CN113580994A - Intelligent optimization method and system for electric vehicle integrated charging - Google Patents

Intelligent optimization method and system for electric vehicle integrated charging Download PDF

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Publication number
CN113580994A
CN113580994A CN202110897408.3A CN202110897408A CN113580994A CN 113580994 A CN113580994 A CN 113580994A CN 202110897408 A CN202110897408 A CN 202110897408A CN 113580994 A CN113580994 A CN 113580994A
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charging
group
electric
electric vehicle
time interval
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CN113580994B (en
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赵奇
杨泽鑫
吕洋
黄学良
田江
高山
霍雪松
高天
王浩伟
丁宏恩
俞瑜
赵慧
孟雨庭
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Southeast University
State Grid Jiangsu Electric Power Co Ltd
Suzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Southeast University
State Grid Jiangsu Electric Power Co Ltd
Suzhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Priority to PCT/CN2021/141421 priority patent/WO2023010778A1/en
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations

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  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

An intelligent optimization method and system for electric vehicle integrated charging. The method comprises the steps that firstly, for each time interval t, electric vehicle information connected to a charging pile in each time interval is collected; then, establishing a mathematical model of the energy requirement of the electric automobile and classifying the electric automobile into a rigid charging group (group 1), a flexible charging group (group 2) and a discharging group (group 3); calculating the charging/discharging priority of the electric automobile according to the remaining residence time of the electric automobile and the SOC state of the battery, and distributing the charging electric quantity of the electric automobile according to the priority; then, the charging electric quantity of the electric automobile needing to be charged in the parking lot is adjusted by combining the real-time charging and discharging conditions of the parking lot; finally, updating the charging data of the electric automobile and selecting to finish the method or optimize the charging and discharging distribution of the next time interval t according to the judgment condition; the invention also discloses an integrated charging intelligent optimization system based on the method.

Description

Intelligent optimization method and system for electric vehicle integrated charging
Technical Field
The invention relates to an intelligent optimization method and system for integrated charging of an electric vehicle, and belongs to the field of energy management of electric vehicle charging stations.
Background
Global warming brings about many environmental problems, people pay more and more attention to environmental protection problems, and how to reduce the emission of greenhouse gases is the focus of research at present. Greenhouse gas emission of transportation means accounts for about 23% of total global emission, so that the use of low-emission or zero-emission electric automobiles instead of fuel vehicles is the key to reduce greenhouse gas emission. Despite the low prevalence of electric vehicles at present, electric vehicle penetration is expected to grow rapidly in the coming years, as encouraged by government policy. The uncoordinated charging of the high-permeability electric automobile can bring different degrees of influence on the safe and stable operation of the power distribution network, such as power loss, voltage deviation, out-of-limit switching and the like. Therefore, how to improve the permeability of the electric automobile on the premise of not influencing the infrastructure of the power distribution network is a problem which needs to be solved urgently at present.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide an intelligent optimization method and system for electric vehicle integrated charging. The invention adopts the following technical scheme:
an intelligent optimization method for integrated charging of an electric automobile comprises the following steps:
step 1: acquiring the information of the electric vehicle connected to the charging pile in each time interval for each time interval t;
step 2: establishing a mathematical model of the energy requirement of the electric automobile;
and step 3: classifying the electric automobiles in groups;
and 4, step 4: respectively calculating the charging and discharging priority value of each electric automobile in each group, and distributing the charging electric quantity of the electric automobiles according to the priority;
and 5: adjusting the charging electric quantity of the electric automobile needing to be charged in the parking lot by combining the real-time charging and discharging conditions of the parking lot;
step 6: updating the charging data of the electric automobile according to the charging and discharging distribution condition in the step 5 to judge whether the charging quantity of the electric automobile meets the charging requirement of the electric automobile group or not; determining whether the current time interval t is the end time, if so, ending the method; otherwise, returning to the step 1, and optimizing the charge-discharge distribution of the next time interval t according to the updated information of the electric vehicle.
In the step of1, the collected information comprises the total number of the electric automobiles connected with the parking lot charging pile
Figure BDA0003198407230000021
Time t when ith electric vehicle arrives at parking loti,sSOC State S when the ith electric vehicle arrives at the parking loti,iniTime t when ith electric vehicle leaves parking loti,eSOC state S expected to be reached by the ith electric vehicle when leaving the parkingi,expectUpper limit of charging power of ith electric vehicle battery in t time interval
Figure BDA0003198407230000022
And the upper limit of discharge power of the ith electric vehicle battery in the t time interval
Figure BDA0003198407230000023
In step 2, the mathematical model includes the following:
the out-of-limit constraint of the connected charging pile transformer meets the following relational expression:
Figure BDA0003198407230000024
wherein L isload,tRepresenting the normal load of the connected charging post transformer during the time interval t,
Figure BDA0003198407230000025
represents the actual charging power of the ith electric vehicle in the t time interval,
Figure BDA0003198407230000026
represents the actual discharge power of the ith electric vehicle in the time interval TnorIndicating the power rating of the connected charging post transformer.
The SOC constraint of each electric vehicle battery in the time interval t meets the following relational expression:
Figure BDA0003198407230000027
wherein S isi,minRepresents the minimum lower limit of SOC of the ith electric vehicle battery, Si,tRepresenting the SOC value of the ith electric vehicle battery in the time interval t, Si,maxRepresenting the maximum upper limit of the SOC of the ith electric vehicle battery; t represents the total time.
