CN106682765A - Charging station optimization layout method and apparatus thereof - Google Patents

Abstract

The invention provides a charging station optimization layout method and an apparatus thereof, and belongs to the new energy technology field. The method comprises the following steps of according to a distribution network basic parameter and a first constraint condition, through a first model, generating a parameter decision variable of a charging station; according to the parameter decision variable, through a second model, acquiring a binary decision variable; updating the parameter decision variable and the binary decision variable for preset times according to a preset rule, wherein the preset rule includes steps of according to the parameter decision variable, through the second model, updating the binary decision variable, and according to the updated binary decision variable and the first constraint condition, through the first model, updating the parameter decision variable; and according to the updated parameter decision variable and the binary decision variable, through the first model, acquiring an investment optimization result, and according to the updated binary decision variable, through the second model, acquiring a satisfaction optimization result. Through mutual influences of the parameter decision variable and the binary decision variable during optimization, an optimal scheme satisfying demands of a user and an investor is acquired.

Description

Charging station Optimal Deployment Method and device

Technical field

The present invention relates to technical field of new energies, in particular to a kind of charging station Optimal Deployment Method and device.

Background technology

With the development of science and technology, and the increasingly consumption of fossil energy, new energy technology is progressively emerged.New In energy technology, electric energy is representational new forms of energy, with the life for being applied to people progressively of the electric automobile of electrical energy drive In work.

In the practical application of current electric automobile, electric automobile needs to fill storage battery after a segment distance is travelled Electricity is in order to travelling again.Due to the road environment that city is complicated, user additional being charged to electric automobile of being in is not Applicable.In city, generally require to arrange charging pile in multiple regions in city, can easily enter in order to electric automobile Row charges.If arranging the website or charging pile of charging pile very little, although can effectively control the construction of enterprise and the money of maintenance Gold, but the user of electric automobile generally requires to take a long time, and traveling is relatively remote or long-time is queued up and could give oneself Electric automobile be charged, the suitability of user so as to strong influence.If arrange charging pile website or charging pile compared with It is many, although the operating range of electric automobile user, traveling duration or queuing time can be effectively reduced, user is met Demand.But the investment of enterprise needs to spend substantial contribution to be built and safeguarded, from being unable to meet business investor Demand.

Therefore, the quantity that the website or charging pile of charging pile are set in city how is effectively calculated, is made with obtaining satisfaction The optimal case of user and investor demand is current industry a great problem.

The content of the invention

In view of this, it is an object of the invention to provide a kind of charging station Optimal Deployment Method and device, it can be effective Calculate the quantity that the website or charging pile of charging pile are set in city, meet the optimum of user and investor demand to obtain Scheme.

What embodiments of the invention were realized in：

In a first aspect, embodiments providing a kind of charging station Optimal Deployment Method, methods described includes：Acquisition is matched somebody with somebody Net basic parameter, according to the distribution basic parameter and the first constraints the Parameter Decision Making of charging station is generated by the first model Variable, binary decision variable is obtained according to the Parameter Decision Making variable by the second model.By the Parameter Decision Making variable and institute State binary decision variable and be updated to preset times according to preset rules, wherein, the preset rules include：According to the parameter Decision variable is by binary decision variable described in second model modification, the binary decision variable and institute further according to renewal The first constraints is stated by Parameter Decision Making variable described in first model modification.According to the ginseng for being updated to preset times Number decision variable obtains investment optimum results with the binary decision variable for being updated to preset times by first model, The binary decision variable according to preset times are updated to obtains satisfaction optimum results by second model.According to institute State investment optimum results and the satisfaction optimum results obtain charging station optimization layout result.

Second aspect, embodiments provides a kind of charging station optimization placement device, and described device includes：At first Reason module, for obtaining distribution basic parameter, is given birth to according to the distribution basic parameter and the first constraints by the first model Into the Parameter Decision Making variable of charging station, binary decision variable is obtained by the second model according to the Parameter Decision Making variable.Second Processing module, for the Parameter Decision Making variable and the binary decision variable to be updated to into default time according to preset rules Number, wherein, the preset rules include：According to the Parameter Decision Making variable by binary decision described in second model modification Variable, further according to the binary decision variable and first constraints for updating by joining described in first model modification Number decision variable.3rd processing module, is updated to the Parameter Decision Making variable of preset times and is updated to default for basis The binary decision variable of number of times obtains investment optimum results by first model, according to the institute for being updated to preset times State binary decision variable and satisfaction optimum results are obtained by second model.Output module；For excellent according to the investment Change result and the satisfaction optimum results obtain charging station optimization layout result.

The beneficial effect of the embodiment of the present invention is：

By obtaining input distribution basic parameter, and distribution basic parameter and the first constraints are passed through into the first model Generate the Parameter Decision Making variable of charging station such that it is able to obtain meeting the charging station of distribution basic parameter and the first constraints Build quantity and corresponding construction position.And according to Parameter Decision Making variable by bringing the second model into, then can pass through second Model obtains the binary decision variable which charging station certain user selects.

At the same time, Parameter Decision Making variable and binary decision variable are updated to into preset times according to preset rules, with Obtain stable Parameter Decision Making variable and binary decision variable.Wherein, preset rules include：According to binary decision variable and first Constraints passes through the second model modification two by the first model modification Parameter Decision Making variable, then by the Parameter Decision Making variable for updating First decision variable.Parameter Decision Making variable is once updated to into the suboptimization to Parameter Decision Making variable by the first model, and By once updating for the second model also it is the suboptimization to binary decision variable by binary decision variable.By determining to parameter Plan variable is logical and binary decision variable constantly updates, and after close preset times, Parameter Decision Making variable is by the first model Update every time and binary decision variable is tended towards stability by each renewal of the second model, be then updated to preset times in satisfaction Afterwards, the renewal to Parameter Decision Making variable and binary decision variable is stopped.

Furthermore, led to the binary decision variable for being updated to preset times according to the Parameter Decision Making variable for being updated to preset times Cross the first model and obtain investment optimum results, and obtained by the second model according to the binary decision variable for being updated to preset times Satisfaction optimum results.Due to investing optimum results and satisfaction optimum results to continue to optimize renewal, and influence each other and obtain Optimum structure is taken, so as to charging station optimization layout result just can be obtained according to investment optimum results and satisfaction optimization.

Therefore, the renewal of continuing to optimize by preset times by the first model to Parameter Decision Making variable, the second model pair The renewal of continuing to optimize by preset times of binary decision variable, and Parameter Decision Making variable and binary decision variable are when updating Influence each other, so as to finally be obtained in that charging station optimizes layout result, and then can obtain and meet user and investor The optimal case of demand.

