CN109800917A - A kind of planing method in electric car parking lot, device and calculate equipment - Google Patents
A kind of planing method in electric car parking lot, device and calculate equipment Download PDFInfo
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Abstract
The invention discloses a kind of planing methods in electric car parking lot, suitable for being executed in calculating equipment, this method comprises: building degree of regretting Mechanism Model, and the automobile user model based on decision-making uncertainty is constructed according to degree of the regretting Mechanism Model, which includes that decision relies on utility function；Pass through the uncertain uncertain model of place for carrying out clustering and constructing electric car to electric car；According to the two-stage programming model of automobile user model and uncertain model of place building electric car parking lot, which includes lower layer's planning of parking lot addressing, constant volume, the upper layer planning of price incentive design and parking lot traffic control；And pre-defined algorithm is respectively adopted, the upper layer and lower layer planning of two-stage programming model is solved, obtain the optimum programming scheme in electric car parking lot.Calculating equipment the invention also discloses the device for planning in corresponding electric car parking lot and for executing this method.
Description
Technical field
The present invention relates to field of power system more particularly to a kind of planing method, device and the meters in electric car parking lot
Calculate equipment.
Background technique
In order to meet extensive electric car to the access demand of electric system, a large amount of electric car electrically-charging equipment also by
Along residential block, market and highway, different types of electric car can be on different electrically-charging equipments for planning
It charges.In electric car and bulk power grid while carrying out charge and discharge on charging pile, the big electricity of power charge factors influencing demand
The load spatial and temporal distributions of net, provide the energy storage largely moved also for electric system.Just as private electric car can be certainly
It charges, can also charge in the fast charge stake of electric car, different types of electrically-charging equipment can expire in the trickle charge stake of family
The charge requirement of the different types of user of foot, wherein best medium is interacted as electric car integrator and user, it is public
Common-battery electrical automobile parking lot is one of charging medium of greatest concern.It is different from the quick charge station for being mainly used for promptly charging,
Public electric car parking lot is because of its parking function, so that electric car is when public electric car parking lot stops here
Between it is longer, also just to be parked in electric car parking lot automobile participate in Demand Side Response provide possibility, make electronic vapour
The medium that vehicle is interacted with bulk power grid.Public electrically-charging equipment appropriate is disposed in planning, that is, passes through intelligent electric automobile parking lot
Carrying out control to electric car access electric system is a good method.
Simultaneously in real world, most of electric cars be it is privately owned, each automobile user can be with different
Mode is the charging of its electric car, and whether user is ready to enter and become using electric car parking lot for a long time to determine investment effect
The big key factor of the one of rate.The fast development of behavior economy has us more to the action selection of automobile user
Proper describing mode.The charging behavior of electric car depends not only on the personal preference of car owner, also by electrically-charging equipment
The influence of geographical conditions and money subsidy.In practical study, investment decision and subsidy policy due to intelligent parking lot are all
Decision variable, at the same the two all can the charging behavior wish to electric car have an impact, so the participation of automobile user
Wish depends on decision variable, so cannot be indicated with given probability distribution, so uncertainties model is relied on using decision,
The function of subsidy and investment decision before the probability distribution of the participation wish of user is expressed as, so that more accurate portrays
User behavior.Do not consider that the estimation intelligent parking lot charging that result is likely to mistake is formulated in the planning of user intention and subsidy policy
The utilization rate of stake, so as to cause non-optimal decision.Therefore consider that decision relies on probabilistic intelligent parking lot project study tool
There is realistic meaning.
Summary of the invention
For this purpose, the present invention provides a kind of planning in electric car parking lot, device and calculates equipment, to try hard to solve or extremely
It is few alleviate above there are the problem of.
According to an aspect of the invention, there is provided a kind of planing method in electric car parking lot, suitable for being set in calculating
Standby middle execution, which comprises construct degree of regretting Mechanism Model according to reflection-reaction normal form, and according to degree of the regretting mechanism
Model construction is based on decision and relies on probabilistic automobile user model, the automobile user model include decision according to
Rely utility function；Pass through the uncertain uncertain scene mould for carrying out clustering and constructing electric car to electric car
Type, the uncertainty model of place include that the institute that electric car parking lot is likely to occur during operation is stateful；According to institute
State the two-stage programming model of automobile user model and uncertain model of place building electric car parking lot, described two
Stage plan model using in power distribution network electric car integrator obtain maximum profit as target comprising parking lot addressing, constant volume,
The upper layer planning of price incentive design and the lower layer of parking lot traffic control plan；And pre-defined algorithm is respectively adopted to described two
The upper layer planning and lower layer's planning of stage plan model are solved, and the optimum programming scheme in electric car parking lot is obtained.
Optionally, in planing method according to the present invention, decision relies on utility function and includes:
Wherein, Wy,iIt is income of the electric car i in time interval y,It is electronic
Automobile i time interval y remuneration and subsidy,It is not convenient cost of the electric car i in time interval y,It is electricity
Battery charging and discharging cost depletions of the electrical automobile i in time interval y.
Optionally, in planing method according to the present invention, the uncertainty of electric car include it is interior raw uncertain and
External uncertainty, wherein interior raw uncertainty includes that electric car participates in the factor, external uncertainty includes electric car
Initial state-of-charge, arrival time and time departure.
Optionally, in planing method according to the present invention, the upper layer planning algorithm of first stage is genetic algorithm, second
Lower layer's planning algorithm in stage is first dual interior point.
Optionally, in planing method according to the present invention, the upper layer objective function of two-stage programming model is power distribution network
The profit F of middle electric car integratorPLIt maximizes, its calculation formula is:
Wherein, ΛOpeIt is year operation income, CInvIt is year equivalent cost of investment,It is in the electronic of node b setting
Vehicle charging station quantity,It is the binary variable for indicating electric car parking lot and whether being established in node b, ΩSIt is scene collection
It closes, Y is 1 year contract period, ρy,sIt is the probability that scene s occurs in time interval y, Λy,sIt is the field in time interval y
The operation of scape s is taken in, kcpIt is the year value operator of charging pile, kldIt is the year value operator in soil, πcpIt is two-way charger investment
Cost.
Optionally, in planing method according to the present invention, the upper layer constraint condition of two-stage programming model are as follows:
Wherein,It is the maximum electric automobile charging station quantity in node b setting, RewbIt is to be arranged to motivate in node b
Subsidized price,It is the maximum excitation subsidized price in node b setting.
Optionally, in planing method according to the present invention, lower layer's objective function of two-stage programming model is parking lot
Maximum revenue is run, decision variable includes whether the charge-discharge electric power in electric car parking lot and user stop with electric car
The binary variable of field signing.
Optionally, in planing method according to the present invention, lower layer's objective function of two-stage programming model are as follows:
Wherein,Indicate the operation income in electric car parking lot,Indicate the operation flower in electric car parking lot
Take,Indicate the contract customisation costs in electric car parking lot, θ indicates the number of days in each cycle of operation, ΩIIt is user
Set,Indicate the discharge power in electric car parking lot,Indicate the electric discharge electricity price of electric car, r is runing time
Interval,Indicate whether automobile user signs the binary variable of price incentive contract with the parking lot s,It indicates
The charging expense of electric car, πomIndicate the day maintenance cost of electric car,Indicate the charging function in electric car parking lot
Rate,Indicate Spot Price, RewbIndicate electric car parking lot and the excitation expense that automobile user is signed.
Optionally, in planing method according to the present invention, lower layer's constraint condition of two-stage programming model includes maximum
Charge-discharge electric power constraint, charge and discharge cannot carry out simultaneously constraint, electric car state-of-charge constraint, meet electric car charging
Constraint of demand, batteries of electric automobile loss constrain, electric car can be constrained with binary system, electric car contract signing amount constrains,
At least one of the constraint of electric car available quantity, the constraint of electric car amount of reach and the constraint of the electric car amount of leaving.
