CN108764554A - A kind of robust Optimal methods that guiding electric vehicle orderly charges - Google Patents

A kind of robust Optimal methods that guiding electric vehicle orderly charges Download PDF

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CN108764554A
CN108764554A CN201810489716.0A CN201810489716A CN108764554A CN 108764554 A CN108764554 A CN 108764554A CN 201810489716 A CN201810489716 A CN 201810489716A CN 108764554 A CN108764554 A CN 108764554A
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price
period
electric vehicle
peak
orderly
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CN108764554B (en
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于艾清
王惠洲
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Shanghai University of Electric Power
University of Shanghai for Science and Technology
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

The present invention relates to a kind of robust Optimal methods that guiding electric vehicle orderly charges, and this method comprises the following steps：(1) automobile user tou power price responsiveness model is established；(2) electric vehicle charging load model after constitution and implementation tou power price；(3) it establishes and considers Demand Side Response and the orderly charge model of electric vehicle that scene is contributed；(4) consider that scene is contributed uncertain, is obtained electric vehicle and is orderly charged Robust Optimization Model；(5) solution is optimized to the model in step (3) and (4) with quanta particle swarm optimization and obtains day part division and corresponding tou power price；(6) it is charged according to Time segments division and corresponding tou power price guiding user.Compared with prior art, the present invention can effectively guide user that initiation of charge time, optimization electric vehicle charging load is selected to realize the effect to regional power grid Fill valley.

Description

A kind of robust Optimal methods that guiding electric vehicle orderly charges
Technical field
The present invention relates to a kind of methods of guiding electric vehicle charging, are orderly filled more particularly, to a kind of guiding electric vehicle The robust Optimal methods of electricity.
Background technology
Electric vehicle and generation of electricity by new energy developed very rapidly in recent years, solve energy crisis to the mankind and environmental pollution is asked Topic brings new opportunity, however the space-time of electric vehicle charging is uncertain and the uncertain of scene output also gives power grid Operation bring new challenge.On the one hand, the spatial and temporal distributions of the regenerative resources such as wind-powered electricity generation and solar energy have very strong fluctuation And it is intermittent, it is continuously improved with the permeability of regenerative resource, stabilizes the uncertainty of its output and efficiently dissolve this kind of Intermittent energy has been the task of top priority.On the other hand, when electric vehicle accesses power grid on a large scale and charged, power grid will appear Phenomena such as " on peak plus peak ", system operation cost improve and power quality declines.
In order to solve the problems, such as above-mentioned both sides, needs orderly to charge electric vehicle and generation of electricity by new energy collaborative combination rises Consider, the field of existing new energy and electric vehicle cooperative scheduling directly controls and indirect control two ways.Directly Control is adopted by charging station operator or regional distribution network control centre on meeting the basic charge requirement of automobile user With technological means, directly controls the charge power size of electric vehicle and start to charge up the time.Indirect control is to use electric power valence Lattice mechanism guiding user actively adjusts charging behavior.The charge power for needing continually to adjust charging pile is directly controlled, it can be to electricity The service life of pond and charger has a negative impact, and therefore, it is difficult to obtain the support of user and practical application.
In terms of processing new energy contributes uncertainty, Probability estimate is generally adopted or historical data deduces the side such as analysis Formula, these methods have the shortcomings that computationally intensive, computational accuracy is difficult to ensure.
Invention content
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of guiding electric vehicles The robust Optimal methods orderly to charge.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of robust Optimal methods that guiding electric vehicle orderly charges, this method comprises the following steps：
(1) automobile user tou power price responsiveness model is established；
(2) electric vehicle charging load model after constitution and implementation tou power price；
(3) it establishes and considers Demand Side Response and the orderly charge model of electric vehicle that scene is contributed；
(4) consider that scene is contributed uncertain, optimizes to obtain electric vehicle and orderly fill to the orderly charge model of electric vehicle Electric Robust Optimization Model；
(5) use quanta particle swarm optimization to the orderly charge model of electric vehicle and electric vehicle orderly charge robust optimization Model optimizes solution and obtains day part division and corresponding tou power price；
(6) it is charged according to Time segments division and corresponding tou power price guiding user.
