CN108944531A - A kind of orderly charge control method of electric car - Google Patents
A kind of orderly charge control method of electric car Download PDFInfo
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- 238000000342 Monte Carlo simulation Methods 0.000 claims abstract description 6
- 238000006467 substitution reaction Methods 0.000 claims abstract description 6
- 238000004088 simulation Methods 0.000 claims abstract description 5
- 238000007476 Maximum Likelihood Methods 0.000 claims description 9
- 230000005540 biological transmission Effects 0.000 claims description 6
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- 238000012986 modification Methods 0.000 description 2
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The present invention discloses a kind of orderly charge control method of electric car, and main contents include, first according to the service condition of electric car, obtaining the probability distribution of electric car daily travel, last time trip finish time and charging duration；Pass through the electric car charging power load distributing situation of 24 hours one day different periods of Monte Carlo method analogue simulation again；Then by the load random access IEEE33 Node power distribution system of electric car, by being pushed forward the calculating power system load flow of back substitution, the voltage magnitude and via net loss of each node of distribution system are calculated；Negative effect according to 24 hours charge power demand models of the electric car and unordered charging to power distribution network, electric car of the building based on electricity price boot scheme orderly charges Optimal Operation Model, the Optimal Operation Model optimizes scheduling as peak-valley difference of the optimization aim to electricity price and power grid to reduce daily load peak-valley difference and reduce automobile user charging cost, alleviates access influence caused by power grid of electric car.
Description
Technical field
The invention belongs to electric car charging technique field, specifically a kind of orderly charge control method of electric car.
Background technique
The electric car that current era generally uses is a kind of vehicles for using petroleum as sole fuel, with society
Expanding economy is increasingly severe to the dependence of fossil resources, the rare difficulty for having become whole world facing of fossil energy
Topic.On the other hand, the burning of a large amount of fossil energies is so that SO2、CO2The discharge amount of equal pernicious gases is just increasing year by year.Traditional vapour
CO in vehicle emission2It is the largest source of pollution for causing greenhouse effects, environmental problem caused by the increase with automobile quantity
It gets worse.Orthodox car industry faces the dual-pressure of the energy and environment, and it is imperative to make the transition.
Electric car is using vehicle power supply as power, with the vehicle of motor driven wheels travel, including pure electric automobile, mixed
Close power vehicle and fuel cell car this three categories.It replaces conventional petroleum fuels as one of new industry, using electric energy
Can effective energy-saving and emission-reduction, be the important method readjusted the energy structure and alleviate environmental problem.Electric car will be as future
The dominant direction of China Automobile Industry has vast potential for future development.The following large-scale electric car floods the market, unordered
Operation to power distribution network economic and reliable, power quality are brought far-reaching influence and challenge, such as load peak-valley difference to increase by charging,
Variation, via net loss etc..
There are no the method that the charging for a kind of pair of electric car occur optimizes scheduling, application numbers in currently existing technology
A kind of region electric automobile charging station load prediction based on user's trip simulation is disclosed for 201810087702.6 patent
Method, according to user's trip purpose and the type of locating region division electric automobile charging station, using discrete markoff process
Travel activity of the analog subscriber between different zones constructs Trip chain；Extracting influences electric automobile charging station in Trip chain fills
The space-time characteristic amount of electric load, and use Probability Distribution Fitting；Charge condition is set according to the trip requirements of user, establishes region
Interior electric automobile charging station load forecasting model；Different types of electric car is calculated using Monte Carlo simulation approach to charge
The load prediction curve stood.However, the patent is only used for predicting the load of charging station, and then studies electric car and connect
Enter influence of the power grid to power grid, without can effectively solve that electric car access power grid is led to the problem of.
Summary of the invention
In response to the problems existing in the prior art, the purpose of the present invention is to provide a kind of orderly charge control sides of electric car
Method, for orderly charging to electric car access power grid and optimizing scheduling, with reduce to greatest extent user charging expense,
It improves the economic benefit of power grid and guarantees that power grid is stably and reliably run.