Description of the drawings

In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to institute in embodiment The accompanying drawing that needs are used is briefly described, it should be apparent that, drawings in the following description are only some enforcements of the present invention Example, for those of ordinary skill in the art, on the premise of not paying creative work, can be being obtained according to these accompanying drawings Obtain other accompanying drawings.By shown in accompanying drawing, the above and other purpose of the present invention, feature and advantage will become apparent from.In whole Identical reference indicates identical part in accompanying drawing.Not deliberately by actual size equal proportion scaling drafting accompanying drawing, emphasis It is the purport for illustrating the present invention.

Fig. 1 shows a kind of structured flowchart of local terminal provided in an embodiment of the present invention；

Fig. 2 shows a kind of flow chart of charging station Optimal Deployment Method provided in an embodiment of the present invention；

Fig. 3 shows the sub-process of step S100 in a kind of charging station Optimal Deployment Method provided in an embodiment of the present invention Figure；

Fig. 4 shows the sub-process of step S200 in a kind of charging station Optimal Deployment Method provided in an embodiment of the present invention Figure；

Fig. 5 shows the sub-process of step S300 in a kind of charging station Optimal Deployment Method provided in an embodiment of the present invention Figure；

Fig. 6 shows the structural representation of planning region in a kind of charging station optimization method provided in an embodiment of the present invention；

Fig. 7 shows a kind of analogous diagram of charging station optimization method provided in an embodiment of the present invention；

Fig. 8 shows that a kind of charging station provided in an embodiment of the present invention optimizes the first structure block diagram of device；

Fig. 9 shows that a kind of charging station provided in an embodiment of the present invention optimizes the second structured flowchart of device；

Figure 10 shows that a kind of charging station provided in an embodiment of the present invention optimizes the 3rd structured flowchart of device.

Icon：100- local terminals；200- charging stations optimize placement device；210- first processing modules；211- inputs are single Unit；212- first processing units；213- second processing units；220- Second processing modules；The processing units of 221- the 3rd；222- Four processing units；The processing modules of 230- the 3rd；240- output modules；101- memorizeies；102- storage controls；103- process Device；104- Peripheral Interfaces；105- input-output units；106- audio units；107- display units.

Specific embodiment

To make purpose, technical scheme and the advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is The a part of embodiment of the present invention, rather than the embodiment of whole.Present invention enforcement generally described and illustrated in accompanying drawing herein The component of example can be arranged and designed with a variety of configurations.

As shown in figure 1, being the block diagram of the local terminal 100.The local terminal 100 includes：Charging station is excellent Change placement device 200, memorizer 101, storage control 102, processor 103, Peripheral Interface 104, input-output unit 105, Audio unit 106, display unit 107.

The memorizer 101, storage control 102, processor 103, Peripheral Interface 104, input-output unit 105, sound Frequency unit 106, each element of display unit 107 are directly or indirectly electrically connected with each other, to realize the transmission or friendship of data Mutually.For example, these elements can be realized being electrically connected with by one or more communication bus or holding wire each other.The charging Optimization placement device 200 of standing can be stored in the memorizer including at least one in the form of software or firmware (firmware) In 101 or the software function module that is solidificated in the operating system of the local terminal 100 (operating system, OS). The processor 103 is used to perform the executable module stored in memorizer 101, such as described charging station optimizes placement device 200 software function modules for including or computer program.

Wherein, memorizer 101 may be, but not limited to, random access memory (Random Access Memory, RAM), read only memory Read Only Memory, ROM), programmable read only memory (Programmable Read-Only Memory, PROM), erasable read-only memory Erasable Programmable Read-Only Memory, EPROM), electricity Erasable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM) etc..Its In, memorizer 101 be used for storage program, the processor 103 after execute instruction is received, perform described program, aforementioned Method performed by the server of the stream process definition that inventive embodiments any embodiment is disclosed can apply to processor 103 In, or realized by processor 103.

A kind of possibly IC chip of processor 103, the disposal ability with signal.Above-mentioned processor 103 can Being general processor, including central processing unit (Central Processing Unit, abbreviation CPU), network processing unit (Network Processor, abbreviation NP) etc.；Can also be digital signal processor (DSP), special IC (ASIC), Ready-made programmable gate array (FPGA) either other PLDs, discrete gate or transistor logic, discrete hard Part component.Can realize or perform disclosed each method in the embodiment of the present invention, step and logic diagram.General processor Can be microprocessor or the processor can also be any conventional processor etc..

The Peripheral Interface 104 is by various input/output devices coupled to processor 103 and memorizer 101.At some In embodiment, Peripheral Interface 104, processor 103 and storage control 102 can be realized in one single chip.Other one In a little examples, they can be realized respectively by independent chip.

Input-output unit 105 is used to be supplied to user input data to realize interacting for user and the local terminal 100. The input-output unit 105 may be, but not limited to, mouse and keyboard etc..

Audio unit 106 provides a user with audio interface, and it may include one or more mikes, one or more raises Sound device and voicefrequency circuit.

Display unit 107 provides interactive interface (such as user operation circle between the local terminal 100 and user Face) or refer to user for display image data.In the present embodiment, the display unit 107 can be liquid crystal display Or touch control display.If touch control display, it can be the capacitance type touch control screen or resistance for supporting single-point and multi-point touch operation Formula touch screen etc..Support that single-point and multi-point touch operation refer to that touch control display can be sensed one on the touch control display Or the touch control operation produced simultaneously at multiple positions, and transfer to processor 103 to carry out calculating the touch control operation for sensing and Process.

Fig. 2 is referred to, is the charging station optimization of the local terminal 100 for being applied to Fig. 1 that present pre-ferred embodiments are provided The flow chart of layout method.In the present embodiment, by the first model to Parameter Decision Making variable by the continuous excellent of preset times Change and update, continue to optimize renewal by preset times of second model to binary decision variable, and Parameter Decision Making variable and two First decision variable influencing each other when updating, so as to finally be obtained in that charging station optimizes layout result, and then can obtain Meet the optimal case of user and investor demand.

Specifically, obtaining the flow process of charging station optimization layout result includes：Step S100, step S200, step S300, step Rapid S400 and step S500.

Step S100：The benchmark data of electric automobile is obtained, filling day in predeterminable area is obtained according to the benchmark data Electric peak power, wherein, the day charging peak power is the parameter in the distribution basic parameter.

Obtain the benchmark data of all kinds of electric automobiles in planning region.Wherein, all kinds of electric automobiles include：Electric Transit Car, electric taxi, electronic private car and electronic officer's car；Benchmark data includes：The quantity of all kinds of electric automobile rows, all kinds of electricity In journey of the electrical automobile row in one day, running time section and all kinds of electric automobile of all kinds of electric automobiles in one day be in one day Charging interval section.The purposes of every kind of electric automobile is different, and its benchmark data is also different.By every kind of electric automobile in benchmark data Data carry out synthesis, then the charging probability, moment probability density function and the duration that obtain electric automobile by probability distribution are general Rate density function.It is according to the preset charged power of default each electric automobile, the charging probability of electric automobile, moment probability is close Degree function and duration probability density function carry out multiple sampling with Monte Carlo Analogue Method, so as to obtain the electricity in planning region The day charging peak power of electrical automobile.And day charging peak power can be used as one of parameter in distribution basic parameter.

