CN103997091A - Scale electric automobile intelligent charging control method - Google Patents

Abstract

The invention provides a scale electric automobile intelligent charging control method. According to the control method, operation states of each electric automobile at all moments in a day are sampled and simulated by using a Markov chain and Monte Carlo. In this way, each electric automobile can be arranged to be connected into a power grid in a non-traveling time period, and a reasonable charging scheme is designed, so that the requirement for charging of users of the electric automobiles is met, standard deviation of the total daily load curve including traditional loads and charging loads of the electric automobiles is smallest, and power fluctuation of the load curve is reduced.

Description

A kind of scale electric automobile intelligent charge control method

Technical field

The present invention relates to a kind of control method, specifically relate to a kind of scale electric automobile intelligent charge control method based on Markov chain and Monte Carlo sampling.

Background technology

At present, along with being on the rise of energy crisis and problem of environmental pollution, increasing people start to pay close attention to and explore the mode of sustainable development that how could realize with environment harmonious coexistence.Have investigation to find, at present, transportation has occupied the consumption of petroleum of about whole world half, and has brought the almost greenhouse gas emissions in the whole world 15%, has produced tremendous influence to the amblent air temperature variation of All Around The World.And for people's closely bound up automobile of living, if to reduce discharge and power consumption be the unrealistic desirability with not meeting development by reducing its use amount.At this moment, the appearance of electric automobile has brought new dawn to undoubtedly the solution of this problem.Electric automobile is as a kind of novel vehicles, at alleviating energy crisis, make full use of regenerative resource, reduces greenhouse gas emission, promotes the aspects such as people and environment harmonious development to have the incomparable advantage of traditional combustion engine automobile.At present, also become the focus that department of national governments, automaker, energy enterprise etc. are paid close attention to.

But, if the random unordered access electrical network of large-scale electric automobile charges, the each side such as the scheduling on whole electric power system, planning, control and protection are produced to very important impact.First, in time scale, extensive electric automobile at any time, random charging may cause the peak load of electrical network greatly to improve, easily there is the phenomenon at " Shang Jia peak, peak " in load boom period especially at dusk, exceed power supply capacity and the ability to bear of existing distribution power distribution network, easily cause the series of problems such as voltage out-of-limit, branch road overload.Secondly, on space scale, electric automobile everywhere, random unordered dispersion access may cause the three-phase imbalance of power distribution network, the quality of power supply of infringement power distribution network and increase electric energy loss, causes new adverse effect.So, adverse effect electrical network being caused in order to reduce the unordered charge mode of electric automobile, existing expert advice designs by certain market economy regulation mechanism, for example utilize the guiding of tou power price policy to realize the trough transfer of charging electric vehicle power to daily load, but, existing document shows, if do not apply effective control, this scheme is also not obvious for the penetration degree of electric automobile in the middle of increase power distribution network, and may form new load peak in the load valley phase, produce the problem making new advances.

Summary of the invention

In order to overcome above-mentioned the deficiencies in the prior art; the invention provides a kind of scale electric automobile intelligent charge control method based on Markov chain and Monte Carlo sampling, utilize the running status in Markov chain and Monte Carlo sampled analog each electric automobile each moment in the middle of one day.So, can be connected to the grid by being arranged in the non-running time section of electric automobile, and charging scheme reasonable in design, thereby both met electric automobile user's charging demand, make the total daily load curve standard deviation minimum including tradition load and charging electric vehicle load simultaneously, reduce the power fluctuation of load curve.

In order to realize foregoing invention object, the present invention takes following technical scheme:

A kind of scale electric automobile intelligent charge control method is provided, said method comprising the steps of:

Step 1: the initial launch state of assessment power distribution network;

Step 2: the parameter of determining electric automobile;

Step 3: determine the optimum charging strategy of electric automobile;

Step 4: the grid-connected rear impact on power distribution network of assessment scale electric automobile;

Step 5: determine that power distribution network is to the maximum of the scale electric automobile ability of dissolving.

