CN114971056A - Charging time optimization method for electric vehicle cluster - Google Patents

Charging time optimization method for electric vehicle cluster Download PDF

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CN114971056A
CN114971056A CN202210639909.6A CN202210639909A CN114971056A CN 114971056 A CN114971056 A CN 114971056A CN 202210639909 A CN202210639909 A CN 202210639909A CN 114971056 A CN114971056 A CN 114971056A
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沈然
胡若云
李伊玲
丁麒
孙钢
张维
金良峰
徐世予
叶方彬
吕诗宁
王庆娟
倪琳娜
汪一帆
项莹洁
章一新
章江铭
邓建丽
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Marketing Service Center of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention belongs to the technical field of electric automobile charging, and particularly relates to a charging time optimization method for an electric automobile cluster. Aiming at the defect that the charging time of the electric automobile cluster vehicle has an improved space, the invention adopts the following technical scheme: a charging time optimization method for an electric vehicle cluster comprises the following steps: modeling a charging process of the electric automobile; establishing an equivalent charging model of the electric automobile; determining an optimized scheduling time period, and calculating the optimized target charging power of the electric automobile according to the power grid power change condition of the optimized scheduling time period; screening out electric automobiles with optimization potential; the charging time is optimized, and the peak clipping and valley filling of the power grid are assisted. The invention has the beneficial effects that: by optimizing the charging time period of the electric automobile, the charging load of the electric automobile in the power grid electricity utilization valley period is increased, and the charging load of the electric automobile in the power grid electricity utilization peak period is reduced, so that the peak clipping and valley filling of the power grid are assisted, and the safe and stable operation of the power grid is maintained.

Description

Charging time optimization method for electric vehicle cluster
Technical Field
The invention belongs to the technical field of electric automobile charging, and particularly relates to a charging time optimization method for an electric automobile cluster.
Background
The power grid needs to ensure real-time supply and demand balance so as to maintain the stability of the system frequency. Because resident's power consumption is concentrated in daytime, and the night power consumption is less, leads to the peak valley difference of electric wire netting to increase constantly. Therefore, in order to maintain the real-time supply and demand balance of the power grid, part of the generator sets need to be reduced or even shut down at night, so that the power generation efficiency is reduced, and the power generation cost is increased.
For the household electric automobile with the private charging pile, most users can choose to start charging after getting home, and the situation of power shortage in late peak is aggravated. The electric vehicle is usually used in the next morning, but the charging is finished early, so that the electricity is used up in the early peak period. The household electric automobile is a demand side load which can be fully utilized, and can assist a power grid to carry out peak clipping and valley filling on the premise of meeting the traveling requirement of the household electric automobile by optimizing the charging starting moment of the electric automobile. Commercial charging piles also have similar regulatory potential.
Disclosure of Invention
The invention provides a charging time optimization method of an electric vehicle cluster aiming at the defect that the charging time of an electric vehicle cluster has an improved space, the charging time period of the electric vehicle is adjusted according to the operating characteristics of a power grid, and the peak-valley difference is reduced under the condition that the charging requirement of a user is not influenced.
In order to achieve the purpose, the invention adopts the following technical scheme: a charging time optimization method for an electric vehicle cluster comprises the following steps:
s1, modeling the charging process of the electric automobile according to the parameters of the electric automobile;
step S2, establishing an equivalent charging model of the electric automobile, and describing the charging process characteristics of the electric automobile by using the average charging power, the charging starting time, the charging ending time and the charging duration;
step S3, determining an optimized scheduling time interval, and calculating the optimized target charging power of the electric automobile according to the power grid power change condition of the optimized scheduling time interval;
s4, screening out the electric automobile with optimized potential according to the charging starting time, the charging duration time and the latest charging ending time of the electric automobile;
and S5, optimizing the charging time of the electric vehicle with the optimized potential screened in the step S4 according to the target charging power of the electric vehicle obtained in the step S3, and assisting the peak clipping and valley filling of the power grid.
