CN105894123B - The determination method and device of electric vehicle charging operational mode - Google Patents
The determination method and device of electric vehicle charging operational mode Download PDFInfo
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- CN105894123B CN105894123B CN201610251610.8A CN201610251610A CN105894123B CN 105894123 B CN105894123 B CN 105894123B CN 201610251610 A CN201610251610 A CN 201610251610A CN 105894123 B CN105894123 B CN 105894123B
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Abstract
A kind of determination method and device of electric vehicle charging operational mode, obtains the daily load curve of charge-discharge electric power, preset vehicle quantity and the feeder line of electric vehicle；The minimum load peak value model for the electric vehicle rear feed electric line for adding charging is set up according to charge-discharge electric power, preset vehicle quantity and daily load curve；Change preset vehicle quantity obtains Current vehicle quantity, distributed using Current vehicle quantity and Current vehicle quantity to the charging quantity of each period as changed factor, optimizing is carried out to minimum load peak value model with the optimizing result for the daily load curve peak value minimum for obtaining the corresponding feeder line of Current vehicle quantity.So, by finding while a kind of electric vehicle normal operation of electric vehicle charging operational mode by planning charging quantity and the charging quantity of each period to ensure maximum quantity, address and existing feeder line are built just so as to improve economy without changing charging station.
Description
Technical field
The present invention relates to the determination method and dress in power grid security field, more particularly to a kind of electric vehicle charging operational mode
Put.
Background technology
In the case of the huge challenge that Present Global auto industry faces financial crisis and energy environment issues, development electricity
Electrical automobile, realizes the electrification of energy source of car dynamical system, promotes the strategic transformation of orthodox car industry, in the world shape
Into extensive common recognition.It is still a very long process that electric automobile will be popularized on a large scale, and first develops city electric vehicle,
The traffic transport power in city can be not only improved, and for energy-saving and emission-reduction, Optimizing City environment has great significance.
During the popularization of electric automobile, one of maximum problem is charging problems.Electric vehicle due to centralized management,
Moving law is strong, is typically all to concentrate charging station to solve charging problems by building.Electric vehicle can by itself
Operational requirements, are selectively concentrating charging station to be charged.But due to concentrating charging station typically to build some wall scroll feed in
On circuit, the maximum appearance of influence, i.e. feeder line is necessarily produced during electric vehicle charging on feeder line where charging station
The charging number of units of electric vehicle will be restricted by measuring, and then constrain total operation quantity of electric vehicle.Solve the side of this problem
Method, one is that the circuit for selecting feeder line capacity redundancy bigger builds charging station, and two be that this feeder line is carried out into upgrading,
It is allowed to meet electric car charging Operational requirements.First method changes the charging planning of electric vehicle, by increase vehicle company
Cost；Second method then changes the present mode of distribution line, and economic expense is big.
The content of the invention
Based on this, it is necessary to which charging station construction address and existing feeder line need not be changed to improve economy by providing one kind
The determination method and device of the electric vehicle charging operational mode of property.
A kind of determination method of electric vehicle charging operational mode, including：
Parameters acquiring procedure：Obtain the daily load of charge-discharge electric power, preset vehicle quantity and the feeder line of electric vehicle
Curve；
Model construction step：Set up according to the charge-discharge electric power, the preset vehicle quantity and the daily load curve
Add the minimum load peak value model of the feeder line after the electric vehicle of charging；
Iteration optimizing step：Change the preset vehicle quantity and obtain Current vehicle quantity, with the Current vehicle quantity
And the Current vehicle quantity is distributed to the charging quantity of each period as changed factor, to the minimum load peak value mould
Type carries out optimizing to obtain the optimizing that the daily load curve peak value of the corresponding feeder line of the Current vehicle quantity is minimum
As a result；When the optimizing result is distributed to each including target vehicle quantity, minimum load peak value and the target vehicle quantity
Between section charging quantity；
Wherein, the target vehicle quantity be the minimum load peak value be less than the feeder line maximum capacity when
Maximum in the Current vehicle quantity.