Step S200：Distribution basic parameter is obtained, according to the distribution basic parameter and the first constraints first is passed through Model generates the Parameter Decision Making variable of charging station, obtains binary decision by the second model according to the Parameter Decision Making variable and becomes Amount.

Distribution basic parameter is obtained, wherein, distribution basic parameter not only includes day charging peak power, distribution basic parameter Also include：The quantity that the position of charging station can be set up in disjunctive programming region is, the fixed cost and line of setting up charging station The loss on road.In the present embodiment, the first constraints is the basic demand parameter of electrical network, and the charging station of foundation needs to meet electricity The requirement of net, therefore obtain in the calculating process of Parameter Decision Making variable, it is necessary to meet the first constraints.First model is upper The solution that layer model, i.e. upper strata model are obtained can represent the investment optimum results of investor, and Parameter Decision Making variable is in decision One of variable of the solution of layer, it is the variable that charging station whether is set up in each predeterminated position.By distribution basic parameter and First constraints generates the Parameter Decision Making variable of charging station by the first model.As a kind of mode, obtain Parameter Decision Making and become The process of amount can carry out continuing to optimize renewal for Parameter Decision Making variable, therefore, need to be by distribution basic parameter and the first constraint Condition is first randomly generated a Parameter Decision Making variable in the first model.Second model is underlying model, i.e. underlying model institute The solution of acquisition can represent the satisfaction optimum results of user.In the present embodiment, the Parameter Decision Making variable band by generating Enter the second model, and be obtained in that the binary decision variable related to Parameter Decision Making variable.Wherein, binary decision variable represents certain Individual user selects the variable that certain charging station charges.Therefore, by random generation parameter decision variable, and become according to Parameter Decision Making Measure and obtain and determine that the optimization that after binary decision variable, just can be iterated is calculated.

Step S300：The Parameter Decision Making variable and the binary decision variable are updated to according to preset rules default Number of times, wherein, the preset rules include：The binary according to the Parameter Decision Making variable is by second model modification is determined Plan variable, further according to the binary decision variable and first constraints for updating by described in first model modification Parameter Decision Making variable.

After get parms decision variable and binary decision variable, can be by Parameter Decision Making variable and binary decision variable root Preset times are updated to according to preset rules, to obtain stable Parameter Decision Making variable and binary decision variable.In the present embodiment In, preset rules can be：According to the first constraints and the binary decision variable for obtaining first by the first model so as to more New parameter decision variable, and again the rear Parameter Decision Making variable for updating is updated into again binary decision variable by the second model.Will Parameter Decision Making variable is once updated to the suboptimization to Parameter Decision Making variable by the first model, and by binary decision variable Also it is the suboptimization to binary decision variable by once updating for the second model.By and binary logical to Parameter Decision Making variable Decision variable constantly updates, after close preset times, each renewal and binary of the Parameter Decision Making variable by the first model Decision variable is tended towards stability by each renewal of the second model, then after satisfaction is updated to preset times, stopped to parameter The renewal of decision variable and binary decision variable.

Step S400：The Parameter Decision Making variable of preset times and described the two of preset times are updated to according to being updated to First decision variable obtains investment optimum results by first model, is become according to the binary decision for being updated to preset times Amount obtains satisfaction optimum results by second model.

The Parameter Decision Making variable for being updated to preset times and the binary decision variable for being updated to preset times can tend to It is stable, it is believed that Parameter Decision Making variable and binary decision variable are changed into optimum state by the renewal of preset times.First In model, Parameter Decision Making variable and binary decision variable are the variable for obtaining investment optimum results.After preset times, will be true Fixed Parameter Decision Making variable and binary decision variable is brought in the first model, and the first model just can export investment optimum results. In second model, binary decision variable is the variable for obtaining satisfaction optimum results, after preset times, will determine binary decision Variable is brought in the second model, and the second model just can export satisfaction optimum results.

Step S500：Charging station optimization layout knot is obtained according to the investment optimum results and the satisfaction optimum results Really.

After investment optimum results and satisfaction optimum results are obtained, due in the first model and the second model calculation Influence each other, investing optimum results can represent the prime investment realization side of the investors' interest in the case where user's service condition is met Formula, and satisfaction optimum results can represent the optimal use satisfaction of the user in the case of investors' interest.So as to throw Money optimum results and satisfaction optimum results carry out collecting after output, and the result of output is has taken into account investment interests and user's profit The charging station optimization layout result of benefit.

Fig. 3 is referred to, Fig. 3 is the sub-process of the step of obtaining day charging peak power S100.It is multiple general by setting up Rate is distributed so as to obtain day charging peak power.

Specifically, the sub-process by setting up multiple probability distribution so as to obtain day charging peak power includes： Step S110, step S120, step S130 and step S140.

Step S110：According to the benchmark data obtain predeterminable area in the electric automobile day operating range away from From normal distribution and the moment probability density function for starting to charge up the moment.

Benchmark data may include：In the journey of the quantity of all kinds of electric automobile rows, all kinds of electric automobile rows in one day, it is all kinds of Charging interval section of running time section and all kinds of electric automobile of the electric automobile in one day in one day.Specifically, electronic public affairs It is 150~200km to hand over the average daily distance travelled of car predeterminable, if maintaining one day Operational requirements at least must once be charged.It is electronic Buses service time and place Relatively centralized, can carry out concentration charging in the low power consumption period, in the peak operation period on daytime (10:30-16:00) fast charge mode, night (22 can be adopted:30-5:30) down time length can adopt trickle charge mode.

For electric taxi is with respect to electric bus, without fixed vehicle line, in the time, spatially have it is stronger with Machine.The average daily distance travelled of electric taxi is 350~500km, is typically replaced by large and small class's mode by 2 taxi drivers Drive, top class in a kindergarten driver once class per 24 hours, bottom class driver once class per 12 hours.Predeterminable electric taxi it is specified Distance travelled is about 300km, is to maintain the requirement of taxi normal operation to be charged at least twice daily.In view of taxi Operation benefits, typically can select to be charged in the period relieved, daytime (11:30-14:30) quick charge side can be adopted Formula, night (2:00-5:00) can be using charging modes at a slow speed.

Electronic private car is mainly used in the upper and lower class of car owner and amusement and recreation etc..Electronic officer's car is mainly used in government official Member exercises a public function.Idle condition is in view of the most of the time in electronic private car and electronic officer's car one day, can be by it It is classified as a class.When private car car owner and government functionary are not used automobile, can be by automobile parking to concentrated charging station using slow Fast charging modes carry out electricity supply.