In described step 1, by reading all nodal informations and line information in power distribution network, in the time there is no electric automobile access power distribution network, calculate the initial launch state of assessment power distribution network by the trend in each moment; Finally obtain node voltage amplitude, line power, losses of distribution network and the load total amount in each moment.

In described step 2, the parameter of electric automobile comprises the vehicle fleet of electric automobile, grid-connected moment, from net moment, battery rated capacity, charge power, the battery electric quantity in grid-connected moment and the battery electric quantity from the net moment.

Electric automobile sum represents with N; Based on historical statistical data formation probability transfer matrix, utilize Markov chain and Monte Carlo simulated sampling to obtain the running status situation of change of each electric automobile in the middle of certain day, thereby obtain the grid-connected moment t of corresponding each electric automobile ₀with from net moment t _d;

Based on probability density curve, sampling obtains the battery rated capacity C of i electric automobile ⁱ, charge power Pev _i, the grid-connected moment battery electric quantity with the battery electric quantity from the net moment

In described step 3, consider user side constraints, taking the daily load standard deviation minimum of electric automobile as controlling target, and taking X as control variables, by control variables X is optimized to value, reduce the daily load standard deviation of electric automobile, finally determine the optimum charging strategy of electric automobile.

Described user's side constraints comprises the user's constraint of constraint of demand, charge power, battery SOC constraint and charging interval constraint while starting to charge of charging;

1) user's constraint of demand that charges:

$80\≤{\mathrm{SOC}}_{t_d}^i\≤100---\left(1\right)$

Wherein, be the battery electric quantity of i electric automobile from the net moment;

2) charge power constraint:

Pev _min≤Pev _i≤Pev _max (2)

Wherein, Pev _ibe the charge power of i electric automobile, suppose 3kW≤Pev _i≤ 4kW; Pev _maxand Pev _minbe respectively the bound of the charge power of i electric automobile;

3) SOC constraint when battery starts to charge:

The battery electric quantity in i grid-connected moment of electric automobile meet and block Gaussian Profile, it meets:

$20\≤{\mathrm{SOC}}_{t_0}^i\≤50---\left(3\right)$

4) charging interval constraint:

t ₀≤t≤t _d-1 (4)

Wherein, t is the charging electric vehicle time; t ₀for the grid-connected moment of electric automobile, the moment that electric automobile stops travelling; t _dfor electric automobile from net the moment, electric automobile starts the moment of transport condition;

T ₀and t _dall obtain by Markov chain and Monte Carlo sampled analog research electric automobile user's behavioural habits, so need to meet:

$0\≤{\mathrm{SOC}}_{t_d}^i-{\mathrm{SOC}}_{t_0}^i\≤\frac{\underset{t=t_0}{\overset{t_d-1}{\mathrm{\Σ}}}(x_t^i\×\mathrm{\ΔT}\×{\mathrm{Pev}}_i)\×\mathrm{\η}}{C^i}\×100---\left(5\right)$

Wherein, for whether t moment i platform electric automobile accepts intelligent charge, represent that i platform electric automobile receives charging, represent that i platform electric automobile does not receive charging; Δ T is step-length computing time, and η is charge efficiency;

In like manner can be obtained by formula (5):

${\mathrm{SOC}}_{t+1}^i={\mathrm{SOC}}_t^i+\frac{(x_t^i\×\mathrm{\ΔT}\×{\mathrm{Pev}}_i)\×\mathrm{\η}}{C^i}\×100---\left(6\right)$

Wherein, represent t+1 moment i platform batteries of electric automobile electric weight; time, expression t moment i platform electric automobile is not accepted charging, and battery electric quantity did not change in the t+1 moment, so after can making charging electric vehicle finish meet consumers' demand.