According to the method for optimizing the charging time of the electric automobile cluster, disclosed by the invention, an electric automobile equivalent charging model is established, an optimized scheduling period is determined, the target charging power after the electric automobile charging is optimized is calculated, the electric automobiles with optimized potential are screened out, and the charging start-stop time of each electric automobile with optimized potential is optimized finally, so that the method is beneficial to maintaining real-time supply and demand balance of a power grid, reducing the peak-valley difference of the power grid, improving the power generation efficiency of a power grid generator set and not influencing the normal use of a user.
In step S1, let M denote the set of all electric vehicles, and for the ith electric vehicle, a charging model of its battery is established:
Figure BDA0003683418920000021
wherein S is SOC,i (t) and S SOC,i (t-1) the percentage of electric quantity of the battery of the ith electric vehicle at the current time t and the last time t-1 respectively, E i (t-1) represents the amount of charge of the i-th electric vehicle from the last time t-1 to the present time t, C i Indicating the capacity of a battery of the ith electric vehicle;
in the charging process of the electric automobile, the maximum charging power of the electric automobile is restricted by the battery and the charging pile and cannot exceed the corresponding allowable value:
Figure BDA0003683418920000022
wherein, P i (t) represents a real-time charging power of the ith electric vehicle,
Figure BDA0003683418920000023
the maximum allowable charging power of the battery of the ith electric vehicle,
Figure BDA0003683418920000024
the maximum charging power allowed by the charging pile of the ith electric automobile is represented, namely the real-time charging power of the electric automobile cannot exceed the smaller value of the maximum charging power allowed by the battery and the maximum charging power allowed by the charging pile;
in order to avoid the phenomenon of overcharging of the battery, the battery capacity needs to meet the following requirements:
Figure BDA0003683418920000025
wherein,
Figure BDA0003683418920000026
represents the maximum allowable charge of the battery;
the charging amount of the battery during charging can be calculated by the following formula:
Figure BDA0003683418920000027
wherein, E c,i Indicates the amount of charge of the i-th electric vehicle, S SOC,i Represents the initial charge percentage before charging of the ith electric vehicle, C i Represents the battery capacity, η, of the ith electric vehicle i Indicates the charging efficiency, t, of the ith electric vehicle s,i Indicates the time point, t, at which charging of the ith electric vehicle is started d,i Indicates the charging time period of the ith electric vehicle, P i (t) represents the real-time charging power of the charging pile of the ith electric automobile;
the equivalent charging process of the ith electric vehicle is represented as follows:
Figure BDA0003683418920000028
Figure BDA0003683418920000031
t e,i ＝t s,i +t d,i
P i,avg represents the average charging power, t, of the ith electric vehicle s,i Indicates the charging start time, t, of the ith electric vehicle d,i Indicates the charging time period of the ith electric vehicle, t e,i Indicating the end of charging of the electric vehicle, E c,i And representing the total charge of the ith electric automobile, wherein the charging time period is provided by the user after evaluation according to the state of the electric automobile.
As an improvement, in step S3, the time of the whole day is divided into an optimized scheduling period and a non-optimized scheduling period according to the charging characteristics of the electric vehicle, where the optimized scheduling period is a main period for charging the electric vehicle.
As an improvement, the average power of the power grid in the optimal scheduling period is represented as the integral of the power grid in the optimal scheduling period divided by the duration of the optimal scheduling period, as shown in the following formula:
Figure BDA0003683418920000032
wherein, P sys,avg Representing the average power, P, of the grid during an optimal scheduling period sys (t) represents the real-time power of the grid during the optimal scheduling period, t sys,s Indicating the starting time, t, of the optimal scheduling period sys,e Indicating the end time of the optimized scheduling period.
As an improvement, the optimized target charging power of the electric vehicle is determined according to the average power and the instantaneous power of the power grid in the optimized scheduling period, that is, when the power consumption of the power grid is high, the charging power of the electric vehicle is reduced, and when the power consumption of the power grid is low, the charging power of the electric vehicle is increased, as shown in the following formula:
Figure BDA0003683418920000033
Figure BDA0003683418920000034
P tar (t) represents the target charging power of the optimized electric automobile, and P (t) represents the sum of the charging powers of all the electric automobiles, and as can be seen from the formula, when the real-time power of the power grid is larger than the average power, the charging power of the electric automobile is increased; and when the real-time power of the power grid is smaller than the average power than the real-time power, reducing the charging power of the electric automobile.