The method for classifying the groups comprises the following steps:
in the time interval t, the residual charging time of the ith electric vehicle is
Figure BDA0003198407230000031
Wherein, ti,eRepresenting the time when the ith electric vehicle leaves the parking lot;
further, the minimum SOC of the ith electric vehicle in the time interval t can be found:
Figure BDA0003198407230000032
wherein E isiAnd the rated capacity of the ith electric automobile is shown.
At this time, the SOC is the minimum SOC state according to each electric automobile in the time interval of ti,min,tTrue SOC State SOC over time ti,nowAnd the desired SOC state Si,expectGrouping electric automobile groups:
the rigid charging population, population 1, satisfies the following condition:
SOCi,now＜SOCi,min,t
the flexible charging population, population 2, satisfies the following condition:
SOCi,min,t＜SOCi,now＜Si,expect
the discharge population, population 3, satisfies the following condition:
Si,except＜SOCi,now＜Si,max
in step 4, for the charging groups, i.e. group 1 and group 2, the charging priority value of the ith electric vehicle in the time interval t satisfies the following relation:
Figure BDA0003198407230000033
for the discharge group, i.e. group 3, the discharge priority value of the ith electric vehicle in the time interval t satisfies the following relation:
Figure BDA0003198407230000034
Figure DA00031984072345837138
for the ith in population 1group1The charge capacity distributed to the vehicle in the time interval t satisfies the following relation:
Figure BDA0003198407230000042
wherein N isgroup1The number of the electric automobiles in the rigid charging group is shown,
Figure BDA0003198407230000043
representing the ith in the rigid charging group of the electric automobilegroup1A charge priority value of the vehicle electric vehicle,
Figure BDA0003198407230000044
representing the ith in the rigid charging group of the electric automobilegroup1The amount of charge allocated to the vehicle; t isres,tRepresenting the deduction of the conventional load L of the connected charging pile transformerload,tThe allowance of the transformer is obtained after that,
Figure BDA0003198407230000045
representing the sum of all electric vehicle charging priority values in the electric vehicle rigid charging group;
charging capacity distributed to each electric vehicle in group 2 in t time interval
Figure BDA0003198407230000046
The calculation method is the same as that of the method: and calculating the ratio of the charging priority value of each electric vehicle in the electric vehicle group 2 to the sum of the charging priority values of all the electric vehicles in the group 2, and multiplying the ratio by the margin of the transformer after the conventional load is deducted.
Step 5 comprises the following steps:
step 501: for each time interval t, first, the maximum charging power is limited
Figure BDA0003198407230000047
Charging is carried out to obtain the charging requirement of the parking lot in the time interval t
Figure BDA0003198407230000048
Step 502: according to Tres,tAnd
Figure BDA0003198407230000049
calculating the charging requirement or the discharging capacity of the electric automobile;
step 503: and adjusting the charge and discharge electric quantity distribution according to the charge requirement and the discharge capacity calculated in the step 502.
In step 501, charging demand of parking lot in time interval t
Figure BDA0003198407230000051
Namely the sum of the maximum charging power upper limits of all the electric automobiles connected with the parking lot charging pile in the time interval t, namely
Figure BDA0003198407230000052
The sum of the upper limits of the maximum charging power of the vehicle electric vehicle.
In step 502, if
Figure BDA0003198407230000053
At the moment, the allowance of the transformer can meet the charging requirement of the parking lot, and the charging method does not need to be changed;
if it is not
Figure BDA0003198407230000054
Reclassifying the electric vehicle groups by using the method in the step 3, calculating the charging and discharging priority value of each electric vehicle in each group by using the method in the step 4, and calculating the charging requirement or the discharging capacity of the group by using the following relation:
charging demand of rigid charging group, i.e. group 1, in t time interval
Figure BDA0003198407230000055
The following relation is satisfied:
Figure BDA0003198407230000056
therein, SOCi1,min,tRepresents the minimum lower limit of the SOC, SOC of the i1 th electric vehicle battery in the t time intervali1,nowRepresenting the true SOC state, E, of the i1 th electric vehicle in population 1 during the t time intervali1Representing the rated capacity of the i1 th electric automobile in the group 1;
flexible charging demand of population, population 2, over time interval t
Figure BDA0003198407230000057
The following relation is satisfied:
Figure BDA0003198407230000058
wherein N isgroup2Indicating the number of electric vehicles in the flexible charging group, Si2,expectIndicating the i2 th motor steam in the group 2Desired SOC State, SOC, of a vehicle when leaving a parking spacei2,nowRepresenting the true SOC state, E, of the i2 th electric vehicle in population 2 during time interval ti2Indicating the rated capacity of the i2 th electric automobile in the group 2,
Figure BDA0003198407230000061
representing the upper limit of the charging power of the i2 th electric vehicle battery in the group 2 in the t time interval;
discharging the population in the t time interval, i.e. the discharge capacity of population 3
Figure BDA0003198407230000062
The following relation is satisfied:
Figure BDA0003198407230000063
wherein N isgroup3Indicates the number of electric vehicles in the discharge group, Si3,expectRepresents the expected SOC state, SOC, of the i3 th electric vehicle in the group 3 when leaving the parkingi2,nowRepresenting the true SOC state, E, of the i3 th electric vehicle in population 3 during time interval ti2Indicating the rated capacity of the i3 th electric automobile in the group 3,
Figure BDA0003198407230000064
and the upper limit of the discharge power of the i3 th electric vehicle battery in the group 3 in the time interval t is shown.