A kind of determining device of electric vehicle charging operational mode, including：
Parameter acquisition module, the day of charge-discharge electric power, preset vehicle quantity and feeder line for obtaining electric vehicle
Load curve；
Model construction module, for according to the charge-discharge electric power, the preset vehicle quantity and the daily load curve
Set up the minimum load peak value model of the feeder line after the electric vehicle for adding charging；
Iteration optimizing module, obtains Current vehicle quantity, with the Current vehicle for changing the preset vehicle quantity
Quantity and the Current vehicle quantity are distributed to the charging quantity of each period as changed factor, to the minimum load peak
Daily load curve peak value that value model carries out optimizing to obtain the corresponding feeder line of the Current vehicle quantity is minimum
Optimizing result；The optimizing result is distributed to each including target vehicle quantity, minimum load peak value and the target vehicle quantity
The charging quantity of individual period；
Wherein, the target vehicle quantity be the minimum load peak value be less than the feeder line maximum capacity when
Maximum in the Current vehicle quantity.
The determination method and device of above-mentioned electric vehicle charging operational mode, obtains the charge-discharge electric power, pre- of electric vehicle
If the daily load curve of vehicle fleet size and feeder line；According to the charge-discharge electric power, the preset vehicle quantity and the day
Load curve sets up the minimum load peak value model of the feeder line after the electric vehicle for adding charging；Change described pre-
If vehicle fleet size obtains Current vehicle quantity, distributed with the Current vehicle quantity and the Current vehicle quantity to each time
The charging quantity of section carries out optimizing to obtain the Current vehicle quantity as changed factor to the minimum load peak value model
The minimum optimizing result of the daily load curve peak value of the corresponding feeder line.In this way, being filled by finding one kind by planning
The electric vehicle charging operational mode of electric quantity and the charging quantity of each period is ensureing the electric vehicle of maximum quantity just
Often while operation, address and existing feeder line are built just so as to improve economy without changing charging station.
Brief description of the drawings
Fig. 1 is a kind of flow chart of the determination method of the electric vehicle charging operational mode of embodiment；
Fig. 2 is the schematic diagram of dicentrics subgroup optimized algorithm；
Fig. 3 is a kind of tune afterload curve map of the feeder line of the electric vehicle charging operational mode of embodiment；
Fig. 4 is a kind of scheduling feelings of the electric driving vehicle of electric bus of the electric vehicle charging operational mode of embodiment
Condition explanation figure；
Fig. 5 schemes for the electric bus charging dispatch situation explanation of Fig. 4 electric vehicle charging operational mode；
Fig. 6 schemes for the battery change situation explanation of the electric bus of Fig. 4 electric vehicle charging operational mode；
The structure chart of Fig. 7 determining devices of the electric vehicle charging operational mode of embodiment always.
Embodiment
For the ease of understanding the present invention, the present invention is described more fully below with reference to relevant drawings.In accompanying drawing
Give the preferred embodiment of the present invention.But, the present invention can be realized in many different forms, however it is not limited to herein
Described embodiment.On the contrary, the purpose for providing these embodiments is to make the understanding to the disclosure more saturating
It is thorough comprehensive.
Unless otherwise defined, all of technologies and scientific terms used here by the article is with belonging to technical field of the invention
The implication that technical staff is generally understood that is identical.Term used in the description of the invention herein is intended merely to description tool
The purpose of the embodiment of body, it is not intended that in the limitation present invention.Term as used herein " or/and " include one or more phases
The arbitrary and all combination of the Listed Items of pass.
As shown in figure 1, the determination method of the electric vehicle charging operational mode for one embodiment of the present invention, including：
S100, parameters acquiring procedure：Obtain the day of charge-discharge electric power, preset vehicle quantity and the feeder line of electric vehicle
Load curve.
Preset vehicle quantity is the quantity of the vehicle of initial estimation.Daily load curve is according to existing vehicle feeder line
Traffic-operating period determine curve.The form of expression of daily load curve can be a functional form.Charge-discharge electric power is one
The charge-discharge electric power of electric vehicle unit interval.
S300, model construction step：According to the charge-discharge electric power, the preset vehicle quantity and the daily load curve
Set up the minimum load peak value model of the feeder line after the electric vehicle for adding charging.
Minimum load peak value model includes object function and constraints.The electricity that the object function charges for addition
The daily load curve peak value of the feeder line is minimum after motor-car.The constraints includes：To the fortune of the electric vehicle
Rule constraint is sought, such as peak time vehicle in use is more, and morning etc., lonely period vehicle in use was less；To the electric vehicle
The constraint for the total amount that charges total amount of discharging day and day, daily all electric vehicles electric discharge total amounts are not more than charging total amount；To with for the moment
Between the section electric vehicle charging quantity with operation quantity constraint, such as operation quantity with charging quantity and no more than public transport
Sum；To the capacity-constrained of the feeder line of the electric vehicle；And the constraint of the charge-discharge electric power of electric vehicle etc.