To achieve the above object, the technical solution adopted by the present invention is that: a kind of orderly charge control method of electric car, packet
Include following steps:
Step A obtains the daily travel and last time trip knot of electric car according to the service condition of electric car
The data at beam moment, and the data of acquisition are normalized, the day is travelled respectively by Maximum Likelihood Estimation Method
The data of mileage and last time trip finish time are fitted, and obtain electric car daily travel, last time is gone on a journey
The probability distribution of finish time and charging duration；
Step B establishes electricity according to the initiation of charge moment of electric car, the probability distribution of charge power and charging duration
24 hours one day charge power demand models of electrical automobile pass through 24 hours one day different periods of Monte Carlo method analogue simulation
Electric car charging power load distributing situation；
Step C, by the load random access IEEE33 Node power distribution system of electric car, by the trend for being pushed forward back substitution
Calculating method, calculates the voltage magnitude and via net loss of each node of distribution system, to verify the unordered charging pair of electric car
The negative effect of power distribution network；
Step D bears power distribution network according to 24 hours charge power demand models of the electric car and unordered charging
Face is rung, and electric car of the building based on electricity price boot scheme orderly charges Optimal Operation Model, and the Optimal Operation Model is to subtract
Small daily load peak-valley difference and reduction automobile user charging cost are that optimization aim is excellent to the peak-valley difference progress of electricity price and power grid
Change scheduling；Guarantee under the premise of meeting automobile user traveling demand, reduces the economy that load peak-valley difference improves power grid
And guarantee that power grid is steadily reliably run, while reducing the charging expense of user.
Specifically, in step A, the service condition of the electric car includes the charge characteristic of electric car, user's traveling
Habit, charging modes etc..
Specifically, in step A, the data of the electric car daily travel are fitted by the Maximum Likelihood Estimation Method
For logarithm normal distribution, the probability density function of the logarithm normal distribution are as follows:
Wherein, μD=3.20, σD=0.88；X represents the distance of traveling；μDAnd σDIt is one group of empirical value, it can be by electronic
The survey data of automobile daily travel obtains；
The data of the last time trip finish time are fitted to normal distribution by the Maximum Likelihood Estimation Method, institute
State the probability density function of normal distribution are as follows:
Wherein, μT=17.6, σT=3.4；X represents the time；μTAnd σTIt is one group of empirical value, it can be by being filled to electric car
The survey data of electric selection of time obtains.
Specifically, in step D, the Optimal Operation Model includes two objective functions, is respectively as follows:
Objective function a, user side guide electricity price minimum:
Wherein, function uses 96 Day Load Curve Forecasting methods, is divided into 96 periods, each period for 24 hours one day
For 15min, pjFor jth (j=1,2 ..., 96) the electricity price of a period, PevIndicate the charge power size of electric car；N be with
Electric car sum in Grid；Value is 0 or 1, is a decision variable, for determining that i-th electric car exists
Whether charging behavior is had in the jth period, and 1, which represents electric car, is in charged state, and 0, which represents electric car, is not at charging shape
State；Δ t indicates each period；TiIndicate that the i-th trolley leaves required charging duration from starting to charge to terminating；
Objective function b, grid side peak-valley difference are minimum:
f2=min (maxP 'Lj-minP′Lj)
Wherein, PLjFor jth period original minus charge values, P 'LjTo be after the jth period is superimposed electric car charging load
System load value, PijFor i-th electric car the jth period load power；A large amount of electric cars can draw when being charged at random
The problems such as playing " on peak plus peak ", the present invention motivate user's charging behavior by formulating Spot Price scheme, and reasonable arrangement is each
Car owner can complete to charge in the load valley period as far as possible for the charging order of user, can thus allow the height of network load
Peak value control in the range of one relatively lower so that load integrally fluctuate more steadily.
Specifically, two objective functions of the user side and grid side are based on, establish a multiple objective function, and using something lost
Propagation algorithm is solved；The multiple objective function is as follows:
Above-mentioned two objective function influences each other, interacts, and to be optimal the two can, is added using linear
The Optimization Solution problem of the multiple objective function is changed into single-goal function optimization problem and solved by the method for power, and right
The single-goal function optimization problem does normalized, and two objective functions is made to be in the same order of magnitude；
The normalization formula are as follows:
Single-goal function after normalized are as follows:
Minf=λ1f1+λ2f2
Wherein, λ1And λ2Respectively f1And f2Weight coefficient, it is respectively shared to respectively indicate charging expense and network load
Specific gravity；λ1+λ2=1, λ1>=0, λ2≥0。
Specifically, in step D, the Optimal Operation Model includes Constraint condition:
TiPi=(1-SOCi)×Bi
Wherein, SOCiFor electric car charging initial quantity of electricity, the initial quantity of electricity be electric car reach power distribution network when
Remaining capacity, the remaining capacity using percentage indicate, the electric car power distribution network charge volume be 1-SOCi, Ti
For the fully charged required duration of electric car；PiElectric car charge power；BiFor accumulator capacity.
