a faster charging mode, and the direct current charging is in a high-power fast charging mode. The power and cost of each charging device are shown in table 1. The direct current charging has high power and high cost, the alternating current charging has low cost and low power, and different configurations of the direct current and alternating current charging equipment can generate larger equipment cost and cause larger charging load peak-valley difference to the power grid.
Step three: establishing an electric vehicle charging load model:
24 hours a day was divided into 96 time instants, one time instant every 15 minutes. The i-th period total charge load is the sum of all the vehicle loads charged at that time. i=1, 2,..96. And (3) assuming that the user charging is not controlled by the power grid, carrying out disordered charging, and linearizing the charging duration to be taken as the charging load at the starting moment. The charging start time of 100 data is summarized to each period of 24 hours according to the charging start time.
Wherein: m is the number of charged automobiles at each moment; x is X i The number of the electric vehicles at the ith moment; m is the total amount of the electric automobile.
Wherein:
the average charging power of the alternating-current 1-level electric automobile.
Wherein:
the average charging power of the alternating-current 2-level electric automobile.
Wherein:
the average charging power of the direct-current charging electric automobile.
Charge load:
wherein: p is the sum of power when the power is distributed according to the proportion of the requirement of the problem; p (P) i The i-th time period total charging load; x is x i ，y i ，z i The electric vehicle comprises an ith alternating current 1 level, an alternating current 2 level and a direct current charging electric vehicle.
Step four: establishing a double objective function of minimum total equipment investment and minimum peak-valley difference of charging load of the electric automobile charging pile:
minF＝100x(k 1 x i +k 2 y i +k 3 z i )
wherein: k (k) 1 、k 2 、k 3 The equipment cost of the electric automobile is respectively AC 1 level, AC 2 level and DC charging; x is x i ，y i ，z i The electric vehicles respectively comprise the ith alternating current 1 level, the alternating current 2 level and direct current charging。
minΔP＝P maxi -P mimi (9)
Wherein: p (P) maxi Charging a maximum value of the load for the ith power class ratio; p (P) mini The minimum value of the load is charged for the ith power class ratio.
Step five: setting constraint conditions of a double objective function:
(1) Different power class duty cycle constraints of electric vehicles:
x+y+z＝1 (10)
wherein: and x, v and z are respectively the percentages of the electric vehicles charged by the alternating current 1 level, the alternating current 2 level and the direct current.
(2) Expected charge capacity constraint:
P≥P s (11)
wherein: p (P) s The user demand is the expected charging electric quantity obtained in the charging connection time; p power sum of different charging power class ratios.
(3) Peak-to-valley difference constraint:
ΔP≤min{ΔP 1 ，ΔP 2 ，ΔP 3 ，...} (12)
wherein: Δp is the peak-valley difference; ΔP 1 ，ΔP 2 ，ΔP 3 Peak-to-valley differences for different power class duty cycles.
Step six: solving a double objective function:
and (3) changing the values of x, y and z to realize different proportion distribution through model arrangement and constraint condition excavation. And then observing power load curves at different ratio values to obtain an optimal solution capable of realizing three targets. As shown in FIG. 5, the green curves are 10%, 40%, 50% (1:4:5); the red curves are 40%, 30% (4:3:3); the blue curves are 40%, 50%, 10% (4:5:1). The three different charge power levels were then compared in different ratios as shown in table 2. It follows that: when the ratio of different charging power levels is 4:5:1, the requirements of users can be met, the equipment investment can be reduced, and the peak-valley difference of unordered charging load of the electric automobile can be reduced.
