CN112356724A - Electric automobile ordered charging control method based on artificial fish swarm algorithm - Google Patents

Electric automobile ordered charging control method based on artificial fish swarm algorithm Download PDF

Info

Publication number
CN112356724A
CN112356724A CN202011107452.1A CN202011107452A CN112356724A CN 112356724 A CN112356724 A CN 112356724A CN 202011107452 A CN202011107452 A CN 202011107452A CN 112356724 A CN112356724 A CN 112356724A
Authority
CN
China
Prior art keywords
charging
time
opt
representing
electric vehicle
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011107452.1A
Other languages
Chinese (zh)
Other versions
CN112356724B (en
Inventor
汪天允
张�浩
赵翔
周斌
张建伟
董自波
郭云翔
王玲
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
NARI Group Corp
State Grid Beijing Electric Power Co Ltd
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
Original Assignee
NARI Group Corp
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by NARI Group Corp, Nari Technology Co Ltd, NARI Nanjing Control System Co Ltd filed Critical NARI Group Corp
Priority to CN202011107452.1A priority Critical patent/CN112356724B/en
Publication of CN112356724A publication Critical patent/CN112356724A/en
Application granted granted Critical
Publication of CN112356724B publication Critical patent/CN112356724B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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
    • B60L53/63—Monitoring or controlling charging stations in response to network capacity
    • 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
    • B60L53/64—Optimising energy costs, e.g. responding to electricity rates
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/40—The network being an on-board power network, i.e. within a vehicle
    • H02J2310/48—The network being an on-board power network, i.e. within a vehicle for electric vehicles [EV] or hybrid vehicles [HEV]
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The invention discloses an electric vehicle ordered charging control method based on an artificial fish swarm algorithm, and a scene optimization model G (t) is constructedopt)，G(topt) The formula is as follows:
Figure DDA0002725797840000011
wherein, F1Representing the charge of the user during disordered charging, F2Representing the grid peak-to-valley difference during disordered charging, F1optRepresenting the charge of the user during the ordered charging, F2optRepresenting the grid peak-to-valley difference, λ, during ordered charging1And λ2Weighting coefficients representing the respective objective functions; using minG (t)opt) As an optimization target of the scene optimization model, the optimized charging time period t is solvedoptOf (2) an optimal solution tbestCombined with charging period tcOptimizing by using an artificial fish swarm algorithm to obtain a final charging time interval
Figure DDA0002725797840000012
According to the final charging period
Figure DDA0002725797840000013
And arranging the electric automobile to be charged in order. The method can scientifically guide the charging behavior of the electric automobile, has important significance for optimizing and scheduling the large-scale charging behavior of the electric automobile, and has strong universality and practicability.