In one or more constraints.
S500, iteration optimizing step：Change the preset vehicle quantity and obtain Current vehicle quantity, with the Current vehicle
Quantity and the Current vehicle quantity are distributed to the charging quantity of each period as changed factor, to the minimum load peak
Daily load curve peak value that value model carries out optimizing to obtain the corresponding feeder line of the Current vehicle quantity is minimum
Optimizing result.
In the present embodiment, can iteration increase or decrease Current vehicle quantity, and made with the charging quantity of each period
For a changed factor of optimizing algorithm, the optimizing algorithms such as particle cluster algorithm, genetic algorithm are used in each iteration to described
Minimum load peak value model carries out optimizing, finally, and the day for obtaining adding the feeder line after the electric vehicle charged is born
The minimum optimizing result of the peak value of lotus curve.
In another embodiment, distributed with the Current vehicle quantity and the Current vehicle quantity to each period
Charging quantity as a changed factor, using optimizing algorithms such as particle cluster algorithm, genetic algorithms to the minimum load peak
Value model carries out the peak value of the daily load curve of the feeder line after the electric vehicle of the optimizing to obtain adding charging most
Small optimizing result.
The optimizing result is distributed to each including target vehicle quantity, minimum load peak value and the target vehicle quantity
The charging quantity of period.Wherein, the target vehicle quantity is that the minimum load peak value is less than and the closest feed
The Current vehicle quantity during maximum capacity of circuit；It can namely to add described after the electric vehicle charged
The daily load curve peak value of feeder line is minimum and daily load curve peak value is less than and working as closest to feeder line maximum capacity
Vehicle in front quantity.
The determination method of above-mentioned electric vehicle charging operational mode, obtains charge-discharge electric power, the preset vehicle of electric vehicle
The daily load curve of quantity and feeder line；It is bent according to the charge-discharge electric power, the preset vehicle quantity and the daily load
Line sets up the minimum load peak value model of the feeder line after the electric vehicle for adding charging；Change the preset vehicle
Quantity obtains Current vehicle quantity, and filling to each period is distributed with the Current vehicle quantity and the Current vehicle quantity
It is corresponding to obtain the Current vehicle quantity that electric quantity carries out optimizing as changed factor, to the minimum load peak value model
The minimum optimizing result of the daily load curve peak value of the feeder line.In this way, a kind of by planning charging quantity by finding
And the electric vehicle charging operational mode of the charging quantity of each period ensures the electric vehicle normal operation of maximum quantity
While, address and existing feeder line are built just so as to improve economy without changing charging station.
In one of the embodiments, S300 is model construction step, including：
Function determines sub-step：Will according to the charge-discharge electric power, the preset vehicle quantity and the daily load curve
The daily load curve added after the electric vehicle of charging is defined as adjusting afterload curve, and with the tune afterload curve
The minimum object function as the minimum load peak value model of peak value；
Constraint determines sub-step：The operation rule constraint to the electric vehicle is obtained, the day of the electric vehicle is put
The constraint for the total amount that charges electric total amount and day, the constraint to the charging quantity and operation quantity of electric vehicle described in the same period,
And the capacity-constrained of the feeder line to the electric vehicle, it is used as the constraint bar of the minimum load peak value model
Part.
Further, using the preset vehicle quantity as base value, increase predetermined number obtains Current vehicle quantity, according to electricity
The motor-car operation rule constraint of one day, the daily load curve and electric vehicle one such as added after the electric vehicle of charging
It running time and charge-discharge electric power, it may be determined that in this prior in the case of vehicle fleet size, the total power consumption of one day, in this, as
Charge total quantity.The charging total amount of each particle must meet the constraint as the total amount that charges day not less than the charging total quantity,
It is all full electric requirement that could so meet when electric vehicle sets out within second day.
In one of the embodiments, S500 is iteration optimizing step, including：
Optimize sub-step：In the case where the quantity of the electric vehicle is the Current vehicle quantity, with described
Current vehicle quantity is distributed to the charging quantity of each period as a particle, using particle swarm optimization algorithm to it is described most
Smaller load peak value model is iterated optimizing, determines optimal particle and the corresponding minimum load peak value of the optimal particle.