Optionally, in planing method according to the present invention, maximum charge-discharge electric power constraint are as follows:
Wherein, γmaxIndicate the maximum charge-discharge electric power of electric automobile charging pile,It indicates in electric car parking lot
Schedulable electric car quantity, t is the period in one day period of time T.
Optionally, in planing method according to the present invention, constraint that charge and discharge cannot carry out simultaneously are as follows:
Optionally, in planing method according to the present invention, the constraint of electric car state-of-charge are as follows:
Wherein, Ey,s,b,tIndicate the total state-of-charge of electric car of current generation, Ey,s,b,t-1The previous stage of expression
The total state-of-charge of electric car, η indicate efficiency for charge-discharge,Indicate the energy capacity of batteries of electric automobile,Indicate electronic
State-of-charge when automobile reaches,Indicate that electric car needs state-of-charge to be achieved,Indicate that electric car stops
Arrival electric car quantity in parking lot,It indicates to leave electric car quantity in electric car parking lot.
Optionally, in planing method according to the present invention, batteries of electric automobile loss constraint are as follows:
Wherein, πdgIndicate the battery loss expense of electric car, ψ indicates that battery loss limitation in electric car parking lot is normal
Amount,Indicate the specified charge power under normal mode.
According to another aspect of the present invention, a kind of device for planning in electric car parking lot is provided, suitable for residing in meter
It calculates and is executed in equipment, described device includes: user model construction unit, suitable for constructing degree of regretting machine according to reflection-reaction normal form
Simulation, and probabilistic automobile user model is relied on based on decision according to degree of regretting Mechanism Model building, it is electronic
User vehicle model includes that decision relies on utility function；Model of place construction unit, suitable for by not known to electric car
Property carry out the uncertain model of place of clustering building electric car, uncertain model of place includes electric during operation
The institute that electrical automobile parking lot is likely to occur is stateful；Plan model construction unit, suitable for according to automobile user model and not
Certainty model of place constructs the two-stage programming model in electric car parking lot, and the two-stage programming model is in power distribution network
It is target that electric car integrator, which obtains maximum profit, comprising the upper layer planning that parking lot addressing, constant volume, price incentive design
It is planned with the lower layer of parking lot traffic control；And model solution unit, suitable for pre-defined algorithm is respectively adopted to two-stage programming
The upper layer planning and lower layer's planning of model are solved, and the optimum programming scheme in electric car parking lot is obtained.
According to another aspect of the invention, a kind of calculating equipment is provided, comprising: one or more processors；Memory；
With one or more programs, wherein the storage of one or more of programs in the memory and is configured as by one
Or multiple processors execute, one or more of programs include the planning for executing electric car parking lot as described above
The instruction of method.
According to another aspect of the invention, a kind of readable storage medium storing program for executing for storing one or more programs is provided, it is described
One or more programs include instruction, and described instruction is when calculating equipment execution, so that calculating equipment execution is as described above
Electric car parking lot planing method.
According to the technique and scheme of the present invention, the outer of electric car is described using probability density function for single motor automobile
It is raw uncertain, while the influence that the conceptual modeling electric car based on virtual power plant orderly charges to distribution system.For
The interior raw uncertainty of electric car, the i.e. investment decision of current generation may be to the when space divisions of following electric car charging load
Cloth generates significant impact, constructs a kind of probabilistic model of description electric car decision dependence.Wherein the present invention is according to anti-
It penetrates-reacts normal form and construct degree of regretting Mechanism Model, to calculate automobile user under different decisions and Contract Incentive
The probability distribution of behavior.The utility function of quantization degree of regretting additionally is described in detail, is finally generated using clustering not true
Qualitative scene completes entire decision and relies on probabilistic modeling.In above-mentioned uncertainties model and statistical scene modeling
On the basis of, the present invention using in power distribution network electric car integrator obtain maximum profit as objective function, formulate all planning
Decision and Incentive contracts design, establish and rely on probabilistic electric car parking area planning model based on decision.
Detailed description of the invention
To the accomplishment of the foregoing and related purposes, certain illustrative sides are described herein in conjunction with following description and drawings
Face, these aspects indicate the various modes that can practice principles disclosed herein, and all aspects and its equivalent aspect
It is intended to fall in the range of theme claimed.Read following detailed description in conjunction with the accompanying drawings, the disclosure it is above-mentioned
And other purposes, feature and advantage will be apparent.Throughout the disclosure, identical appended drawing reference generally refers to identical
Component or element.
Fig. 1 shows the schematic diagram according to an embodiment of the invention for calculating equipment 100；
Fig. 2 shows the flow charts of the planing method 200 in electric car parking lot according to an embodiment of the invention；
Fig. 3 shows the structural block diagram of the device for planning 300 in electric car parking lot according to an embodiment of the invention；
Fig. 4 shows the frame diagram of two-stage programming model according to an embodiment of the invention；
Fig. 5 shows two-stage programming model solution schematic diagram according to an embodiment of the invention；And
Fig. 6 shows IEEE12 node system schematic diagram according to an embodiment of the invention.
Specific embodiment
Exemplary embodiments of the present disclosure are described in more detail below with reference to accompanying drawings.Although showing the disclosure in attached drawing
Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here
It is limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure
It is fully disclosed to those skilled in the art.
Fig. 1 is the block diagram of Example Computing Device 100.In basic configuration 102, calculating equipment 100, which typically comprises, is
System memory 106 and one or more processor 104.Memory bus 108 can be used for storing in processor 104 and system
Communication between device 106.
Depending on desired configuration, processor 104 can be any kind of processing, including but not limited to: microprocessor
(μ P), microcontroller (μ C), digital information processor (DSP) or any combination of them.Processor 104 may include such as
The cache of one or more rank of on-chip cache 110 and second level cache 112 etc, processor core
114 and register 116.Exemplary processor core 114 may include arithmetic and logical unit (ALU), floating-point unit (FPU),
Digital signal processing core (DSP core) or any combination of them.Exemplary Memory Controller 118 can be with processor
104 are used together, or in some implementations, and Memory Controller 118 can be an interior section of processor 104.
Depending on desired configuration, system storage 106 can be any type of memory, including but not limited to: easily
The property lost memory (RAM), nonvolatile memory (ROM, flash memory etc.) or any combination of them.System storage
Device 106 may include operating system 120, one or more is using 122 and program data 124.In some embodiments,
It may be arranged to be operated using program data 124 on an operating system using 122.Program data 124 includes instruction, in root
According in calculating equipment 100 of the invention, program data 124 includes for executing the planing method 200 in electric car parking lot
Instruction.
Calculating equipment 100 can also include facilitating from various interface equipments (for example, output equipment 142, Peripheral Interface
144 and communication equipment 146) to basic configuration 102 via the communication of bus/interface controller 130 interface bus 140.Example
Output equipment 142 include graphics processing unit 148 and audio treatment unit 150.They can be configured as facilitate via
One or more port A/V 152 is communicated with the various external equipments of such as display or loudspeaker etc.Outside example
If interface 144 may include serial interface controller 154 and parallel interface controller 156, they, which can be configured as, facilitates
Via one or more port I/O 158 and such as input equipment (for example, keyboard, mouse, pen, voice-input device, touch
Input equipment) or the external equipment of other peripheral hardwares (such as printer, scanner etc.) etc communicated.Exemplary communication is set
Standby 146 may include network controller 160, can be arranged to convenient for via one or more communication port 164 and one
A or multiple other calculate communication of the equipment 162 by network communication link.