In step 503, if
Figure BDA0003198407230000065
The transformer margin at this time can meet the charging requirement of the group 1, the charging requirement of the group 1 is unchanged, and the charging requirement of the group 2 cannot be completely met, so that the remaining energy is distributed according to the charging priority:
Figure BDA0003198407230000066
wherein the content of the first and second substances,
Figure BDA0003198407230000071
indicating the ith in the flexible charging group of the electric automobilegroup2A charge priority value of the vehicle electric vehicle,
Figure BDA0003198407230000072
indicating the ith in the flexible charging group of the electric automobilegroup2The amount of charge allocated to the vehicle;
Figure BDA0003198407230000073
representing the sum of all electric vehicle charging priority values in the electric vehicle flexible charging group;
no charge is allocated to the group 3;
if it is not
Figure BDA0003198407230000074
The remaining energy of the voltage transformation at this time cannot satisfy the charging demand of the group 1, and the discharging capacity of the group 3 is required at this time
Figure BDA0003198407230000075
And distributing the charging capacity.
According to the discharge capacity of the population 3
Figure BDA0003198407230000076
The method for distributing the charging capacity comprises the following steps:
if it is not
Figure BDA0003198407230000077
The discharge capacity of the population 3 at this time cannot meet the remaining charge demand of the population 1, and the energy allocation of the population 1 at this time is:
Figure BDA0003198407230000078
if it is not
Figure BDA0003198407230000079
The discharging capacity of the group 3 at this time can satisfy the remaining charging demand of the group 1, and the energy distribution of the group 1 at this time satisfies the following relational expression:
Figure BDA00031984072300000710
at this time, the group 3 also supplies the charging energy to the group 2, and the energy distribution of the group 2 satisfies the following relational expression:
Figure BDA0003198407230000081
the invention also discloses an electric vehicle integrated charging intelligent optimization system based on the electric vehicle integrated charging intelligent optimization method, which comprises a data acquisition module, an electric vehicle group classification module, a charging and discharging priority calculation module, a charging electric quantity distribution module, a transformer margin comparison module, a charging demand calculation module and a discharging capacity calculation module:
the data acquisition module acquires the information of the electric automobiles connected to the parking lot charging piles within a time interval t, wherein the information comprises the total number of the electric automobiles connected with the parking lot charging piles, the time of each electric automobile arriving at the parking lot, the time of each electric automobile leaving the parking lot, the expected SOC state of each electric automobile when leaving the parking lot, the upper charging power limit of each electric automobile battery within the time interval t and the upper discharging power limit of the ith electric automobile battery within the time interval t, and the acquired data are input to all other modules;
the electric vehicle group classification module divides electric vehicles into a group 1, a group 2 and a group 3 according to the real SOC state of each electric vehicle in the t time interval, the minimum SOC state of each electric vehicle in the t time interval, the SOC state expected to be reached by each electric vehicle when the electric vehicle leaves the parking period and the maximum upper limit of the SOC of each electric vehicle battery, and the group 1, the group 2 and the group 3 respectively represent a rigid charging group, a flexible charging group and a discharging group;
the charging and discharging priority calculation module calculates the charging priority values of the group 1 and the group 2 and the discharging priority value of the group 3, and inputs the calculation results to the charging electric quantity distribution module;
the charging demand calculation module calculates the charging demands of the group 1 and group 2 electric vehicles and inputs the results to the transformer allowance module;
the discharge capacity calculation module calculates the discharge capacity of the group 3 electric vehicles and inputs the result to the transformer margin comparison module;
the transformer margin module compares the transformer margin with the charging requirement of the group 1 and the discharging capacity of the group 3, and inputs the comparison result to the charging electric quantity distribution module;
and the charging electric quantity distribution module calculates, adjusts and distributes the charging electric quantities of the group 1 and the group 2 according to the comparison result of the transformer margin module.
Compared with the prior art, the invention has the beneficial effects that:
1. the coupling relation between the charging requirement of each electric vehicle and the current battery state at the same time is fully considered, and the coordination of charging and discharging distribution is carried out by combining the requirements of all electric vehicles at the same time and the real-time charging and discharging conditions.
2. The method intelligently groups the electric automobile groups, sorts the charging and discharging priorities, and optimizes the charging behavior of the electric automobile groups according to the relationship between the charging demand and the transformer allowance so as to meet the requirement of simultaneously charging more electric automobiles and realize the maximization of the permeability of the electric automobiles.
3. The algorithm provided by the invention can rapidly group the electric vehicles and accurately analyze the relationship between the group charging requirement of the electric vehicles and the allowance of the transformer so as to maximize the charging efficiency.
Drawings
Fig. 1 is a flowchart of an intelligent optimization approach method for integrated charging of an electric vehicle and a system implementation thereof.
FIG. 2 is a graph of a conventional load curve for a power distribution network;
FIG. 3 is a load graph of 200 electric vehicles charging uncoordinated;
FIG. 4 is a graph of electric vehicle charge load at different permeabilities after applying the intelligent strategy presented herein.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
An intelligent optimization approach method and system for electric vehicle integrated charging are disclosed, wherein the flow diagram of the method is shown in fig. 1, and the method specifically comprises the following steps:
step 1: collecting the information of the electric vehicles connected to the charging piles in each time interval through a control device of the parking lot for each time interval t, wherein the information comprises the total number of the electric vehicles connected with the charging piles in the parking lot
Figure BDA0003198407230000091
Time t when ith electric vehicle arrives at parking loti,sSOC State S when the ith electric vehicle arrives at the parking loti,iniTime t when ith electric vehicle leaves parking loti,eSOC state S expected to be reached by the ith electric vehicle when leaving the parkingi,expectUpper limit of charging power of ith electric vehicle battery in t time interval
Figure BDA0003198407230000092
And the upper limit of discharge power of the ith electric vehicle battery in the t time interval
Figure BDA0003198407230000101
Step 2: and establishing a mathematical model of the energy requirement of the electric automobile.