Specifically, in step D, the Optimal Operation Model further include charge power constraint condition, network node voltage about
Beam condition and power distribution network transimission power constraint condition；
The charge power constraint condition are as follows:
0≤Pi≤PN
The network node voltage constraint condition are as follows:
The power distribution network transimission power constraint condition are as follows:
Wherein, PNFor electric car charging power-handling capability,For minimal network node voltage,For maximum web
Network node voltage；For power distribution network minimum transmission power,For power distribution network maximum transmission power.
Compared with prior art, the beneficial effects of the present invention are: the present invention is according to 24 hours charge powers of electric car
The negative effect of demand model and unordered charging to power distribution network, electric car of the building based on electricity price boot scheme orderly charge excellent
Change scheduling model, the Optimal Operation Model is to reduce daily load peak-valley difference and reduce automobile user charging cost as optimization mesh
Mark optimizes scheduling to the peak-valley difference of electricity price and power grid；Under the premise of meeting user's traveling demand, by formulating electricity in real time
Valence scheme motivates the user to charge behavior, and the charging order of each user of reasonable arrangement makes car owner low in load as far as possible
The paddy period completes charging；Not only consider to have saved the cost to charge from the angle of user, while also station is at the angle of grid side
Degree considers, reduces peak-valley difference, has achieved the effect that " peak load shifting ", makes power distribution network is relatively reliable economically to run.
Detailed description of the invention
Fig. 1 is the flow diagram for calculating charging load in the present invention based on Monte Carlo method；
Fig. 2 is the Optimizing Flow schematic diagram of genetic algorithm in the present invention；
Fig. 3 is a kind of flow diagram of the orderly charge control method of electric car of the present invention.
Specific embodiment
Below in conjunction with the attached drawing in the present invention, technical solution of the present invention is clearly and completely described, it is clear that
Described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Based on the implementation in the present invention
Example, those of ordinary skill in the art's all other embodiment obtained under the conditions of not making creative work belong to
The scope of protection of the invention.
As shown in Figures 1 to 3, a kind of orderly charge control method of electric car is present embodiments provided, comprising the following steps:
Step A obtains the daily travel and last time trip knot of electric car according to the service condition of electric car
The data at beam moment, and the data of acquisition are normalized, the day is travelled respectively by Maximum Likelihood Estimation Method
The data of mileage and last time trip finish time are fitted, and obtain electric car daily travel, last time is gone on a journey
The probability distribution of finish time and charging duration；
Step B establishes electricity according to the initiation of charge moment of electric car, the probability distribution of charge power and charging duration
24 hours one day charge power demand models of electrical automobile pass through 24 hours one day different periods of Monte Carlo method analogue simulation
Electric car charging power load distributing situation；
Step C, by the load random access IEEE33 Node power distribution system of electric car, by the trend for being pushed forward back substitution
Calculating method, calculates the voltage magnitude and via net loss of each node of distribution system, to verify the unordered charging pair of electric car
The negative effect of power distribution network；
Step D bears power distribution network according to 24 hours charge power demand models of the electric car and unordered charging
Face is rung, and electric car of the building based on electricity price boot scheme orderly charges Optimal Operation Model, and the Optimal Operation Model is to subtract
Small daily load peak-valley difference and reduction automobile user charging cost are that optimization aim is excellent to the peak-valley difference progress of electricity price and power grid
Change scheduling；Guarantee under the premise of meeting automobile user traveling demand, reduces the economy that load peak-valley difference improves power grid
And guarantee that power grid is steadily reliably run, while reducing the charging expense of user.