Network communication link can be an example of communication media.Communication media can be usually presented as in such as carrier wave
Or computer readable instructions, data structure, program module in the modulated data signal of other transmission mechanisms etc, and can
To include any information delivery media." modulated data signal " can such signal, one in its data set or more
It is a or it change can the mode of encoded information in the signal carry out.As unrestricted example, communication media can be with
Wired medium including such as cable network or private line network etc, and it is such as sound, radio frequency (RF), microwave, infrared
(IR) the various wireless mediums or including other wireless mediums.Term computer-readable medium used herein may include depositing
Both storage media and communication media.
Calculating equipment 100 can be implemented as server, such as file server, database server, application program service
Device and WEB server etc. also can be implemented as a part of portable (or mobile) electronic equipment of small size, these electronic equipments
It can be such as cellular phone, personal digital assistant (PDA), personal media player device, wireless network browsing apparatus, individual
Helmet, application specific equipment or may include any of the above function mixing apparatus.Calculating equipment 100 can also be real
It is now the personal computer for including desktop computer and notebook computer configuration.In some embodiments, equipment 100 is calculated
It is configured as executing the planing method 200 in the electric car parking lot according to invention.
It should be appreciated that automobile user can be accustomed to according to factum and preference in the environment of free market
Freely decide whether to sign charging contract with electric car integrator.If electric car is ready to sign with electric car integrator
Contract, and determine the feature (for example, daily time of using cars and desired SOC) of its billing requirements, electric car parking lot can
Therefrom to take over and provide corresponding service for it.During operation, automobile user obtains electricity according to established contract
The charging service in electrical automobile parking lot and the service charge that the buying energy is paid to electric car parking lot.If automobile user
Selection does not sign contract with electric car parking lot user, then it tends to meet its charge requirement under conventional charge mode.
In this case, it is assumed that automobile user is inserted directly into power grid when completing destination stroke and obtains electric power from power grid,
It charges without control, and extracts power supply immediately when reaching required SOC.So in such a scenario, automobile user
The energy that charging cost is only from them uses, which uses and directly calculated by the power grid based on retail price.
In practice, automobile user may always to whether select electric car parking lot and select which electronic vapour
Vehicle parking lot and it is irresolute benefit can be brought to him because participating in the plan not only, it is also possible to generate some extra costs.Example
Such as, the not convenient cost of distance trip or the cell degradation cost due to the operation generation under controlled mode.Therefore, in order to attract
The charging plan of electric car parking lot is added in the electric vehicle owner, and electric car polymerize the incentives strategy that quotient may use and mentions
For Incentive contracts, make its participant income obtained always greater than the conventional income charged under case.In fact, due to electronic
The excitation of automobile directly affects the economy of the operation in electric car parking lot, it is therefore necessary to according to programmed decision-making optimization design
The arrangement of contract and incentive policy, to ensure the profitability of electric car parking lot project.
For this purpose, the present invention provides a kind of new electric car parking planing methods.Fig. 2 shows one according to the present invention
The flow chart of the planing method 200 in the electric car parking lot of embodiment is such as calculating equipment suitable for executing in calculating equipment
It is executed in 100.As described in Figure 2, this method starts from step S210.
In step S210, degree of regretting Mechanism Model is constructed according to reflection-reaction normal form, and according to degree of the regretting mechanism mould
Type building relies on probabilistic automobile user model based on decision, and the automobile user model includes that decision relies on
Utility function.
The invention mainly relates to pure electric automobile BEV, each electric vehicle as an independent individual from the point of view of, Mei Ge electricity
The time that electrical automobile starts to charge is a stochastic variable, submits to one and determines people's trip habit and vehicle use habit
The initial state-of-charge SOC (state-of-charge) of single motor automobile is also one about upper to probability density function simultaneously
The random function of total kilometres after primary charging.In this case, in order to determine single motor vehicle can charge-discharge electric power just
Need to obtain the probability distribution of each electric vehicle stroke.The probability-distribution function of electric vehicle trip stroke d obeys similar normal state point
Cloth:
Wherein, d is the trip stroke of electric vehicle；μ is the average value of trip stroke, generally 16.1km；σ is trip stroke
Standard deviation, generally 9.3km.Assuming that the trip distance of SOC and electric car be it is linear, electric car is starting to charge
SOC can be according to SOCi=(1-d/dR) × 100% is estimated, wherein SOCiIt is the initial SOC of electric car, d is electric car
Trip distance, obey normal distribution above, dRThe maximum trip distance of electric car, representative value are 100 kilometers.Assuming that
All electric vehicles all return to the charging of electric car parking lot in one day, then available new probability density function are as follows:
The charging time started of single electric vehicle and initial SOC are simply to be easy to describe, but in active distribution network
Or in an electric car parking lot, there are hundreds of electric cars, if by each of which as charge independence
Individual will greatly increase the difficulty of optimization problem, and be difficult to optimize control, so the present invention is made with electronic parking lot
Consider for unit, and introduces the concept of virtual power plant.The control of a totality is constructed namely between electric car and power grid
Preparative layer, this control layer and present microgrid, energy aggregation concept are all much like, but it is more like a virtual power plant, because
As long as can be controlled for the electric car in electric car parking lot, to show overall effect to power grid.Virtually
Power plant is played for power grid as an entirety to allow certain some not associated unit of electrical sheet after integrated scheduling
Effect as traditional power plants shows the characteristic in power plant, from some terms, helping the optimization control of active distribution network
System.
Single electric car has very strong randomness, although can be described by probability density function, nothing
Method optimizes control, and when being considered with electronic parking lot model, it can only see the SOC constraint in electronic parking lot from grid side
It is constrained with charge-discharge electric power, SOC constraint and charge-discharge electric power constraint of the electronic parking lot under 24 sections, need in order to obtain
It clusters to obtain typical scene by statistical method or sign an agreement with user.Need to know that each car reaches daily from user side
With the stroke for leaving the time in parking lot, each car reaches the SOC in parking lot and each car is gone on a journey, this is if fining modeling
The thing to be considered；And if only being left and being reached with discontinuity surface when knowing each only from the point of view of electric car parking lot
The quantity in electronic parking lot and the expectation of electric automobile during traveling stroke, so that it may determine the modeling peace treaty in electric car parking lot
Beam.
Assuming that each car can only return to fixed electric car parking lot in one day, and it was divided into 24 periods for one day
Wherein 1 refers to since the when discontinuity surface terminated any to two o'clock, and 24 refer to since morning zero point to the when interruption that any terminates
Face.Assuming that AtDiscontinuity surface reaches the electric car quantity in electronic parking lot, D when being ttDiscontinuity surface leaves the electronic of parking lot when being t
Automobile quantity, NtIt is the electric car quantity that may participate in scheduling, Nt=Nt-1+At-DtT=1,2 ... 24.For it is each when be interrupted
Face NtControl strategy in, it is necessary to got out Dt+1Vehicle will leave in subsequent time, and must when discontinuity surface t satisfaction have
Dt+1Vehicle meets the trip requirements of user, i.e. SOCset, in a model, it is assumed that the SOC of each carsetIt is all mutually all 80%, and its
As long as the SOC of his vehicle is not less than the minimum requirements (being assumed to be 20%) of battery loss, therefore:
0.2·(Nt-Dt+1)·SOCmax+Dt+1·SOCset≤SOCNt+Pt·Δt≤NtSOCmaxT=1,2 ... 24 (3)
SOCNt=SOCNt-1+Pt-1·Δt+SOCAt-SOCDtT=1,2 ... 24 (4)
SOC in formulaNtN when discontinuity surface when for ttIt may participate in the gross energy of the electric car of scheduling, SOCAtDiscontinuity surface when for t
When AtReach the gross energy of the electric car in electronic parking lot, SOCDtD when discontinuity surface when for ttReach electronic parking lot
The gross energy of electric car, wherein SOCAtIt can be obtained according to statistical data, SOCDtFor the number of electric car parking lot setting
According to.Assuming that the electric energy SOC that electric car trip consumes in one dayconstantIt is absorbed from power grid within one day with electric car stop
Electric energy is equal, that is,
In general, the mainspring that selection participates in the client for the charging plan that electric car polymerization quotient provides is to be passed through
Ji interests.However, in actual implementation, since the charging plan that electric car polymerization quotient provides independently is formulated and is grasped by oneself
Make, thus automobile user polymerize before quotient signs a contract with electric car hardly know they will from participation the project
Obtain how many return.Therefore, in order to achieve the above objectives, most probable strategy is by observing the income having been carried out before it
Estimate the profitability of charging plan that electric car polymerization quotient provides, and considers this point in following action.By
In this learning ability, automobile user is tended to follow to the participative behavior for the charging plan that electric car polymerization quotient provides
" reflection-reaction " (RR) normal form in action, can explain are as follows: for each decision, if result before proves this
A little action can generate more pleasant result or higher return, and user can be switched to other rows from his current action
It is dynamic.Meanwhile the strategy with more high repayment is considered to have higher possibility to a certain extent.In order to correctly consider this
It kind influences, the present invention constructs degree of regretting mechanism (RM) model to describe under " reflection-reaction " mode automobile user not
Certainty.