Further, the step 2 is as follows:
calculating the residence time of the ith electric automobile in the parking lot:
ti,stop＝ti,s-ti,e
wherein, ti,sIs the time when the ith electric vehicle arrives at the parking lot, ti,eIs the time when the ith electric vehicle leaves the parking lot.
Assuming that the ith electric vehicle is charged with the maximum charging power all the time during the stay time, the theoretical state of charge (SOC) of the battery at the end of charging satisfies the following condition:
Figure BDA0003198407230000102
wherein, S'i,finIs the theoretical SOC, S at the end of charging of the ith electric vehiclei,iniIs the SOC state when the ith electric vehicle arrives at the parking lot, Si,maxIs the maximum upper limit of the SOC of the ith electric vehicle battery,
Figure BDA0003198407230000103
is the charging power upper limit of the ith electric automobile battery in the t time interval,
Figure BDA0003198407230000104
is the charging efficiency of the battery.
Obtaining the actual SOC of the ith electric automobile when the ith electric automobile leaves the parking lot according to the SOC requirement of the user
Figure BDA0003198407230000105
Wherein S isi,expectIs the SOC state that the ith electric vehicle is expected to reach when leaving the parking length.
In the charging process, each electric vehicle needs to satisfy the following constraint conditions:
and (3) restricting the charging power of each electric vehicle:
Figure BDA0003198407230000106
wherein the content of the first and second substances,
Figure BDA0003198407230000107
the method comprises the following steps that an arbitrary time interval of actual charging power of an ith electric automobile in a time interval T is represented, and T represents total time;
the discharge power constraint of each electric automobile meets the following relational expression:
Figure BDA0003198407230000111
wherein the content of the first and second substances,
Figure BDA0003198407230000112
representing the actual discharge power of the ith electric automobile in the t time interval;
each electric automobile cannot simultaneously charge and discharge, and the constraint meets the following relational expression:
Figure BDA0003198407230000113
the out-of-limit constraint of the connected charging pile transformer meets the following relational expression:
Figure BDA0003198407230000114
wherein L isload,tRepresenting the normal load of the connected charging post transformer during the time interval t,
Figure BDA0003198407230000115
represents the total number of the electric automobiles connected with the parking lot charging pile,
Figure BDA0003198407230000116
represents the actual charging power of the ith electric vehicle in the t time interval,
Figure BDA0003198407230000117
represents the actual discharge power of the ith electric vehicle in the time interval TnorIndicating the power rating of the connected charging post transformer.
The SOC constraint of each electric vehicle battery in the time interval t meets the following relational expression:
Figure BDA0003198407230000118
wherein S isi,minRepresents the minimum lower limit of SOC of the ith electric vehicle battery, Si,tRepresenting the SOC value of the ith electric vehicle battery in the t time interval;
and step 3: according to the remaining charging time of each electric automobile, calculating the minimum SOC state SOC of each electric automobile in each time intervali,min,tAnd then dividing the electric automobile group connected with the charging piles into a rigid charging group, a flexible charging group and a discharging group, namely a group 1, a group 2 and a group 3, according to the SOC state and the charging requirement of each electric automobile in a time interval t.
Further, the step 3 is as follows:
in the time interval t, the residual charging time of the ith electric vehicle is
Figure BDA0003198407230000121
Wherein, ti,eIndicating the time when the ith electric vehicle leaves the parking lot.
Further, the minimum SOC of the ith electric vehicle in the time interval t can be obtained
Figure BDA0003198407230000122
Wherein E isiThe rated capacity of the ith electric vehicle is indicated.
At this time, the SOC is the minimum SOC state according to each electric automobile in the time interval of ti,min,tWithin a time interval of tTrue SOC state SOCi,nowAnd the desired SOC state Si,expectGrouping electric automobile groups:
rigid charging group, group 1 satisfies the conditions
SOCi,now＜SOCi,min,t
Flexible charging group, group 2 satisfies conditions
SOCi,min,t＜SOCi,now＜Si,expect
Discharge population, population 3 satisfies the conditions
Si,except＜SOCi,now＜Si,max
And 4, step 4: and respectively calculating the charging and discharging priority value of each electric automobile in each group, and distributing the charging electric quantity of the electric automobiles according to the priority.
Further, the step 4 is as follows:
for the charging groups, namely group 1 and group 2, the charging priority value of the ith electric vehicle in the t time interval satisfies the following relation:
Figure BDA0003198407230000123
for the discharge group, i.e. group 3, the discharge priority value of the ith electric vehicle in the time interval t satisfies the following relation:
Figure BDA0003198407230000131
when charging or discharging, the charging electric quantity needs to be respectively distributed according to the charging priority value and the discharging priority value. Taking the charging behavior of the rigid charging group as an example, for the ith charging group in the rigid charging groupgroup1The charge capacity distributed to the vehicle in the time interval t satisfies the following relation:
Figure BDA0003198407230000132
wherein N isgroup1The number of the electric automobiles in the rigid charging group is shown,
Figure BDA0003198407230000138
representing the ith in the rigid charging group of the electric automobilegroup1A charge priority value of the vehicle electric vehicle,
Figure BDA0003198407230000134
representing the ith in the rigid charging group of the electric automobilegroup1The amount of charge allocated to the vehicle; t isres,tRepresenting the deduction of the conventional load L of the connected charging pile transformerload,tThe allowance of the transformer is obtained after that,
Figure BDA0003198407230000135
and the sum of all electric vehicle charging priority values in the electric vehicle rigid charging group is represented.