As a modification, in step S4, the screening process is as follows:
first, the charging start period of the electric vehicle needs to be within the screening period, as shown in the following formula:
Figure BDA0003683418920000035
t s indicating the start of the screening period, t e Indicating an end time of the screening period;
the starting time of the screening period is not later than the starting time of the optimized scheduling period, and the ending time of the screening period does not exceed the ending time of the optimized scheduling period:
t s ≤t sys,e ≤t e ≤t sys,s
the charging time of the electric automobile needs to satisfy a certain duration:
Figure BDA0003683418920000041
wherein, t d,i Indicates the charging time period, t, of the ith electric vehicle d,min Represents the shortest charging time period for screening;
the latest charging end time of the electric automobile needs to meet the following requirements:
Figure BDA0003683418920000042
wherein, t e,i Denotes the firstLatest charge end time, t, of i electric vehicles e,min Indicating the earliest end of charge time for screening, i.e. only usage demand at t e,min The electric automobile has optimization potential. .
As an improvement, in step S4, based on the aforementioned screening criteria, all electric vehicles are divided into portions with optimization potential and portions without optimization potential, which are expressed as:
M＝P+Q
P(t)＝P p (t)+P q (t)
Figure BDA0003683418920000043
Figure BDA0003683418920000044
wherein P represents a set of electric vehicles with optimization potential, Q represents a set of electric vehicles without optimization potential, P (t) represents the sum of charging powers of all electric vehicles, P p (t) represents the sum of the real-time charging powers, P, of the electric vehicles with optimization potential q (t) represents the sum of real-time charging power of electric vehicles without optimization potential.
As an improvement, in step S5, for the electric vehicle set P with optimization potential, the optimized target power is as follows:
Figure BDA0003683418920000045
wherein,
Figure BDA0003683418920000046
representing the sum of the optimized target optimized powers, P, of the electric vehicles with optimization potential tar (t) represents the sum of the target charging powers of the optimized electric vehicles, P (t) represents the sum of the charging powers of all the electric vehicles, P p (t) shows electromotion with optimization potentialSum of real-time charging power of the car.
As an improvement, in step S5, the optimization goal is to minimize the integral value of the square of the difference between the sum of the optimized electric vehicle powers with optimization potential and the sum of the target optimized powers, as shown in the following formula:
Figure BDA0003683418920000051
wherein,
Figure BDA0003683418920000052
represents the sum of the optimized electric automobile power with optimization potential,
Figure BDA0003683418920000053
and representing the sum of the optimized target optimized power of the electric automobile with optimization potential.
As a modification, in step S5, for the ith electric vehicle with optimization potential, the initial charging start time is t s,i Receiving a command t of charging in a delayed manner w,i Thereafter, the final charge start time is t s,i +t w,i Therefore, the optimized charging power of the electric vehicle can be expressed as:
Figure BDA0003683418920000054
wherein,
Figure BDA0003683418920000055
representing the charging power, P, of the ith electric vehicle with optimization potential avg,i Representing the average charging power of the ith electric automobile with optimization potential;
therefore, the sum of the charging powers of all electric vehicles with optimized potential is:
Figure BDA0003683418920000056
Figure BDA0003683418920000057
representing the optimized charging power of the ith electric vehicle;
electric automobile after optimizing charges constantly and can't be earlier than its arrival and fill electric pile's moment, and its completion time that charges also needs to be earlier than the latest electric automobile finish time that charges, needs satisfy:
Figure BDA0003683418920000058
the method for optimizing the charging time of the electric automobile cluster has the beneficial effects that: by optimizing the charging time of the electric automobile, the charging load in the power grid electricity utilization peak period is transferred to the power grid electricity utilization valley period, the efficiency between the electric automobile and the power grid supply and demand interaction is improved, the peak clipping and valley filling capacity of the electric automobile is fully utilized, the power of the power grid in the late peak period is effectively reduced, meanwhile, the power in the early valley period is improved, the electric automobile is utilized to assist the power grid in peak clipping and valley filling, and the real-time regulation and control pressure of the power grid is relieved.