Description

Electric automobile ordered charging control method based on artificial fish swarm algorithm
Technical Field
The invention relates to an electric automobile ordered charging control method based on an artificial fish swarm algorithm, and belongs to the technical field of electric automobile charging.
Background
In recent years, along with the continuous improvement of the economic level and the scientific level of China, the happiness index of the life of people is obviously improved, but a series of problems of shortage of fossil energy, global warming, excessive pollutant emission and the like are brought while the society is rapidly developed, and great challenge is brought to the development of the modern society.
The new energy automobile adopts clean energy, and compared with the traditional fuel oil automobile, the pressure of exhaust emission on the environment can be effectively relieved, so that countries in the world start to vigorously support the development of the new energy automobile industry, and the electric automobile and related industries thereof also meet the rapid development period. Meanwhile, the charging behavior of the large-scale electric automobile which is not guided to be connected into the power grid brings huge negative effects to the power system, and the negative effects are particularly shown in the aspects of enlarging the peak-valley difference of the power distribution network, generating harmonic pollution, causing the limit of a transformer to be out of limit, reducing the voltage level of the power grid and the like. Therefore, how to solve the charging control problem of the electric vehicle is a technical problem which needs to be solved urgently by those skilled in the art.
Disclosure of Invention
The purpose is as follows: in order to overcome the defects in the prior art, the invention provides an electric vehicle ordered charging control method based on an artificial fish swarm algorithm, so as to realize safe and stable operation of a power grid and minimum charging cost of a user.
The technical scheme is as follows: in order to solve the technical problems, the technical scheme adopted by the invention is as follows:
an electric automobile ordered charging control method based on an artificial fish swarm algorithm comprises the following steps:
step 1: construction of a scene optimization model G (t)opt)，G(topt) The formula is as follows:
Figure BDA0002725797820000011
wherein, F1Representing the charge of the user during disordered charging, F2Representing the grid peak-to-valley difference during disordered charging, F1optRepresenting the charge of the user during the ordered charging, F2optRepresenting the grid peak-to-valley difference, λ, during ordered charging1And λ2Weighting coefficients representing the respective objective functions;
step 2: using minG (t)opt) As an optimization target of the scene optimization model, the optimized charging period to is solvedptOf (2) an optimal solution tbestCombined with charging period tcOptimizing by using an artificial fish swarm algorithm to obtain a final charging time interval
Figure BDA0002725797820000021
And step 3: according to the final charging period
Figure BDA0002725797820000022
And arranging the electric automobile to be charged in order.
Preferably, the charging period tcThe calculation formula is as follows:
Figure BDA0002725797820000023
tback＜tc＜tstart
wherein, tcDenotes a chargeable period, tcWhen the time is 1, the electric vehicle is charging at the moment, tcWhen the time is equal to 0, the electric automobile is not charged at the moment; t is tcShould be in the range of tstartAnd tbackT isstartAt the time of trip, tbackIs the return time; t is t0Represents the starting time of the charging in stages; t ischargeIndicating a periodic charging period.
Preferably, the user charging fee F in the disordered charging1The calculation formula is as follows:
Figure BDA0002725797820000024
wherein N is the total number of electric vehicles in the region; t is tbackTo return to time, tstartAt the time of trip, tcDenotes a chargeable period, PevTo charging power, ccRepresents the electricity price at the charging time;
the power grid peak-valley difference F during the disordered charging2The calculation formula is as follows:
F2＝Lmax-Lmin
Figure BDA0002725797820000025
Figure BDA0002725797820000026
wherein ltRepresenting the basic load of residents at the time t;
user charging fee F during ordered charging1optThe calculation formula is as follows:
Figure BDA0002725797820000027
wherein N is the total number of electric vehicles in the region; t is tbackTo return to time, tstartAt the time of trip, toptRepresents an optimized charging period, PevTo charging power, ccRepresents the electricity price at the charging time;
the difference F between the peak and the valley of the power grid during the orderly charging2optThe calculation formula is as follows:
F2＝Lmax-Lmin
Figure BDA0002725797820000031
Figure BDA0002725797820000032
wherein ltRepresenting the resident base load at time t.
As a preferred scheme, the optimization of the artificial fish swarm algorithm comprises the following specific steps:
2.1 pairs of tcPerforming a random action RB to obtain an initial one
Figure BDA0002725797820000033
The RB calculation formula is as follows:
Figure BDA0002725797820000034
wherein step represents the moving step length, rand represents the random variable, and step-rand represents the randomly extracted moving step length;
2.2 after the above-mentioned operations
Figure BDA0002725797820000035
Executing the FB of foraging behavior to obtain
Figure BDA0002725797820000036
The FB calculation formula is as follows:
Figure BDA0002725797820000037
G(tc)、
Figure BDA0002725797820000038
is G (to)pt) To inptBy tc、
Figure BDA0002725797820000039
Alternative calculation is carried out;
2.3 after the above-mentioned operations
Figure BDA00027257978200000310
Value to t'cAnd to new t'cPerforming clustering action SB to obtain
Figure BDA00027257978200000311
The SB calculation is as follows:
Figure BDA00027257978200000312
Figure BDA00027257978200000313
wherein R isvFor a dispatching range, m is the number of cars being charged in the dispatching range, tiCharging an ith automobile, wherein N is the total number of electric automobiles in the area; sigma is a self-defined crowding factor; g (t)k)、G(t′c) Respectively combine G (t)opt) T in (1)optBy tk、t‘cAlternative calculation is carried out;
2.4 after the above-mentioned operations
Figure BDA0002725797820000041
Assigned value to t'cAnd for new t "cExecuting the rear-end collision behavior REB to obtain
Figure BDA0002725797820000042
REB calculation formula is as follows:
Figure BDA0002725797820000043
wherein, tbestRepresenting an optimized target minG (t) within a scheduling scopeopt) Middle toptOf G (t)best)、G(t″c) Respectively combine G (t)opt) T in (1)optBy tbest、t”cAnd alternative calculation.
Preferably, λ is1+λ2＝1。
Preferably, P isev·tcThe constraints of (2) are as follows:
Figure BDA0002725797820000044
wherein ltRepresenting the basic load of the residents at time t, t being tstartAnd tbackAll time in between; pMTo representThe maximum capacity of the transformer; mu represents the maximum capacity threshold multiple which can be carried by the transformer; t is tcDenotes a chargeable period, PevIs the charging power; and N is the total number of electric automobiles in the region.
Preferably, t isstartThe calculation formula of (a) is as follows:
Figure BDA0002725797820000045
wherein, tstartTo the time of trip, musExpected value, σ, of trip timesVariance of time of trip, fs(tstart) Is a probability density function of the travel time distribution.
Preferably, t isbackThe calculation formula of (a) is as follows:
Figure BDA0002725797820000046
wherein, tbackTo return to time, μeTo return the expected value of the time of day, σeAs variance of the return time, fe(tback) Is a probability density function of the distribution of the return moments.
Preferably, T ischargeThe calculation formula of (a) is as follows:
Figure BDA0002725797820000051
therein, SOC0Is the initial state of charge rate of the electric vehicle, Q is the battery capacity, PevIs the charging power.
Preferably, T ischargeThe constraints of (2) are as follows:
Figure BDA0002725797820000052
wherein, Tn,chargeRepresenting the charging time of the nth electric vehicle; pevRepresenting the charging power of the electric vehicle; qnRepresenting the battery capacity of the nth electric vehicle; η represents the charging efficiency; SOCn,eIndicates that the nth electric vehicle is at tstartUser desired SOC value at time; SOCn,0Indicates that the nth electric vehicle is at tbackInitial SOC value at time.
Has the advantages that: the invention provides an electric vehicle ordered charging control method based on an artificial fish swarm algorithm, which optimizes the charging behavior of electric vehicles in residential communities. The basic idea is that firstly, a disordered charging model is established based on a Monte Carlo method; secondly, establishing a charging constraint model according to a charging scene of the electric automobile; then, the problems of safe and stable operation of a power grid and the charging cost of a user are comprehensively considered, and an ordered charging optimization scheduling scheme is provided; and finally, constructing a residential community scene optimization model, and finishing scheduling optimization of the electric automobile by using an artificial fish swarm algorithm. The method can scientifically guide the charging behavior of the electric automobile, has important significance for optimizing and scheduling the large-scale charging behavior of the electric automobile, and has strong universality and practicability.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The present invention will be further described with reference to the following examples.