Step (1) is specially：
Electricity price is divided into peak, flat, three periods of paddy, establishes automobile user tou power price responsiveness model, it is described Tou power price responsiveness model is tou power price responsiveness curve, tou power price responsiveness curve Yi Feng, flat, the paddy period electricity Price differential is abscissa, using automobile user tou power price responsiveness as ordinate, the automobile user tou power price Responsiveness is specially：In the case of electric vehicle charges load from high rate period to the transfer amount of low rate period and single price The ratio of former period charging load.
Automobile user tou power price responsiveness model is specially：
Wherein, r is automobile user tou power price responsiveness, and d is that electricity price is poor, d1For electricity price difference dead zone threshold, d1It indicates Electricity price when automobile user begins with response is poor, d2For electricity price difference saturation degree threshold, d2Indicate automobile user there is no Electricity price when response is poor, and k is tou power price responsiveness curve linear area slope, rmaxIt is responded for automobile user tou power price Spend maximum value.
Step (2) is specially：
24 periods were divided by one day, peak, flat, paddy Time segments division are carried out to each period, define the attribute of the i-th period vi：
vi∈ { 1,2,3 }, i=1,2 ..., 24,
viIndicate that the i-th period was peak period, v when=1iIndicate that the i-th period was peak period, v when=2iWhen indicating i-th when=3 The Duan Weifeng periods；
Determine that electric vehicle charging load model is specially after implementing tou power price according to peak, flat, the paddy period division：
PiFor the fitting load of the i-th period after implementation tou power price, TpFor peak period, TfFor usually section, TvFor the paddy period, Pini,iFor the actual measurement load of the i-th period before implementation tou power price, PpTo implement the total load of tou power price leading peak period, PfFor reality Apply before tou power price the usually total load of section, rpfTo be transferred to the automobile user Respondence to the Price of Electric Power degree of usually section from the peak period, rpvTo be transferred to the automobile user Respondence to the Price of Electric Power degree of paddy period, r from the peak periodpfTo be transferred to usually section from the peak period Automobile user Respondence to the Price of Electric Power degree, rfvTo be transferred to the automobile user Respondence to the Price of Electric Power degree of paddy period from usually section.
The orderly charge model of step (3) electric vehicle is specially：
Object function：
Wherein, F is load curve sum of squares of deviations, Pl,iFor the regional power grid original loads, P in the i-th periodw,iIt is i-th The region wind power output power in period, Ps,iFor the region photovoltaic output power, P in the i-th periodiAfter implementing tou power price The fitting load of i-th period, PavFor the average value of optimization cycle internal loading, T is to optimize total period；
Constraints：
(a) electricity price difference inequality constraints：
dpv.min≤dpv≤dpv.max,
dpf.min≤dpf≤dpf.max,
dfv.min≤dfv≤dfv.max,
(b) electricity price difference equality constraint：
dpv=dpf+dfv,
(c) charging load constraint：
minPi>=0,
Wherein, dpvPoor, the d for peak interval of time electricity pricepfFor peak, usually section electricity price is poor, dfvPoor, the d for Pinggu period electricity pricepv.min For peak interval of time electricity price difference minimum value, dpv.maxFor peak interval of time electricity price difference maximum value, dpf.minFor peak, usually section electricity price difference is minimum Value, dpf.maxFor peak usually section electricity price difference maximum value, dfv.minFor Pinggu period electricity price difference minimum value, dfv.maxFor Pinggu period electricity Price differential maximum value.
Step (4) is specially：
Consider that scene output is uncertain, activity of force is gone out according to the region wind power output power and photovoltaic in day part Different demarcation is several scenes；
For different scenes, determine that the electric vehicle Robust Optimization Model that orderly charges is specially：
Object function：
Min τ,
Constraints：
Wherein, τ is opposite robust parameter, FΩFor the object function in the orderly charge model of electric vehicle under corresponding scene,For the target function value solved by the orderly charge model of electric vehicle under corresponding scene.
Step (5) is specially：
For each scene, uses with the orderly charge model of quanta particle swarm optimization electric vehicle solve first It is divided and corresponding tou power price provisional value, then, the Robust Optimization Model that orderly charged using electric vehicle progress to day part Optimization Solution, the day part for obtaining meeting all different scenes divide and corresponding tou power price.