Drawings
Fig. 1 is a schematic diagram of an electric vehicle equivalent charging process of a charging time optimization method for an electric vehicle cluster according to an embodiment of the present invention.
Fig. 2 is a comparison schematic diagram before and after the charging time optimization of the method for optimizing the charging time of the electric vehicle cluster according to the embodiment of the invention.
Fig. 3 is a comparison effect diagram before and after optimization of the method for optimizing the charging time of the electric vehicle cluster according to the embodiment of the invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be explained and explained below with reference to the drawings of the embodiments of the present invention, but the embodiments described below are only preferred embodiments of the present invention, and are not all embodiments. Other embodiments obtained by persons skilled in the art without any inventive work based on the embodiments in the embodiment belong to the protection scope of the invention.
Referring to fig. 1 to 3, the charging time optimization method for an electric vehicle cluster of the present invention includes:
s1, modeling the charging process of the electric automobile according to the parameters of the electric automobile;
step S2, establishing an equivalent charging model of the electric automobile, and describing the charging process characteristics of the electric automobile by using the average charging power, the charging starting time, the charging ending time and the charging duration;
step S3, determining an optimized scheduling time interval, and calculating the optimized target charging power of the electric automobile according to the power grid power change condition of the optimized scheduling time interval;
s4, screening out the electric automobile with optimized potential according to the charging starting time, the charging duration time and the latest charging ending time of the electric automobile;
and S5, optimizing the charging time of the electric vehicle with the optimized potential screened in the step S4 according to the target charging power of the electric vehicle obtained in the step S3, and assisting the peak clipping and valley filling of the power grid.
The battery is the main equipment for the supply and demand interaction of the electric automobile and the power grid, the electric automobile obtains electric energy from the power grid through the charging pile to charge the battery, and the charging process and the charging demand of the electric automobile depend on the capacity, the real-time electric quantity and the maximum charging power of the battery. Different electric vehicles have different parameters and different real-time electric quantities.
In this embodiment, in step S1, let M denote the set of all electric vehicles, and for the ith electric vehicle, a charging model of the battery is established:
Figure BDA0003683418920000061
wherein S is SOC,i (t) and S SOC,i (t-1) respectively represents the electric quantity percentage of the battery of the ith electric automobile at the current time t and the last time t-1, E i (t-1)Represents the charging amount of the ith electric vehicle from the last time t-1 to the current time t, C i Indicating the capacity of a battery of the ith electric vehicle;
in the charging process of the electric automobile, the maximum charging power of the electric automobile is restricted by the battery and the charging pile and cannot exceed the corresponding allowable value:
Figure BDA0003683418920000071
wherein, P i (t) represents a real-time charging power of the ith electric vehicle,
Figure BDA0003683418920000072
the maximum allowable charging power of the battery of the ith electric vehicle,
Figure BDA0003683418920000073
the maximum charging power allowed by the charging pile of the ith electric automobile is represented, namely the real-time charging power of the electric automobile cannot exceed the smaller value of the maximum charging power allowed by the battery and the maximum charging power allowed by the charging pile;
in order to avoid the phenomenon of overcharging of the battery, the battery capacity needs to meet the following requirements:
Figure BDA0003683418920000074
wherein,
Figure BDA0003683418920000075
represents the maximum allowable charge of the battery;
the charging amount of the battery during charging can be calculated by the following formula:
Figure BDA0003683418920000076
wherein E is c,i Represents the theoretical charge quantity of the ith electric vehicle, S SOC,i Represents the initial charge percentage before charging of the ith electric vehicle, C i Represents the battery capacity, η, of the ith electric vehicle i Indicates the charging efficiency, t, of the ith electric vehicle s,i Indicates the time point, t, at which charging of the ith electric vehicle is started d,i Indicates the charging time period, P, of the ith electric vehicle i (t) represents the real-time charging power of the charging pile of the ith electric automobile; the theoretical charging amount of the electric vehicle is equal to the charging pile power, however, the relationship between the charging pile power and the actual charging amount of the electric vehicle is represented by charging efficiency, that is, the charging pile power charging efficiency is the actual charging amount of the electric vehicle, the charging efficiency can be regarded as a constant, and the actual charging amount of the electric vehicle is obtained through the charging pile power and the charging efficiency;
the equivalent charging process of the ith electric vehicle is represented as follows:
Figure BDA0003683418920000077
Figure BDA0003683418920000078
t e,i ＝t s,i +t d,i
P i,avg represents the average charging power, t, of the ith electric vehicle s,i Indicates the charging start time, t, of the ith electric vehicle d,i Indicates the charging time period, t, of the ith electric vehicle e,i Indicating the end of charging of the electric vehicle, E c,i And the total charge of the ith electric automobile is represented, wherein the charging time is provided by a user after the user evaluates the state of the electric automobile, and the charging time can also be calculated by software according to historical data.