Using the charging vehicle number of each period as element one data acquisition system of formation, as that can be array
Form, and a particle is made with this.
Iteration sub-step：The Current vehicle quantity is increased into predetermined number, and repeats the optimization sub-step, until institute
When stating minimum load peak value more than the maximum capacity, the institute of the corresponding Current vehicle quantity of a upper iteration and determination is returned
State optimal particle and the minimum load peak value.
So so that vehicle fleet size Step wise approximation maximum vehicle number amount.
Further, the particle swarm optimization algorithm is dicentrics subgroup optimized algorithm.The optimization sub-step, bag
Include：
Initialization step：Initialized according to the Current vehicle quantity and the constraints of the minimum load peak value model
Particle, and initialize individual optimal particle and global optimum's particle.
In the present embodiment, distributed using Current vehicle quantity to the charging quantity of each period and be used as a particle.It is individual
Body optimal particle is that a particle constantly updates the minimum particle of fitness in obtained particle in searching process.Global optimum
Particle is constantly updated in all particles in obtained population in searching process for the molecular population of multiple grains and adapted to
Spend minimum particle.The quantity of particle is determined by the scale of population in population.The scale of population can use default value,
Or be set by the user.
Initialization population mode be：Determined according to the trend of the daily load curve after the electric vehicle for adding charging
Each fixed period charging vehicle number number.Filled in the rush hour section limitation of the user power utilization of the feeder line of charging station
An electric number, in case the peak value for raising the feeder line load of charging station makes it exceed the maximum capacity limit that the feeder line allows
System.And then increase a charging number in the low ebb period of user power utilization, to play a part of Fill valley.
Particle updates step：Population is updated, the fitness of relatively more contemporary particle, previous generation particles and default particle is more
The new individual optimal particle, is determined in broad sense central particles and narrow sense according to the individual optimal particle and the contemporary particle
Heart particle is to update global optimum's particle.
Contemporary particle obtains to update population in particle swarm optimization algorithm implementation procedure, in the renewal step being carrying out
Particle.Previous generation particles update for the last particle performed and the particle that population is obtained are updated in step.
Relatively conventional particle cluster algorithm, dicentrics swarm optimization determines broad sense according to individual optimal particle and contemporary particle
Central particles and narrow sense central particles, the fitness of fitness and narrow sense central particles further according to broad sense central particles update
Global optimum's particle.Wherein, broad sense central particles are the corresponding particle of average value of the fitness of individual optimal particle；In narrow sense
Heart particle is the corresponding particle of average value of the fitness of contemporary particle.In this way, finding the several of the optimal particle of adaptive optimal control degree
Rate is higher, i.e. daily load curve peak value minimum and the day for finding the feeder line after the electric vehicle for adding charging are negative
The probability that lotus peak of curve is less than the charging quantity of the electric vehicle of each period of feeder line maximum capacity is higher.
As shown in Fig. 2 the default particle is included along previous generation particlesIt is pre- that the direction of renewal updates first obtained
If particle A, and first along previous generation particlesThe direction V of renewalid oldFurther along history optimal particle pbestiThe direction of renewal
Update the default particle B of second obtained.
The first default particle A, a second default B and previous generation particle are added in the individual optimal particle due to updatingAnd history optimal particleFitness comparison, hunting zone is wider, therefore, finds the probability of adaptive optimal control degree more
Height, that is, find the daily load curve peak value minimum and daily load curve of the feeder line after the electric vehicle for adding charging
The probability that peak value is less than the charging quantity of the electric vehicle of each period of feeder line maximum capacity is higher.
Iteration step：Repeat the particle and update step, until during particle fitness iteration convergence, by the overall situation
Optimal particle is used as the corresponding minimum load peak value of the optimal particle and the optimal particle and the charging quantity.
In one of the embodiments, the initialization step, including：
First sub-step：According to the Current vehicle quantity, the daily load curve and the electric vehicle one day
Running time and the charge-discharge electric power determine daily load curve after adjusting.
Wherein, daily load curve is the daily load curve after addition electric vehicle charging after tune.
Second sub-step：The charging vehicle of each period is determined according to the tune afterload curve and the maximum capacity
Weight.