In order to describe the modeling of degree of regretting mechanism, the present invention uses finite aggregateIndicate for electric vehicle
The potential charging station at family, wherein ΩBWithIndicate the both candidate nodes and normal charge node in whole system.In operation mould
During quasi-, each automobile user i ∈ ΩIIt will be assumed to be that facing one selects asking for which charging station in appointed interval
Topic, each interval corresponds to each contract terms herein, is considered four months (120 days).According to regretting machine processed
The definition of system, for any section y, using strategyProbability depend on user and regret angle valueIt is as follows:
Wherein,It is the binary variable whether electric car i is ready potential charging station bb, usual 1 is to be ready,
0 is to be unwilling.When the subscript of z parameter is plus variables such as τ or y, then it represents that whether τ moment or y time interval remove charging station
Binary variable.As shown in above formula, the angle value of regretting of automobile user is defined as all knots before some period
After fruit occurs, if previous selection(the case where actually having been achieved with, such as) be replaced by
One different selection, the increase of brought profit.Wherein the profit of electric car passes through utility function Wi:R+→ R carrys out amount
Change, and multiplication by constants 1/y is normalized each section.Formula (5) indicates, if individual comes from strategy's
The strategy that expected profit selects before being higher thanProfit so user will regret oneself decision, and to regretting the amount of progress
Turn to GY, ss, i.Otherwise he will not have any sorry, that is, the assignment for degree of regretting:
Based on defined above, if it is assumed that a movementIt is taken, then being transformed into newly in the y+1 time interval
StrategyProbability can by decision rely on model be described as follows:
WhereinIndicate selection strategyProbability；μiIt is one predefined
Constant, it guarantees that the summation of all probability is 1.Formula (8) shows that, for each run the period, automobile user can protect
Stay the strategy before itOr from ΩBBSelect a new strategyThe probability that each strategy is selected
It is one about regretting angle value MY, ss, iThe function of (), regrets that angle value is bigger, and the probability that may be selected is higher.In addition,
If each automobile user in research is acted all in accordance with the principle of formula (8),Actual distribution most
Correlated equilibrium collection t → ∞ can be converged to eventually.On this equilibrium point, because everyone decision can consider that other people is latent
In strategy, and it is converted into everyone optimal system benefit, so, all automobile users will not all regret them
The decision of selection.Degree of the regretting model and the imagination of such as RR example are completely the same, can be very good description automobile user and exist
Behavior under programmed decision-making and Contract Incentive.
According to formula (8), automobile user selects the probability in parking lot that will determine with the correlation of electric car integrator
And change, i.e.,Wherein RewbbIt is the incentive price of potential charge node bb,Be electric car i whether with potential charging station bb signing binary variable, usual 1 is to have contracted, 0 be it is unsigned,It is the charge power and discharge power at potential charging station bb respectively.This makesResult ours
It works in model as interior life (decision dependence) uncertainty.In actual optimization, give electric car integrator EVA's
Each plug-in electromobile PEVi ∈ Ω at 1 determination section y of algorithm can be used in history decisionIProbability distribution
It generatesThe method of probability distribution is as follows:
It is relied in uncertainty models in the decision of electric car, utility function is as portraying the important of every kind of optional program
Index.According to one embodiment, the income W of the electric car i of electric car parking lot charging plan is participated inY, iBe considered with often
The remuneration and subsidy of a interval ySubtract not convenient costWith battery loss costNamely decision relies on effect
It can be with function are as follows:
Wherein,It is requirement state-of-charge SOC when electric car i leaves parking lot,It is that electric car i arrival stops
State-of-charge when parking lot,WithIt is respectively whether electric car i is ready potential charging station in time interval y
Point bb or the binary variable whether contracted with charging station bb, θ is the number of days that a time interval y is included,It is electronic
The energy capacity (kWh) of automobile batteries,WithIt is time departure and the arrival time of electric car i, Rew respectively1,bbWith
Rew2,bbIt is residence time and the excitation electricity price of charge capacity of potential charge node bb, h respectivelybb,iIt is destination to electronic vapour
Equivalent distances (km) between vehicle parking lot,It is distance costs ($/km), πdgBe batteries of electric automobile degradation cost (/
KWh), t is a period of one day T,It is battery loss cost under conventional charge mode.
Normal charge cost is deducted from formula (12) under normal circumstancesTo ensureResult only reflect automobile user participate in electric car parking lot scheduling " increment " cost.Portraying effectiveness letter
In several processes, in order to calculateWe assume that electric car integrator has used the advanced scheduling strategy based on equity
It is run to carry out the Real-Time Scheduling of battery, therefore for automobile user, caused by the operation of electric car parking lot
Deterioration of battery can always be counted as the mean allocation when the automobile users for participating in operation all under the period.Furthermore if electricity
Electrical automobile user selects conventional charge mode, i.e.,So his profit WY, iIt will be 0.
Then, in step S220, electric car is constructed by the uncertain clustering that carries out to electric car
Uncertain model of place, the uncertainty model of place include during operation electric car parking lot be likely to occur it is all
State.
It should be appreciated that uncertainty relevant to electric car can usually be attributed to two aspects: travel mode and filling
The selection of electric scheme.The former uncertainty is external, because their statistical nature is fixed and can be by pre-
Fixed probabilistic model indicates, and the uncertainty of the latter is interior life, because its probability is not constant, with electricity
Electrical automobile polymerize the decision of quotient and develops.According to one embodiment, the uncertainty of electric car include it is interior raw uncertain and
External uncertainty, wherein the interior raw uncertain participation willingness factor including automobile user
External uncertain initial state-of-charge, arrival time and time departure including electric car.Namely about each electronic
The information of the behavior of user vehicle i can be by vector ΦiIt indicates are as follows:
By synthesis to PEV all in system statistics, can the charge requirement scene of model may be expressed as:
Ψs={ Φi|i∈ΩI} (14)
Wherein, ΨSIt is the expression of scene s, indicates that electric car parking facility is possible to the state occurred during operation,
ΩIIt is automobile user set.If it is assumed that ΦiIn all uncertain variables be all independent, then can pass through cover it is special
Monte Carlo Simulation of Ions Inside (MCS) process generates the operation operation scenario in public electric car parking lot in each section y, as shown in algorithm 2:
Then, in step S230, electric car is constructed according to automobile user model and uncertain model of place
The two-stage programming model in parking lot, the two-stage programming model obtain maximum profit with electric car integrator in power distribution network and are
Target comprising lower layer's planning of upper layer planning and parking lot traffic control that parking lot addressing, constant volume, price incentive design.