In this embodiment, in step S3, the time of the whole day is divided into an optimized scheduling period and a non-optimized scheduling period according to the charging characteristics of the electric vehicle, where the optimized scheduling period is a main period for charging the electric vehicle.
In this embodiment, the average power of the power grid during the optimal scheduling period is represented as an integral of the power grid during the optimal scheduling period divided by the duration of the optimal scheduling period, as shown in the following formula:
Figure BDA0003683418920000081
wherein, P sys,avg Representing the average power, P, of the grid during an optimal scheduling period sys (t) represents the real-time power of the grid during the optimal scheduling period, t sys,s Indicating the starting time, t, of the optimal scheduling period sys,e Indicating the end time of the optimized scheduling period.
In this embodiment, the optimized target charging power of the electric vehicle is determined according to the average power and the instantaneous power of the power grid in the optimized scheduling period, that is, when the power consumption of the power grid is high, the charging power of the electric vehicle is reduced, and when the power consumption of the power grid is low, the charging power of the electric vehicle is increased, as shown in the following formula:
Figure BDA0003683418920000082
Figure BDA0003683418920000083
P tar (t) represents the target charging power of the optimized electric automobile, and P (t) represents the sum of the charging powers of all the electric automobiles, and as can be seen from the formula, when the real-time power of the power grid is larger than the average power, the charging power of the electric automobile is increased; and when the real-time power of the power grid is smaller than the average power than the real-time power, reducing the charging power of the electric automobile.
In this embodiment, in step S4, the screening process is as follows:
first, the charging start period of the electric vehicle needs to be within the screening period, as shown in the following formula:
Figure BDA0003683418920000084
t s indicating the start of the screening period, t e Indicating an end time of the screening period;
the starting time of the screening period is not later than the starting time of the optimized scheduling period, and the ending time of the screening period does not exceed the ending time of the optimized scheduling period:
t s ≤t sys,e ≤t e ≤t sys,s
the charging time of the electric automobile needs to satisfy a certain duration:
Figure BDA0003683418920000085
wherein, t d,i Indicates the charging time period, t, of the ith electric vehicle d,min Represents the shortest charging time period for screening;
the latest charging end time of the electric automobile needs to meet the following requirements:
Figure BDA0003683418920000086
wherein, t e,i Represents the latest charging end time, t, of the ith electric vehicle e,min Indicating the earliest end of charge time for screening, i.e. only usage demand at t e,min The electric automobile has optimization potential. .
In this embodiment, in step S4, based on the aforementioned screening criteria, all electric vehicles are divided into portions with optimization potential and portions without optimization potential, which are expressed as:
M＝P+Q
P(t)＝P p (t)+P q (t)
Figure BDA0003683418920000091
Figure BDA0003683418920000092
wherein P represents a set of electric vehicles with optimization potential, Q represents a set of electric vehicles without optimization potential, P (t) represents the sum of charging powers of all electric vehicles, P p (t) represents the sum of the real-time charging powers, P, of the electric vehicles with the potential for optimization q (t) represents the sum of real-time charging power of electric vehicles without optimization potential.
In this embodiment, in step S5, for the electric vehicle set P with optimization potential, the optimized target power is as follows:
Figure BDA0003683418920000093
wherein,
Figure BDA0003683418920000094
representing the sum of the optimized target optimized powers, P, of the electric vehicles with optimization potential tar (t) represents the sum of the target charging powers of the optimized electric vehicles, P (t) represents the sum of the charging powers of all the electric vehicles, P p (t) represents the sum of the real-time charging powers of the electric vehicles with the potential for optimization.