As shown in fig. 1, an electric vehicle ordered charging control method based on an artificial fish swarm algorithm includes the following steps:
step 1, establishing a disordered charging model based on a Monte Carlo method. The chaotic charging model is as follows,
1.1 electric automobile travel time model
Figure BDA0002725797820000061
Wherein, tstartTo the time of trip, musExpected value, σ, of trip timesVariance of time of trip, fs(tstart) Is a probability density function of the travel time distribution.
1.2 electric vehicle Return time model
Figure BDA0002725797820000062
Wherein, tbackTo return to time, μeTo return the expected value of the time of day, σeAs variance of the return time, fe(tback) Is a probability density function of the distribution of the return moments.
1.3 electric automobile is long model during charging
Figure BDA0002725797820000063
Therein, SOC0Is the initial state of charge rate of the electric vehicle, Q is the battery capacity, PevFor charging power, TchargeIs the charging period.
1.4 relationship between electric vehicle return time and charging start time
The charging start time and the return time of the vehicle are considered to be approximately the same
tstchar＝tback (4)
Wherein, tstcharTo the charging start time, tbackIs the return time.
And 2, establishing a charging constraint model according to the charging scene of the electric automobile. The charging constraint model is as follows,
2.1 Total Charge constraint
The charging load distributed by the charging pile to the electric vehicle should satisfy the State of Charge (SOC) of the battery expected by the user when charging is completed.
Figure BDA0002725797820000064
Therein, SOCn,eIndicates that the nth electric vehicle is at tstartUser desired SOC value at time, tstartExtracting by the travel time model in the step 1.1; SOCn,0Indicates that the nth electric vehicle is at tbackInitial SOC value at time, i.e. tstcharInitial SOC value at time, tbackExtracted from the return time model of step 1.2; t isn,chargeThe charging time length of the nth electric automobile is represented and determined by the charging time length model in the step 1.3; η represents the charging efficiency; pevIs the charging power; qnThe battery capacity of the nth electric vehicle.
2.2 electric vehicle charging duration constraint
Figure BDA0002725797820000071
Wherein, Tn,chargeThe charging time length of the nth electric automobile is represented and determined by the charging time length model in the step 1.3; pevRepresenting the charging power of the electric vehicle; qnThe battery capacity of the nth electric vehicle is shown.
2.3 electric vehicle chargeable time period constraint
The chargeable period of the electric automobile is distributed in any period between the return time and the departure time of the electric automobile.
Figure BDA0002725797820000072
tback＜tc＜tstart (8)
Wherein, tcDenotes a chargeable period, tcWhen the time is 1, the electric vehicle is charging at the moment, tcWhen the value is 0, the electric vehicle is not charged at that time. t is tcShould be in the range of tstartAnd tbackT isstartAnd tbackThe determination is carried out by steps 1.1 and 1.2 respectively; t is t0Indicating the start of the staged charging, the initial value of which is the start of charging t in step 1.4stchar；TchargeThe representative periodic charge period is determined by the charge period model of step 1.3.
2.4 Charge continuity constraints
Frequent opening and stopping fills the life-span that electric pile can loss car battery and fill electric pile components and parts, consequently need set for and fill the minimum interval time that stops of electric pile.
Figure BDA0002725797820000073
Figure BDA0002725797820000074
Wherein, tinterFor the set minimum interval time of starting and stopping the charging pile, the charging starting time needs to be reduced due to the calculation of the continuous working time, so the t is calculatedinterA subtraction of 1 is required.
2.5 Peak load constraint
The peak load requirement for electric vehicle charging is lower than the transformer capacity.
Figure BDA0002725797820000081
Wherein ltRepresenting the basic load of the residents at time t, t being tstartAnd tbackAll moments in between, tstartAnd tbackThe determination is carried out by steps 1.1 and 1.2 respectively; pMRepresents the maximum capacity of the transformer; mu represents the maximum capacity threshold multiple which can be carried by the transformer; pevIs the charging power; and N is the total number of electric automobiles in the region.
