CN110084443A - A kind of electrical changing station optimal operation model analysis method based on QPSO optimization algorithm - Google Patents
A kind of electrical changing station optimal operation model analysis method based on QPSO optimization algorithm Download PDFInfo
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Abstract
The electrical changing station optimal operation model analysis method based on QPSO optimization algorithm that the invention discloses a kind of, the analysis method include the following steps: step 1: establishing electrical changing station service model；Step 2: the optimization object function of electric service model is changed in definition: scene-load deviation rate, wind-light storage is purchased and operating cost, energy-storage battery service life, generation of electricity by new energy account for the ratio of electric car rechargeable electrical energy；Step 3: the electrical changing station dynamic operation strategy under electrical changing station service model is obtained based on QPSO optimization algorithm according to objective function.Highway electrical changing station wind-light storage capacity configuration from far-off regions in electric system and operation reserve are optimized according to analysis method of the present invention, as a result electric car generates electricity rapidly after getting up, highway independence micro-capacitance sensor construction retrofit scheme in electric system containing electrical changing station provides theoretical basis, while also changing the work such as electricity scheduling to electrical changing station for following needs system and providing theoretical foundation.
Description
Technical field
The invention belongs to electric car electric charging infrastructure construction fields in electric system, are related to a kind of study and change electricity
Stand wind-light storage capacity ratio and optimization operation analysis method, and in particular to one kind based on quantum behavior population (QPSO) it is excellent
Change the electrical changing station optimal operation model analysis method of algorithm.
Background technique
Under the development of auto industry and the increasingly increased background of automobile occupancy volume, people are increased to petroleum resources
Consumption, aggravated the severity of environmental pollution.Traditional fossil energy consumption is reduced, renewable energy is made full use of to carry out
Power generation, the important means for becoming energy-saving and emission-reduction using oneself for promoting electric car.
Meanwhile China Expressway develops towards the direction of " green high speed, Ecological Freeway, wisdom high speed " at present,
Since the highway periphery of remote districts has the green scene such as wild animals and plants, nature reserve area mostly, how to guarantee
Ecological protection and prevention and control of soil erosion measure are carried out in Highways ' while traffic, is utmostly reduced to ecological environment, open country
The destruction of lively plant and aquatic life habitat, effectively eliminate along geological disaster hidden danger, avoiding soil erosion is into one
What step needed to study.
To sum up, in order to avoid the bottleneck that highway from far-off regions becomes the following Development of Electric Vehicles, and utmostly
Realization highway low-carbon from far-off regions theme, establish the electric charging station containing electric car in highway from far-off regions
Wind-light storage independence micro-capacitance sensor undoubtedly has important practical value and meaning.
Summary of the invention
In order to establish independent micro-capacitance sensor in highway from far-off regions, meets the electric car run at high speed and changes electricity demanding,
The electrical changing station optimal operation model analysis method based on QPSO optimization algorithm that the present invention provides a kind of.According to of the present invention
Analysis method optimizes highway electrical changing station wind-light storage capacity configuration from far-off regions in electric system and operation reserve,
As a result electric car generates electricity rapidly after getting up, the highway independence micro-capacitance sensor construction in electric system containing electrical changing station changes
The scheme of making provides theoretical basis, at the same also for following needs system to electrical changing station change the work such as electricity scheduling provide it is theoretical according to
According to.
The purpose of the present invention is what is be achieved through the following technical solutions:
A kind of electrical changing station optimal operation model analysis method based on QPSO optimization algorithm, includes the following steps:
Step 1: electrical changing station service model is established；
Step 2: the optimization object function of electric service model is changed in definition: scene-load deviation rate, wind-light storage purchase and
Operating cost, energy-storage battery service life, generation of electricity by new energy account for the ratio of electric car rechargeable electrical energy；
Step 3: the dynamic of the electrical changing station under electrical changing station service model is obtained based on QPSO optimization algorithm according to objective function
Operation reserve.
Compared with the prior art, the present invention has the advantage that
1, the present invention combines electric automobile charging station, it is contemplated that load from far-off regions is small, grid-connected somewhat expensive, exists very
More wild animals and plants and the natural scenic spot feature high to environmental requirement, while considering highway vehicle flowrate from far-off regions
Characteristic and actual user's changes electricity demanding.