Utilize self-adapted genetic algorithm taking the daily load standard deviation minimum of electric automobile as controlling target, described self-adapted genetic algorithm comprises initialization of population, selection.Crossover and mutation; The daily load standard deviation of electric automobile represents have with σ:

$\mathrm{\σ}=\min \left\{\sqrt{\frac{1}{T}\·\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{[\underset{i=1}{\overset{N}{\mathrm{\Σ}}}(x_t^i\×{\mathrm{Pev}}_i)+{\mathrm{Pload}}_t-\mathrm{Pavg}]}^2}\right\}---\left(7\right)$

Wherein, T is total calculating duration; N is electric automobile sum; Pload _tfor the conventional load total amount size in t moment power distribution network, the kW of unit; Pavg is daily load mean value, is expressed as:

$\mathrm{Pavg}=\frac{1}{T}\·\underset{t=1}{\overset{T}{\mathrm{\Σ}}}[\underset{i=1}{\overset{N}{\mathrm{\Σ}}}(x_t^i\×{\mathrm{Pev}}_i)+{\mathrm{Pload}}_t]---\left(8\right)$

And N total charge power of electric automobile t moment for:

$\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{x}_t^i\×{\mathrm{Pev}}_i=x_t^1\×{\mathrm{Pev}}_1+x_t^2\×{\mathrm{Pev}}_2+...+x_t^N\×{\mathrm{Pev}}_N---\left(9\right).$

Whether be charged as independent variable with each electric automobile in each moment, form T × N independent variable described control variables X matrix notation, has:

Described step 4 comprises the following steps:

Step 4-1: all electric automobiles load is distributed according to the ratio that in power distribution network, each node conventional load accounts for total load, obtain being arranged at the concrete quantity N of the electric automobile that node j charges _j, be expressed as:

$N_j=\frac{{\mathrm{Pload}}_j}{{\mathrm{\Σ}}_{j=1}^M{\mathrm{Pload}}_j}\×N---\left(11\right)$

Wherein, Pload _jfor the conventional load size that node j connects, M is node number in power distribution network, for conventional load size total amount in power distribution network;

Step 4-2: in conjunction with original conventional load in power distribution network and the electric automobile load that newly adds, again power distribution network is carried out to the trend at full-time quarter and calculate, obtain scale electric automobile and be incorporated to the running status situation of change of all electric automobiles after power distribution network.

In described step 5, based on grid side constraints, by increasing electric automobile sum, again sample according to step 2, determine that power distribution network is to the maximum of scale electric automobile dissolve ability, the i.e. maximum permeability of electric automobile.

Described grid side constraints comprises generating set units limits, node voltage constraint and the constraint of circuit through-put power;

1) generating set units limits:

$\left\{\begin{array}{c}{P}_{\mathrm{Gj}}^{\min }\≤P_{\mathrm{Gj}}^t\≤P_{\mathrm{Gj}}^{\max }\\ Q_{\mathrm{Gj}}^{\min }\≤Q_{\mathrm{Gj}}^t\≤Q_{\mathrm{Gj}}^{\max }\end{array}\right.---\left(12\right)$

Wherein, with be respectively the meritorious of node j place generating set and exert oneself and idle exerting oneself, with be respectively the meritorious bound of exerting oneself of node j place generating set, with be respectively the idle bound of exerting oneself of node j place generating set;

2) node voltage constraint:

$U_j^{\min }\≤U_j^t\≤U_j^{\max }---\left(13\right)$

Wherein, for the voltage of t moment node j, with be respectively the voltage bound of t moment node j;

3) circuit through-put power constraint:

$\left|{P_l}^t\right|\≤{P_l}^{\max }---\left(14\right)$

Wherein, for circuit l in power distribution network is in the through-put power in t moment, for the through-put power upper limit of circuit l permission.