And step 3, comprehensively considering the problems of safe and stable operation of the power grid and the charging cost of a user, and providing an ordered charging optimization scheduling scheme. The specific ordered charging optimized scheduling scheme is as follows,
3.1 user Charge cost model
Figure BDA0002725797820000082
Wherein, Pev·tcThe constraint condition of the step 2.4 is satisfied; n is the total number of electric vehicles in the region; t is tbackTo return to time, tstartAt the time of trip, tstartAnd tbackThe determination is carried out by steps 1.1 and 1.2 respectively; c. CcRepresenting the electricity price at the moment of charging. Determining a user charge fee F based on this step1。
3.2 Power grid Peak-valley Difference model
Figure BDA0002725797820000083
Figure BDA0002725797820000084
F2＝Lmax-Lmin (15)
Wherein, PevRepresenting the charging power of the electric vehicle, Pev·tcThe constraint condition of the step 2.4 is satisfied; l ismaxRepresents the highest peak load throughout the day; l isminRepresenting the lowest trough load throughout the day. Determining a grid peak-to-valley difference F based on the step2。
And 4, constructing a residential community scene optimization model, and finishing scheduling optimization of the electric automobile by using an artificial fish swarm algorithm.
4.1 scene optimization model
Figure BDA0002725797820000085
Wherein, G (to)pt) Optimizing the model for the scene; f1Representing the charge of the user during disordered charging, F2Representing the grid peak-to-valley difference during chaotic charging, the charging period t determined in step 2.3cDetermination of F by substitution into Steps 3.1 and 3.2, respectively1And F2，F1And F2The reference value used as the reference of the subsequent operation is fixed; f1optRepresenting the charge of the user during the ordered charging, F2optRepresenting the peak-valley difference of the power grid during ordered charging, and optimizing the charging time period t after each stepoptSubstituting into steps 3.1 and 3.2 respectively for tcDetermination of F1optAnd F2opt，F1optAnd F2optWill vary accordingly with the optimization process, toptDetermined by step 4.2; lambda [ alpha ]1And λ2Representing the weighting coefficients of the objective functions and satisfying lambda1+λ 21 is the requirement.
4.2 Charge period optimization
With G (t) in step 4.1opt) Min G (t)opt) As optimization objective of the model, the chargeable period t determined in step 2.3cOptimizing by using an artificial fish swarm algorithm, and taking a variable t as the optimized charging time periodoptDenotes, toptThe charge continuity constraint in step 2.5 should be met.
First for tcPerforming a random action RB to obtain an initial one
Figure BDA0002725797820000091
The expression of RB is as follows,
Figure BDA0002725797820000092
wherein step represents the moving step length, rand represents the random variable, and step and rand describe the randomly extracted moving step length;
after the above operations are carried out
Figure BDA0002725797820000093
The foraging behaviour FB is executed,
Figure BDA0002725797820000094
G(topt) A scene optimization model, determined by step 4.1; g (t)c)、
Figure BDA0002725797820000095
To step 4.1G (t)opt) T in (1)optBy tc、
Figure BDA0002725797820000096
And alternative calculation.
After the above operations are carried out
Figure BDA0002725797820000097
Value to t'cAnd to new t'cThe clustering action SB is performed in such a way that,
Figure BDA0002725797820000098
Figure BDA0002725797820000099
wherein R isvFor a dispatching range, m is the number of cars being charged in the dispatching range, tiCharging an ith automobile, wherein N is the total number of electric automobiles in the area; sigma is a self-defined crowding factor; t is tkStoring the coordinates at the center of the primary scheduling range as temporary variables; g (t)opt) A scene optimization model, determined by step 4.1; g (t)k)、G(t′c) To respectively combine step 4.1G (t)opt) T in (1)optBy tk、t‘cAlternative calculation is carried out; RB stands for random behavior.
After the above operations are carried out
Figure BDA0002725797820000101
Assigned value to t'cAnd for new t "cThe rear-end collision action REB is executed,
Figure BDA0002725797820000102
wherein, tbestRepresenting an optimized target minG (t) within a scheduling scopeopt) Optimal solution, G (t)opt) A scene optimization model, determined by step 4.1; g (t)best)、G(t″c) To respectively combine step 4.1G (t)opt) T in (1)optBy tbest、t”cReplacing, and calculating; FB for foraging behavior.
The optimized charging time interval is obtained through the steps
Figure BDA0002725797820000103
Namely the ordered charging time period obtained by the ordered charging control method of the electric vehicle, the model optimization target realized by the ordered charging time period is stored in min G (t)opt) In (1).
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (10)

