CN112356724B - 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

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CN112356724B
CN112356724B CN202011107452.1A CN202011107452A CN112356724B CN 112356724 B CN112356724 B CN 112356724B CN 202011107452 A CN202011107452 A CN 202011107452A CN 112356724 B CN112356724 B CN 112356724B
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CN112356724A (en
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汪天允
张�浩
赵翔
周斌
张建伟
董自波
郭云翔
王玲
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NARI Group Corp
State Grid Beijing Electric Power Co Ltd
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
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NARI Group Corp
State Grid Beijing Electric Power Co Ltd
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
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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
    • 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

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  • 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 period
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 expanding the peak-valley difference of the power distribution network, generating harmonic pollution, causing the limit of a transformer to be exceeded, 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 GDA0003577069020000011
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 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 GDA0003577069020000021
And step 3: according to the final charging period
Figure GDA0003577069020000022
And arranging the electric automobile to be charged in order.
Preferably, the charging period tcThe calculation formula is as follows:
Figure GDA0003577069020000023
tback＜tc＜tstart
wherein, tcDenotes a chargeable period, tcWhen 1, it means that the electric vehicle is charging at that time, 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 GDA0003577069020000024
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 GDA0003577069020000025
Figure GDA0003577069020000026
wherein ltRepresenting the basic load of residents at the time t;
user charging fee F during ordered charging1optThe calculation formula is as follows:
Figure GDA0003577069020000027
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:
F2opt＝L′max-L′min
Figure GDA0003577069020000031
Figure GDA0003577069020000032
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 GDA0003577069020000033
The RB calculation formula is as follows:
Figure GDA0003577069020000034
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 GDA0003577069020000035
Executing the FB of foraging behavior to obtain
Figure GDA0003577069020000036
The FB calculation formula is as follows:
Figure GDA0003577069020000037
G(tc)、
Figure GDA0003577069020000038
is G (t)opt) T in (1)optBy tc、
Figure GDA0003577069020000039
Alternative calculation is carried out;
2.3 after the above-mentioned operations
Figure GDA00035770690200000310
Assigned to t'cAnd to new t'cPerforming clustering action SB to obtain
Figure GDA00035770690200000311
The SB calculation is as follows:
Figure GDA00035770690200000312
Figure GDA00035770690200000313
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 GDA0003577069020000041
Assigned value to t'cAnd for new t "cExecuting the rear-end collision behavior REB to obtain
Figure GDA0003577069020000042
REB calculation formula is as follows:
Figure GDA0003577069020000043
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 GDA0003577069020000044
wherein ltRepresenting 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.
Preferably, t isstartThe calculation formula of (c) is as follows:
Figure GDA0003577069020000045
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 GDA0003577069020000046
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 GDA0003577069020000051
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 GDA0003577069020000052
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 beneficial effects 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 GDA0003577069020000061
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 GDA0003577069020000062
Wherein, tbackTo return the time of day, 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 GDA0003577069020000063
Therein, SOC0Is the initial state of charge rate of the electric vehicle, Q is the battery capacity, PevIn order to charge the power, the charging power,
Tchargeis the charging period.
1.4 relationship between electric vehicle return time and charging start time
Consider that the charge start time is approximately the same as the vehicle return time
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 GDA0003577069020000064
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, tbackFrom the return time model of step 1.2Taking; 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 GDA0003577069020000071
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 the chargeable period of the electric automobile restricts that the chargeable period of the electric automobile should be distributed in any period of time between the return time and the departure time of the electric automobile.
Figure GDA0003577069020000072
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 GDA0003577069020000073
Figure GDA0003577069020000074
Wherein, tinterFor the set minimum interval time of starting and stopping the charging pile, because the charging starting moment needs to be subtracted from the calculation of the continuous working time, t is calculatedinterA subtraction of 1 is required.
2.5 Peak load constraint the peak load requirement for charging an electric vehicle is lower than the transformer capacity.
Figure GDA0003577069020000075
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 the 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. A specific ordered charging optimal scheduling scheme is as follows,
3.1 user Charge cost model
Figure GDA0003577069020000081
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 tbackRespectively comprises the following steps1.1 and 1.2; 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 GDA0003577069020000082
Figure GDA0003577069020000083
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 GDA0003577069020000084
Wherein, G (t)opt) 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 GDA0003577069020000091
The expression of RB is as follows,
Figure GDA0003577069020000092
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 GDA0003577069020000093
The foraging behaviour FB is executed,
Figure GDA0003577069020000094
G(topt) A scene optimization model, determined by step 4.1; g (t)c)、
Figure GDA0003577069020000095
To step 4.1G (t)opt) T in (1)optBy tc、
Figure GDA0003577069020000096
And alternative calculation.
After the above operations are carried out
Figure GDA0003577069020000097
Value to t'cAnd to new t'cThe clustering action SB is performed in such a way that,
Figure GDA0003577069020000098
Figure GDA0003577069020000099
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 GDA0003577069020000101
Assigned value to t'cAnd for new t "cThe rear-end collision action REB is executed,
Figure GDA0003577069020000102
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 inoptBy tbest、t”cReplacing, and calculating to obtain; FB for foraging behavior.
The optimized charging time interval is obtained through the steps
Figure GDA0003577069020000103
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 (7)