By each benchmark data, so as to can obtain electric automobile in predeterminable area day operating range apart from normal state point Cloth：

Wherein, d represents the day operating range of electric automobile；μ 1 and σ 1 is respectively the day of electric bus and electric taxi Operating range is expected and standard deviation；μ 2 and σ 2 is respectively electronic private car and expects and standard with the day operating range of electronic officer's car Difference.

Furthermore, according to the charging interval distribution of each electric automobile in said reference data, so as to obtain in predeterminable area Electric automobile starts to charge up the moment probability density function at moment.

Wherein, μ 3 and σ 3 is expectation and the standard deviation for starting to charge up the moment.

Step S120：The charging electric vehicle duration is obtained according to described apart from normal distribution and preset charged power Duration probability density function.

In the present embodiment, preset various electric automobiles charge power meet in the range of 2 to 3KW be uniformly distributed for Preset charged power.If meet electric automobile day operating range variable and charge power variable it is separate, so as to can To obtain charging interval length t of electric automobile_cIt is expressed as：

f_pc(p_c)=1 2≤P_c≤ 3 or f_pc(p_c)=0

Wherein, ω_dRepresent the power consumption of the 100KM of electric automobile；η represents the charge efficiency of charger；P_cRepresent charger Charge power.

By length t_cFunction and apart from normal distribution can obtain charging electric vehicle duration duration probability it is close Degree function：

Wherein, f₃(t_c) represent the charging duration probability density function of electric bus and electric taxi, f₄(t_c) represent The charging duration probability density function of electronic private car and electronic officer's car, and a in formula represents ω_d/1.61×η。

Step S130：The duration probability density function is multiplied with the moment probability density function and obtains described electronic Charging probability of certain moment in charged state in car day.

As a kind of mode, if the charging start time of electric automobile and charging interval length are also separate.And one Electric automobile can be divided into just in charged state and uncharged state, state X of its certain moment t in one day in the state of a day_t, X_t=1 is and the X just in charged state_t=0 is uncharged state.Such that it is able to obtain the probability P of electric automobile uncharged state (X_t=0) and electric automobile just in charged state just in the probability P (X of charged state_t=1), it is represented by：

P(X_t=0)=F₃₄(t<t_b,t_b+t_c≤t+24)+

F₃₄(t_b+t_c≤t)

P(X_t=1)=1-P (X_t=0)

In formula, F₃₄Represent the joint probability distribution function of charge start time and charging interval length, you can think electronic Charging probability of certain moment in charged state in car day.As a kind of mode, F₃₄=F₃×F₄, wherein, F₃Represent split The moment probability density function at moment of beginning to charge is integrated and obtains the moment probability-distribution function that starts to charge up the moment, and F₄ Then represent the duration probability density function to charging duration to be integrated and obtain the duration probability-distribution function of charging duration.Will F₃And F₄Probability multiplication superposition, be obtained in that electric automobile in one day certain moment in charged state charging probability.

Step S140：According to the charging probability, the moment probability density function, the preset charged power and described Duration probability density function carries out multiple sampling and obtains the day charging peak power in predeterminable area, wherein, the day Charging peak power is the parameter in the distribution basic parameter.

In the present embodiment, according to the preset charged power of default each electric automobile, by the charging probability of electric automobile, One charging electric vehicle of each moment is needed during moment probability density function and duration probability density function simultaneous get up to obtain one day Seek the probability distribution of power.Wherein, multiple sampling solution is carried out by Monte Carlo Analogue Method.By many to an electric automobile Secondary sampling obtains the charge power of sampling every time, then the charge power that multiple sampling is obtained is carried out averagely such that it is able to obtain The charge power demand of one electric automobile in one day.According in planning region, the quantity of default electric automobile is multiplied by one Charge power demand of the electric automobile in one day such that it is able to obtain the default day for obtaining the electric automobile in planning region Charging peak power.And day charging peak power can be used as one of parameter in distribution basic parameter.

Fig. 4 is referred to, Fig. 4 shows the subflow of the step of getting parms decision variable and binary decision variable first S200 Journey.By the particle cluster algorithm in the quantum genetic algorithm in the first model and the second model can get parms first decision-making become Amount and binary decision variable.

Specifically, can be obtained first by the particle cluster algorithm in the quantum genetic algorithm in the first model and the second model Taking the sub-process of Parameter Decision Making variable and binary decision variable includes：Step S210, step S220 and step S230.

Step S210：Obtain the distribution basic parameter.

Distribution basic parameter is obtained, wherein, distribution basic parameter not only includes day charging peak power, distribution basic parameter Also include：The quantity that the position of charging station can be set up in disjunctive programming region is, the fixed cost and line of setting up charging station The loss on road.

Step S220：According to the distribution basic parameter and first constraints by first model with quantum Genetic algorithm generates the Parameter Decision Making variable of charging station.

First model is upper layer model, and upper layer model can be used in the investment amount of optimization of investment enterprise.To charging station During planning, investment enterprise should consider to cause power network line during the Construction and operation cost of newly-built charging station and charging station operation The cost for being lost and paying, considers again the time-consuming cost that the client of enterprise produces in charging process.Charger is that charging station is consolidated The deciding factor of fixed investment, charger quantity is more, and the vehicle of service is more, and floor space is bigger, corresponding soil purchase The investment for being set to this and other auxiliary equipment is also bigger, and operation expense is also bigger.Using Construction and operation cost as charging The function of machine quantity.Therefore, business investor will be minimised as object function and sets up upper strata mould with the year cost of investment of charging station Type, and under the constraint of the constraints of charging station capacity, electric network swim and investment budgey etc. first on layer model target letter Number is solved.Wherein, upper layer model is represented by：

Min C=C₁+C₂+C₃

Wherein, δ i represent Parameter Decision Making variable, λ ij represent binary decision variable, minC represent the gross investment of charging station into Originally, C₁Represent charging station year Construction and operation cost, C₂Annual via net loss cost, the C of Utilities Electric Co. is paid in expression₃Represent and use Family charging behavior time-consuming cost, n_iRepresent charger quantity, f in i-th charging station_i(n_i) represent that the year of i-th charging station builds Cost, g_i(n_i) represent i-th charging station annual operating and maintenance cost, r₀Represent that discount rate, τ represent that the operation time limit, W represent charging station Fixed cost；q₁Represent equivalent factors of investment, the q of charger unit price₂Represent equivalent factors of investment, the T of charger quantity_yearTable Show that the natural law of 1 year, e represent circuit identity network cost depletions, Δ P_k,iRepresent that i-th charging station causes in kth bar feeder line 1 day Active power loss, M represent 1 day in certain number of users, the t that charge at moment_ijRepresent that j-th user is travelled to i-th from demand point Desired summation of interior charging interval, C are expected and stood to the running time of seat charging station₀Represent long-run cost rate.