2, the present invention is in the case where changing electricity and concentrating the unmatched situation of charging with electrical changing station, with different weather wind light generation power
In conjunction with throughput sub-line is that particle swarm optimization algorithm finally obtains electrical changing station capacity configuration and operating scheme.
3, the present invention is that micro-capacitance sensor progress power is cut using reserve battery in electrical changing station to dissolve clean energy resource as far as possible
The service of peak load.
4, the present invention is to provide theoretical basis in electric system electric car electric charging station infrastructure construction scheme,
The work such as electricity scheduling also, which are changed, for subsequent electrical changing station simultaneously provides theoretical foundation.
5, the present invention can achieve the Complete Convergence of full probability in changing electric service model using QPSO algorithm, obtain
Optimum results can satisfy any time electric car and change electricity demanding.
Detailed description of the invention
Fig. 1 is independent micro-capacitance sensor structure chart；
Fig. 2 is that electrical changing station changes electricity service schematic diagram；
Fig. 3 is that different innovation parameters follow the example of QPSO iterativecurve；
Fig. 4 is electrical changing station QPSO algorithm optimal operation model；
Fig. 5 is electrical changing station practical operation situation under different weather type；
Fig. 6 is electrical changing station practical operation situation under special operation condition.
Specific embodiment
Further description of the technical solution of the present invention with reference to the accompanying drawing, and however, it is not limited to this, all to this
Inventive technique scheme is modified or replaced equivalently, and without departing from the spirit and scope of the technical solution of the present invention, should all be covered
Within the protection scope of the present invention.
The electrical changing station optimal operation model analysis method based on QPSO optimization algorithm that the present invention provides a kind of, described point
Analysis method includes the following steps:
Step 1: electrical changing station service model is established.
Reserve battery has following four state in electrical changing station: full electricity, charging, etc. it is to be charged, just in discharge condition.
Electrical changing station provided for electric car change electricity service when, the battery of electricity is exhausted with Full Charge Capacity battery swap electric vehicle, so changing electricity
The total amount of reserve battery remains unchanged in standing, and the battery total quantity in four kinds of states is constant at any time.Due to electrical changing station
It is electric asynchronous that interior reserve battery concentrates charging to change with electric automobile on highway, so optimization charging strategy can reduce scene
Capacity is stored up, abandonment is avoided to abandon light.
Step 2: the optimization object function of electric service model is changed in definition: scene-load deviation rate, wind-light storage purchase and
Operating cost, energy-storage battery service life, generation of electricity by new energy account for the ratio of electric car rechargeable electrical energy.
Before carrying out QPSO algorithm optimization operation reserve, target to be optimized in electrical changing station operation reserve is first determined
Function.
Step 3: the dynamic of the electrical changing station under electrical changing station service model is obtained based on QPSO optimization algorithm according to objective function
Operation reserve.
This step includes following sub-step:
Step 3 one: setting highway changes the dimensionality of particle of the QPSO optimization algorithm of electric service model；
Step 3 two: attractor is calculated；
Step 3 three: particle position updates, and selects the innovation parameter of suitable electrical changing station service model；
Step 3 four: electrical changing station dynamic operation strategy is obtained based on QPSO algorithm.
Electric car charging and conversion electric facility is connected with bulk power grid at present, and the present invention considers clean scene completely
Micro-capacitance sensor is stored up as electrical changing station energy supply, reserve cell adjusts the equilibrium of supply and demand by discharging to provide for micro-capacitance sensor in consideration electrical changing station
Service.So in this case, being different from bulk power grid electrical changing station optimization problem, there are four types of states for battery in electrical changing station, change
In power station there is coupling in the tense conversion of battery status.Therefore, for emerging electrical changing station problem, Optimized model becomes
The challenge of close coupling between multiple constraint, particle.Standard particle group algorithm can not solve problems, it may appear that do not restrain or
The case where not exclusively restraining.The control parameter of QPSO algorithm is less compared with PSO algorithm, in addition in QPSO algorithm quantized system grain
Son not set route before measuring, but any position of feasible zone is appeared in certain probability distribution, to increase
Strong algorithm ability of searching optimum and constringency performance.