Compared with prior art, the invention has the advantages that：
(1) the method for the present invention can effectively carry out peak Pinggu Time segments division and corresponding tou power price, guiding user's selection Initiation of charge time, optimization electric vehicle charging load realize the effect to regional power grid Fill valley；
(2) uncertainty that the present invention is contributed using robust Optimal methods processing scene, in different honourable output scenes The good operation that can ensure electric system down has effectively handled the fluctuation of uncertain parameter, while having been played to load curve Peak load shifting acts on；
(3) present invention has flexibility, faces more severe honourable output scene, and opposite robust can independently be selected to join Number reduces the conservative of robust optimization, formulates rational electricity price scheme, reduce sum of squares of deviations, the peak-valley ratio of load curve, Stabilize the fluctuation caused by power grid of scene output uncertainty.
Description of the drawings
Fig. 1 is the flow diagram for the robust Optimal methods that present invention guiding electric vehicle orderly charges；
Fig. 2 is automobile user tou power price responsiveness curve graph of the present invention；
Fig. 3 is quanta particle swarm optimization flow chart of the present invention；
Fig. 4 is the load chart before and after the unordered charging of electric vehicle under the different scenes of one embodiment of the invention；
Fig. 5 is charging price and Time segments division figure under different scenes of the present invention；
Fig. 6 is the charging price and Time segments division figure in the case of the opposite robust optimization of the present invention；
Fig. 7 is the front and back load chart of robust optimization that orderly charges under different scenes of the present invention.
Specific implementation mode
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.Note that the following embodiments and the accompanying drawings is said Bright is substantial illustration, and the present invention is not intended to be applicable in it object or its purposes is defined, and the present invention does not limit In the following embodiments and the accompanying drawings.
Embodiment
As shown in Figure 1, a kind of robust Optimal methods that guiding electric vehicle orderly charges, this method comprises the following steps：
(1) automobile user tou power price responsiveness model is established；
(2) electric vehicle charging load model after constitution and implementation tou power price；
(3) it establishes and considers Demand Side Response and the orderly charge model of electric vehicle that scene is contributed；
(4) consider that scene is contributed uncertain, optimizes to obtain electric vehicle and orderly fill to the orderly charge model of electric vehicle Electric Robust Optimization Model；
(5) use quanta particle swarm optimization to the orderly charge model of electric vehicle and electric vehicle orderly charge robust optimization Model optimizes solution and obtains day part division and corresponding tou power price；
(6) it is charged according to Time segments division and corresponding tou power price guiding user.
Step (1) is specially：
Electricity price is divided into peak, flat, three periods of paddy, establishes automobile user tou power price responsiveness model, it is described Tou power price responsiveness model is tou power price responsiveness curve, tou power price responsiveness curve Yi Feng, flat, the paddy period electricity Price differential is abscissa, using automobile user tou power price responsiveness as ordinate, the automobile user tou power price Responsiveness is specially：In the case of electric vehicle charges load from high rate period to the transfer amount of low rate period and single price The ratio of former period charging load.
As shown in Fig. 2, when electric vehicle charges and carries out tou power price, user can be according to the charging of three periods of peak Pinggu Price difference selects corresponding charge period.The present invention defines automobile user tou power price responsiveness and charges for electric vehicle Load is from high rate period to the ratio of former period charging load in the case of the transfer amount of low rate period and single price；It is used in combination Piecewise linear function indicates automobile user tou power price responsiveness, and the abscissa of responsiveness curve is peak Pinggu period Price difference (peak-paddy, peak-flat, flat-three kinds of paddy situation), ordinate is then responsiveness of the user to different situations electricity price difference.
Automobile user tou power price responsiveness model is specially：
Wherein, r is automobile user tou power price responsiveness, and d is that electricity price is poor, d1For electricity price difference dead zone threshold, d1It indicates Electricity price when automobile user begins with response is poor, d2For electricity price difference saturation degree threshold, d2Indicate automobile user there is no Electricity price when response is poor, and k is tou power price responsiveness curve linear area slope, rmaxIt is responded for automobile user tou power price Spend maximum value.