By add charging the electric vehicle after daily load curve, that is, adjust afterload curve in each period load with
The distance of the maximum constrained capacity of feeder line can determine the size of the period charging vehicle weight.Specifically, in one day
Each period charging vehicle weight Yt' calculation formula it is as follows：
In formula, LhignRepresent the maximum capacity of feeder line；PLminRepresent to add daily load after the electric vehicle charged
The minimum valley of curve；PLtRepresent the load of i-th of period；I value is 1 to T (the time hop count included for one day).YtTable
Show the ratio of the load of i-th period and the distance of maximum capacity and the distance of minimum load and maximum capacity in one day.Yt'
Represent the weight of charging vehicle, i.e., the Y of each periodtAccount for one day YtSummation ratio, the size of this ratio determines charging number
Amount number.
The maximum capacity that the load curve of feeder line allows closer to feeder line, the then probability that charges is smaller, distance feedback
The maximum capacity that electric line allows is more remote, then the probability that charges is bigger, can so obtain preferably particle.
3rd sub-step：Filling for each period is initialized according to the charging vehicle weight and the Current vehicle quantity
Electric quantity.
Using Current vehicle quantity as base value, according to the traveling of charge-discharge electric power and electric vehicle the operation rule constraint of one day
In the case of the Time Calculation base value, the total power consumption of one day, in this, as charging total quantity；Further according to charging total quantity and charging
Vehicle weight can determine that the charging quantity of each period.Specifically, charging total quantity is multiplied by the charging vehicle of each period
Weight just can determine that the charging quantity of each period.
Specifically, in initialization, determined respectively by the charging total quantity of one day and the charging vehicle weight of each period
The charging quantity of individual period, is consequently formed the array of multiple charging quantity compositions, in this, as a particle of initialization, its
Remaining particle can be randomly generated, so that the particle populations initialized.Wherein, the number of array element is the period in one day
Number.
Further, according to the dump energy situation of storage battery of electric motor, the minimum preceding charging quantity of electricity is electronic
Vehicular charging.
In wherein one specific embodiment, by taking electric bus as an example, the tune afterload of feeder line is illustrated in figure 3
Curve, the maximum capacity that the feeder line allows is limited to 3600kw/h, the traveling arrangement of electric bus each period of one day
For RunNumber=[2 8933333333893332000000 0] (notes：2 representatives have 20%
Electric bus is in traveling), charge-discharge electric power is respectively Pcm=-22kw/h, Pfm=28kw/h.
In the present embodiment, each period is 1 hour.Refer to the electric vehicle work period in one day or one day one day,
Can be from the 6 of first day:The 6 of 00~the second day:00.Assuming that preset vehicle quantity is 60.
According to the travel situations RunNumber of the one of electric bus day and the charging quantity of each period, with few electricity
The principle of many electricity travelings of charging can arrange the operation of vehicle.It is illustrated in figure 4 the scheduling feelings of the electric driving vehicle of electric bus
Each unit has 6 cars in condition, the 1-10 of title bar, i.e., 1 is 1-6 cars, and 2 be 7-12 cars, by that analogy.1 generation in figure
The table period, for example 1-6 cars were respectively 7 in traveling:00-8:00,9:00-10:00,11:00-12:00,17:00-19:
00,20:00-21:00 moment was used to travel carrying.The charging dispatch situation of electric bus is as shown in figure 5,1 in figure represents
This period all charges 1 hour in charging.For example, 1-6 cars are respectively 8:00-9:00,12:00-13:00,19:00-
20:00,21:00-23:00,1:00-2:00,3:00-4:00,5:00-6:00 moment access charging station charging.Wherein 5:00-6:
00 only need to fill 0.64 hour i.e. 39 minutes without the charging of whole hour can be fully charged.The battery change situation of electric bus
As shown in fig. 6, disclosure satisfy that normal work demand.
As shown in fig. 7, the present invention also provides a kind of determination of electric vehicle charging operational mode corresponding with the above method
Device, including：
Parameter acquisition module 100, charge-discharge electric power, preset vehicle quantity and feeder line for obtaining electric vehicle
Daily load curve；
Model construction module 300, for bent according to the charge-discharge electric power, the preset vehicle quantity and the daily load
Line sets up the minimum load peak value model of the feeder line after the electric vehicle for adding charging；
Iteration optimizing module 500, obtains Current vehicle quantity, with the current vehicle for changing the preset vehicle quantity
Quantity and the Current vehicle quantity are distributed to the charging quantity of each period as changed factor, to the minimum load
It is minimum to obtain the daily load curve peak value of the corresponding feeder line of the Current vehicle quantity that peak value model carries out optimizing
Optimizing result；The optimizing result including target vehicle quantity, minimum load peak value and the target vehicle quantity distribute to
The charging quantity of each period；
Wherein, the target vehicle quantity be the minimum load peak value be less than the feeder line maximum capacity when
Maximum in the Current vehicle quantity.