Claims (1)
1. The electric vehicle charging mode optimization method based on the charging power level is characterized by comprising the following steps of:
step one: according to the charging record of 100 electric vehicles in a certain city for a week, the charging behavior of the electric vehicles is analyzed:
(1) Analyzing the charging starting time to obtain the probability density function as follows:
wherein f s (t) represents a probability density function of a charge start time, t represents a vehicle charge start time, μ s Representing the expected value, sigma, of the probability density function s Representing the variance of the probability density function;
(2) Analyzing the charging connection time length, wherein the distribution rule of the charging connection time length of the electric automobile generally shows poisson distribution;
(3) And analyzing the charging electric quantity to obtain the probability density function of the charging electric quantity as follows:
wherein f c (x) A probability density function representing the charge quantity, x represents the charge quantity of the vehicle, and the value of x ranges from 0 to 45kWh and mu c Representing the expected value, sigma, of the probability density function c Representing the variance of the probability density function;
step two: and analyzing the relation between the charging mode and the charging power level of the electric automobile:
now, assume that the charging station has three charging modes of AC 1 level, AC 2 level and DC charging; EV users can select three charging and discharging modes of AC 1 level, AC 2 level and DC charging, wherein the AC 1 level is a slow charging mode, the AC 2 level is a faster charging mode, and the DC charging is a high-power fast charging mode; the direct current charging has high power and high cost, and the alternating current charging has low cost and low power;
step three: establishing an electric vehicle charging load model:
dividing 24 hours a day into 96 moments, one moment every 15 minutes; the i-th period total charge load is the sum of all the vehicle loads charged at that time; i=1, 2, 96; the user charging is not controlled by a power grid, disordered charging is carried out, and the charging duration is linearized and is regarded as the charging load at the starting moment; according to the charging start time of 100 data, summarizing the charging start time into each period of 24 hours;
wherein: m is the number of charged automobiles at each moment; x is X i The number of the electric vehicles at the ith moment; m is the total amount of the electric automobile;
wherein:
the average charging power of the alternating-current 1-level electric automobile;
wherein:
the average charging power of the alternating-current 2-level electric automobile;
wherein:
the average charging power of the direct-current charging electric automobile is;
charge load:
wherein: p is the sum of charging power when three charging modes are distributed according to a given proportion requirement; p (P) i The i-th time period total charging load; x is x i ，y i ，z i The electric vehicle comprises an i-th alternating current 1 level, an alternating current 2 level and a direct current charging electric vehicle;
step four: establishing a double objective function with minimum total equipment investment and minimum peak-valley difference of charging load of the electric automobile charging pile:
minF＝100×(k 1 x i +k 2 y i +k 3 z i )
wherein: k (k) 1 、k 2 、k 3 The equipment cost of the electric automobile is respectively AC 1 level, AC 2 level and DC charging; x is x i ，y i ，z i The electric vehicle comprises an i-th alternating current 1 level, an alternating current 2 level and a direct current charging electric vehicle;
min△P＝P maxi -P mini (9)
wherein: p (P) maxi Charging a maximum value of the load for the ith power class ratio; p (P) mini A minimum value of the charging load for the ith power class ratio;
step five: setting constraint conditions of a double objective function:
(1) Different power class duty cycle constraints of electric vehicles:
x+y+z＝1 (10)
wherein: x, y and z are respectively the percentages of the electric vehicles charged by the alternating current 1 level, the alternating current 2 level and the direct current;
(2) Expected charge capacity constraints:
P≥P s (11)
wherein: p (P) s The user demand is the expected charging electric quantity obtained in the charging connection time; p power sum of different charging power class ratios;
(3) Peak-to-valley difference constraint:
△P≤min{△P 1 ,△P 2 ,△P 3 } (12)
wherein: Δp is the peak-to-valley difference; deltaP 1 ，△P 2 ，△P 3 Peak-to-valley differences for different power class duty cycles;
step six: solving a double objective function:
the values of x, y and z are changed through model arrangement and excavation of constraint conditions, so that different proportion distribution is realized; then observing power load curves at different ratio values to obtain optimal solutions capable of realizing three targets; and comparing the different proportions of the three different charging power levels to obtain: when the ratio of different charging power levels is 4:5: and 1, the requirements of users can be met, the equipment investment can be reduced, and the peak-valley difference of unordered charging load of the electric automobile can be reduced.
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| WO2012171147A1 (en) * | 2011-06-17 | 2012-12-20 | 辽宁省电力有限公司 | Coordination and control system for regulated charging and discharging of pure electric vehicle in combination with wind power generation |
| CN108470240A (en) * | 2018-03-02 | 2018-08-31 | 东南大学 | A kind of energy storage two-phase optimization method based on requirement management |
| CN108520314A (en) * | 2018-03-19 | 2018-09-11 | 东南大学 | In conjunction with the active distribution network dispatching method of V2G technologies |
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