Fig. 4 shows the planning framework of Two-stage model according to an embodiment of the invention.The frame is by the decision visual field
Be divided into two stages, the first stage be directed generally to processing position relevant to electric car parking lot plan, Capacity Selection and
Contract (excitation) design, this is corresponding in the decision of planning stage with electric car integrator；And in second stage, pass through operation
Simulation program assesses the economy of suggested solution, while considering the operation scene of various electric cars.The present invention is for reality
Show effective simulation of electric car, the external and interior raw uncertain of electric car is considered in frame.Wherein electric car
The uncertain and external uncertainty of interior life is generated and is indicated by scene collection, the ginseng of interior life uncertainty i.e. automobile user
It is associated with willingness factor with the programmed decision-making of electric car and operational decisions by degree of regretting mechanism, that is, described before
Decision rely on it is uncertain.
Statistical scene is being carried out to indoor raw uncertain and external uncertainty models for electric vehicle and scene
Under the basis of modeling, for electric car integrator acquisition maximum profit as objective function, formulation is all using in power distribution network by the present invention
Programmed decision-making and Incentive contracts design, while the electric car in entire power grid being made within the cycle of operation to obtain optimal power output
With the planned value for absorbing power from major network.Because the decision of the polymerization quotient of electric car can have very big shadow to the operation of electric car
It rings, therefore the present invention uses two-stage Stochastic Programming Model, the optimal addressing and incentive policy in Lai Shixian electric car parking lot
Design.
According to one embodiment of present invention, the upper layer objective function of two-stage programming model is electric car in power distribution network
The profit F of integratorPLIt maximizes, its calculation formula is:
max FPL=ΛOpe-CInv (15)
Wherein, ΛOpeIt is year operation income, CInvIt is year equivalent cost of investment,It is in the electronic of node b setting
Vehicle charging station quantity,It is the binary variable for indicating electric car parking lot and whether being established in node b, ΩBIt is in system
Both candidate nodes, ΩSIt is scene set, Y is contract period, ρy,sIt is the probability that scene s occurs in time interval y, Λy,sIt is
The operation income of scene s, k in time interval ycpIt is the year value operator of charging pile, kldIt is the year value operator in soil, πcp
It is two-way charger cost of investment.
In order to allow the time scale of cost of investment and operation cost to be consistent, by the present invention in that with the capital recovery factor
Year equivalent cost of investment and year operation are taken in into be converted into equal years value k=[ζ (1+ ζ)d]/ [(1+ζ)d- 1], wherein ζ generation
The service life of table equipment, d represent average annual discount rate.
According to one embodiment of present invention, the upper layer constraint condition of two-stage programming model are as follows:
Wherein,It is the maximum electric automobile charging station quantity in node b setting, RewbIt is to be arranged to motivate in node b
Subsidized price, that is, the excitation expense that electric car parking lot and automobile user are signed,It is to be arranged in node b
Maximum excitation subsidized price.
Because of ΛOpeCorresponding to the expected electric car collection based on power grid market and electric car charge and discharge electric interactions
At the income of quotient.In fact, the expense standard that each electric car parking factory imposes depends on electric car integrator and its visitor
Bilateral agreements between family, that is, Incentive contracts.In the case where not being general situation, it is assumed that the charging in electric car parking lot
Cost is always equal to the charging cost of regular situation, it may be assumed that
Wherein,Indicate the charging expense of electric car, βY, s, iIndicate batteries of electric automobile full charge of total time,
θ indicates the number of days in each cycle of operation,Indicate arrival time,Indicate the specified charging effect under conventional charge mode
Rate,Indicate Spot Price,Indicate the electric car state-of-charge needed when leaving,Indicate electronic when reaching
The state-of-charge of automobile,Indicate the energy capacity of batteries of electric automobile, ηslIndicate the specified charge efficiency of electric car.
According to another embodiment of the invention, lower layer's objective function of two-stage programming model is that parking lot runs income
It maximizes, decision variable includes whether the charge-discharge electric power in electric car parking lot and user contract with electric car parking lot
Binary variable.Lower layer's objective function of two-stage programming model are as follows:
Wherein,Indicate the operation income in electric car parking lot,Indicate the operation flower in electric car parking lot
Take,Indicate the contract customisation costs in electric car parking lot,Indicate the discharge power in electric car parking lot,
Indicating the electric discharge electricity price of electric car, r is runing time interval,Indicate whether automobile user is signed with the parking lot s
The binary variable of price incentive contract,Indicate the charging expense of electric car, πomIndicate the day maintenance expense of electric car
With,Indicate the charge power in electric car parking lot,Indicate Spot Price,It is electric car i in the time
The binary variable whether contracted with candidate parking lot b when being spaced y,Electric car i is respectively represented in time interval y
Scene s at time departure and arrival time,Indicate state-of-charge when electric car reaches,Indicate electronic vapour
Vehicle needs state-of-charge to be achieved, Rew1,bAnd Rew2,bIt is residence time and the charging electricity of candidate electric car parking lot b respectively
The excitation electricity price of amount.
According to another embodiment of the invention, lower layer's constraint condition of two-stage programming model includes maximum charge and discharge electric work
Rate constraint, charge and discharge cannot carry out simultaneously constraint, electric car state-of-charge constraint, meet charging demand for electric vehicles about
Beam, batteries of electric automobile loss constraint, electric car can be constrained with binary system, electric car contract signing amount constrains, electronic vapour
At least one of the constraint of vehicle available quantity, the constraint of electric car amount of reach and the constraint of the electric car amount of leaving.Wherein, maximum charge and discharge
Electrical power constraint are as follows:
Wherein, γmaxIndicate the maximum charge-discharge electric power of electric automobile charging pile,It indicates in electric car parking lot
Schedulable electric car quantity, t is a period in a time T.
In addition, constraint, electric car state-of-charge SOC constraint and batteries of electric automobile damage that charge and discharge cannot carry out simultaneously
Consumption constraint is respectively following three formula:
Wherein, Ey,s,b,tIndicate the total state-of-charge of electric car of current generation, Ey,s,b,t-1The previous stage of expression
The total state-of-charge of electric car, η indicate efficiency for charge-discharge,Indicate the arrival electric car number in electric car parking lot
Amount,It indicates to leave electric car quantity in electric car parking lot.πdgIndicate the battery loss expense of electric car, ψ
Indicate that electric car parking lot battery loss limits constant.
Electric car can be constrained with binary system are as follows:
Wherein,Indicate whether plug-in electromobile is in the binary variable of plug-in state,With
It is the binary variable whether electric car arrives and departs from respectively.
Electric car available quantityConstraint, electric car amount of reachConstraint and the electric car amount of leavingAbout
Beam is respectively following three formula:
Then, in step S240, pre-defined algorithm is respectively adopted, the upper layer planning of two-stage programming model and lower layer is advised
It draws and is solved, obtain the optimum programming scheme in electric car parking lot.Wherein, the upper layer of first stage, which is planned, to be with algorithm
Lower layer's planning algorithm of genetic algorithm, second stage can be first dual interior point, the design parameter details of algorithm, this field
Technical staff can sets itself according to actual needs, this is not limited by the present invention.