The determining device of above-mentioned electric vehicle charging operational mode, obtains charge-discharge electric power, the preset vehicle of electric vehicle
The daily load curve of quantity and feeder line；It is bent according to the charge-discharge electric power, the preset vehicle quantity and the daily load
Line sets up the minimum load peak value model of the feeder line after the electric vehicle for adding charging；Change the preset vehicle
Quantity obtains Current vehicle quantity, and filling to each period is distributed with the Current vehicle quantity and the Current vehicle quantity
It is corresponding to obtain the Current vehicle quantity that electric quantity carries out optimizing as changed factor, to the minimum load peak value model
The minimum optimizing result of the daily load curve peak value of the feeder line.In this way, a kind of by planning charging quantity by finding
And the electric vehicle charging operational mode of the charging quantity of each period ensures the electric vehicle normal operation of maximum quantity
While, address and existing feeder line are built just so as to improve economy without changing charging station.
In one of the embodiments, model construction model 300, including：
Function determination sub-module, for bent according to the charge-discharge electric power, the preset vehicle quantity and the daily load
Daily load curve after the electric vehicle for adding charging is defined as adjusting afterload curve by line, and bent with the tune afterload
The minimum object function as the minimum load peak value model of the peak value of line；
Determination sub-module is constrained, for obtaining the operation rule constraint to the electric vehicle, to the electric vehicle
The constraint for the total amount that charges total amount of discharging day and day, to the pact of the charging quantity and operation quantity of electric vehicle described in the same period
Beam, and the feeder line to the electric vehicle capacity-constrained, be used as the constraint of the minimum load peak value model
Condition.
In one of the embodiments, the iteration optimizing module 500, including：
Optimize submodule, in the case of being the Current vehicle quantity in the quantity of the electric vehicle, with described
Current vehicle quantity is distributed to the charging quantity of each period as a particle, using particle swarm optimization algorithm to it is described most
Smaller load peak value model is iterated optimizing, determines optimal particle and the corresponding minimum load peak value of the optimal particle；
Iteration submodule, for the Current vehicle quantity to be increased into predetermined number, and repeats the optimization sub-step, directly
When being more than the maximum capacity to the minimum load peak value, the corresponding Current vehicle quantity of a upper iteration and determination are returned
The optimal particle and the minimum load peak value.
In one of the embodiments, the particle swarm optimization algorithm is dicentrics subgroup optimized algorithm；The optimization
Submodule, including：
Initialization unit, for according at the beginning of the Current vehicle quantity and the constraints of the minimum load peak value model
Beginningization particle, and initialize individual optimal particle and global optimum's particle；
Particle updating block, for updating population, relatively more contemporary particle and previous generation particles and default particle it is suitable
Response updates the individual optimal particle, according to the individual optimal particle and the contemporary particle determine broad sense central particles and
Narrow sense central particles are to update global optimum's particle；
Iteration unit, step is updated for repeating the particle, until during particle fitness iteration convergence, will be described
Global optimum's particle is used as the corresponding minimum load peak value of the optimal particle and the optimal particle and the charging number
Amount；
Wherein, the direction that the default particle includes updating along previous generation particles updates the default particle of first obtained,
And the direction first updated along previous generation particles updates the second obtained default grain further along the direction that history optimal particle updates
Son.
In one of the embodiments, the initialization unit, including：
First subelement, for according to the Current vehicle quantity, the daily load curve and the electric vehicle one
It running time and the charge-discharge electric power determine daily load curve after adjusting；
Second subelement, the charging for determining each period according to the tune afterload curve and the maximum capacity
Vehicle weight；
3rd subelement, for initializing each period according to the charging vehicle weight and the Current vehicle quantity
Charging quantity.
Because said apparatus is corresponding with the above method, repeated no more to save length specific descriptions.