Compared with prior art, beneficial effect of the present invention is:

Scale electric automobile intelligent charge control method based on Markov chain and Monte Carlo sampling provided by the invention has the tactful premium properties of the optimum charging of quick generation; realize while considering extensive charging electric vehicle load the control target of total load power curve standard deviation minimum.Meeting user's charge capacity demand and do not transforming existing network and under prerequisite that can safe operation, make total load curve smooth as much as possible.The method is coordinated to control for the optimization that realizes in the future scale electric automobile and electrical network has important practical significance.

Brief description of the drawings

Fig. 1 is the scale electric automobile intelligent charge control method flow chart based on Markov chain and Monte Carlo sampling provided by the invention;

Fig. 2 is the network diagram of IEEE33 node power distribution network in the embodiment of the present invention;

Fig. 3 is Markov chain state transitions graph of a relation in the embodiment of the present invention;

Fig. 4 is the Markov chain state probability scatter chart of a day in the embodiment of the present invention;

Fig. 5 realizes the every load chart after optimizing in the embodiment of the present invention;

Fig. 6 restrains effect schematic diagram in the embodiment of the present invention;

Fig. 7 is load curve comparison diagram when intelligent charge control method and unordered charging control in the embodiment of the present invention.

Embodiment

Below in conjunction with accompanying drawing, the present invention is described in further detail.

The present invention, by the control method of existing various electric automobile access electrical networks is analysed in depth, on existing investigation basis, has set up and has comprised the Mathematical Modeling of the scale electric automobile access network optimization control of comprehensive information more.The constraint of user's side and grid side constraints when the method has at length been considered extensive electric automobile access electrical network, specifically comprise: user charge constraint of demand, charge power constraint, storage battery while starting to charge in SOC (state-of-charge) constraint, charging interval constraint and power distribution network generating set units limits, node voltage constraint, circuit through-put power retrain.Owing to possessing abundant constraints, this Mathematical Modeling has been reacted the real scene of extensive electric automobile access grid charging in practical power systems well, and consider that controlling target is that to realize total daily load curve smooth as much as possible, reduce the standard deviation of load curve power fluctuation.So, the present invention proposes to utilize self-adapted genetic algorithm to solve this typical " nonlinear integer programming " problem, the method has the tactful premium properties of the optimum charging of quick generation, realize while considering extensive charging electric vehicle load the control target of total load power curve standard deviation minimum.Meeting user's charge capacity demand and do not transforming existing network and under prerequisite that can safe operation, make total load curve smooth as much as possible.The method is coordinated to control for the optimization that realizes in the future scale electric automobile and electrical network has important practical significance.

As shown in Figure 1, the scale electric automobile intelligent charge control method based on Markov chain and Monte Carlo sampling provided by the invention comprises following step:

The first step: the initial launch state of critic network.Read in network the parameters such as all nodes, circuit, consider that the initial launch state of network can pass through each moment the trend of (T=24, Δ T=1 hour) and calculate to assess in the time not having electric automobile (EV) to access electric power system.Finally obtain each moment lower node voltage magnitude, line power, via net loss, load total amounts etc., store these important data and compare for same subsequent calculations result.

Second step: each parameter of initialization electric automobile.Here suppose initial electric automobile sum N=600, utilize Markov chain and Monte Carlo simulated sampling to obtain the running status situation of change of each electric automobile in the middle of one day.For simplifying the analysis, the rated cell capacity C of unified each electric automobile of supposition ⁱ=40kWh; Specified charge power Pev _i=4kW; Battery electric quantity while starting to charge obedience is blocked Gaussian Profile, and its average is 40, and variance is 20, minimum be 25, maximum be 50; The charge capacity that user expects is charging scheme is (suppose electric automobile in the middle of a day go on a journey the last time between moment of day trip first finish time to the second) trickle charge in the charging interval section arranging, if the too short charge capacity that can not meet user's expectation of charging interval, keep electric automobile is charged state as much as possible always, approaches user's request as far as possible; Here temporarily do not consider to calculate maximum electric automobile infiltration situation.