Charging capacity distributed to each electric vehicle in group 2 in t time interval
Figure BDA0003198407230000136
The calculation method is the same as that of the method: calculating the ratio of the charging priority value of each electric vehicle in the electric vehicle group 2 to the sum of the charging priority values of all the electric vehicles in the group 2, and multiplying the ratio by the margin of the transformer after deducting the conventional load; the discharge electric quantity of each electric automobile in the group 3 in the time interval t
Figure BDA0003198407230000137
The calculation method is the same as that of the method: and (3) calculating the ratio of the discharge priority value of each electric vehicle in the electric vehicle group 3 to the sum of the discharge priority values of all the electric vehicles in the group 3, and multiplying the ratio by the margin of the transformer after the conventional load is deducted.
And 5: adjusting the charging electric quantity of the electric automobile needing to be charged in the parking lot by combining the real-time charging and discharging conditions of the parking lot;
further, the step 5 includes the following steps:
step 501: for each time interval t, first, the maximum charging power is limited
Figure BDA0003198407230000141
Charging is carried out to obtain the charging requirement of the parking lot in the time interval t
Figure BDA0003198407230000142
Parking lot charging requirement in t time interval
Figure BDA0003198407230000143
Namely the sum of the maximum charging power upper limits of all the electric automobiles connected with the parking lot charging pile in the time interval t, namely
Figure BDA0003198407230000144
The sum of the upper limits of the maximum charging power of the electric vehicles;
step 502: according to Tres,tAnd
Figure BDA0003198407230000145
calculating the charging requirement or the discharging capacity of the electric automobile;
if it is not
Figure BDA0003198407230000146
At the moment, the transformer allowance can meet the charging requirement of the parking lot, so the charging method does not need to be changed.
If it is not
Figure BDA0003198407230000147
Reclassifying the electric vehicle groups by using the method in the step 3, calculating the charging and discharging priority value of each electric vehicle in each group by using the method in the step 4, and calculating the charging requirement or the discharging capacity of the group by using the following relation;
a rigid charging population is present during the t time interval,i.e. the charging requirements of the population 1
Figure BDA0003198407230000148
The following relation is satisfied:
Figure BDA0003198407230000149
therein, SOCi1,min,tRepresents the minimum lower limit of the SOC, SOC of the i1 th electric vehicle battery in the t time intervali1,nowRepresenting the true SOC state, E, of the i1 th electric vehicle in population 1 during the t time intervali1Representing the rated capacity of the i1 th electric automobile in the group 1;
flexible charging demand of population, population 2, over time interval t
Figure BDA0003198407230000151
The following relation is satisfied:
Figure BDA0003198407230000152
wherein N isgroup2Indicating the number of electric vehicles in the flexible charging group, Si2,expectRepresents the expected SOC state, SOC, of the i2 th electric vehicle in the group 2 when leaving the parkingi2,nowRepresenting the true SOC state, E, of the i2 th electric vehicle in population 2 during time interval ti2Indicating the rated capacity of the i2 th electric automobile in the group 2,
Figure BDA0003198407230000153
representing the upper limit of the charging power of the i2 th electric vehicle battery in the group 2 in the t time interval;
discharging the population in the t time interval, i.e. the discharge capacity of population 3
Figure BDA0003198407230000154
The following relation is satisfied:
Figure BDA0003198407230000155
wherein N isgroup3Indicates the number of electric vehicles in the discharge group, Si3,expectRepresents the expected SOC state, SOC, of the i3 th electric vehicle in the group 3 when leaving the parkingi2,nowRepresenting the true SOC state, E, of the i3 th electric vehicle in population 3 during time interval ti2Indicating the rated capacity of the i3 th electric automobile in the group 3,
Figure BDA0003198407230000156
representing the upper limit of the discharge power of the i3 th electric vehicle battery in the group 3 in the time interval t;
step 503: according to the charging requirements of the group 1 and the group 2 and the discharging capacity of the group 3, the distribution of the charging and discharging electric quantity is adjusted; the adjustment mode is as follows:
if it is not
Figure BDA0003198407230000161
The transformer margin at this time can meet the charging requirement of the group 1, the charging requirement of the group 1 is unchanged, and the charging requirement of the group 2 cannot be completely met, so that the remaining energy is distributed according to the charging priority:
Figure BDA0003198407230000162
wherein the content of the first and second substances,
Figure BDA0003198407230000163
indicating the ith in the flexible charging group of the electric automobilegroup2A charge priority value of the vehicle electric vehicle,
Figure BDA0003198407230000164
indicating the ith in the flexible charging group of the electric automobilegroup2The amount of charge allocated to the vehicle;
Figure BDA0003198407230000165
and the sum of all electric vehicle charging priority values in the electric vehicle flexible charging group is represented.
The charge amount is not allocated to the group 3.
If it is not
Figure BDA0003198407230000166
The remaining energy of the voltage transformation at this time cannot satisfy the charging demand of the group 1, and the discharging capacity of the group 3 is required at this time
Figure BDA0003198407230000167
And (3) distribution:
case 1: if it is not
Figure BDA0003198407230000168
The discharge capacity of the population 3 at this time cannot meet the remaining charge demand of the population 1, and the energy allocation of the population 1 at this time is:
Figure BDA0003198407230000169
case 2: if it is not
Figure BDA00031984072300001610
The discharging capacity of the group 3 at this time can meet the remaining charging demand of the group 1, and the energy allocation of the group 1 at this time is as follows:
Figure BDA0003198407230000171
at this time, the group 3 also supplies the energy for charging the group 2
Figure BDA0003198407230000172
Step 6: updating the charging data of the electric automobile according to the charging and discharging distribution condition in the step 5 to judge whether the charging quantity of the electric automobile meets the charging requirement of the electric automobile group or not; determining whether the current time interval t is the end time, if so, ending the method; otherwise, returning to the step 1, and optimizing the charge-discharge distribution of the next time interval t according to the updated information of the electric vehicle.