In the present embodiment, upper layer model can carry out meeting the computing of the first constraints.First constraints is electricity The basic demand parameter of net, the charging station of foundation needs the requirement for meeting electrical network, therefore obtains the computing in Parameter Decision Making variable During, it is necessary to meet the first constraints.First constraints is represented by：

V_{a min}≤V_a≤V_{a max} a∈A

|I_ab|≤I_{ab max} a,b∈A

C≤C_all

Wherein, P_maxFor maximum charge power, V that power distribution network allows to access_aFor the voltage amplitude of urban power distribution network interior joint a Value, Vamin and Vamax are respectively the upper and lower bound of node a voltage magnitudes, I_abAnd I_abmaxAb feeder lines respectively in power distribution network Actual current and the feeder line maximum current, the C that allow to flow through_allFor concentrated charging station overall cost of ownership budget, N_maxTo concentrate The first formula in maximum, first constraints of the newly-built quantity of type charging station represents day charging peak power no more than electrical network The second formula in allowable value, the first constraints represent charging station access that point voltage is out-of-limit, in the first constraints the Three formulas represent the electric current for flowing through feeder line ab less than the 4th formula expression cost of investment in allowable value, the first constraints Represent the newly-built charging station quantity of enterprise less than the given upper limit less than the 5th formula in given budget, the first constraints.

After distribution basic parameter is obtained, distribution basic parameter is corresponded in upper layer model as preset parameter.It is logical Cross variable of the quantum genetic algorithm in the case where the first constraints is met in layer model to solve.Quantum genetic is calculated Method has the characteristic continued to optimize, so as to upper layer model with quantum genetic algorithm can generate charging station position and its capacity, Charger quantity.Due to also needing follow-up continuation to optimize, the capacity and charger quantity of the charging station for now generating do not do into The process of one step.And the position for generating charging station is the variable that charging station whether is set up in each predeterminated position, as Parameter Decision Making Variable.Parameter Decision Making variable is also a variable in the second model, is entered so as to bring Parameter Decision Making variable into second model Row is solved.It should be noted that the Parameter Decision Making variable that upper layer model is generated for the first time is random generation.

Step S230：According to the Parameter Decision Making variable and the second constraints by second model with population calculation Method obtains the binary decision variable.

In the present embodiment, the second model is upper layer model, and underlying model can be used in optimizing the satisfaction of user.As A kind of mode, different according to the experience of the time of user, user is consuming or is receiving to produce satisfaction in various degree in service process Phenomenon is referred to as Time-satisfaction degree.The time experience of user is an extremely complex process, provided that service time meet use Expect that user's performance is satisfied with attitude, can otherwise show dissatisfied attitude in family.The present embodiment is by user satisfaction to charge user Experiencing the satisfactory level of charging service carries out unifying to portray, and represents that unit charges using the improved function of flexure (Sigmoid) User satisfaction S (t under power_ij), it is represented by：

Wherein, S (t_ij) represent user satisfaction, max T whole user satisfaction.

And according to the improved function of flexure, the underlying model that target is to the maximum with user satisfaction T can be set up, it can be represented For：

Wherein, λ ij represent the binary decision variable, t_ijRepresent that j-th user is travelled to i-th charging station from demand point Running time expect and stand in the charging interval desired summation, P_jFor the charge volume of j-th user.

In underlying model, underlying model needs the computing for carrying out meet the constraint condition.Its constraints is the second constraint Condition.Specifically, the second constraints is represented by：

λ_ij-δ_i≤0

In formula, P_{i max}Charge user is represented for the first formula in i-th charging station maximum charge power, the second constraints The second formula in charging station charging, second constraints can only be selected to represent that j-th charge user selects i-th charging station The premise being charged is exactly that the 3rd formula that must be set up in i-th charging station, the second constraints represents j-th charge user The charge power that i-th charging station is charged is selected no more than the maximum charge power that i-th charging station is provided.

Parameter Decision Making variable is a variable in underlying model, Parameter Decision Making variable is brought into after underlying model, lower floor Model obtains binary decision variable λ ij by particle cluster algorithm according to Parameter Decision Making variable.Specifically, in particle cluster algorithm In, each is with tool per family it is considered that being a particle, the speed of user's traveling is the speed of the particle, by speed Particle continuous computing such that it is able to obtain optimum binary decision variable λ ij, i.e. certain user selects certain charging station to fill The result of electricity.

Refer to Fig. 5, Fig. 5 show Parameter Decision Making variable and binary decision variable are updated to according to preset rules it is pre- If the sub-process of number of times.By according to preset rules undated parameter decision variable and binary decision variable to preset times, to obtain Parameter Decision Making variable that must be stable and binary decision variable.

Specifically, by according to preset rules undated parameter decision variable and binary decision variable to preset times, to obtain The step of Parameter Decision Making variable that must be stable and binary decision variable, includes：Step S310 and step S320.

Step S310：The Parameter Decision Making variable and the binary decision variable are updated to according to preset rules default Number of times, wherein, the preset rules include：Second model is passed through according to the second constraints and the Parameter Decision Making variable The binary decision variable is updated with particle cluster algorithm, and according to the binary decision variable for updating and the first constraint bar Part updates the Parameter Decision Making variable by first model with quantum genetic algorithm.

When acquisition once gets Parameter Decision Making variable by upper layer model with quantum genetic algorithm, if according to the parameter Upper strata model solution is obtained investment optimum results by decision variable, then the investment optimum results may be unsatisfactory for the need of user Ask.And when binary decision variable is once got with particle cluster algorithm by underlying model, if according to the binary decision variable Underlying model is solved and satisfaction optimum results are obtained, then the satisfaction optimum results may be unsatisfactory for the investment need of enterprise Ask.Accordingly, it would be desirable to be calculated with population by Parameter Decision Making variable and by underlying model with quantum genetic algorithm by upper layer model Method binary decision variable is updated to preset times according to preset rules.

Starting for preset rules is after the layer model on gets parms decision variable, by Parameter Decision Making variable and binary Decision variable updates once.Specifically, Parameter Decision Making variable is got in the case where the first constraints is met by upper layer model Afterwards, the decision variable that will get parms is brought into underlying model, so as to meet the second constraints under so that binary decision variable Updated.During due to upper layer model generation parameter decision variable, upper layer model make a living into Parameter Decision Making variable and generate two The binary decision variable that obtained by Parameter Decision Making variable in first decision variable and underlying model is simultaneously differed.So as to again by lower floor The binary decision variable that model is obtained brings layer model into, so that the Parameter Decision Making variable in upper layer model is updated, and then Parameter Decision Making variable and binary decision variable are once updated in preset rules.