The 3rd step: utilize intelligent algorithm to solve optimum charging and arrange.Wherein, the design parameter of genetic algorithm is set to: genetic iteration number of times 70 times, 200 of individual total amounts in population, maximum crossover probability P _{c_max}=0.9, minimum crossover probability P _{c_min}=0.9, maximum variation probability P _{m_max}=0.1, minimum variation probability P _{m_min}=0.01.

The 4th step: the access grid-connected rear impact on electrical network of assessment scale electric automobile.The ratio that whole electric automobile loads are accounted for to total conventional load according to conventional load under each node in network is distributed; obtain being dispensed on the concrete quantity of the electric automobile that each node charges; again network is carried out to the trend at 24 hours full-time quarters and calculate, the running status situation of change of system after obtaining scale electric automobile and being connected to the grid.

The 5th step: analyze relatively and export final result of calculation.Every result of output comprises: daily load curve variation before and after electric automobile access operation of power networks, network trend, the contents such as variation, branch road loading condition, via net loss.

Fig. 2 is IEEE33 node distribution network system network diagram, and IEEE33 node distribution network system data are as table 1:

Table 1

Load curve comparison diagram when Fig. 7 is intelligent charge control method provided by the invention and unordered charging control, load curve standard deviation when intelligent charge control method and unordered charging control is relatively as table 2:

Table 2

Charging strategy	Load criterion is poor
		Intelligent charge	2063.5368
Unordered charging	3238.4577

In the embodiment of the present invention, the design parameter of certain electric automobile and this electric automobile status data of a day are respectively as table 3 and table 4:

Table 3

Battery capacity (kWh)	40
		Charge power (kW)	4
The grid-connected time	20
		From the net time	7
Initial SOC	42.1993
		User's request SOC	>90
Can be calculated the charging interval (h) of minimum needs	5
		SOC while leaving	92.1993

Table 4

Wherein, first row represents the time point that electric automobile is grid-connected, for this emulation testing scheme, suppose that electric automobile is and net state after the same day, last trip finished during trip first in second day, and no matter electric automobile is to be parked in family, market or unit; Secondary series represents the time point of the concrete charging of electric automobile, is all positioned at grid-connected time range; The situation of change of battery electric quantity SOC is shown in the 3rd list, and in the time having charge power to inject, battery electric quantity changes until meet user's demand; Battery charge power size is shown in the 4th list, is assumed to rated value here; Battery electric quantity SOC variation percentage is in a period of time shown in the 5th list.As can be seen from Table 4, not charging at once after this electric automobile is grid-connected, but in order to realize the control target of total poor minimum of load criterion, in the moment in morning, the low ebb phase of tradition load charges, in addition 7 points in the morning, be user while leaving, electric weight has reached desired value, so the method is effective and feasible.

Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit, although the present invention is had been described in detail with reference to above-described embodiment, those of ordinary skill in the field are to be understood that: still can modify or be equal to replacement the specific embodiment of the present invention, and do not depart from any amendment of spirit and scope of the invention or be equal to replacement, it all should be encompassed in the middle of claim scope of the present invention.

Claims (11)

1. a scale electric automobile intelligent charge control method, is characterized in that: said method comprising the steps of:

Step 1: the initial launch state of assessment power distribution network;

Step 2: the parameter of determining electric automobile;

Step 3: determine the optimum charging strategy of electric automobile;

Step 4: the grid-connected rear impact on power distribution network of assessment scale electric automobile;

Step 5: determine that power distribution network is to the maximum of the scale electric automobile ability of dissolving.

2. scale electric automobile intelligent charge control method according to claim 1, it is characterized in that: in described step 1, by reading all nodal informations and line information in power distribution network, in the time there is no electric automobile access power distribution network, calculate the initial launch state of assessment power distribution network by the trend in each moment; Finally obtain node voltage amplitude, line power, losses of distribution network and the load total amount in each moment.