The invention also discloses an electric vehicle integrated charging intelligent optimization system which comprises a data acquisition module, an electric vehicle group classification module, a charging and discharging priority calculation module, a charging electric quantity distribution module, a transformer margin comparison module, a charging demand calculation module and a discharging capacity calculation module.
The data acquisition module acquires the information of the electric automobiles connected to the parking lot charging piles within a time interval t, wherein the information comprises the total number of the electric automobiles connected with the parking lot charging piles, the time of each electric automobile arriving at the parking lot, the time of each electric automobile leaving the parking lot, the expected SOC state of each electric automobile when leaving the parking lot, the upper charging power limit of each electric automobile battery within the time interval t and the upper discharging power limit of the ith electric automobile battery within the time interval t, and the acquired data are input to all other modules;
the electric vehicle group classification module divides electric vehicles into a group 1, a group 2 and a group 3 according to the real SOC state of each electric vehicle in the t time interval, the minimum SOC state of each electric vehicle in the t time interval, the SOC state expected to be reached by each electric vehicle when the electric vehicle leaves the parking period and the maximum upper limit of the SOC of each electric vehicle battery, and the group 1, the group 2 and the group 3 respectively represent a rigid charging group, a flexible charging group and a discharging group;
the charging and discharging priority calculation module calculates the charging priority values of the group 1 and the group 2 and the discharging priority value of the group 3, and inputs the calculation results to the charging electric quantity distribution module;
the charging demand calculation module calculates the charging demands of the group 1 and group 2 electric vehicles and inputs the results to the transformer allowance module;
the discharge capacity calculation module calculates the discharge capacity of the group 3 electric vehicles and inputs the result to the transformer margin comparison module;
the transformer margin module compares the transformer margin with the charging requirement of the group 1 and the discharging capacity of the group 3, and inputs the comparison result to the charging electric quantity distribution module;
and the charging electric quantity distribution module calculates, adjusts and distributes the charging electric quantities of the group 1 and the group 2 according to the comparison result of the transformer margin module.
The focus of the study herein is on parking lots at workplaces, parking during the day. Assuming that a parking lot has 500 parking spaces, each parking space is equipped with an electric vehicle charging facility having G2V and V2G functions. Assuming that all electric vehicles in the parking lot are controlled by the charging coordinator, the intelligent charging of the electric vehicles is performed by the charging coordinator.
Fig. 2 is a typical 24 hour conventional load profile for a transformer in a parking lot. As can be seen, the conventional power load of the area varies from 180kW to 380kW, the power peak period is 13:00 to 19:00, the power valley period is 22:00 to 08:00, and the remaining time is the normal power utilization period.
For the electric vehicles arriving at the parking lot by adopting the method of charging and charging, the charging scene of 200 electric vehicles is simulated, and as shown in fig. 3, the electric vehicles cause serious power peak demands at charging loads of 07:00-12: 00. This situation seriously affects the safe and stable operation of the power grid.
The integrated charging intelligent optimization method provided by the invention is applied to a parking lot in a workplace, and realizes the optimization integration of the electric automobile. Assuming that the parking lot has 500 parking spaces, the permeability of the electric vehicle is discretized into 10 levels, 10%, 20%, …, and 100%, respectively (considering the increment step size as 10%). As can be seen from fig. 4, after the intelligent charging strategy proposed herein is applied, the maximum permeability of the electric vehicle that can be accommodated in the parking lot is effectively improved.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.

Claims (12)

1. An integrated charging intelligent optimization seeking method for an electric vehicle is characterized by comprising the following steps:
step 1: acquiring the information of the electric vehicle connected to the charging pile in each time interval for each time interval t;
step 2: establishing a mathematical model of the energy requirement of the electric automobile;
and step 3: classifying the electric automobiles in groups;
and 4, step 4: respectively calculating the charging and discharging priority value of each electric automobile in each group, and distributing the charging electric quantity of the electric automobiles according to the priority;
and 5: adjusting the charging electric quantity of the electric automobile needing to be charged in the parking lot by combining the real-time charging and discharging conditions of the parking lot;
step 6: updating the charging data of the electric automobile according to the charging and discharging distribution condition in the step 5 to judge whether the charging quantity of the electric automobile meets the charging requirement of the group of electric automobiles; determining whether the current time interval t is the end time, if so, ending the method; otherwise, returning to the step 1, and optimizing the charge-discharge distribution of the next time interval t according to the updated information of the electric vehicle.