In this embodiment, in step S5, since the power is not adjustable and the charging process is not interrupted during the optimization process of the electric vehicle optimization process, and there is a deviation between the power of the electric vehicle after final optimization and the target power, the optimization goal is to minimize the integral value of the square of the difference between the sum of the powers of the electric vehicles with optimization potential after optimization and the sum of the target optimization power, as shown in the following formula:
Figure BDA0003683418920000095
wherein,
Figure BDA0003683418920000096
represents the sum of the optimized electric automobile power with optimization potential,
Figure BDA0003683418920000097
and representing the sum of the optimized target optimized power of the electric automobile with optimization potential.
In this embodiment, in step S5, since the charging power of the electric vehicle is not adjustable, the charging process is not interrupted, and only the time when charging is started can be changed. By utilizing the charging reservation function of the existing electric automobile, a user can connect the electric automobile to the charging pile and then set the charging delay time of the electric automobile. For the ith electric vehicle with optimization potential, the initial charging starting time is t s,i Receiving a command t for charging with a delay w,i Thereafter, the final charge start time is t s,i +t w,i Therefore, the optimized charging power of the electric vehicle can be expressed as:
Figure BDA0003683418920000101
wherein,
Figure BDA0003683418920000102
representing the charging power, P, of the ith electric vehicle with optimization potential avg,i Representing the average charging power of the ith electric vehicle with optimization potential;
therefore, the sum of the charging powers of all electric vehicles with the potential for optimization is:
Figure BDA0003683418920000103
Figure BDA0003683418920000104
representing the optimized charging power of the ith electric vehicle;
the electric automobile after optimizing charges the moment and can't be earlier than its moment of reaching the electric pile, and its completion time that charges also needs to be earlier than the latest electric automobile end time that charges, needs to satisfy:
Figure BDA0003683418920000105
as can be seen from fig. 2, before optimization, the charging time of the electric vehicle mostly starts at the night and rapidly reaches the peak, and reaches the lowest near the morning, and the charging curve shows a right-hand distribution. And (5) after optimization. And delaying the charging time of part of electric automobiles, wherein the charging curve is close to unbiased distribution.
As can be seen from fig. 3, prior to optimization, the electric vehicle charging power rose rapidly from 18 pm, peaked at 24 pm, and decreased at a faster rate, substantially 0 in the morning at 8. After optimization, the electric vehicle charging power rises at a slower rate from eighteen pm, rises at a faster rate at 23 pm, reaches the peak at 2 am, and falls at a lower rate after reaching the peak than before optimization, and does not decrease to 0 even after 8 am.
The method for optimizing the charging time of the electric automobile cluster has the advantages that: according to electric automobile parameters and charging requirements provided by a user in advance, electric automobiles with optimized potential are screened out, the charging load of the electric automobiles in the power grid electricity utilization valley period is increased by optimizing the charging time period of the electric automobiles, and the charging load of the electric automobiles in the power grid electricity utilization peak period is reduced, so that the peak clipping and valley filling of the power grid are assisted, and the safe and stable operation of the power grid is maintained.
While the invention has been described with reference to specific embodiments thereof, it will be understood by those skilled in the art that the invention is not limited thereto but is intended to cover all modifications and equivalents as may be included within the spirit and scope of the invention. Any modification which does not depart from the functional and structural principles of the invention is intended to be included within the scope of the following claims.

Claims (10)

1. A charging time optimization method of an electric vehicle cluster is characterized by comprising the following steps: the method for optimizing the charging time of the electric automobile cluster comprises the following steps:
s1, modeling the charging process of the electric automobile according to the parameters of the electric automobile;
step S2, establishing an equivalent charging model of the electric automobile, and describing the charging process characteristics of the electric automobile by using the average charging power, the charging starting time, the charging ending time and the charging duration;
step S3, determining an optimized scheduling time interval, and calculating the optimized target charging power of the electric automobile according to the power grid power change condition of the optimized scheduling time interval;
s4, screening out the electric automobile with optimized potential according to the charging starting time, the charging duration time and the latest charging ending time of the electric automobile;
and S5, optimizing the charging time of the electric vehicle with the optimized potential screened in the step S4 according to the target charging power of the electric vehicle obtained in the step S3, and assisting the peak clipping and valley filling of the power grid.