Further description of the technical solution of the present invention combined with specific embodiments below:
Step 1: electrical changing station service model is established；
Fig. 1 show the independent micro-capacitance sensor structure chart of the present embodiment foundation.Wherein power supply includes wind-driven generator, photovoltaic
Solar panel and diesel-driven generator.Wherein, diesel-driven generator is as reserve battery, for coping with extreme weather from far-off regions, increases
Add the reliability of stand alone generating system.When generation of electricity by new energy power can satisfy electrical changing station operation, then do not consider using diesel oil
Generator.Load is electric automobile charging station, it plays the role of " working along both lines ", is on the one hand ground for remote highway
Electric service is changed in the electric car offer for studying carefully section, and the reserve battery in another aspect electrical changing station provides tracking for independent micro-capacitance sensor
The effect of power.
Be divided into 24 periods for 24 hours one day, if in i moment electrical changing station in full electricity, charging, etc. it is to be charged, put
The number of batteries of electric four kinds of states is respectively Ns (i), Nc (i), Nw (i), Nd (i).Four kinds of states of subsequent time are obtained by formula
Number of batteries:
In formula, Ns(i-1) the power battery pack quantity of full power state is in for (i-1)-th period； NcIt (i-1) is (i-1)-th
A period is in the power battery pack quantity of charged state；Nd(i-1) power electric of discharge condition is in for (i-1)-th period
Pond group quantity；Nw(i-1) the power battery pack quantity to be changed from electric car in (i-1)-th period；Nc_start(i-1) it is
The power battery pack quantity that (i-1)-th period starts to charge；Nc_finish(i-1) power battery fully charged for (i-1)-th period
Group quantity；Nd_start(i-1) start the power battery pack quantity of electric discharge for (i-1)-th period；
Nd_finish(i-1) the power battery pack quantity of electricity is drained for (i-1)-th period；NEV_needIt (i-1) is the i-1 period
Electric car unloading exhausts Electronic power batteries.
It is as shown in Figure 2 that electrical changing station changes electricity service schematic diagram.To guarantee to change electric service availability, needed when formulating operational plan
Want each period electrical changing station that can be greater than the quantity for changing electric car with the quantity of battery, formula is as follows:
Ns(i)≥NEV_need(i)。
Step 2: the optimization object function of electric service model is changed in definition.
Before carrying out QPSO algorithm optimization, first determines objective function, obtained according to objective function by QPSO optimization algorithm
To the electrical changing station dynamic operation strategy changed under electric service model.
Objective function is respectively as follows:
(1) scene-load deviation rate:
In formula,For the mean power of loads all in the time cycle, unit kw；PPV(i)、 PWT(i) when being i respectively
Carve photovoltaic, wind turbine power generation power；N is number of samples；PL(i) be i moment load watt level, unit kw.
(2) wind-light storage is purchased and operating cost:
The objective function be under the premise of meeting electric automobile on highway and changing electricity, by proportion wind-light storage capacity and
The strategy that battery in suitable electrical changing station concentrates charging is found, the income of electric automobile charging station is maximized.
C=TCd-(Cin+Cop+CFC)；
In formula, CdIt is that average odd-numbered day electric car changes electric income summation, unit Wan Yuan； CopFor electrical changing station equipment dimension
Protect cost, unit Wan Yuan；CinIt is electrical changing station equipment investment cost, unit Wan Yuan；CFCIt is diesel generating set fuel cost
It is Wan Yuan with unit；T is the number of days of electrical changing station profit.
(3) energy-storage battery service life:
By the corresponding relationship of ferric phosphate lithium cell depth of discharge (Depth Of Discharge, DOD) and cycle-index
It carries out curve fitting, it is as follows that the present embodiment uses N rank Function Fitting to obtain cubic function formula:
Nm=-2083DOD 3+8750DOD 2-13170DOD+11200；
In formula, NmIt is energy-storage battery cycle-index.
Energy storage life time decay rate in duty cycle are as follows:
In formula, n is electric car charge and discharge cycles number.
The service life (year) of energy storage is calculated:
In formula, γEVIt is odd-numbered day electric automobile during traveling energy consumption attenuation rate (%)；γdIt is attenuation rate of discharging in odd-numbered day electrical changing station
(%)；NEV、NbIt is electric automobile on highway total amount and electrical changing station internally-powered battery total amount respectively.