Furthermore, its binary decision variable is updated by bringing the Parameter Decision Making variable that upper layer model is obtained into underlying model, Its binary decision variable will be updated again brings layer model undated parameter decision variable into.So as to after constantly updating, enable to The optimal solution of upper layer model and the optimal solution of underlying model are close to each other.In the present embodiment, preset times are 100 times.Join Number decision variable and binary decision variable update 100 times.Parameter Decision Making variable and binary decision variable update preset times energy Enough regional stabilities, i.e., after preset times are updated, according to the Parameter Decision Making variable and renewal preset times that update after preset times Upper strata model solution is obtained binary decision variable afterwards investment optimum results can most meet the same of business investor demand When also meet the demand of user.And underlying model is solved according to the binary decision variable updated after preset times and is satisfied with Degree optimum results also comply with the interests of business investor while the demand of user is met.

Step S320：When the Parameter Decision Making variable and the binary decision variable are updated to according to the preset rules During preset times, judgement meets termination condition.

When parameter decision variable and binary decision variable are updated to preset times according to preset rules, i.e., update 100 After secondary.Parameter Decision Making variable and binary decision variable now tends towards stability, so as to judge that the process for updating iteration terminates.

Fig. 6 is referred to, for the implementation procedure of said method, below this is described in detail with a specific embodiment Bright method.

Setting planning region area is about as a example by certain area of city of 150km2 and is charged station optimization layout, and planning region shows It is intended to as shown in fig. 6, business investor can arrange charging station, and each charging station in 6 predeterminated positions (being represented with circle) Usable area it is unfettered.The automobile pollution in default planning level year is about 200,000, and wherein the ratio of electric automobile is about For 10%, electric bus, electric taxi, electronic private car and electronic officer's car percentage are respectively 6%, 13%, 79% and 2%.

The benchmark data and distribution basic parameter of the electric automobile in acquisition planning region is as shown in the table：

μ1	σ1	μ2	σ2	μ3	σ3
						155.02	41.53	3.20	0.88	17.6	3.4
Nmin	Nmax	ωl	η	r0	τ
						5	15	0.15kw*h/km	0.9	0.07	1.0 yuan/kw
e1	e2	Tyear	A	c0
						100000 yuan	30000 yuan/platform * platforms	365 days	2000000 yuan	20 yuan/h

Charging probability, moment probability density function, preset charged power and duration probability density according to each electric automobile Function, carries out multiple sampling and obtains day charging peak power 12.5MW obtained in predeterminable area by Monte Carlo Analogue Method. Default business investor provides the user charging service can set up 3 to 5 charging stations in predeterminated position.According to acquired day Charging peak power 12.5MW, and by above-mentioned upper layer model and underlying model, using quantum genetic algorithm and population Algorithm updates iteration 100 times according to preset rules, obtains shown in two electric automobile charging station program results following tables (in bracket Numeral configures quantity for charger)：

Scheme	δi(Ni/ platform)	Ten thousand yuan of C/	Satisfaction
				1	1(10)、5(9)、6(9)	674.22	56
2	2(7)、4(7)、5(8)、6(8)	722.17	79

Understand that scheme 1 proposes that the cost of investment of newly-built 3 concentrated charging stations is proposed newly than scheme 2 refering to data in table Build low during 4 charging stations, but the satisfaction of charge user is substantially low than scheme 2.The position word of charging station is set in scheme 2 Mother will be marked in Fig. 5, would know that 4 charging station locations are distributed evenly in planning region refering to Fig. 5, although charging station construction Cost increased about 7.11%, but charge user satisfaction greatly improves about 41.07%.

In the case that in charging station year, overall cost of ownership is more or less the same, how newly-built a business investor charging station be more excellent Gesture and adaptability.For example, charge user easily reaches charging station in planning region, has saved user time cost depletions, can More consumer's purchase enterprise electric automobiles are encouraged, and then improves enterprise competitiveness；The configuration capacity of each charging station is reduced, just In the dilatation demand for meeting charging load growth in future.

Furthermore, go up used in the charging station Optimal Deployment Method that provides of the present embodiment layer model and lower floor's mould to verify again The advantage of type, respectively the single level programming model with cost of investment and user satisfaction as object function be analyzed：1. will charge The cost of investment stood is added in Prescribed Properties, and with charge user Maximum Satisfaction as object function as constraints, Set up the model 1 of charging station single level programming model；2. Constrained bar is added using the satisfaction of charge user as constraints In part, and with the minimum object function of charging station cost of investment, set up the model 2 of charging station single level programming model.

Two above single-layer model is equally using the basic parameter in above-mentioned specific embodiment, the constraints of model 1 It is that the cost of investment of business investor is not higher than 7,000,000 yuan, the constraints of model 2 is that the satisfaction of user is not less than 90 lists Position.Two above model is solved using quantum genetic algorithm, its result is as shown in the table：

Scene	δi(Ni/ platform)	Ten thousand yuan of C/	Satisfaction
				1	1(9)、5(9)、6(9)	686.53	63
2	2(7)、4(7)、7(7)、8(7)	742.36	94

From the data result in upper table, though scene 1 can guarantee that cost of investment is minimum, user satisfaction is relatively It is little；Scene 2 can guarantee that user satisfaction reaches maximum (lifting about 49.21% relative to scene 1), but cost of investment increases (increasing Plus about 8.13%), run counter to business investor theory of investment.Compare with the scheme 2 in above-mentioned specific embodiment, by upper Coupling Decision-making Function in layer model and underlying model between different policymaker, both can guarantee that company investment cost was in reasonable water It is flat, also take into account the comfort level of Consumer's Experience, i.e. enterprise and the respective interests of user have obtained equilibrium.

Refer to Fig. 7, in Fig. 7 solid line A be user satisfaction, dotted line B be company investment cost, X be enterprise's gross investment into This (ten thousand yuan), Y are fixed cost (ten thousand yuan), Z is user satisfaction.

As a kind of mode, for business investor, it is ensured that enterprise getting profit and enhancing enterprise market occupation rate are most Whole purpose.The newly-built concentrated charging station of enterprise can not only strengthen the desire that consumer buys electric automobile, moreover it is possible to rely on charging station Charge and obtain operation benefits.The initial stage is promoted in electric automobile, newly-built charging station early stage need to put into substantial amounts of fund, if enterprise's urgency In cost of recouping capital outlay, high charging service expense, and then the development of obstruction electric automobile can be collected, therefore for enterprise comes Say that control charging station cost of investment is enterprises pay attention emphasis.Charging station year, Construction and operation cost proportion was heavier, and its middle age transports Row cost is substantially more or less the same, and fixed cost and equivalent cost of investment can be according to enterprise practical situations in year construction cost Or program budget determines that is, year construction cost is variable.Therefore, by obtaining different fixed costs to final in the present embodiment The impact of charging station program results is as shown in Figure 7.

By obtaining refering to Fig. 7, the increase of concentrated charging station fixed cost can be such that charging station overall cost of ownership constantly increases Plus, charge user satisfaction is also gradually lifted.In other words, enterprise can pass through the investment for increasing charging station (as increased charger number Amount, update charging technique etc.) improving charging station efficiency of service, and then increase automobile user and charge the satisfaction of experience, The consumer around existing automobile user can also be stimulated to buy the desire of electric automobile.