2. The method for optimizing the charging time of the electric vehicle cluster according to claim 1, wherein the method comprises the following steps: in step S1, let M denote the set of all electric vehicles, and for the ith electric vehicle, a charging model of its battery is established:
Figure FDA0003683418910000011
wherein S is SOC,i (t) and S SOC,i (t-1) the percentage of electric quantity of the battery of the ith electric vehicle at the current time t and the last time t-1 respectively, E i (t-1) represents the amount of charge of the i-th electric vehicle from the last time t-1 to the present time t, C i Indicating the capacity of a battery of the ith electric vehicle;
in the charging process of the electric automobile, the maximum charging power of the electric automobile is restricted by the battery and the charging pile and cannot exceed the corresponding allowable value:
Figure FDA0003683418910000012
wherein, P i (t) represents a real-time charging power of the ith electric vehicle,
Figure FDA0003683418910000013
the maximum allowable charging power of the battery of the ith electric vehicle,
Figure FDA0003683418910000014
the maximum charging power allowed by the charging pile of the ith electric automobile is represented, namely the real-time charging power of the electric automobile cannot exceed the smaller value of the maximum charging power allowed by the battery and the maximum charging power allowed by the charging pile;
in order to avoid the phenomenon of overcharging of the battery, the battery capacity needs to meet the following requirements:
Figure FDA0003683418910000015
wherein,
Figure FDA0003683418910000016
represents the maximum allowable charge of the battery;
the charging amount of the battery during charging can be calculated by the following formula:
Figure FDA0003683418910000017
wherein E is c,i Indicates the amount of charge of the i-th electric vehicle, S SOC,i Represents the initial charge percentage before charging of the ith electric vehicle, C i Represents the battery capacity, η, of the ith electric vehicle i Indicates the charging efficiency, t, of the ith electric vehicle s,i Indicates the time of starting charging of the ith electric vehicle，t d,i Indicates the charging time period, P, of the ith electric vehicle i (t) represents the real-time charging power of the charging pile of the ith electric automobile;
the equivalent charging process of the ith electric vehicle is represented as follows:
Figure FDA0003683418910000021
Figure FDA0003683418910000022
t e,i ＝t s,i +t d,i
P i,avg represents the average charging power, t, of the ith electric vehicle s,i Indicates the charging start time, t, of the ith electric vehicle d,i Indicates the charging time period, t, of the ith electric vehicle e,i Indicating the end of charging of the electric vehicle, E c,i And representing the total charge of the ith electric automobile, wherein the charging time period is provided by the user after evaluation according to the state of the electric automobile.
3. The method for optimizing the charging time of the electric automobile cluster according to claim 1, wherein the method comprises the following steps: in step S3, the time of the whole day is divided into an optimized scheduling period and a non-optimized scheduling period according to the charging characteristics of the electric vehicle, where the optimized scheduling period is a main period during which the electric vehicle is charged.
4. The method for optimizing the charging time of the electric automobile cluster according to claim 3, wherein the method comprises the following steps: in step S3, the average power of the grid during the optimal scheduling period is represented as an integral of the grid power during the optimal scheduling period divided by the duration of the optimal scheduling period, as shown in the following formula:
Figure FDA0003683418910000023
wherein, P sys,avg Representing the average power, P, of the grid during an optimal scheduling period sys (t) represents the real-time power of the grid during the optimal scheduling period, t sys,s Indicating the starting time, t, of the optimal scheduling period sys,e Indicating the end time of the optimized scheduling period.
5. The method for optimizing the charging time of the electric automobile cluster according to claim 4, wherein the method comprises the following steps: in step S3, determining the optimized target charging power of the electric vehicle according to the average power and the instantaneous power of the power grid during the optimized scheduling period, that is, when the power consumption of the power grid is high, reducing the charging power of the electric vehicle, and when the power consumption of the power grid is low, increasing the charging power of the electric vehicle, as shown in the following formula:
Figure FDA0003683418910000024
Figure FDA0003683418910000025
P tar (t) represents the target charging power of the optimized electric automobile, and P (t) represents the sum of the charging powers of all the electric automobiles, and as can be seen from the formula, when the real-time power of the power grid is larger than the average power, the charging power of the electric automobile is increased; and when the real-time power of the power grid is smaller than the average power than the real-time power, reducing the charging power of the electric automobile.