Specifically, in step A, the service condition of the electric car includes the charge characteristic of electric car, user's traveling
Habit, charging modes etc..
Specifically, in step A, the data of the electric car daily travel are fitted by the Maximum Likelihood Estimation Method
For logarithm normal distribution, the probability density function of the logarithm normal distribution are as follows:
Wherein, μD=3.20, σD=0.88；μDAnd σDIt is one group of empirical value, it can be by electric car daily travel
Survey data obtains.
The data of the last time trip finish time are fitted to normal distribution by the Maximum Likelihood Estimation Method, institute
State the probability density function of normal distribution are as follows:
Wherein, μT=17.6, σT=3.4；μTAnd σTIt is one group of empirical value, it can be by selecting the electric car charging time
Survey data obtain.
Further, charging duration needed for electric car are as follows:
Wherein, W is per 100 km power consumption；S is daily travel；PcFor electric car charge power.
Specifically, in step C, described the step of being pushed forward back substitution are as follows:
S1, it is assumed that give the topological structure of fixed emitting distribution network, share n node, root node voltage is U0,
The load of remaining node is Pi+Qi(i=1,2 ..., n-1), wherein PiFor active power, QiFor reactive power；Rij+jXijIt is adjacent
Impedance between two node of i, j, each node voltage, node Injection Current, Branch Power Flow and via net loss are amount to be asked；Each section
The Injection Current of point are as follows:
S2 calculates the electric current in each branch of distribution system, any branch by the structure and combination above formula of network topology
Electric current be the branch end-node electric current and all the sum of Injection Currents positioned at the downstream branch node, IiExpression formula such as
Shown in lower:
S3 combines known branch impedance to calculate each from root node after the branch current of entire distribution system has been calculated
The voltage drop on road, and each node voltage is updated by following formula, following formula indicates that beginning number is i, the end for the branch that end number is j
Hold voltage；
Uj=Ui-Ii(Rij+jXij)
S4 calculates the voltage magnitude correction amount of each node:
ΔUj=| Uj-Uj(0)|
S5, repetition are pushed forward branch current and back substitution node voltage, until meeting iterated conditional, obtain the distribution system most
Whole calculation of tidal current.
Specifically, in step D, the Optimal Operation Model includes two objective functions, is respectively as follows:
Objective function a, user side guide electricity price minimum:
Wherein, function uses 96 Day Load Curve Forecasting methods, is divided into 96 periods, each period for 24 hours one day
For 15min, pjFor jth (j=1,2 ..., 96) the electricity price of a period, PevIndicate the charge power size of electric car；N be with
Electric car sum in Grid；Value is 0 or 1, is a decision variable, for determining that i-th electric car exists
Whether charging behavior is had in the jth period, and 1, which represents electric car, is in charged state, and 0, which represents electric car, is not at charging shape
State；Δ t indicates each period；TiIndicate that the i-th trolley leaves required charging duration from starting to charge to terminating；
Objective function b, grid side peak-valley difference are minimum:
f2=min (maxP 'Lj-minP′Lj)
Wherein, PLjFor jth period original minus charge values, P 'LjTo be after the jth period is superimposed electric car charging load
System load value, PijFor i-th electric car the jth period load power；A large amount of electric cars can draw when being charged at random
The problems such as playing " on peak plus peak ", the present invention motivate user's charging behavior by formulating Spot Price scheme, and reasonable arrangement is each
Car owner can complete to charge in the load valley period as far as possible for the charging order of user, can thus allow the height of network load
Peak value control in the range of one relatively lower so that load integrally fluctuate more steadily.
Specifically, two objective functions of the user side and grid side are based on, establish a multiple objective function, and using something lost
Propagation algorithm is solved；The multiple objective function is as follows:
Above-mentioned two objective function influences each other, interacts, and to be optimal the two can, is added using linear
The Optimization Solution problem of the multiple objective function is changed into single-goal function optimization problem and solved by the method for power, and right
The single-goal function optimization problem does normalized, and two objective functions is made to be in the same order of magnitude；
The normalization formula are as follows:
Single-goal function after normalized are as follows:
Minf=λ1f1+λ2f2
Wherein, λ1And λ2Respectively f1And f2Weight coefficient, it is respectively shared to respectively indicate charging expense and network load
Specific gravity；λ1+λ2=1, λ1>=0, λ2≥0。
Specifically, in step D, the Optimal Operation Model includes Constraint condition:
TiPi=(1-SOCi)×Bi
Wherein, SOCiFor electric car charging initial quantity of electricity, the initial quantity of electricity be electric car reach power distribution network when
Remaining capacity, the remaining capacity using percentage indicate, the electric car power distribution network charge volume be 1-SOCi, Ti
For the fully charged required duration of electric car；PiElectric car charge power；BiFor accumulator capacity.