1. An electric automobile ordered charging control method based on an artificial fish swarm algorithm is characterized by comprising the following steps: the method comprises the following steps:
step 1: construction of a scene optimization model G (t)opt)，G(topt) The formula is as follows:
Figure FDA0002725797810000011
wherein, F1Representing the charge of the user during disordered charging, F2Representing the grid peak-to-valley difference during disordered charging, F1optRepresenting the charge of the user during the ordered charging, F2optRepresenting the grid peak-to-valley difference, λ, during ordered charging1And λ2Weighting coefficients representing the respective objective functions;
step 2: using minG (t)opt) As an optimization objective of the scene optimization model, solving the optimized charging timeSegment toptOf (2) an optimal solution tbestCombined with charging period tcOptimizing by using an artificial fish swarm algorithm to obtain a final charging time interval
Figure FDA0002725797810000012
And step 3: according to the final charging period
Figure FDA0002725797810000013
And arranging the electric automobile to be charged in order.
2. The electric vehicle ordered charging control method based on the artificial fish swarm algorithm according to claim 1, characterized in that: the charging period tcThe calculation formula is as follows:
Figure FDA0002725797810000014
tback＜tc＜tstart
wherein, tcDenotes a chargeable period, tcWhen the time is 1, the electric vehicle is charging at the moment, tcWhen the time is equal to 0, the electric automobile is not charged at the moment; t is tcShould be in the range of tstartAnd tbackT isstartAt the time of trip, tbackIs the return time; t is t0Represents the starting time of the charging in stages; t ischargeIndicating a periodic charging period.
3. The electric vehicle ordered charging control method based on the artificial fish swarm algorithm according to claim 1, characterized in that: user charging fee F during unordered charging1The calculation formula is as follows:
Figure FDA0002725797810000015
wherein N is the total number of electric vehicles in the region; t is tbackTo return to time, tstartAt the time of trip, tcDenotes a chargeable period, PevTo charging power, ccRepresents the electricity price at the charging time;
the power grid peak-valley difference F during the disordered charging2The calculation formula is as follows:
F2＝Lmax-Lmin
Figure FDA0002725797810000021
Figure FDA0002725797810000022
wherein, ItRepresenting the basic load of residents at the time t;
user charging fee F during ordered charging1optThe calculation formula is as follows:
Figure FDA0002725797810000023
wherein N is the total number of electric vehicles in the region; t is tbackTo return to time, tstartAt the time of trip, toptRepresents an optimized charging period, PevTo charging power, ccRepresents the electricity price at the charging time;
the difference F between the peak and the valley of the power grid during the orderly charging2optThe calculation formula is as follows:
F2＝Lmax-Lmin
Figure FDA0002725797810000024
Figure FDA0002725797810000025
wherein, ItRepresenting the resident base load at time t.
4. The electric vehicle ordered charging control method based on the artificial fish swarm algorithm according to claim 1, characterized in that: the specific steps of the artificial fish swarm algorithm for optimization are as follows:
2.1 pairs of tcPerforming a random action RB to obtain an initial one
Figure FDA0002725797810000026
The RB calculation formula is as follows:
Figure FDA0002725797810000027
wherein step represents the moving step length, rand represents the random variable, and step-rand represents the randomly extracted moving step length;
2.2 after the above-mentioned operations
Figure FDA0002725797810000028
Executing the FB of foraging behavior to obtain
Figure FDA0002725797810000029
The FB calculation formula is as follows:
Figure FDA0002725797810000031
G(tc)、
Figure FDA0002725797810000032
is G (t)opt) T in (1)optBy tc、
Figure FDA0002725797810000033
Alternative calculation is carried out;
2.3 after the above-mentioned operations
Figure FDA0002725797810000034
Value to t'cAnd to new t'cPerforming clustering action SB to obtain
Figure FDA0002725797810000035
The SB calculation is as follows:
Figure FDA0002725797810000036
Figure FDA0002725797810000037
wherein R isvFor a dispatching range, m is the number of cars being charged in the dispatching range, tiCharging an ith automobile, wherein N is the total number of electric automobiles in the area; sigma is a self-defined crowding factor; g (t)k)、G(t′c) Respectively combine G (t)opt) T in (1)optBy tk、t′cAlternative calculation is carried out;
2.4 after the above-mentioned operations
Figure FDA0002725797810000038
Assigned a value of t ″cAnd for new t ″)cExecuting the rear-end collision behavior REB to obtain
Figure FDA0002725797810000039
REB calculation formula is as follows:
Figure FDA00027257978100000310
wherein, tbestRepresenting an optimized target minG within a scheduling scope(topt) Middle toptOf G (t)best)、G(t″c) Respectively combine G (t)opt) T in (1)optBy tbest、t″cAnd alternative calculation.
5. The electric vehicle ordered charging control method based on the artificial fish swarm algorithm according to claim 3, characterized in that: pev·tcThe constraints of (2) are as follows:
Figure FDA0002725797810000041
wherein, ItRepresenting the basic load of the residents at time t, t being tstartAnd tbackAll time in between; pMRepresents the maximum capacity of the transformer; mu represents the maximum capacity threshold multiple which can be carried by the transformer; t is tcDenotes a chargeable period, PevIs the charging power; and N is the total number of electric automobiles in the region.
6. The electric vehicle ordered charging control method based on the artificial fish swarm algorithm according to claim 1, characterized in that: said t isstartThe calculation formula of (a) is as follows:
Figure FDA0002725797810000042
wherein, tstartTo the time of trip, musExpected value, σ, of trip timesVariance of time of trip, fs(tstart) Is a probability density function of the travel time distribution.
7. The electric vehicle ordered charging control method based on the artificial fish swarm algorithm according to claim 1, characterized in that: said t isbackThe calculation formula of (a) is as follows:
Figure FDA0002725797810000043
wherein, tbackTo return to time, μeTo return the expected value of the time of day, σeAs variance of the return time, fe(tback) Is a probability density function of the distribution of the return moments.
8. The electric vehicle ordered charging control method based on the artificial fish swarm algorithm according to claim 1, characterized in that: the T ischargeThe calculation formula of (a) is as follows:
Figure FDA0002725797810000044
therein, SOC0Is the initial state of charge rate of the electric vehicle, Q is the battery capacity, PevIs the charging power.
9. The electric vehicle ordered charging control method based on the artificial fish swarm algorithm according to claim 1, characterized in that: the T ischargeThe constraints of (2) are as follows:
Figure FDA0002725797810000045
wherein, Tn，chargeRepresenting the charging time of the nth electric vehicle; pevRepresenting the charging power of the electric vehicle; qnRepresenting the battery capacity of the nth electric vehicle; η represents the charging efficiency; SOCn，eIndicates that the nth electric vehicle is at tstartUser desired SOC value at time; SOCn，0Indicates that the nth electric vehicle is at tbackInitial SOC value at time.
10. The artificial fish swarm algorithm-based electric vehicle ordering according to claim 1The charging control method is characterized in that: lambda [ alpha ]1+λ2＝1。
CN202011107452.1A 2020-10-15 2020-10-15 Electric automobile ordered charging control method based on artificial fish swarm algorithm Active CN112356724B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011107452.1A CN112356724B (en) 2020-10-15 2020-10-15 Electric automobile ordered charging control method based on artificial fish swarm algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011107452.1A CN112356724B (en) 2020-10-15 2020-10-15 Electric automobile ordered charging control method based on artificial fish swarm algorithm