Step (2) is specially：
24 periods were divided by one day, peak, flat, paddy Time segments division are carried out to each period, define the attribute of the i-th period vi：
vi∈ { 1,2,3 }, i=1,2 ..., 24,
viIndicate that the i-th period was peak period, v when=1iIndicate that the i-th period was peak period, v when=2iWhen indicating i-th when=3 The Duan Weifeng periods；
Determine that electric vehicle charging load model is specially after implementing tou power price according to peak, flat, the paddy period division：
PiFor the fitting load of the i-th period after implementation tou power price, TpFor peak period, TfFor usually section, TvFor the paddy period, Pini,iFor the actual measurement load of the i-th period before implementation tou power price, PpTo implement the total load of tou power price leading peak period, PfFor reality Apply before tou power price the usually total load of section, rpfTo be transferred to the automobile user Respondence to the Price of Electric Power degree of usually section from the peak period, rpvTo be transferred to the automobile user Respondence to the Price of Electric Power degree of paddy period, r from the peak periodpfTo be transferred to usually section from the peak period Automobile user Respondence to the Price of Electric Power degree, rfvTo be transferred to the automobile user Respondence to the Price of Electric Power degree of paddy period from usually section.
The orderly charge model of step (3) electric vehicle is specially：
Object function：
Wherein, F is load curve sum of squares of deviations, Pl,iFor the regional power grid original loads, P in the i-th periodw,iIt is i-th The region wind power output power in period, Ps,iFor the region photovoltaic output power, P in the i-th periodiAfter implementing tou power price The fitting load of i-th period, PavFor the average value of optimization cycle internal loading, T is to optimize total period；
Constraints：
(a) electricity price difference inequality constraints：
dpv.min≤dpv≤dpv.max,
dpf.min≤dpf≤dpf.max,
dfv.min≤dfv≤dfv.max,
(b) electricity price difference equality constraint：
dpv=dpf+dfv,
(c) charging load constraint：
minPi>=0,
Wherein, dpvPoor, the d for peak interval of time electricity pricepfFor peak, usually section electricity price is poor, dfvPoor, the d for Pinggu period electricity pricepv.min For peak interval of time electricity price difference minimum value, dpv.maxFor peak interval of time electricity price difference maximum value, dpf.minFor peak, usually section electricity price difference is minimum Value, dpf.maxFor peak usually section electricity price difference maximum value, dfv.minFor Pinggu period electricity price difference minimum value, dfv.maxFor Pinggu period electricity Price differential maximum value.
Step (4) is specially：
Consider that scene output is uncertain, activity of force is gone out according to the region wind power output power and photovoltaic in day part Different demarcation is several scenes；
For different scenes, determine that the electric vehicle Robust Optimization Model that orderly charges is specially：
Object function：
Min τ,
Constraints：
Wherein, τ is opposite robust parameter, FΩFor the object function in the orderly charge model of electric vehicle under corresponding scene,For the target function value solved by the orderly charge model of electric vehicle under corresponding scene.
Step (5) is specially：
For each scene, uses with the orderly charge model of quanta particle swarm optimization electric vehicle solve first It is divided and corresponding tou power price provisional value, then, the Robust Optimization Model that orderly charged using electric vehicle progress to day part Optimization Solution, the day part for obtaining meeting all different scenes divide and corresponding tou power price.
In solution procedure, in order to further decrease the conservative of robust optimization, the wind power output field that comparison is severe is avoided Scape leads to not find out the case where electricity price scheme effectively solves, can specify that one can tolerate under different scenes it is maximum τmax, τmax≥τ*, τ*The minimum τ that the Robust Optimization Model that orderly charges for electric vehicle solves, then following constraints herein Feasible solution is found out on the basis of condition：
The decision variable for the Robust Optimization Model that the present invention is built includes price difference and the charging of time-sharing charging electricity price Peak Pinggu Time segments division, wherein electricity price difference value can be decimal, and the period integer representation of peak Pinggu；Therefore the optimization problem For non-linear mixed integer optimization problem.The present invention solves object function using quanta particle swarm optimization, compared to basic grain Swarm optimization, the algorithm encode the current location of particle using quantum bit, and the search of particle is realized with Quantum rotating gate, Particle variations are realized to avoid Premature Convergence with quantum non-gate, and quanta particle swarm optimization flow chart is as shown in Figure 3.