2. The intelligent optimization approach method for the integrated charging of the electric automobile according to claim 1, characterized in that:
in the step 1, the collected information comprises the total number of the electric vehicles connected with the charging piles of the parking lot
Figure FDA0003198407220000011
Time t when ith electric vehicle arrives at parking loti,sSOC State S when the ith electric vehicle arrives at the parking loti,iniThe ith electric vehicle leaves the parking lotTime t ofi,eSOC state S expected to be reached by the ith electric vehicle when leaving the parkingi,expectUpper limit of charging power of ith electric vehicle battery in t time interval
Figure FDA0003198407220000012
And the upper limit of discharge power of the ith electric vehicle battery in the t time interval
Figure FDA0003198407220000013
3. The intelligent optimization approach method for the integrated charging of the electric automobile according to claim 2, characterized in that:
in step 2, the mathematical model includes the following:
the out-of-limit constraint of the connected charging pile transformer meets the following relational expression:
Figure FDA0003198407220000014
wherein L isload,tRepresenting the normal load of the connected charging post transformer during the time interval t,
Figure FDA0003198407220000021
represents the actual charging power of the ith electric vehicle in the t time interval,
Figure FDA0003198407220000022
represents the actual discharge power of the ith electric vehicle in the time interval TnorIndicating the rated power of the connected charging pile transformer;
the SOC constraint of each electric vehicle battery in the time interval t meets the following relational expression:
Figure FDA0003198407220000023
wherein S isi,minRepresents the minimum lower limit of SOC of the ith electric vehicle battery, Si,tRepresenting the SOC value of the ith electric vehicle battery in the time interval t, Si,maxRepresenting the maximum upper limit of the SOC of the ith electric vehicle battery; t represents the total time.
4. The intelligent optimization approach method for electric vehicle integrated charging according to claim 2 or 3, characterized in that:
the method for classifying the population comprises the following steps:
in time interval t, the residual charging time of the ith electric vehicle
Figure FDA0003198407220000024
Is composed of
Figure FDA0003198407220000025
Wherein, ti,eRepresenting the time when the ith electric vehicle leaves the parking lot;
further, the minimum SOC, i.e., SOC of the ith electric vehicle in the time interval t can be obtainedi,min,t：
Figure FDA0003198407220000026
Wherein E isiThe rated capacity of the ith electric automobile is represented;
at this time, the SOC is the minimum SOC state according to each electric automobile in the time interval of ti,min,tTrue SOC State SOC over time ti,nowAnd the desired SOC state Si,expectGrouping electric automobile groups:
the rigid charging population, population 1, satisfies the following condition:
SOCi,now＜SOCi,min,t
the flexible charging population, population 2, satisfies the following condition:
SOCi,min,t＜SOCi,now＜Si,expect
the discharge population, population 3, satisfies the following condition:
Si,except＜SOCi,now＜Si,max
5. the intelligent optimization approach method for the integrated charging of the electric automobile according to claim 4, characterized in that:
in step 4, for the charging groups, i.e. group 1 and group 2, the charging priority value of the ith electric vehicle in the t time interval satisfies the following relation:
Figure FDA0003198407220000031
for the discharge group, i.e. group 3, the discharge priority value of the ith electric vehicle in the time interval t satisfies the following relation:
Figure FDA0003198407220000032
6. the intelligent optimization approach method for the integrated charging of the electric automobile according to claim 5, characterized in that:
for the ith in population 1group1The charge capacity distributed to the vehicle in the time interval t satisfies the following relation:
Figure FDA0003198407220000033
wherein N isgroup1The number of the electric automobiles in the rigid charging group is shown,
Figure FDA0003198407220000034
rigid charging group for electric automobileIn vivo ithgroup1A charge priority value of the vehicle electric vehicle,
Figure FDA0003198407220000041
representing the ith in the rigid charging group of the electric automobilegroup1The amount of charge allocated to the vehicle; t isres,tRepresenting the deduction of the conventional load L of the connected charging pile transformerload,tThe allowance of the transformer is obtained after that,
Figure FDA0003198407220000042
representing the sum of all electric vehicle charging priority values in the electric vehicle rigid charging group;
charging capacity distributed to each electric vehicle in group 2 in t time interval
Figure FDA0003198407220000043
The calculation method is the same as that of the method: and calculating the ratio of the charging priority value of each electric vehicle in the electric vehicle group 2 to the sum of the charging priority values of all the electric vehicles in the group 2, and multiplying the ratio by the margin of the transformer after the conventional load is deducted.
7. The intelligent optimization approach method for the integrated charging of the electric automobile according to claim 6, characterized in that:
the step 5 comprises the following steps:
step 501: for each time interval t, first, the maximum charging power is limited
Figure FDA0003198407220000044
Charging is carried out to obtain the charging requirement of the parking lot in the time interval t
Figure FDA0003198407220000045
Step 502: according to Tres,tAnd
Figure FDA0003198407220000046
calculating the charging requirement or the discharging capacity of the electric automobile;
step 503: and adjusting the charge and discharge electric quantity distribution according to the charge requirement and the discharge capacity calculated in the step 502.
8. The intelligent optimization approach method for the integrated charging of the electric automobile according to claim 7, characterized in that:
in step 501, charging demand of parking lot in time interval t
Figure FDA0003198407220000047
Namely the sum of the maximum charging power upper limits of all the electric automobiles connected with the parking lot charging pile in the time interval t, namely
Figure FDA0003198407220000048
The sum of the upper limits of the maximum charging power of the vehicle electric vehicle.