Refer to Fig. 7, Fig. 7 is to apply the charging station in the local terminal 100 shown in Fig. 1 to optimize the of placement device 200 One structured flowchart.The charging station optimization placement device 200 includes：At first processing module 210, Second processing module the 220, the 3rd Reason module 230 and output module 240.

First processing module 210, for obtaining distribution basic parameter, according to the distribution basic parameter and the first constraint bar Part generates the Parameter Decision Making variable of charging station by the first model, and two are obtained by the second model according to the Parameter Decision Making variable First decision variable.

Second processing module 220, for by the Parameter Decision Making variable and the binary decision variable according to preset rules Preset times are updated to, wherein, the preset rules include：Second model is passed through more according to the Parameter Decision Making variable The new binary decision variable, further according to the binary decision variable and first constraints for updating described first is passed through Parameter Decision Making variable described in model modification.

3rd processing module 230, is updated to the Parameter Decision Making variable of preset times and is updated to default for basis The binary decision variable of number of times obtains investment optimum results by first model, according to the institute for being updated to preset times State binary decision variable and satisfaction optimum results are obtained by second model.

Output module 240；Charging station optimization cloth is obtained according to the investment optimum results and the satisfaction optimum results Office's result.

Refer to Fig. 8, Fig. 8 is to apply the charging station in the local terminal 100 shown in Fig. 1 to optimize the of placement device 200 Two structured flowcharts.Wherein, first processing module 210 includes：Input block 211, first processing units 212 and second processing unit 213。

Input block 211, for obtaining the distribution basic parameter.

First processing units 212, for according to the distribution basic parameter and first constraints by described the One model generates the Parameter Decision Making variable of charging station with quantum genetic algorithm.

Second processing unit 213, for passing through second mould according to the Parameter Decision Making variable and the second constraints Type obtains the binary decision variable with particle cluster algorithm.

Refer to Fig. 9, Fig. 9 is to apply the charging station in the local terminal 100 shown in Fig. 1 to optimize the of placement device 200 Two structured flowcharts.Wherein, Second processing module 220 includes：3rd processing unit 221 and fourth processing unit 222.

3rd processing unit 221, for the Parameter Decision Making variable and the binary decision variable to be preset according to described Rule is updated to preset times, wherein, the preset rules include：According to the second constraints and the Parameter Decision Making variable The binary decision variable is updated with the particle cluster algorithm by second model, and according to the binary decision for updating Variable and first constraints update the Parameter Decision Making variable by first model with the quantum genetic algorithm User.

Fourth processing unit 222, for when the Parameter Decision Making variable and the binary decision variable are according to described default When rule is updated to preset times, judgement meets termination condition.

It should be noted that being convenience and the letter of description because those skilled in the art can be understood that Clean, the specific work process of the system, device and unit of foregoing description may be referred to corresponding in preceding method embodiment Journey, will not be described here.

In sum, a kind of charging station Optimal Deployment Method and device are being embodiments provided, in the charging station In Optimal Deployment Method, by obtaining input distribution basic parameter, and distribution basic parameter and the first constraints are passed through First model generates the Parameter Decision Making variable of charging station such that it is able to obtain meeting distribution basic parameter and the first constraints The construction quantity of charging station and corresponding construction position.And then can by bringing the second model into according to Parameter Decision Making variable The binary decision variable which charging station certain user selects is obtained by the second model.Parameter Decision Making variable and binary are determined Plan variable is updated to preset times according to preset rules, to obtain stable Parameter Decision Making variable and binary decision variable.Its In, preset rules include：According to binary decision variable and the first constraints by the first model modification Parameter Decision Making variable, then The Parameter Decision Making variable for updating is passed through into the second model modification binary decision variable.By Parameter Decision Making variable by the first model The suboptimization to Parameter Decision Making variable is once updated to, and is also by the once renewal that binary decision variable passes through the second model A suboptimization to binary decision variable.Constantly updated by and binary decision variable logical to Parameter Decision Making variable, be close to After preset times, Parameter Decision Making variable passes through each renewal of the first model and binary decision variable by each of the second model Renewal tends towards stability, then after satisfaction is updated to preset times, stop to Parameter Decision Making variable and binary decision variable more Newly.Furthermore, according to the Parameter Decision Making variable that is updated to preset times and the binary decision variable for being updated to preset times by the One model obtains investment optimum results, and obtains satisfaction by the second model according to the binary decision variable for being updated to preset times Degree optimum results.Due to investing optimum results and satisfaction optimum results to continue to optimize renewal, and influence each other and obtain most Excellent structure, so as to charging station optimization layout result just can be obtained according to investment optimum results and satisfaction optimization.By first Continue to optimize renewal by preset times of the model to Parameter Decision Making variable, the second model is to binary decision variable by default time Several continues to optimize renewal, and Parameter Decision Making variable and binary decision variable influencing each other when updating, so as to final energy Charging station optimization layout result is enough obtained, and then the optimal case for meeting user and investor demand can be obtained.

Claims (10)

1. a kind of charging station Optimal Deployment Method, it is characterised in that methods described includes：

Distribution basic parameter is obtained, charging station is generated by the first model according to the distribution basic parameter and the first constraints Parameter Decision Making variable, according to the Parameter Decision Making variable by the second model obtain binary decision variable；

The Parameter Decision Making variable and the binary decision variable are updated to into preset times according to preset rules, wherein, institute Stating preset rules includes：The binary decision variable according to the Parameter Decision Making variable passes through second model modification, then root Become by Parameter Decision Making described in first model modification according to the binary decision variable and first constraints that update Amount；

Led to the binary decision variable for being updated to preset times according to the Parameter Decision Making variable for being updated to preset times Cross first model and obtain investment optimum results, according to being updated to the binary decision variable of preset times by described the Two models obtain satisfaction optimum results；

Charging station optimization layout result is obtained according to the investment optimum results and the satisfaction optimum results.

2. charging station Optimal Deployment Method according to claim 1, it is characterised in that obtain distribution basic parameter, according to The distribution basic parameter and the first constraints generate the Parameter Decision Making variable of charging station by the first model, according to the ginseng The step of number decision variable obtains binary decision variable by the second model, including：

Obtain the distribution basic parameter；

According to the distribution basic parameter and first constraints by first model with quantum genetic algorithm generation The Parameter Decision Making variable of charging station；

Described two are obtained according to the Parameter Decision Making variable and the second constraints with particle cluster algorithm by second model First decision variable.