3. scale electric automobile intelligent charge control method according to claim 1; it is characterized in that: in described step 2, the parameter of electric automobile comprises the vehicle fleet of electric automobile, grid-connected moment, from net moment, battery rated capacity, charge power, the battery electric quantity in grid-connected moment and the battery electric quantity from the net moment.

4. scale electric automobile intelligent charge control method according to claim 3, is characterized in that: electric automobile sum N represents; Based on historical statistical data formation probability transfer matrix, utilize Markov chain and Monte Carlo simulated sampling to obtain the running status situation of change of each electric automobile in the middle of certain day, thereby obtain the grid-connected moment t of corresponding each electric automobile ₀with from net moment t _d;

Based on probability density curve, sampling obtains the battery rated capacity C of i electric automobile ⁱ, charge power Pev _i, the grid-connected moment battery electric quantity with the battery electric quantity from the net moment

5. scale electric automobile intelligent charge control method according to claim 1; it is characterized in that: in described step 3; consider user's side constraints; taking the daily load standard deviation minimum of electric automobile as controlling target; and taking X as control variables; by control variables X is optimized to value, reduce the daily load standard deviation of electric automobile, finally determine the optimum charging strategy of electric automobile.

6. scale electric automobile intelligent charge control method according to claim 5, is characterized in that: described user's side constraints comprises the user's constraint of constraint of demand, charge power, battery SOC constraint and charging interval constraint while starting to charge of charging;

1) user's constraint of demand that charges:

$80\≤{\mathrm{SOC}}_{t_d}^i\≤100---\left(1\right)$

Wherein, be the battery electric quantity of i electric automobile from the net moment;

2) charge power constraint:

Pev _min≤Pev _i≤Pev _max (2)

Wherein, Pev _ibe the charge power of i electric automobile, suppose 3kW≤Pev _i≤ 4kW; Pev _maxand Pev _minbe respectively the bound of the charge power of i electric automobile;

3) SOC constraint when battery starts to charge:

The battery electric quantity in i grid-connected moment of electric automobile meet and block Gaussian Profile, it meets:

$20\≤{\mathrm{SOC}}_{t_0}^i\≤50---\left(3\right)$

4) charging interval constraint:

t ₀≤t≤t _d-1 (4)

Wherein, t is the charging electric vehicle time; t ₀for the grid-connected moment of electric automobile, the moment that electric automobile stops travelling; t _dfor electric automobile from net the moment, electric automobile starts the moment of transport condition;

T ₀and t _dall obtain by Markov chain and Monte Carlo sampled analog research electric automobile user's behavioural habits, so need to meet:

$0\≤{\mathrm{SOC}}_{t_d}^i-{\mathrm{SOC}}_{t_0}^i\≤\frac{\underset{t=t_0}{\overset{t_d-1}{\mathrm{\Σ}}}(x_t^i\×\mathrm{\ΔT}\×{\mathrm{Pev}}_i)\×\mathrm{\η}}{C^i}\×100---\left(5\right)$

Wherein, for whether t moment i platform electric automobile accepts intelligent charge, represent that i platform electric automobile receives charging, represent that i platform electric automobile does not receive charging; Δ T is step-length computing time, and η is charge efficiency;

In like manner can be obtained by formula (5):

${\mathrm{SOC}}_{t+1}^i={\mathrm{SOC}}_t^i+\frac{(x_t^i\×\mathrm{\ΔT}\×{\mathrm{Pev}}_i)\×\mathrm{\η}}{C^i}\×100---\left(6\right)$

Wherein, represent t+1 moment i platform batteries of electric automobile electric weight; time, expression t moment i platform electric automobile is not accepted charging, and battery electric quantity did not change in the t+1 moment, so after can making charging electric vehicle finish meet consumers' demand.