9. The intelligent optimization approach method for electric vehicle integrated charging according to claim 7 or 8, characterized in that:
in said step 502, if
Figure FDA0003198407220000051
At the moment, the allowance of the transformer can meet the charging requirement of the parking lot, and the charging method does not need to be changed;
if it is not
Figure FDA0003198407220000052
Reclassifying the electric vehicle groups by using the method in the step 3, calculating the charging and discharging priority value of each electric vehicle in each group by using the method in the step 4, and calculating the charging requirement or the discharging capacity of the group by using the following relation:
charging demand of rigid charging group, i.e. group 1, in t time interval
Figure FDA0003198407220000053
The following relation is satisfied:
Figure FDA0003198407220000054
therein, SOCi1,min,tRepresents the minimum lower limit of the SOC, SOC of the i1 th electric vehicle battery in the t time intervali1,nowRepresenting the true SOC state, E, of the i1 th electric vehicle in population 1 during the t time intervali1Representing the rated capacity of the i1 th electric automobile in the group 1;
flexible charging demand of population, population 2, over time interval t
Figure FDA0003198407220000055
The following relation is satisfied:
Figure FDA0003198407220000056
wherein N isgroup2Indicating the number of electric vehicles in the flexible charging group, Si2,expectRepresents the expected SOC state, SOC, of the i2 th electric vehicle in the group 2 when leaving the parkingi2,nowRepresenting the true SOC state, E, of the i2 th electric vehicle in population 2 during time interval ti2Indicating the rated capacity of the i2 th electric automobile in the group 2,
Figure FDA0003198407220000057
representing the upper limit of the charging power of the i2 th electric vehicle battery in the group 2 in the t time interval;
discharging the population in the t time interval, i.e. the discharge capacity of population 3
Figure FDA0003198407220000061
The following relation is satisfied:
Figure FDA0003198407220000062
wherein N isgroup3Indicates the number of electric vehicles in the discharge group, Si3,expectRepresents the expected SOC state, SOC, of the i3 th electric vehicle in the group 3 when leaving the parkingi2,nowRepresenting the true SOC state, E, of the i3 th electric vehicle in population 3 during time interval ti2Indicating the rated capacity of the i3 th electric automobile in the group 3,
Figure FDA0003198407220000063
and the upper limit of the discharge power of the i3 th electric vehicle battery in the group 3 in the time interval t is shown.
10. The intelligent optimization approach method for the integrated charging of the electric automobile according to claim 9, characterized in that:
in said step 503, if
Figure FDA0003198407220000064
The transformer margin at this time can meet the charging requirement of the group 1, the charging requirement of the group 1 is unchanged, and the charging requirement of the group 2 cannot be completely met, so that the remaining energy is distributed according to the charging priority:
Figure FDA0003198407220000065
wherein the content of the first and second substances,
Figure FDA0003198407220000066
indicating the ith in the flexible charging group of the electric automobilegroup2A charge priority value of the vehicle electric vehicle,
Figure FDA0003198407220000067
indicating the ith in the flexible charging group of the electric automobilegroup2The amount of charge allocated to the vehicle;
Figure FDA0003198407220000068
representing the sum of all electric vehicle charging priority values in the electric vehicle flexible charging group;
no charge is allocated to the group 3;
if it is not
Figure FDA0003198407220000071
The remaining energy of the voltage transformation at this time cannot satisfy the charging demand of the group 1, and the discharging capacity of the group 3 is required at this time
Figure FDA0003198407220000072
And distributing the charging capacity.
11. The intelligent optimization approach method for the integrated charging of the electric automobile according to claim 10, characterized in that:
according to the discharge capacity of the population 3
Figure FDA0003198407220000073
The method for distributing the charging capacity comprises the following steps:
if it is not
Figure FDA0003198407220000074
The discharge capacity of the population 3 at this time cannot meet the remaining charge demand of the population 1, and the energy allocation of the population 1 at this time is:
Figure FDA0003198407220000075
if it is not
Figure FDA0003198407220000076
The discharging capacity of the group 3 at this time can satisfy the remaining charging demand of the group 1, and the energy distribution of the group 1 at this time satisfies the following relational expression:
Figure FDA0003198407220000077
at this time, the group 3 also supplies the charging energy to the group 2, and the energy distribution of the group 2 satisfies the following relational expression:
Figure FDA0003198407220000078
12. the intelligent optimization seeking system for the integrated charging of the electric vehicle according to any one of claims 1 to 11 comprises a data acquisition module, an electric vehicle group classification module, a charging and discharging priority calculation module, a charging electric quantity distribution module, a transformer margin comparison module, a charging demand calculation module and a discharging capacity calculation module, and is characterized in that:
the data acquisition module acquires the information of the electric automobiles connected to the parking lot charging piles within a time interval t, wherein the information comprises the total number of the electric automobiles connected with the parking lot charging piles, the time of each electric automobile arriving at the parking lot, the time of each electric automobile leaving the parking lot, the expected SOC state of each electric automobile when leaving the parking lot, the upper limit of the charging power of each electric automobile battery within the time interval t and the upper limit of the discharging power of the ith electric automobile battery within the time interval t, and the acquired data are input to all other modules;
the electric vehicle group classification module divides electric vehicles into a group 1, a group 2 and a group 3 according to the real SOC state of each electric vehicle in the t time interval, the minimum SOC state of each electric vehicle in the t time interval, the SOC state expected to be reached by each electric vehicle when the electric vehicle leaves the parking period and the maximum upper limit of the SOC of each electric vehicle battery, and the group 1, the group 2 and the group 3 respectively represent a rigid charging group, a flexible charging group and a discharging group;
the charging and discharging priority calculation module calculates the charging priority values of the group 1 and the group 2 and the discharging priority value of the group 3, and inputs the calculation results to the charging electric quantity distribution module;
the charging demand calculation module calculates the charging demands of the group 1 and group 2 electric vehicles and inputs the results to the transformer allowance module;
the discharge capacity calculation module calculates the discharge capacity of the group 3 electric vehicles and inputs the result to the transformer margin comparison module;
the transformer margin module compares the transformer margin with the charging requirement of the group 1 and the discharging capability of the group 3, and inputs the comparison result to the charging electric quantity distribution module;
and the charging electric quantity distribution module calculates, adjusts and distributes the charging electric quantities of the group 1 and the group 2 according to the comparison result of the transformer margin module.
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