(4) generation of electricity by new energy accounts for the ratio of electric car rechargeable electrical energy:
Power battery charging energy source is accounted in photovoltaic and wind power generating set and diesel-driven generator, generation of electricity by new energy
Energy storage rechargeable electrical energy ratio (The Percentage of New Energy in Total EV Charging Energy,
PNTC) indicate that clean energy resource accounts for the ratio of whole rechargeable electrical energies for power battery charging, as shown by the equation:
In formula, NWT、NPVIt is wind-driven generator, photovoltaic battery panel number respectively；NcFor in electrical changing station just in rechargeable battery number
Amount；PcIt is electrical changing station charge power, unit kw.
Step 3: the dynamic of the electrical changing station under electrical changing station service model is obtained based on QPSO optimization algorithm according to objective function
Operation reserve.
Step 3 one: it is tieed up according to the particle that the QPSO optimization algorithm that highway changes electric service model is arranged in objective function
Degree.
The present embodiment changes electric service model according to what step 1 was established, is located in the search space of one 27 dimension, each
Population is made of 100 particles, i.e. X={ x1,...xi,...x100}.27 dimension particles are set as x=(NPV,NWT,Nb,Nc_start
(1),Nc_start(2) ..., Nc_start(24))T.It is photovoltaic battery panel, wind-driven generator, electrical changing station internally-powered that 1-3, which ties up particle,
Number of batteries, 4-27 dimension particle are the number of batteries that electrical changing station starts to charge per hour.Pass through 27 dimension particle of selection, electrical changing station
Odd-numbered day collection, which fills variable, can determine.
Step 3 two: attractor is calculated.
In quantum-behaved particle swarm optimization, the state of particle is by the wave function in Schrodinger equationTo retouch
It states, each particle passes through an attractor pi=[pi,1pi,2...pi,n] certain area is converged to, attractor can be under
Formula is calculated:
In formula, pbest_iIt is i-th of particle history desired positions in current iteration；gbestIt is current global optimum's grain
Son；piIt is attractor, the update for i-th of particle position.
Step 3 three: particle position updates, and selects the innovation parameter of suitable electrical changing station service model.
Particle position more new formula are as follows:
In formula, xiIt is the position of i-th of particle；α is innovation parameter；μ be obeyed between (0,1) it is equally distributed with
Machine number.Formula takes+or-probability be respectively 0.5.
The value of α depends on the circumstances, and can immobilize, and can choose several wounds according to certain mode dynamic change
New parameter value mode:
In formula, TmaxIt is the maximum times of iteration；Itrtn is current iteration number.
Multiple objective function of the invention is processed into single-goal function by weigthed sums approach, so fitting when multiple-objection optimization
It answers angle value value is smaller in the range of not less than 1 and illustrate that effect of optimization is better.Utilize fine day Simulation Example QPSO optimization algorithm
Influence of the different innovation function value formulas to electrical changing station running optimizatin result.It is bent that different innovation parameters follow the example of QPSO iteration
Line is as shown in Fig. 3.
The different innovation parameters of table 1 follow the example of QPSO algorithm simulating experimental result
According to Fig. 3, when innovation parameter is according to α1Formula value, restrained in 3862 iteration；When innovation parameter according to
α2Formula value, restrained in 201 iteration；When innovation parameter is according to α3Formula value, restrained in 1158 iteration.
In conjunction with shown in chart, QPSO algorithm still can have precocious trend.The model of this Premature Convergence and algorithm is not related,
But it is related with control mode with the value of innovation parameter α.From the point of view of on overall trend, innovation parameter is small, and iteration speed is fast, no
It can guarantee that global optimum's particle fitness value is best；Innovation parameter is big, and long to the convergent time, effect of optimization is good.So knot
Actual optimization model is closed, suitable method control innovation parameter is selected, makes particle close to or far from the position of p point, is that QPSO is calculated
Indispensable link in method.The comparison of fitness value from table, innovation parameter select α1Mode choose, effect of optimization is most
It is good.
Step 3 four: electrical changing station dynamic operation strategy is obtained based on QPSO algorithm.