Publications (2)

Publication Number Publication Date
CN112356724A true CN112356724A (en) 2021-02-12
CN112356724B CN112356724B (en) 2022-07-01

Family

ID=74508174

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011107452.1A Active CN112356724B (en) 2020-10-15 2020-10-15 Electric automobile ordered charging control method based on artificial fish swarm algorithm

Country Status (1)

Country Link
CN (1) CN112356724B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113256444A (en) * 2021-04-30 2021-08-13 北京中电飞华通信有限公司 Low-voltage transformer area household transformation relation identification method and device
CN115402141A (en) * 2022-11-02 2022-11-29 国网浙江省电力有限公司金华供电公司 Electric automobile flexible charging control method and device based on load following

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20140124974A (en) * 2013-04-17 2014-10-28 주식회사 케이티 Method and system of efficiency optimization control of battery charging and discharging for power trading in vehicle to grid
CN105162151A (en) * 2015-10-22 2015-12-16 国家电网公司 Intelligent energy storage system grid-connected real-time control method based on artificial fish swarm algorithm
CN107745650A (en) * 2017-10-26 2018-03-02 电子科技大学 A kind of orderly charge control method of electric automobile based on Peak-valley TOU power price
CN108390421A (en) * 2018-01-19 2018-08-10 上海电力学院 Meter and the double scale charging bootstrap techniques of the electric vehicle of user satisfaction and system
CN109299817A (en) * 2018-09-04 2019-02-01 东北电力大学 Take into account the electric car charge and discharge electricity price optimization method of car owner's response and power grid cost
CN109886501A (en) * 2019-03-06 2019-06-14 昆明理工大学 A kind of electric car charge and discharge Multipurpose Optimal Method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20140124974A (en) * 2013-04-17 2014-10-28 주식회사 케이티 Method and system of efficiency optimization control of battery charging and discharging for power trading in vehicle to grid
CN105162151A (en) * 2015-10-22 2015-12-16 国家电网公司 Intelligent energy storage system grid-connected real-time control method based on artificial fish swarm algorithm
CN107745650A (en) * 2017-10-26 2018-03-02 电子科技大学 A kind of orderly charge control method of electric automobile based on Peak-valley TOU power price
CN108390421A (en) * 2018-01-19 2018-08-10 上海电力学院 Meter and the double scale charging bootstrap techniques of the electric vehicle of user satisfaction and system
CN109299817A (en) * 2018-09-04 2019-02-01 东北电力大学 Take into account the electric car charge and discharge electricity price optimization method of car owner's response and power grid cost
CN109886501A (en) * 2019-03-06 2019-06-14 昆明理工大学 A kind of electric car charge and discharge Multipurpose Optimal Method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113256444A (en) * 2021-04-30 2021-08-13 北京中电飞华通信有限公司 Low-voltage transformer area household transformation relation identification method and device
CN115402141A (en) * 2022-11-02 2022-11-29 国网浙江省电力有限公司金华供电公司 Electric automobile flexible charging control method and device based on load following