7. scale electric automobile intelligent charge control method according to claim 5; it is characterized in that: utilize self-adapted genetic algorithm taking the daily load standard deviation minimum of electric automobile as controlling target, described self-adapted genetic algorithm comprises initialization of population, selection.Crossover and mutation; The daily load standard deviation of electric automobile represents have with σ:

$\mathrm{\σ}=\min \left\{\sqrt{\frac{1}{T}\·\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{[\underset{i=1}{\overset{N}{\mathrm{\Σ}}}(x_t^i\×{\mathrm{Pev}}_i)+{\mathrm{Pload}}_t-\mathrm{Pavg}]}^2}\right\}---\left(7\right)$

Wherein, T is total calculating duration; N is electric automobile sum; Pload _tfor the conventional load total amount size in t moment power distribution network, the kW of unit; Pavg is daily load mean value, is expressed as:

$\mathrm{Pavg}=\frac{1}{T}\·\underset{t=1}{\overset{T}{\mathrm{\Σ}}}[\underset{i=1}{\overset{N}{\mathrm{\Σ}}}(x_t^i\×{\mathrm{Pev}}_i)+{\mathrm{Pload}}_t]---\left(8\right)$

And N total charge power of electric automobile t moment for:

$\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{x}_t^i\×{\mathrm{Pev}}_i=x_t^1\×{\mathrm{Pev}}_1+x_t^2\×{\mathrm{Pev}}_2+...+x_t^N\×{\mathrm{Pev}}_N---\left(9\right).$

8. scale electric automobile intelligent charge control method according to claim 5, is characterized in that: whether be charged as independent variable with each electric automobile in each moment, form T × N independent variable described control variables X matrix notation, has:

9. scale electric automobile intelligent charge control method according to claim 1, is characterized in that: described step 4 comprises the following steps:

Step 4-1: all electric automobiles load is distributed according to the ratio that in power distribution network, each node conventional load accounts for total load, obtain being arranged at the concrete quantity N of the electric automobile that node j charges _j, be expressed as:

$N_j=\frac{{\mathrm{Pload}}_j}{{\mathrm{\Σ}}_{j=1}^M{\mathrm{Pload}}_j}\×N---\left(11\right)$

Wherein, Pload _jfor the conventional load size that node j connects, M is node number in power distribution network, for conventional load size total amount in power distribution network;

Step 4-2: in conjunction with original conventional load in power distribution network and the electric automobile load that newly adds, again power distribution network is carried out to the trend at full-time quarter and calculate, obtain scale electric automobile and be incorporated to the running status situation of change of all electric automobiles after power distribution network.

10. scale electric automobile intelligent charge control method according to claim 1; it is characterized in that: in described step 5; based on grid side constraints; by increasing electric automobile sum; again sample according to step 2; determine that power distribution network is to the maximum of scale electric automobile dissolve ability, the i.e. maximum permeability of electric automobile.

11. scale electric automobile intelligent charge control methods according to claim 1, is characterized in that: described grid side constraints comprises generating set units limits, node voltage constraint and the constraint of circuit through-put power;

1) generating set units limits:

$\left\{\begin{array}{c}{P}_{\mathrm{Gj}}^{\min }\≤P_{\mathrm{Gj}}^t\≤P_{\mathrm{Gj}}^{\max }\\ Q_{\mathrm{Gj}}^{\min }\≤Q_{\mathrm{Gj}}^t\≤Q_{\mathrm{Gj}}^{\max }\end{array}\right.---\left(12\right)$

Wherein, with be respectively the meritorious of node j place generating set and exert oneself and idle exerting oneself, with be respectively the meritorious bound of exerting oneself of node j place generating set, with be respectively the idle bound of exerting oneself of node j place generating set;

2) node voltage constraint:

$U_j^{\min }\≤U_j^t\≤U_j^{\max }---\left(13\right)$

Wherein, for the voltage of t moment node j, with be respectively the voltage bound of t moment node j;

3) circuit through-put power constraint:

$\left|{P_l}^t\right|\≤{P_l}^{\max }---\left(14\right)$

Wherein, for circuit l in power distribution network is in the through-put power in t moment, for the through-put power upper limit of circuit l permission.