In the embodiment, flat, Pinggu period each parameter of automobile user tou power price responsiveness curve in peak valley, peak Value：Linear zone slope is 1, and dead zone threshold is 0 yuan/kWh, and saturation degree threshold is 1 yuan/kWh, automobile user peak response Degree is 1.Do not consider that the original power grid base load of scene output uses the typical daily load in certain city, and is gone out with the cityscape Structure new energy output scene collection based on power historical data, minimum scene is contributed, medium honourable output, maximum scene are contributed and divided It Wei not scene 1, scene 2, scene 3；Assuming that charge power is invariable power, 2.5kW is taken, 100 are simulated using Monte Carlo Analogue Method The case where ten thousand electric vehicles unordered charging, the load curve before and after the unordered charging of electric vehicle under different scenes can be obtained, As shown in figure 4, Fig. 4 (a), Fig. 4 (b) and Fig. 4 (c) correspond respectively to scene 1, scene 2 and the unordered charging of 3 times electric vehicles of scene Front and back load curve.
Orderly charging robust optimisation technique according to the present invention can obtain charging price under different scenes and when Section divides, as shown in Figure 5；Charging price in the case of opposite robust optimization and Time segments division figure, as shown in Figure 6.
Fig. 7 (a), Fig. 7 (b) and Fig. 7 (c) are corresponded respectively under scene 1, scene 2 and scene 3 these three different scenes orderly Charge the front and back load curve of robust optimization, it can be seen from figure 7 that under unordered charging situation, the charging of electric vehicle is negative Lotus demand peaks will appear near the peak value of original loads, cause " peak overlap of peaks " phenomenon；And use electricity proposed by the present invention Electrical automobile orderly charges after robust optimisation technique, and the peak value of load curve and fluctuation are under each scene relative to unordered charging feelings Condition has significant reduction；With the raising of opposite robust parameter, the peak value of load curve and fluctuation increase, but still excellent In the electric vehicle unordered charging the case where.Therefore by robust optimisation technique, formulate rational electricity price, to automobile user into Row charging guiding, can handle the uncertainty of scene output well, and playing good peak load shifting to region load makees With, therefore the model of the present invention and algorithm are that robust is effective.And Robust Optimization Model proposed by the present invention is adjustable Opposite robust Model can select receptible opposite robust parameter, reasonably reduce as needed in practical engineering application The conservative of robust optimization, avoids the occurrence of the case where can not obtaining effective electricity price under the severe honourable output scene of comparison, therefore The present invention has engineering practical value.
The above embodiment is only to enumerate, and does not indicate that limiting the scope of the invention.These embodiments can also be with other Various modes are implemented, and can make in the range of not departing from technical thought of the invention it is various omit, displacement, change.

Claims (7)

1. a kind of robust Optimal methods that guiding electric vehicle orderly charges, which is characterized in that this method comprises the following steps：
(1) automobile user tou power price responsiveness model is established；
(2) electric vehicle charging load model after constitution and implementation tou power price；
(3) it establishes and considers Demand Side Response and the orderly charge model of electric vehicle that scene is contributed；
(4) consider that scene is contributed uncertain, is optimized to obtain electric vehicle to the orderly charge model of electric vehicle and is orderly charged Shandong Stick Optimized model；
(5) quanta particle swarm optimization is used orderly to charge Robust Optimization Model the orderly charge model of electric vehicle and electric vehicle It optimizes solution and obtains day part division and corresponding tou power price；
(6) it is charged according to Time segments division and corresponding tou power price guiding user.
2. a kind of robust Optimal methods that guiding electric vehicle orderly charges according to claim 1, which is characterized in that step Suddenly (1) is specially：
Electricity price is divided into peak, flat, three periods of paddy, establishes automobile user tou power price responsiveness model, the timesharing Respondence to the Price of Electric Power degree model is tou power price responsiveness curve, and tou power price responsiveness curve Yi Feng, flat, the electricity price of paddy period is poor For abscissa, using automobile user tou power price responsiveness as ordinate, automobile user tou power price response Degree is specially：Electric vehicle charge load from high rate period to it is former in the case of the transfer amount of low rate period and single price when The ratio of section charging load.