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 FDA0003577069010000011
wherein, F1Representing the charge rate 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 time period t is solvedoptOptimal solution t ofbestCombined with charging period tcOptimizing by using an artificial fish swarm algorithm to obtain a final charging time interval
Figure FDA0003577069010000012
And step 3: according to the final chargingTime period
Figure FDA0003577069010000013
Arranging the electric automobile to be charged in order;
user charging fee F during unordered charging1The calculation formula is as follows:
Figure FDA0003577069010000014
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 FDA0003577069010000015
Figure FDA0003577069010000016
wherein ltRepresenting the basic load of residents at the time t;
user charging fee F during ordered charging1optThe calculation formula is as follows:
Figure FDA0003577069010000017
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, ccElectricity representing charging timeA price;
the difference F between the peak and the valley of the power grid during the orderly charging2optThe calculation formula is as follows:
F2opt＝L′max-L′min
Figure FDA0003577069010000021
Figure FDA0003577069010000022
wherein ltRepresenting the basic load of residents at the time t;
the optimization of the artificial fish school algorithm comprises the following specific steps:
2.1 pairs of tcPerforming a random action RB to obtain an initial one
Figure FDA0003577069010000023
The RB calculation formula is as follows:
Figure FDA0003577069010000024
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 FDA0003577069010000025
Executing the FB of foraging behavior to obtain
Figure FDA0003577069010000026
The FB calculation formula is as follows:
Figure FDA0003577069010000027
G(tc)、
Figure FDA0003577069010000028
is G (t)opt) T in (1)optBy tc、
Figure FDA0003577069010000029
Alternative calculation is carried out;
2.3 after the above-mentioned operations
Figure FDA00035770690100000210
Value to t'cAnd to new t'cPerforming clustering action SB to obtain
Figure FDA00035770690100000211
The SB calculation is as follows:
Figure FDA00035770690100000212
Figure FDA00035770690100000213
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 FDA0003577069010000031
Assigned value to t'cAnd for new t "cExecuting the rear-end collision behavior REB to obtain
Figure FDA0003577069010000032
REB calculation formula is as follows:
Figure FDA0003577069010000033
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”cAlternative calculation is carried out;
the method is characterized in that: pev·tcThe constraints of (2) are as follows:
Figure FDA0003577069010000034
wherein ltRepresenting 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.
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 FDA0003577069010000035
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 ordered charging control method for the electric vehicle 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 FDA0003577069010000036
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.
4. 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 FDA0003577069010000041
wherein, tbackTo return the time of day, 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.
5. The electric vehicle ordered charging control method based on the artificial fish swarm algorithm according to claim 2, characterized in that: the T ischargeThe calculation formula of (a) is as follows:
Figure FDA0003577069010000042
wherein, SOC0Is the initial state of charge rate of the electric vehicle, Q is the battery capacity, PevIs the charging power.
6. The electric vehicle ordered charging control method based on the artificial fish swarm algorithm according to claim 2, characterized in that: said T ischargeThe constraint of (2) is as follows:
Figure FDA0003577069010000043
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.
7. The electric vehicle ordered charging control method based on the artificial fish swarm algorithm according to claim 1, characterized in that: lambda [ alpha ]1+λ2＝1。
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