Above example only expresses the several embodiments of the present invention, and it describes more specific and detailed, but can not
Therefore it is interpreted as the limitation to the scope of the claims of the present invention.It should be pointed out that for the person of ordinary skill of the art,
Without departing from the inventive concept of the premise, multiple modification and improvement can also be made, these belong to the protection model of the present invention
Enclose.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (6)
1. a kind of determination method of electric vehicle charging operational mode, it is characterised in that including：
Parameters acquiring procedure：Obtain the daily load curve of charge-discharge electric power, preset vehicle quantity and the feeder line of electric vehicle；
Model construction step：Set up and added according to the charge-discharge electric power, the preset vehicle quantity and the daily load curve
The minimum load peak value model of the feeder line after the electric vehicle of charging；The minimum load peak value model includes mesh
Scalar functions and constraints；The daily load of the feeder line is bent after the electric vehicle of the object function to add charging
Line peak value is minimum；The constraints includes：Operation rule constraint to the electric vehicle, put the day to the electric vehicle
Electric total amount and the day charging constraint of total amount, the charging quantity to electric vehicle described in the same period with run quantity constraint,
The constraint of the charge-discharge electric power of capacity-constrained and electric vehicle to the feeder line of the electric vehicle；
Iteration optimizing step：Change the preset vehicle quantity and obtain Current vehicle quantity, with the Current vehicle quantity and institute
State Current vehicle quantity to distribute to the charging quantity of each period as changed factor, the minimum load peak value model is entered
Row optimizing is with the minimum optimizing result of the daily load curve peak value for obtaining the corresponding feeder line of the Current vehicle quantity；
The optimizing result is distributed to each period including target vehicle quantity, minimum load peak value and the target vehicle quantity
Charge quantity；
Wherein, the target vehicle quantity be the minimum load peak value be less than the feeder line maximum capacity when it is described
Maximum in Current vehicle quantity；
The model construction step includes：
Function determines sub-step：It will be added according to the charge-discharge electric power, the preset vehicle quantity and the daily load curve
Daily load curve after the electric vehicle of charging is defined as adjusting afterload curve, and with the peak value of the tune afterload curve
The minimum object function as the minimum load peak value model；
Constraint determines sub-step：The operation rule constraint to the electric vehicle is obtained, it is total to discharging day for the electric vehicle
The constraint for the total amount that charges amount and day, the constraint to the charging quantity and operation quantity of electric vehicle described in the same period, to institute
The capacity-constrained of the feeder line of electric vehicle is stated, and the constraint of the charge-discharge electric power of electric vehicle is used as the minimum
The constraints of load peak model；
The iteration optimizing step, including：
Optimize sub-step：In the case where the quantity of the electric vehicle is the Current vehicle quantity, with the Current vehicle
Quantity is distributed to the charging quantity of each period as a particle, using particle swarm optimization algorithm to the minimum load peak
Value model is iterated optimizing, determines optimal particle and the corresponding minimum load peak value of the optimal particle；
Iteration sub-step：The Current vehicle quantity is increased into predetermined number, and repeats the optimization sub-step, described in most
When Smaller load peak value is more than the maximum capacity, return the corresponding Current vehicle quantity of a upper iteration and determination it is described most
Excellent particle and the minimum load peak value.
2. the determination method of electric vehicle charging operational mode according to claim 1, it is characterised in that the population
Optimized algorithm is dicentrics subgroup optimized algorithm；The optimization sub-step, including：
Initialization step：According to the Current vehicle quantity and the constraints of minimum load peak value model initialization grain
Son, and initialize individual optimal particle and global optimum's particle；
Particle updates step：Population is updated, the fitness of relatively more contemporary particle and previous generation particles and default particle updates
The individual optimal particle, broad sense central particles and narrow sense center are determined according to the individual optimal particle and the contemporary particle
Particle is to update global optimum's particle；
Iteration step：Repeat the particle and update step, until during particle fitness iteration convergence, by the global optimum
Particle is used as the corresponding minimum load peak value of the optimal particle and the optimal particle and the charging quantity；
Wherein, the direction that the default particle includes updating along previous generation particles updates the default particle of first obtained, and first
The direction updated along previous generation particles updates the second obtained default particle further along the direction that history optimal particle updates.
3. the determination method of electric vehicle charging operational mode according to claim 2, it is characterised in that the initialization
Step, including：
First sub-step：According to the Current vehicle quantity, the daily load curve and the electric vehicle traveling of one day
Time and the charge-discharge electric power determine daily load curve after adjusting；
Second sub-step：The charging vehicle power of each period is determined according to the tune afterload curve and the maximum capacity
Weight；
3rd sub-step：The charging number of each period is initialized according to the charging vehicle weight and the Current vehicle quantity
Amount.