Present invention employs the two-stage stochastic programming method for solving for being based on genetic algorithm (GA), genetic algorithm is used to solve
The certainly first stage variable in the planning of upper layer, such asAnd δb,Then using in traditional former antithesis
Point method (PIPM) solves the problems, such as the running optimizatin of lower layer, is considering the operation constraint of electric car parking lot and electric car behavior about
While beam, the profit of each cycle of operation is estimated.Corresponding operation result (s
∈ΩS, b ∈ ΩB, t ∈ T, i ∈ ΩI) will be returned to according to fitness value the first stage operation planning decision.Pass through weight
Multiple interative computation, may finally obtain the optimal solution of entire planning operation model based on the process in Fig. 5.This be based on heredity calculate
In the algorithm of method, the candidate solutions of electric car integrator are indicated by a series of chromosomes created at random.Each group
A total of 3 × Ω of memberBA component part (gene),Indicate the position of electric car parking lot configuration,Expression will be pacified
The charging of dress is counted, RewbIndicate the value of Incentive contracts provided by each electric car parking lot.It is commented using fitness function
The performance for estimating each chromosome carrys out the superiority and inferiority degree of each chromosome of comparison, obtains the planning decision-making of suggestion: Fitness=FPL-
PF Fitness=FPL- PF, wherein FPLIndicate the value of the OF defined by the formula, PF is a penalty factor.
That is, strictly speaking in simulations if chromosome violates constraint, the optimization of lower layer will not converge on the
Two stage optimization, penalty factor will be arranged to number (10 greatly8) activate punishment, it is on the contrary then 0 will be set to.In addition, in order to
The calculated performance of genetic algorithm is improved, present invention employs the improvement renewal processes based on dynamic Niche Technique.It is lost with tradition
Propagation algorithm is different, and modified method is defined by minimum by space length by using a kind of adaptively selected operator
The similitude of microhabitat cluster generates population, to keep the diversity of individual.Therefore, will allow search process always with iteration
The global optimum of problem is moved towards together.When meeting following any preassigned standard, integrated solution algorithm will be terminated: reach
To the maximum number of iterations (β of permissionmax), or without the greatest iteration time (β of adaptability improvementuch).After termination algorithm, entirely
Optimized individual in population is considered as the final solution of entire electric car parking area planning moving model.
In the case where the distribution system that the present invention is accessed for high permeability electric car, a kind of electronic vapour of promotion is proposed
The electrically-charging equipment planning for relying on probabilistic electric car parking lot based on decision of vehicle effective integration is customized with Incentive contracts
Frame establishes two stage optimization model, and the DYNAMIC DISTRIBUTION of the participation rate of automobile user is described using degree of regretting mechanism
Decision dependence with electric car integrator acts on, and emphasis has studied and relies on probabilistic electric car parking based on decision
Field planing method.
Fig. 3 shows a kind of device for planning 300 in electric car parking lot according to an embodiment of the invention, suitable for staying
It stays in calculate and be executed in equipment.As shown in figure 3, the device includes user model construction unit 310, model of place construction unit
320, plan model construction unit 330 and plan model solve unit 340.
User model construction unit 310 is suitable for constructing degree of regretting Mechanism Model according to reflection-reaction normal form, and according to after this
Regret degree Mechanism Model building and probabilistic automobile user model, the automobile user model packet are relied on based on decision
It includes decision and relies on utility function.
Model of place construction unit 320 is suitable for constructing electronic vapour by the uncertain clustering that carries out to electric car
The uncertain model of place of vehicle, the uncertainty model of place are likely to occur including electric car parking lot during operation
Institute it is stateful.
Plan model construction unit 330 is suitable for according to the automobile user model and uncertain model of place building
The two-stage programming model in electric car parking lot, two-stage programming model are obtained maximum with electric car integrator in power distribution network
Profit is target comprising the lower layer of upper layer planning and parking lot traffic control that parking lot addressing, constant volume, price incentive design
Planning.
Plan model solves the upper layer planning that unit 340 is suitable for being respectively adopted pre-defined algorithm to the two-stage programming model
It plans and is solved with lower layer, obtain the optimum programming scheme in electric car parking lot.
The device for planning 300 in electric car parking lot according to the present invention, detail is based on Fig. 1-Fig. 5's
It is disclosed in detail in description, details are not described herein.
Improvement IEEE12 node system (as shown in Figure 6) used below come to model proposed by the invention carry out calculate and
Planning, it is known that the parameters such as IEEE12 system deployment and voltage rating and electric car capacity, maximum range number,
It is charged to the parameters such as 80% electricity time fastly, art technology human eye can obtain P according to the prior artsl, inconvenience cost πdu, it is every
The specified charge/discharge power and efficiency of a electric automobile charging pile, the SOC range of batteries of electric automobile, each electric car
The cost of investment of charging pile and maximum service life (MSL), discount rate, the maximum BC quantity in each electric car parking lot, electric car
Charge and discharge electricity price, electric car parking lot to automobile user node geographic distance.
In order to verify the validity of proposed frame, in the case where incentive mechanism only considers independent price subsidies, setting
Five kinds of different schemes are simulated.The wherein external uncertainty of scheme one consideration electric car, using incentive policy
Automobile user is motivated, so that electric car be made to contract with electric car parking lot；Other schemes are examined simultaneously
The external uncertain and interior life for considering electric car is uncertain, that is, considers the excitation political affairs that automobile user is used according to parking lot
Plan is reacted, to influence the planning and operation in entire electric automobile operation quotient and electric car parking lot.The scene of setting
Specifying information is as shown in table 1.Wherein, scheme one and scheme two use the lower excitation amount of money, scheme three using relative cost compared with
The high excitation amount of money, scheme four will motivate the amount of money to be set as an optimized variable in the genetic algorithm of upper layer, use this paper institute
The algorithm of proposition optimizes, and scheme five will motivate the amount of money to be set as one in the genetic algorithm of upper layer and electric car station
The excitation amount of money in each electric car parking lot that relevant optimized variable, i.e. Optimization Solution go out is different, thus to entire
The planing method and incentive policy mechanism that model proposes carry out more comprehensive analysis and research.
The scene of 1 example of table
Using two-stage stochastic programming derivation algorithm constructed by the present invention can for all example scenes in table 1 into
Row solves, and table 2 gives addressing constant volume optimum results and electric car parking lot incentive policy in the upper layer planning of algorithm
The optimal amount of money.Entire upper layer program results can be described as follows: wherein the selection of scheme 1 is in 633 nodes, 645 nodes and 675 nodes
50 charging piles are respectively built, the selection of scheme 2 builds 50 charging piles in 633 nodes, and the selection of scheme 3 builds 38 chargings in 633 nodes
Stake, 645 nodes build 44 charging piles, and the selection of scheme 4 is 633 nodes build 43 charging piles, 645 nodes build 50 charging piles, together
When by incentive policy price set 79.72 dollars of each period, the selection of scheme 5 builds 50 charging piles in 633 nodes, simultaneously will
Incentive policy price is set as 91.63 dollars of each period, builds 50 charging piles in 645 nodes, while by incentive policy price
It is set as 74.72 dollars of each period.
2 upper layer program results of table
Simultaneously in order to there is more deep understanding to entire Two-stage model, and entire program results are carried out better
The response of the EVA cost/profit calculated of lower layer's operation result under every kind of scene is listed in table 3 by analysis assessment, the present invention
In:
Table 3 operation phase result (dollar $)
The specific Installed capital cost of each scheme, maintenance cost, incentive cost, operation income and net income is specific
It lists, facilitates the difference for analyzing more each scheme, thus the concrete reason of analyzing influence program results.Emphasis of the invention exists
In by decision rely on uncertain (the interior raw uncertain) combination with PEVs utilize electric car parking area planning with
In excitation customization decision.By studying the user in each electric car parking lot in each Energizing cycle expected participation rate (PR)
Variation can analyze electric car parking lot under each schemeElectric car selection, the wherein electric car in scene s
The expection participation rate of parking lot b is defined as being ready the electric car signed a contract in the y of section with the electric car parking lot
Quantity, i.e.,
In scheme one, the expection participation rate in electric car parking lot seem at any time carry out holding stabilization, it is biggish
It is expected that participation rate means that the utilization efficiency in electric car parking lot is higher, electric car integrator will obtain more during operation
Big investment repayment.However, working as includes interior raw uncertain effect, that is, it is for electric vehicle when there is decision dependence uncertainty
The charging behavior at family is associated with the decision of electric car integrator.In scheme two, the influence of excitation is very low, electric car
The expection participation rate in parking lot will be with the passage of time and sharply decline, and lead to similarly advising for electric car integrator
It draws under the result of decision, final profit is well below scheme one.In order to illustrate the advantage that decision relies on method, by by scheme 1
Solution is inserted under the application scenarios of scheme two value for quantifying interior raw uncertain (VEU).Decision relies under model
Target value about scheme one is calculated as 5370.26 dollars.Wherein VEU positive value (10289.03 dollars -5370.26 dollars=
4918.77 dollars) show that the solution obtained in scheme two can provide the bigger electric car than obtaining in scheme one
The economic benefit of integrator.Therefore, the result of frame proposed by the invention in practical applications is better than traditional stochastic programming
Method.