3. charging station Optimal Deployment Method according to claim 2, it is characterised in that by the Parameter Decision Making variable and institute State binary decision variable and be updated to preset times according to preset rules, wherein, the preset rules include：According to the parameter Decision variable is by binary decision variable described in second model modification, the binary decision variable and institute further according to renewal The first constraints is stated by described in first model modification the step of Parameter Decision Making variable, including：

The Parameter Decision Making variable and the binary decision variable are updated to into preset times according to the preset rules, its In, the preset rules include：According to the second constraints and the Parameter Decision Making variable by second model with described Particle cluster algorithm updates the binary decision variable, and according to the binary decision variable and first constraints for updating The Parameter Decision Making variable user is updated with the quantum genetic algorithm by first model；

When the Parameter Decision Making variable and the binary decision variable are updated to preset times according to the preset rules, sentence Surely termination condition is met.

4. charging station Optimal Deployment Method according to claim 1, it is characterised in that obtaining distribution basic parameter, root The Parameter Decision Making variable of charging station is generated by the first model according to the distribution basic parameter and the first constraints, according to described Before the step of Parameter Decision Making variable obtains binary decision variable by the second model, also include：

The benchmark data of electric automobile is obtained, the day charging peak power in predeterminable area is obtained according to the benchmark data, its In, the day charging peak power is the parameter in the distribution basic parameter.

5. charging station Optimal Deployment Method according to claim 4, it is characterised in that obtain the base value of electric automobile According to, the day charging peak power in predeterminable area is obtained according to the benchmark data, wherein, the day charging peak power is institute The step of stating the parameter in distribution basic parameter includes：

According to the benchmark data obtain the electric automobile in predeterminable area day operating range apart from normal distribution and Start to charge up the moment probability density function at moment；

According to the duration probability density that the charging electric vehicle duration is obtained apart from normal distribution and preset charged power Function；

The duration probability density function is multiplied certain in the acquisition electric automobile one day with the moment probability density function Charging probability of the moment in charged state；

According to the charging probability, the moment probability density function, the preset charged power and the duration probability density Function carries out multiple sampling and obtains the day charging peak power in predeterminable area, wherein, the day charging peak power For the parameter in the distribution basic parameter.

6. charging station Optimal Deployment Method according to claim 1, it is characterised in that first model is：

Min C=C₁+C₂+C₃

$\left\{\begin{array}{c}{C}_1=\underset{i=1}{\overset{N}{\&Sigma;}}{\mathrm{\&delta;}}_i\&lsqb;f_i\left(n_i\right)\frac{r_0{(1+r_0)}^{\mathrm{\&tau;}}}{{(1+r_0)}^{\mathrm{\&tau;}}-1}+g_i\left(n_i\right)\&rsqb;\\ f_i\left(n_i\right)={\mathrm{\&delta;}}_i(W+q_1{n}_i+q_2{n}_i^2)\end{array}\right.$

$C_2=T_{year}\underset{i=1}{\overset{N}{\&Sigma;}}(e\&CenterDot;\underset{k=1}{\overset{K}{\&Sigma;}}{\mathrm{\&Delta;P}}_{k,i})$

$C_3=\underset{i=1}{\overset{N}{\&Sigma;}}\underset{j=1}{\overset{M}{\&Sigma;}}{\mathrm{\&lambda;}}_{ij}{t}_{ij}{c}_0$

Wherein, δ i represent that the Parameter Decision Making variable, λ ij represent that the binary decision variable, minC represent total throwing of charging station Money cost, C₁Represent charging station year Construction and operation cost, C₂Annual via net loss cost, the C of Utilities Electric Co. is paid in expression₃Table Show that user's charging behavior takes cost, n_iRepresent charger quantity, f in i-th charging station_i(n_i) represent i-th charging station year Construction cost, g_i(n_i) represent i-th charging station annual operating and maintenance cost, r₀Represent that discount rate, τ represent that the operation time limit, W are represented and filled Power station fixed cost；q₁Represent equivalent factors of investment, the q of charger unit price₂The equivalent factors of investment of expression charger quantity, T_yearRepresent that the natural law of 1 year, e represent circuit identity network cost depletions, Δ P_k,iRepresent that i-th charging station causes kth bar feeder line Certain moment charges during active power loss, M are represented 1 day in 1 day number of users, t_ijRepresent that j-th user travels from demand point Desired summation of interior charging interval, C are expected and stood to running time to i-th charging station₀Represent long-run cost rate.

7. charging station Optimal Deployment Method according to claim 1, it is characterised in that second model is：

$S\left(t_{ij}\right)=\frac{2}{1+e^{-t_{ij}}}-1$

$\begin{array}{cc}\max & T=\underset{i=1}{\overset{N}{\&Sigma;}}\underset{j=1}{\overset{M}{\&Sigma;}}{\mathrm{\&lambda;}}_{ij}\&CenterDot;S\left(t_{ij}\right)\&CenterDot;p_j\end{array}$

Wherein, S (t_ij) represent that user satisfaction, max T whole user satisfaction, λ ij represent the binary decision variable, t_ijTable Show that j-th user travels to the running time of i-th charging station expect and stand desired summation of interior charging interval, P from demand point_j For the charge volume of j-th user.

8. a kind of charging station optimizes placement device, it is characterised in that described device includes：

First processing module, for obtaining distribution basic parameter, passes through according to the distribution basic parameter and the first constraints First model generates the Parameter Decision Making variable of charging station, and binary decision is obtained by the second model according to the Parameter Decision Making variable Variable；

Second processing module, for the Parameter Decision Making variable and the binary decision variable to be updated to according to preset rules Preset times, wherein, the preset rules include：According to the Parameter Decision Making variable by described in second model modification two First decision variable, further according to the binary decision variable and first constraints for updating first model modification is passed through The Parameter Decision Making variable；

3rd processing module, is updated to the Parameter Decision Making variable of preset times and is updated to the institute of preset times for basis State binary decision variable and investment optimum results are obtained by first model, determined according to the binary for being updated to preset times Plan variable obtains satisfaction optimum results by second model；

Output module；Charging station optimization layout result is obtained according to the investment optimum results and the satisfaction optimum results.

9. charging station according to claim 8 optimizes placement device, it is characterised in that the first processing module includes：

Input block, for obtaining the distribution basic parameter；

First processing units, for according to the distribution basic parameter and first constraints by first model with Quantum genetic algorithm generates the Parameter Decision Making variable of charging station；

Second processing unit, for passing through second model with particle according to the Parameter Decision Making variable and the second constraints Group's algorithm obtains the binary decision variable.

10. charging station according to claim 9 optimizes placement device, it is characterised in that the Second processing module includes：

3rd processing unit, for by the Parameter Decision Making variable and the binary decision variable according to the preset rules more Newly to preset times, wherein, the preset rules include：According to the second constraints and the Parameter Decision Making variable by described Second model updates the binary decision variable with the particle cluster algorithm, and according to the binary decision variable and institute for updating State the first constraints and the Parameter Decision Making variable user is updated with the quantum genetic algorithm by first model；

Fourth processing unit, for when the Parameter Decision Making variable and the binary decision variable according to the preset rules more When newly to preset times, judgement meets termination condition.