3. a kind of robust Optimal methods that guiding electric vehicle orderly charges according to claim 2, which is characterized in that electricity Electrical automobile user time-sharing Respondence to the Price of Electric Power degree model is specially：
Wherein, r is automobile user tou power price responsiveness, and d is that electricity price is poor, d1For electricity price difference dead zone threshold, d1Indicate electronic Electricity price when user vehicle begins with response is poor, d2For electricity price difference saturation degree threshold, d2Indicate that there is no responses for automobile user When electricity price it is poor, k be tou power price responsiveness curve linear area slope, rmaxMost for automobile user tou power price responsiveness Big value.
4. a kind of robust Optimal methods that guiding electric vehicle orderly charges according to claim 1, which is characterized in that step Suddenly (2) are specially：
24 periods were divided by one day, peak, flat, paddy Time segments division are carried out to each period, define the attribute v of the i-th periodi：
vi∈ { 1,2,3 }, i=1,2 ..., 24,
viIndicate that the i-th period was peak period, v when=1iIndicate that the i-th period was peak period, v when=2iIndicate that the i-th period was when=3 The peak period；
Determine that electric vehicle charging load model is specially after implementing tou power price according to peak, flat, the paddy period division：
PiFor the fitting load of the i-th period after implementation tou power price, TpFor peak period, TfFor usually section, TvFor paddy period, Pini,i For the actual measurement load of the i-th period before implementation tou power price, PpTo implement the total load of tou power price leading peak period, PfTo implement to divide When electricity price before usually section total load, rpfTo be transferred to the automobile user Respondence to the Price of Electric Power degree of usually section, r from the peak periodpvFor From peak, the period is transferred to the automobile user Respondence to the Price of Electric Power degree of paddy period, rpfTo be transferred to the electronic vapour of usually section from the peak period Automobile-used family Respondence to the Price of Electric Power degree, rfvTo be transferred to the automobile user Respondence to the Price of Electric Power degree of paddy period from usually section.
5. a kind of robust Optimal methods that guiding electric vehicle orderly charges according to claim 4, which is characterized in that step Suddenly the orderly charge model of (3) electric vehicle is specially：
Object function：
Wherein, F is load curve sum of squares of deviations, Pl,iFor the regional power grid original loads, P in the i-th periodw,iFor the i-th period The interior region wind power output power, Ps,iFor the region photovoltaic output power, P in the i-th periodiWhen for i-th after implementation tou power price The fitting load of section, PavFor the average value of optimization cycle internal loading, T is to optimize total period；
Constraints：
(a) electricity price difference inequality constraints：
dpv.min≤dpv≤dpv.max,
dpf.min≤dpf≤dpf.max,
dfv.min≤dfv≤dfv.max,
(b) electricity price difference equality constraint：
dpv=dpf+dfv,
(c) charging load constraint：
minPi>=0,
Wherein, dpvPoor, the d for peak interval of time electricity pricepfFor peak, usually section electricity price is poor, dfvPoor, the d for Pinggu period electricity pricepv.minFor peak Paddy period electricity price difference minimum value, dpv.maxFor peak interval of time electricity price difference maximum value, dpf.minFor peak usually section electricity price difference minimum value, dpf.maxFor peak usually section electricity price difference maximum value, dfv.minFor Pinggu period electricity price difference minimum value, dfv.maxIt is poor for Pinggu period electricity price Maximum value.
6. a kind of robust Optimal methods that guiding electric vehicle orderly charges according to claim 5, which is characterized in that step Suddenly (4) are specially：
Consider that scene output is uncertain, the difference of activity of force is gone out according to the region wind power output power and photovoltaic in day part It is divided into several scenes；
For different scenes, determine that the electric vehicle Robust Optimization Model that orderly charges is specially：
Object function：
Min τ,
Constraints：
Wherein, τ is opposite robust parameter, FΩFor the object function in the orderly charge model of electric vehicle under corresponding scene,For The target function value solved by the orderly charge model of electric vehicle under corresponding scene.
7. a kind of robust Optimal methods that guiding electric vehicle orderly charges according to claim 6, which is characterized in that step Suddenly (5) are specially：
It is each using being solved to obtain with the orderly charge model of quanta particle swarm optimization electric vehicle first for each scene Time segments division and corresponding tou power price provisional value, then, the Robust Optimization Model that orderly charged using electric vehicle are optimized It solves, the day part for obtaining meeting all different scenes divides and corresponding tou power price.
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