6. The method for optimizing the charging time of the electric automobile cluster according to claim 3, wherein the method comprises the following steps: in step S4, the screening process is as follows:
first, the charging start period of the electric vehicle needs to be within the screening period, as shown in the following formula:
Figure FDA0003683418910000031
t s indicating the start of the screening period, t e Indicating an end time of the screening period;
the starting time of the screening period is not later than the starting time of the optimized scheduling period, and the ending time of the screening period does not exceed the ending time of the optimized scheduling period:
t s ≤t sys,e ≤t e ≤t sys,s
the charging time of the electric automobile needs to satisfy a certain duration:
Figure FDA0003683418910000032
wherein, t d,i Indicates the charging time period, t, of the ith electric vehicle d,min Represents the shortest charging time period for screening;
the latest charging end time of the electric automobile needs to meet the following requirements:
Figure FDA0003683418910000033
wherein, t e,i Represents the latest end time of charging, t, of the ith electric vehicle e,min Indicating the earliest end of charge time for screening, i.e. only usage demand at t e,min The electric automobile has optimization potential.
7. The method for optimizing the charging time of the electric automobile cluster as claimed in claim 6, wherein the method comprises the following steps: in step S4, based on the aforementioned screening criteria, all electric vehicles are divided into portions with optimization potential and portions without optimization potential, which are expressed as:
M＝P+Q
P(t)＝P p (t)+P q (t)
Figure FDA0003683418910000034
Figure FDA0003683418910000035
wherein P represents a set of electric vehicles with optimization potential, Q represents a set of electric vehicles without optimization potential, P (t) represents the sum of charging powers of all electric vehicles, P p (t) represents the sum of the real-time charging powers, P, of the electric vehicles with the potential for optimization q (t) represents the sum of real-time charging power of electric vehicles without optimization potential.
8. The method for optimizing the charging time of the electric vehicle cluster according to claim 7, characterized in that: in step S5, for the set P of electric vehicles with optimization potential, the optimized target power is as follows:
Figure FDA0003683418910000041
wherein,
Figure FDA0003683418910000042
representing the sum of the optimized target optimized powers, P, of the electric vehicles with optimization potential tar (t) represents the sum of the target charging powers of the optimized electric vehicles, P (t) represents the sum of the charging powers of all the electric vehicles, P p (t) represents the sum of the real-time charging powers of the electric vehicles with the potential for optimization.
9. The method for optimizing the charging time of the electric vehicle cluster according to claim 8, wherein the method comprises the following steps: in step S5, the optimization goal is to minimize the integral value of the square of the difference between the sum of the optimized electric vehicle powers with the optimization potential and the sum of the target optimization powers, as shown in the following formula:
Figure FDA0003683418910000043
wherein,
Figure FDA0003683418910000044
represents the sum of the optimized electric automobile power with optimization potential,
Figure FDA0003683418910000045
and representing the sum of the optimized target optimized power of the electric automobile with optimization potential.
10. The method for optimizing the charging time of the electric vehicle cluster according to claim 9, wherein the method comprises the following steps: in step S5, for the ith electric vehicle with optimization potential, the initial charging start time is t s,i Receiving a command t for charging with a delay w,i Thereafter, the final charge start time is t s,i +t w,i Therefore, the optimized charging power of the electric vehicle can be expressed as:
Figure FDA0003683418910000046
wherein,
Figure FDA0003683418910000047
representing the charging power, P, of the ith electric vehicle with optimization potential avg,i Representing the average charging power of the ith electric automobile with optimization potential;
therefore, the sum of the charging powers of all electric vehicles with optimized potential is:
Figure FDA0003683418910000048
Figure FDA0003683418910000049
representing the optimized charging power of the ith electric vehicle;
the electric automobile after optimizing charges the moment and can't be earlier than its moment of reaching the electric pile, and its completion time that charges also needs to be earlier than the latest electric automobile end time that charges, needs to satisfy:
Figure FDA00036834189100000410
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