Specifically, in step D, the Optimal Operation Model further include charge power constraint condition, network node voltage about
Beam condition and power distribution network transimission power constraint condition；
The charge power constraint condition are as follows:
0≤Pi≤PN
The network node voltage constraint condition are as follows:
The power distribution network transimission power constraint condition are as follows:
Wherein, PNFor electric car charging power-handling capability,For minimal network node voltage,For maximum web
Network node voltage；For power distribution network minimum transmission power,For power distribution network maximum transmission power.
Specifically, as shown in Fig. 2, in step D, to reduce daily load peak-valley difference and reduce automobile user charging cost
The method for optimizing scheduling for peak-valley difference of the optimization aim to electricity price and power grid is to optimize scheduling using genetic algorithm,
Steps are as follows for its Optimized Operation:
The first step, initialization population S, the coding mode of selection target function, according to objective function and constraint conditional definition
The scale N of population, crossover probability P is arranged in one fitness function f (x)c, mutation probability Pm, genetic algebra T；
Second step calculates the fitness of population at individual using fitness function:
fi=f (Si)
Third step, if meeting iterated conditional, algorithm is terminated, and is taken fitness highest as optimal result, is otherwise calculated
Probability:
4th step, selection duplication, steps are as follows:
(1) in t generation, f is calculatedsumWith P (Si)；
(2) the random number rand () for generating { 0,1 }, seeks s=rand () * fsum；
(3) it asksWith the smallest k, then k-th of individual is selected；
(4) n times (2), (3) operation are carried out, individual is obtained, is S1；
5th step, according to crossover probability PcIn above-mentioned S1In randomly select the individuals of respective numbers, and these individuals are carried out
Crossover operation, and original individual is replaced with newly generated individual, obtain S2；
6th step, according to mutation probability PmIn S2In randomly select the individual of respective numbers, and make a variation to these individuals
Operation replaces original individual with newly generated individual, obtains S3；
Algebra is increased 1, t=t+1 by the 7th step, by by selecting, intersecting, mutation operation obtains as kind of new generation
Group S3It is back to third step to be verified, until obtaining optimal result.
Fig. 3 is a kind of whole flow process figure of the orderly charge control method of electric car of the present embodiment, by combining Fig. 1 and figure
2, the influence according to the charging load of electric vehicle and unordered charging to power grid, electric car of the building based on electricity price boot scheme has
Sequence charging Optimal Operation Model, the Optimal Operation Model is to reduce daily load peak-valley difference and reduce automobile user charging cost
Scheduling is optimized for peak-valley difference of the optimization aim to electricity price and power grid, obtains the highest optimal result of fitness；Guarantee full
Under the premise of sufficient automobile user traveling demand, reduces load peak-valley difference and improve the economy of power grid and guarantee that power grid is steady
Reliable operation, while reducing the charging expense of user.
It although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
A variety of variations, modification, replacement can be carried out to these embodiments without departing from the principles and spirit of the present invention by understanding
And modification, the scope of the present invention is defined by the appended.