Electrical changing station QPSO algorithm optimal operation model is as shown in figure 4, specific Optimization Steps are as follows:
(1) electricity demanding prediction and wind light generation power prediction are changed in input in changing electric service model, obtain electrical changing station a few days ago
Change electric plan；
(2) electrical changing station actual motion state is obtained, particle group parameters, population position and speed are initialized, executes step
(3), rolling optimization is carried out；
(3) particle rapidity and position are updated, judges that selecting particle whether to meet changes electric service model, if not meeting
First setting penalty function constrains fitness value, then executes step (4)；If met, directly execution step (4)；
(4) fitness value and more new particle optimal location and speed are assessed, if pbest≤Gbest, then follow the steps (5)；
If pbest> Gbest, then follow the steps (6), in which: pbest、 GbestRespectively current population optimal value and global optimum；
(5) global optimum's particle position and speed are updated；
(6) if itrtn >=T, the particle of global optimum is obtained；If irtn < Tmax, then follow the steps (3).
If prediction data does not have error, then electrical changing station can be executed fully according to operational plan a few days ago, reach preferable
Operational effect.However generation of electricity by new energy and change electricity demanding prediction and include prediction error, the accuracy of prediction directly affects operation
Plan implementation effect, prediction error not can avoid again.On the basis of operational plan a few days ago, in conjunction with actual operating data and ultrashort
Phase power prediction data carry out dynamic adjustment and optimization to operational plan a few days ago, obtain electrical changing station transition operation control strategy.
If the particle that QPSO algorithm is selected does not meet inequality constraints (electric car changes electricity demanding), or occurs changing electricity
It stands tense conversion logic mistake, then penalty function is set, fitness value is constrained.If selecting particle in changing electric service model
It is feasible after verification, then the fitness value of current population is directly calculated, next iteration operation is carried out.
Emulation obtains different weather according to quantum behavior particle swarm optimization algorithm using northwest section highway as background
Electrical changing station practical operation situation such as Fig. 5 under type.Different weather type simulation result is as shown in table 2.
2 different weather type simulation result of table
In order to improve systems stay power supply capacity, a kind of special operation condition (such as haze weather), that is, photovoltaic cell are considered
Plate does not generate electricity.It is not to be able to satisfy electrical changing station to change electricity demanding in this case, only leaning on wind turbine power generation.So, it has to
It is generated electricity using backup power source (diesel-driven generator).In optimization algorithm, no matter from the angle of economic cost or from environmental protection
Angle set out, be still adhere to utmostly reduce diesel-driven generator power generation principle.Electrical changing station is real under special operation condition
Border operating condition such as Fig. 6.Special operation condition simulation result is as shown in table 3.
3 special operation condition type simulation result of table
By the analysis to different type weather and special operation condition electrical changing station operating condition, electrical changing station wind-solar-diesel storage is obtained
Capacity ratio, cost of investment and electrical changing station on-road efficiency, as shown in table 4, table 5.
4 electrical changing station cost of investment of table
5 electrical changing station simulation result of table and on-road efficiency
Claims (9)
1. a kind of electrical changing station optimal operation model analysis method based on QPSO optimization algorithm, it is characterised in that the analysis method
Include the following steps:
Step 1: electrical changing station service model is established；
Step 2: the optimization object function of electric service model is changed in definition: scene-load deviation rate, wind-light storage purchase and run at
Sheet, energy-storage battery service life, generation of electricity by new energy account for the ratio of electric car rechargeable electrical energy；
Step 3: the electrical changing station dynamic operation plan under electrical changing station service model is obtained based on QPSO optimization algorithm according to objective function
Slightly.
2. the electrical changing station optimal operation model analysis method according to claim 1 based on QPSO optimization algorithm, feature
It is in the step 1, electrical changing station service model meets the following conditions:
Be divided into 24 periods for 24 hours one day, if in i moment electrical changing station in full electricity, charging, etc. to be charged, four kinds of electric discharge
The number of batteries of state is respectively Ns (i), Nc (i), Nw (i), Nd (i), obtains four kinds of state battery numbers of subsequent time by formula
Amount:
In formula, Ns(i-1) the power battery pack quantity of full power state is in for (i-1)-th period；NcIt (i-1) is (i-1)-th period
Power battery pack quantity in charged state；Nd(i-1) the power battery pack number of discharge condition is in for (i-1)-th period
Amount；Nw(i-1) the power battery pack quantity to be changed from electric car in (i-1)-th period；Nc_startIt (i-1) is (i-1)-th
The power battery pack quantity that period starts to charge；Nc_finish(i-1) the power battery pack quantity fully charged for (i-1)-th period；
Nd_start(i-1) start the power battery pack quantity of electric discharge for (i-1)-th period；Nd_finish(i-1) it is drained for (i-1)-th period
The power battery pack quantity of electricity；NEV_need(i-1) Electronic power batteries are exhausted for the unloading of i-1 period electric car；
To guarantee to change electric service availability, needs each period electrical changing station that can be greater than with the quantity of battery when formulating operational plan and change
The quantity of electric car, formula are as follows:
Ns(i)≥NEV_need(i)。
3. the electrical changing station optimal operation model analysis method according to claim 1 based on QPSO optimization algorithm, feature
It is that the scene-load deviation rate calculation formula is as follows:
In formula,For the mean power of loads all in the time cycle；PPV(i)、PWTIt (i) is i moment photovoltaic, blower hair respectively
Electrical power；N is number of samples；PL(i) be i moment load watt level.