In practice, since individual may have different behavior patterns according to different behavioural characteristics, in electricity
It should be understood that considering the interior raw uncertain that is, for electric vehicle of automobile user in the decision of electrical automobile parking area planning
Family relies on the decision of electric car parking area planning decision and excitation decision uncertain.Otherwise, electric car integrator
The profitability of electric car parking area planning project may be over-evaluated and make invalid investment decision.When the excitation that PL is provided
When emolument changes, the Rew in electric car parking lot such as in scheme threebWhen higher, the expected participation rate of automobile user is at any time
Between variation reduce it is less, it means that electric car parking lot can be more effectively utilized during operation.But it is larger
Excitation expense cannot be guaranteed that the profit of electric car integrator is higher.Work as RewbWhen rising to 150 dollars of each dispatching cycle,
The total income of electric car integrator is well below scheme two.It is, with incentive cost RewbGrowth, electric car collection
Tend to increase at the income of quotient, but its operation cost can also rise.However, since the increment of income is not so good as the increase of cost, electricity
The profit of electrical automobile integrator is generally lower than scheme two.
Therefore, in order to maximize the interests of electric car integrator, consider automobile user simultaneously under same frame
Incentive contracts design be vital.Scheme four is no longer using fixed incentive policy, using incentive policy as planning rank
The decision variable of section, to improve the participation rate of electric car in optimization.And scheme five is different using different nodes
The method of excitation optimizes incentive policy, to realize the profit maximization of electric car polymerization quotient.As shown in table 3, best to swash
The profit of the electric car polymerization quotient of scheme four and five can be improved in the measure of encouraging.Moreover, wherein using the excitation side based on node
Crime will obtain better arousal effect, to confirm the advantage of the complex optimum frame constructed in the present invention.
In above-mentioned analysis, it is assumed that pay polymerize with electric car the user that quotient signs a contract excitation reward led to
It crosses based on fixed scheme realization.Next it is simulated and (is determined in such as formula (10) using different incentive structures in a model
Justice), to determine the variation of its analog result.Wherein electricity excitation (such as shown in formula (10B)) is according to the charge volume of electric car
Size is subsidized, synthetic incentive (such as formula (10B) shown in) according to electric car charge volume size and rest on parking lot
Time subsidized.
Table 4 operation phase result (dollar $)
As can be seen that the plans under the conditions of synthetic incentive are to provide in electric car polymerization than constant excitation and electricity
Measure profit (B bigger under incentive programIPL=$ 40093.13).In constant excitation scheme, electric car polymerize quotient must be to
Fixed remuneration is paid with the user that electric car parking lot is signed a contract, participates in management and running to reward them.Therefore, it establishes
The signing that electric car parking lot with firm customer basis will need a large amount of expenditures to be used for Incentive contracts, but it returns efficiency
Lower, the planning solution under constant excitation scheme seems economically to be less effective.And under electricity incentive program, it is electronic
The received remuneration of automobile depends on the energy (charging capacity that they extract from electric car parking lot )。
Charge requirement is higher, and the income of acquisition is more.Therefore, if the short electric car car owner of daily stroke distances participates in contract and swashs
The plan of encouraging can only obtain very limited income, to reduce the chance to charge during operation using the option.This
Kind negative effect can undoubtedly reduce efficiency of investment.However, automobile user not only disappears in energy in the scheme of synthetic incentive
Consumption aspect receives awards, and receives awards in terms of the scheduled availability in electric car parking lot.Since availability payment can
Liberally to reflect automobile user to the actual contribution of system, it will provide stronger power (especially short distance for client
Go on a journey user) it is added in the contract customized solution in electric car parking lot, to bring bigger receipts for electric car integrator
Benefit.
According to the technique and scheme of the present invention, for electric vehicle indoor raw uncertain with external uncertainty models, with
And on the basis of scene carries out statistical model of place, using in power distribution network electric car integrator obtain maximum profit as target
Function formulates all programmed decision-making and Incentive contracts design, while making the electronic vapour in entire power grid within the cycle of operation
Vehicle obtains optimal power output and absorbs the planned value of power from major network.Two-stage Stochastic Programming Model is used, simultaneously to realize electricity
The optimal addressing in electrical automobile parking lot and incentive policy design, and establish and rely on probabilistic electric car parking lot based on decision
Plan model, and the model is solved using two-stage derivation algorithm, obtains the optimum programming in electric car parking lot.
A8, the method as described in A7, wherein lower layer's objective function of the two-stage programming model are as follows: Wherein,Indicate the operation income in electric car parking lot,Table
Show that the operation in electric car parking lot is spent,Indicate the contract customisation costs in electric car parking lot, θ indicates each fortune
Number of days in the row period, ΩIIt is user's set,Indicate the discharge power in electric car parking lot,Indicate electronic vapour
The electric discharge electricity price of vehicle, r are runing time intervals,Indicate whether automobile user is signed price incentive with the parking lot s and closed
Same binary variable,Indicate the charging expense of electric car, πomIndicate the day maintenance cost of electric car,Table
Show the charge power in electric car parking lot,Indicate Spot Price, RewbIndicate electric car parking lot with it is for electric vehicle
The excitation expense that family is signed.
A9, the method as described in A7, wherein lower layer's constraint condition of the two-stage programming model includes maximum charge and discharge
Constraint that power constraint, charge and discharge cannot carry out simultaneously, meets charging demand for electric vehicles about at the constraint of electric car state-of-charge
Beam, batteries of electric automobile loss constraint, electric car can be constrained with binary system, electric car contract signing amount constrains, electronic vapour
At least one of the constraint of vehicle available quantity, the constraint of electric car amount of reach and the constraint of the electric car amount of leaving.A10, such as right are wanted
Method described in asking 9, wherein the maximum charge-discharge electric power constraint are as follows:
Wherein, γmaxIndicate the maximum charge-discharge electric power of electric automobile charging pile,It indicates in electric car parking lot
Schedulable electric car quantity, t is a period in a time T.A11, the method as described in A9, wherein described to fill
The constraint that electric discharge cannot carry out simultaneously are as follows:
A12, the method as described in A9, wherein the electric car state-of-charge constraint are as follows:
Wherein, Ey,s,b,tIndicate the total state-of-charge of electric car of current generation, Ey,s,b,t-1The previous stage of expression
The total state-of-charge of electric car, η indicate efficiency for charge-discharge,Indicate the energy capacity of batteries of electric automobile,Indicate electronic
State-of-charge when automobile reaches,Indicate that electric car needs state-of-charge to be achieved,Indicate electric car parking
Arrival electric car quantity in,It indicates to leave electric car quantity in electric car parking lot.
A13, the method as described in A9, wherein the batteries of electric automobile loss constraint are as follows:
Wherein, πdgIndicate the battery loss expense of electric car, ψ indicates that battery loss limitation in electric car parking lot is normal
Amount,Indicate the specified charge power under normal mode.