Claims (6)
1. a kind of orderly charge control method of electric car, which comprises the following steps:
Step A, according to the service condition of electric car, at the end of the daily travel and last time for obtaining electric car are gone on a journey
The data at quarter, and the data of acquisition are normalized, by Maximum Likelihood Estimation Method respectively to the daily travel
It is fitted with the data of last time trip finish time, obtains electric car daily travel, last time trip terminates
The probability distribution of moment and charging duration；
Step B establishes electronic vapour according to the initiation of charge moment of electric car, the probability distribution of charge power and charging duration
Che Yitian 24 hours charge power demand models pass through the electronic of 24 hours one day different periods of Monte Carlo method analogue simulation
Automobile charging power load distributing situation；
Step C, by the load random access IEEE33 Node power distribution system of electric car, by the Load flow calculation for being pushed forward back substitution
Method, calculates the voltage magnitude and via net loss of each node of distribution system, to verify the unordered charging of electric car to distribution
The negative effect of net；
Step D, according to 24 hours charge power demand models of the electric car and the unordered negative shadow to charge to power distribution network
It rings, electric car of the building based on electricity price boot scheme orderly charges Optimal Operation Model, and the Optimal Operation Model is to reduce day
Load peak-valley difference and reduction automobile user charging cost are that optimization aim optimizes tune to the peak-valley difference of electricity price and power grid
Degree.
2. the orderly charge control method of a kind of electric car according to claim 1, which is characterized in that described in step A
The data of electric car daily travel are fitted to logarithm normal distribution, the lognormal by the Maximum Likelihood Estimation Method
The probability density function of distribution are as follows:
Wherein, μD=3.20, σD=0.88；X represents the distance of traveling；μDAnd σDIt is one group of empirical value；
The data of last time trip finish time are fitted to normal distribution by the Maximum Likelihood Estimation Method, it is described just
The probability density function of state distribution are as follows:
Wherein, μT=17.6, σT=3.4；X represents the time；μTAnd σTIt is one group of empirical value.
3. the orderly charge control method of a kind of electric car according to claim 1, which is characterized in that described in step D
Optimal Operation Model includes two objective functions, is respectively as follows:
Objective function a, user side guide electricity price minimum:
Wherein, function uses 96 Day Load Curve Forecasting methods, is divided into 96 periods for 24 hours one day, and each period is
15min, pjFor the electricity price of jth (j=1,2 ..., 96) a period, PevIndicate the charge power size of electric car；N is distribution
Electric car sum in web area；Value is 0 or 1, is a decision variable, for determining i-th electric car in jth
Whether charging behavior is had in period, and 1, which represents electric car, is in charged state, and 0, which represents electric car, is not at charged state；
Δ t indicates each period；TiIndicate that the i-th trolley leaves required charging duration from starting to charge to terminating；
Objective function b, grid side peak-valley difference are minimum:
f2=min (maxP 'Lj-minP’Lj)
Wherein, PLjFor jth period original minus charge values, P 'LjFor the system loading after the jth period is superimposed electric car charging load
Value, PijFor i-th electric car the jth period load power.
4. the orderly charge control method of a kind of electric car according to claim 3, which is characterized in that be based on the user
Two objective functions in side and grid side, establish a multiple objective function:
Using the method for linear weighted function, the Optimization Solution problem of the multiple objective function is changed into single-goal function optimization problem
It is solved, and normalized is done to the single-goal function optimization problem；
The normalization formula are as follows:
Single-goal function after normalized are as follows:
Minf=λ1f1+λ2f2
Wherein, λ1And λ2Respectively f1And f2Weight coefficient, respectively indicate charging expense and network load respectively shared specific gravity；
λ1+λ2=1, λ1>=0, λ2≥0。
5. the orderly charge control method of a kind of electric car according to claim 1, which is characterized in that described in step D
Optimal Operation Model includes Constraint condition:
TiPi=(1-SOCi)×Bi
Wherein, SOCiFor the initial quantity of electricity of electric car charging, the initial quantity of electricity is residue when electric car reaches power distribution network
Electricity, the remaining capacity indicate that the electric car is 1-SOC in the charge volume of power distribution network using percentagei, TiIt is electronic
Duration needed for automobile is fully charged；PiElectric car charge power；BiFor accumulator capacity.
6. the orderly charge control method of a kind of electric car according to claim 1, which is characterized in that described in step D
Optimal Operation Model further includes charge power constraint condition, network node voltage constraint condition and the constraint of power distribution network transimission power
Condition；
The charge power constraint condition are as follows:
0≤Pi≤PN
The network node voltage constraint condition are as follows:
The power distribution network transimission power constraint condition are as follows:
Wherein, PNFor the rated power of electric car charging；For minimal network node voltage,For maximum network node
Voltage；For power distribution network minimum transmission power,For power distribution network maximum transmission power.
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