Also Published As

Publication number Publication date
CN112356724B (en) 2022-07-01

Similar Documents

Publication Publication Date Title
CN103793758B (en) Multi-objective optimization scheduling method for electric vehicle charging station including photovoltaic power generation system
CN111431198B (en) Power distribution network energy storage peak regulation method considering V2G capacity of electric vehicle under peak-valley electricity price
CN105024432B (en) A kind of electric automobile discharge and recharge Optimization Scheduling based on virtual electricity price
CN111626527B (en) Intelligent power grid deep learning scheduling method considering fast/slow charging/discharging form of schedulable electric vehicle
CN112356724B (en) Electric automobile ordered charging control method based on artificial fish swarm algorithm
CN108493959B (en) Electric automobile participation power grid frequency modulation control and analysis method based on Markov model
CN107919675B (en) Charging station load scheduling model comprehensively considering benefits of vehicle owners and operators
CN112238781B (en) Electric automobile ordered charging control method based on layered architecture
CN111845453A (en) Electric vehicle charging station double-layer optimization charging and discharging strategy considering flexible control
CN111738518B (en) Electric vehicle charging and discharging scheduling method based on average discharge rate
CN115239032B (en) Highway service area microgrid planning method and system considering energy self-consistency rate
CN114274800A (en) Group aggregation charging behavior prediction method for commercial electric vehicle and related device
CN114498635B (en) Power distribution network optimal scheduling method and system considering charging priority of electric automobile
CN112070628A (en) Intelligent power grid multi-target economic dispatching method considering environmental factors
CN110861508B (en) Charging control method and system shared by residential area direct current chargers and storage medium
CN113067355A (en) Electric automobile flexibility mining and cooperative regulation and control method for improving reliability of power grid
CN107154628B (en) Electric car networking electric discharge price cap and networking power relation calculation method
CN115099572B (en) Electric vehicle charging queuing method and device of charging station
Verzijlbergh et al. The role of electric vehicles on a green island
CN116345476A (en) EV intelligent real-time scheduling method and system based on vehicle-station-network information interaction
CN115833199A (en) Mobile energy storage scheduling method, system and storage medium
Usmani et al. Power management for hybrid energy storage system in electric vehicles
CN111953023B (en) Charging pile-containing regional energy network management method based on MACPSO algorithm
CN115063039B (en) Electric bus electricity exchanging method for carbon emission reduction
CN117592803A (en) Urban vehicle network interaction carbon reduction potential evaluation method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
TA01 Transfer of patent application right

Effective date of registration: 20211213

Address after: 210000 19 Chengxin Avenue, Jiangning District, Nanjing, Jiangsu Province

Applicant after: NARI Group Corp.

Applicant after: NARI TECHNOLOGY Co.,Ltd.

Applicant after: NARI NANJING CONTROL SYSTEM Co.,Ltd.

Applicant after: STATE GRID BEIJING ELECTRIC POWER Co.

Address before: 210000 19 Chengxin Avenue, Jiangning District, Nanjing, Jiangsu Province

Applicant before: NARI Group Corp.

Applicant before: NARI TECHNOLOGY Co.,Ltd.

Applicant before: NARI NANJING CONTROL SYSTEM Co.,Ltd.

TA01 Transfer of patent application right
GR01 Patent grant
GR01 Patent grant