4. a kind of determining device of electric vehicle charging operational mode, it is characterised in that including：
Parameter acquisition module, the daily load of charge-discharge electric power, preset vehicle quantity and feeder line for obtaining electric vehicle
Curve；
Model construction module, is added for being set up according to the charge-discharge electric power, Current vehicle quantity and the daily load curve
The minimum load peak value model of the feeder line after the electric vehicle of charging；The minimum load peak value model includes mesh
Scalar functions and constraints；The daily load of the feeder line is bent after the electric vehicle of the object function to add charging
Line peak value is minimum；The constraints includes：Operation rule constraint to the electric vehicle, put the day to the electric vehicle
Electric total amount and the day charging constraint of total amount, the charging quantity to electric vehicle described in the same period with run quantity constraint,
The constraint of the charge-discharge electric power of capacity-constrained and electric vehicle to the feeder line of the electric vehicle；
Iteration optimizing module, obtains Current vehicle quantity, with the Current vehicle quantity for changing the preset vehicle quantity
And the Current vehicle quantity is distributed to the charging quantity of each period as changed factor, to the minimum load peak value mould
Type carries out optimizing to obtain the optimizing that the daily load curve peak value of the corresponding feeder line of the Current vehicle quantity is minimum
As a result；When the optimizing result is distributed to each including target vehicle quantity, minimum load peak value and the target vehicle quantity
Between section charging quantity；
Wherein, the target vehicle quantity be the minimum load peak value be less than the feeder line maximum capacity when it is described
Maximum in Current vehicle quantity；
The model construction module, including：
Function determination sub-module, for being incited somebody to action according to the charge-discharge electric power, the Current vehicle quantity and the daily load curve
The daily load curve added after the electric vehicle of charging is defined as adjusting afterload curve, and with the tune afterload curve
The minimum object function as the minimum load peak value model of peak value；
Determination sub-module is constrained, for obtaining the operation rule constraint to the electric vehicle, the day of the electric vehicle is put
The constraint for the total amount that charges electric total amount and day, the constraint to the charging quantity and operation quantity of electric vehicle described in the same period,
To the capacity-constrained of the feeder line of the electric vehicle, and the charge-discharge electric power of electric vehicle constraint as described
The constraints of minimum load peak value model；
The iteration optimizing module, including：
Optimize submodule, in the case of being the Current vehicle quantity in the quantity of the electric vehicle, with described current
Vehicle fleet size is distributed to the charging quantity of each period as a particle, using particle swarm optimization algorithm to the minimal negative
Lotus peak value model is iterated optimizing, determines optimal particle and the corresponding minimum load peak value of the optimal particle；
Iteration submodule, for the Current vehicle quantity to be increased into predetermined number, and repeats the optimization sub-step, until institute
When stating minimum load peak value more than the maximum capacity, the institute of the corresponding Current vehicle quantity of a upper iteration and determination is returned
State optimal particle and the minimum load peak value.
5. the determining device of electric vehicle charging operational mode according to claim 4, it is characterised in that the population
Optimized algorithm is dicentrics subgroup optimized algorithm；The optimization submodule, including：
Initialization unit, for according to the Current vehicle quantity and the initialization of the constraints of the minimum load peak value model
Particle, and initialize individual optimal particle and global optimum's particle；
Particle updating block, for updating population, the fitness of relatively more contemporary particle and previous generation particles and default particle
The individual optimal particle is updated, broad sense central particles and narrow sense are determined according to the individual optimal particle and the contemporary particle
Central particles are to update global optimum's particle；
Iteration unit, step is updated for repeating the particle, until during particle fitness iteration convergence, by the overall situation
Optimal particle is used as the corresponding minimum load peak value of the optimal particle and the optimal particle and the charging quantity；
Wherein, the direction that the default particle includes updating along previous generation particles updates the default particle of first obtained, and first
The direction updated along previous generation particles updates the second obtained default particle further along the direction that history optimal particle updates.
6. the determining device of electric vehicle charging operational mode according to claim 5, it is characterised in that the initialization
Unit, including：
First subelement, for according to the Current vehicle quantity, the daily load curve and the electric vehicle one day
Running time and the charge-discharge electric power determine daily load curve after adjusting；
Second subelement, the charging vehicle for determining each period according to the tune afterload curve and the maximum capacity
Weight；
3rd subelement, for initializing filling for each period according to the charging vehicle weight and the Current vehicle quantity
Electric quantity.
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