4. the electrical changing station optimal operation model analysis method according to claim 1 based on QPSO optimization algorithm, feature
It is that the wind-light storage is purchased and operating cost calculation formula is as follows:
C=TCd-(Cin+Cop+CFC)；
In formula, CdIt is that average odd-numbered day electric car changes electric income summation；CopFor electrical changing station cost of equipment maintenance；CinIt is electrical changing station
Equipment investment cost；CFCIt is diesel generating set fuel cost；T is the number of days of electrical changing station profit.
5. the electrical changing station optimal operation model analysis method according to claim 1 based on QPSO optimization algorithm, feature
It is that the calculation formula of the energy-storage battery service life is as follows:
In formula, γEVIt is odd-numbered day electric automobile during traveling energy consumption attenuation rate；γdIt is attenuation rate of discharging in odd-numbered day electrical changing station；NEV、NbPoint
It is not electric automobile on highway total amount and electrical changing station internally-powered battery total amount.
6. the electrical changing station optimal operation model analysis method according to claim 1 based on QPSO optimization algorithm, feature
Be the generation of electricity by new energy account for the ratio of electric car rechargeable electrical energy calculation formula it is as follows:
In formula, NWT、NPVIt is wind-driven generator, photovoltaic battery panel number respectively；NcFor in electrical changing station just in rechargeable battery quantity；Pc
It is electrical changing station charge power.
7. the electrical changing station optimal operation model analysis method according to claim 1 based on QPSO optimization algorithm, feature
It is that the step 3 includes following sub-step:
Step 3 one: the dimensionality of particle that highway changes the QPSO optimization algorithm of electric service model is arranged according to objective function；
Step 3 two: attractor is calculated, wherein the attractor is calculated by following formula:
In formula, pbest_iIt is i-th of particle history desired positions in current iteration；gbestIt is current global optimum's particle；piIt is
Attractor, the update for i-th of particle position；
Step 3 three: particle position updates, and selects the innovation parameter of suitable electrical changing station service model, wherein the particle position
More new formula are as follows:
In formula, xiIt is the position of i-th of particle；α is innovation parameter；μ is that equally distributed random number is obeyed between (0,1)；
Step 3 four: electrical changing station dynamic operation strategy is obtained based on QPSO algorithm.
8. the electrical changing station optimal operation model analysis method according to claim 7 based on QPSO optimization algorithm, feature
It is that the value mode of the α is as follows:
In formula, TmaxIt is the maximum times of iteration；Itrtn is current iteration number.
9. the electrical changing station optimal operation model analysis method according to claim 7 based on QPSO optimization algorithm, feature
It is that specific step is as follows for the step 3 four:
(1) electricity demanding prediction and wind light generation power prediction are changed in input in changing electric service model, obtain electrical changing station and change electricity a few days ago
Plan；
(2) electrical changing station actual motion state is obtained, particle group parameters, population position and speed are initialized, is executed step (3), into
Row rolling optimization；
(3) particle rapidity and position are updated, judges that selecting particle whether to meet changes electric service model, is first arranged if not meeting
Penalty function constrains fitness value, then executes step (4)；If met, directly execution step (4)；
(4) fitness value and more new particle optimal location and speed are assessed, if pbest≤Gbest, then follow the steps (5)；If
pbest> Gbest, then follow the steps (6), in which: pbest、GbestRespectively current population optimal value and global optimum；
(5) global optimum's particle position and speed are updated；
(6) if itrtn >=T, the particle of global optimum is obtained；If irtn < Tmax, then follow the steps (3).
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