Various technologies described herein are realized together in combination with hardware or software or their combination.To the present invention
Method and apparatus or the process and apparatus of the present invention some aspects or part can take insertion tangible media, such as it is soft
The form of program code (instructing) in disk, CD-ROM, hard disk drive or other any machine readable storage mediums,
Wherein when program is loaded into the machine of such as computer etc, and is executed by the machine, the machine becomes to practice this hair
Bright equipment.In the case where program code executes on programmable computers, calculates equipment and generally comprise processor, processor
Readable storage medium (including volatile and non-volatile memory and or memory element), at least one input unit, and extremely
A few output device.Wherein, memory is configured for storage program code；Processor is configured for according to the memory
Instruction in the said program code of middle storage executes the planing method in electric car parking lot of the invention.With example rather than
The mode of limitation, computer-readable medium include computer storage media and communication media.Computer-readable medium includes calculating
Machine storage medium and communication media.Computer storage medium store such as computer readable instructions, data structure, program module or
The information such as other data.Communication media is generally calculated with the modulated message signals such as carrier wave or other transmission mechanisms to embody
Machine readable instruction, data structure, program module or other data, and including any information transmitting medium.Above is any
Combination be also included within the scope of computer-readable medium.
In description of the invention, algorithm and display be not intrinsic with any certain computer, virtual system or other equipment
It is related.Various general-purpose systems can also be used together with example of the invention.As described above, this kind of system is constructed to be wanted
The structure asked is obvious.In addition, the present invention is also not directed to any particular programming language.It should be understood that can use each
Kind programming language realizes summary of the invention described herein, and the description done above to language-specific is to disclose this
The preferred forms of invention.
Those skilled in the art should understand that the module of the equipment in example disclosed herein or unit or groups
Part can be arranged in equipment as depicted in this embodiment, or alternatively can be positioned at and the equipment in the example
In different one or more equipment.Module in aforementioned exemplary can be combined into a module or furthermore be segmented into multiple
Submodule.Those skilled in the art will understand that can be carried out adaptively to the module in the equipment in embodiment
Change and they are arranged in one or more devices different from this embodiment.It can be the module or list in embodiment
Member or component are combined into a module or unit or component, and furthermore they can be divided into multiple submodule or subelement or
Sub-component.Other than such feature and/or at least some of process or unit exclude each other, it can use any
Combination is to all features disclosed in this specification (including adjoint claim, abstract and attached drawing) and so disclosed
All process or units of what method or apparatus are combined.Unless expressly stated otherwise, this specification is (including adjoint power
Benefit require, abstract and attached drawing) disclosed in each feature can carry out generation with an alternative feature that provides the same, equivalent, or similar purpose
It replaces.In addition, be described as herein can be as the processor of computer system or as described in executing by some in the embodiment
The combination of method or method element that other devices of function are implemented.Therefore, have for implementing the method or method element
The processor of necessary instruction form the device for implementing this method or method element.
Claims (10)
1. a kind of planing method in electric car parking lot, suitable for being executed in calculating equipment, which comprises
Construct degree of regretting Mechanism Model according to reflection-reaction normal form, and according to degree of regretting Mechanism Model building based on decision according to
Rely probabilistic automobile user model, the automobile user model includes that decision relies on utility function；
By the uncertain uncertain model of place for carrying out clustering and constructing electric car to electric car, it is described not
Certainty model of place includes that the institute that electric car parking lot is likely to occur during operation is stateful；
According to the two-stage programming of the automobile user model and uncertain model of place building electric car parking lot
Model, the two-stage programming model obtain maximum profit as target using electric car integrator in power distribution network comprising parking
Lower layer's planning of upper layer planning and parking lot traffic control that field addressing, constant volume, price incentive design；And
Pre-defined algorithm is respectively adopted to solve the upper layer planning and lower layer's planning of the two-stage programming model, obtains electronic
The optimum programming scheme of car park.
2. the method for claim 1, wherein the decision dependence utility function includes:
Wherein, Wy,iIt is income of the electric car i in time interval y,It is remuneration and benefit of the electric car i in time interval y
Patch,It is not convenient cost of the electric car i in time interval y,It is that battery of the electric car i in time interval y fills
Discharge loss cost.
3. the method for claim 1, wherein the uncertainty of electric car includes interior raw uncertain and external not true
It is qualitative, wherein the interior raw uncertain participation willingness factor for including automobile user, external uncertainty include electric car
Initial state-of-charge, arrival time and time departure.
4. the method for claim 1, wherein the upper layer planning algorithm of first stage is genetic algorithm, second stage
Lower layer's planning algorithm is first dual interior point.
5. such as method of any of claims 1-4, wherein the upper layer objective function of the two-stage programming model is
The profit F of electric car integrator in power distribution networkPLIt maximizes, its calculation formula is:
max FPL=ΛOpe-CInv
Wherein, ΛOpeIt is year operation income, CInvIt is year equivalent cost of investment,It is that the electric car being arranged in node b fills
Power station quantity,It is the binary variable for indicating electric car parking lot and whether being established in node b, ΩBIt is the candidate in system
Node, ΩSIt is scene set, Y is contract period, ρy,sIt is the probability that scene s occurs in time interval y, Λy,sIt is in the time
The operation income of scene s, k when being spaced ycpIt is the year value operator of charging pile, kldIt is the year value operator in soil, πcpIt is two-way
Charger cost of investment.
6. method as claimed in claim 5, wherein the upper layer constraint condition of the two-stage programming model are as follows:
Wherein,It is the maximum electric automobile charging station quantity in node b setting, RewbIt is that excitation subsidy valence is set in node b
Lattice,It is the maximum excitation subsidized price in node b setting.
7. such as method of any of claims 1-6, wherein lower layer's objective function of the two-stage programming model is
Parking lot run maximum revenue, decision variable include electric car parking lot charge-discharge electric power and user whether with electronic vapour
The binary variable of vehicle parking lot signing.
8. a kind of device for planning in electric car parking lot is executed suitable for residing in calculate in equipment, described device includes:
User model construction unit, suitable for constructing degree of regretting Mechanism Model according to reflection-reaction normal form, and according to degree of the regretting machine
Simulation building relies on probabilistic automobile user model based on decision, and the automobile user model includes decision
Rely on utility function；
Model of place construction unit, suitable for constructing electric car not by the uncertain clustering that carries out to electric car
Certainty model of place, the uncertainty model of place include during operation electric car parking lot be likely to occur it is all
State；
Plan model construction unit is suitable for constructing electronic vapour according to the automobile user model and uncertain model of place
The two-stage programming model in vehicle parking lot, the two-stage programming model obtain maximum benefit with electric car integrator in power distribution network
Profit is target comprising lower layer's rule of upper layer planning and parking lot traffic control that parking lot addressing, constant volume, price incentive design
It draws；And
Plan model solves unit, suitable for pre-defined algorithm is respectively adopted to the upper layer planning of the two-stage programming model and lower layer
Planning is solved, and the optimum programming scheme in electric car parking lot is obtained.
9. a kind of calculating equipment, comprising:
At least one processor；And
At least one processor including computer program instructions；
At least one processor and the computer program instructions are configured as making together at least one described processor
It obtains the calculating equipment and executes such as method of any of claims 1-7.
10. a kind of computer readable storage medium for storing one or more programs, one or more of programs include instruction,
Described instruction by server when being executed, so that the server executes appointing in method described in -7 according to claim 1
One method.
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| CN110838243A (en) * | 2019-11-13 | 2020-02-25 | 腾讯科技（深圳）有限公司 | Parking space data processing method and device and computer readable storage medium |
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| CN110838243A (en) * | 2019-11-13 | 2020-02-25 | 腾讯科技（深圳）有限公司 | Parking space data processing method and device and computer readable storage medium |
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