CN106712061B - A kind of in a few days priority scheduling method based on the schedulable ability of electric car - Google Patents

A kind of in a few days priority scheduling method based on the schedulable ability of electric car Download PDF

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CN106712061B
CN106712061B CN201611070501.2A CN201611070501A CN106712061B CN 106712061 B CN106712061 B CN 106712061B CN 201611070501 A CN201611070501 A CN 201611070501A CN 106712061 B CN106712061 B CN 106712061B
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electric car
charge
discharge
distribution network
power distribution
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CN106712061A (en
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张有兵
任帅杰
杨晓东
杨捷伦
翁国庆
戚军
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Zhejiang University of Technology ZJUT
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/64—Optimising energy costs, e.g. responding to electricity rates
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L55/00—Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles

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  • General Physics & Mathematics (AREA)
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  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
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Abstract

A kind of in a few days priority scheduling method based on the schedulable ability of electric car, the present invention propose a kind of event driven for single EV in a few days priority scheduling method on the basis of schedulable conceptions of ability.The comprehensive vehicle history charging behavior of this method and current inbound information, establish the schedulable Capacity Analysis Model of EV；It is accustomed in conjunction with EV user with vehicle, with the minimum index of distribution total load peak-valley difference, determines the schedulable ability optimal threshold of EV；Finally according to the numerical relation of new networking EV schedulable capability evaluation result and optimal threshold, judging electric car, whether there is or not priority scheduling power, so that it is determined that the charge and discharge mode of each networking EV.

Description

A kind of in a few days priority scheduling method based on the schedulable ability of electric car
Technical field
The present invention relates to a kind of in a few days priority scheduling methods based on the schedulable ability of electric car.
Background technique
The fast development and scale of electric car (electric vehicle, EV) are accessed to the optimization fortune of electric system Row brings opportunities and challenges, and EV has huge potentiality in terms of energy-saving and emission-reduction, and a large amount of EV are in conventional load at the same time Evening peak charging, exacerbates the peak-valley difference of distribution network systems, so that load peak is limited more than transformer capacity, affects transformer Safe and reliable operation.
In order to guarantee the stable operation of electric system, it is particularly important to formulate reasonable EV charge and discharge control strategy, V2G The it is proposed of (vehicle to grid) thought, so that carrying out reasonable charge and discharge scheduling to scale EV provides approach, not only Economic benefit directly can be brought for user, moreover it is possible to realize peak load shifting, provide the ancillary services such as frequency modulation, reactive compensation for power grid Improve operation of power networks reliability.
From the point of view of current electricity market situation, according to the difference of optimization period, it is main that Optimized Operation is implemented to EV charge and discharge There are two classes: dispatching and in a few days dispatch a few days ago.Wherein, the formulation of scheduling strategy only obtains vehicle with the statistical data of database a few days ago Driving parameters there is the single vehicle of larger randomness especially for trip situation when for practical controlling, have certain Limitation.Compared to dispatching a few days ago, the in a few days scheduling of EV has more practical significance.In the correlative study in a few days dispatched, electronic vapour The formulation that vehicle V2G regulation of energy ability is specifically quantified as electric car charge and discharge control strategy provides effective foundation.It realizes grid-connected The accurate evaluation of the controllable ability of EV energy is that the operation tune of power distribution network how is effectively participated in using EV as mobile energy-storage units Control is realized to one of key technology of scale EV Optimized Operation.
Summary of the invention
In order to guarantee the stable operation of electric system, reasonable EV charge and discharge control strategy is formulated, the present invention provides one kind In a few days priority scheduling method based on the schedulable ability of electric car.
The technical scheme is that
A kind of in a few days priority scheduling method based on the schedulable ability of electric car, the in a few days priority scheduling method towards Electric car (electric vehicle, EV) charge-discharge facility cluster in local power distribution network, with event driven decision service Mechanism follows the EV to network on current point in time, to the EV of (i.e. described local power distribution network) of networking on current point in time Charge and discharge mode carries out decision, method includes the following steps:
S1: one day continuous time for 24 hours was subjected to sliding-model control, J period is divided into, for any kth time period, has K ∈ { 1,2 ..., J }, and the when a length of Δ t of kth time period, draw conventional load curve in local power distribution network, formulate and use towards EV The charge and discharge electricity price at family；
S2: setting the sum of the EV of pre-access local power distribution network as n, when there is new electric car l access, and l ∈ 1, 2 ..., n }, the relevant information of electric car l: turn-on time T is obtained by charge-discharge facilityin,l, the initial charged shape of EV battery State (i.e. SOC, State of Charge) S0,l, SOC indicates the ratio of battery remaining power and battery capacity, therefore has 0≤S0,l ≤1；
S3:EV user inputs expected time departure Tout,lAnd desired state-of-charge S when leavingE,l, and have 0≤SE,l≤ 1；
S4: if the duration of electric car l access local power distribution network, which is greater than, charges to S for the battery of electric car lE,l Required most grows in short-term, thens follow the steps S5, and EV user is otherwise allowed independently to choose whether to be ready to modify Tout,lAnd SE,lIf EV User agree to execute modification then skips to step S3, if EV user refuse execute modification if not to the charge and discharge mode of the EV user into Row decision；
S5: the schedulable ability of electric car (schedulable ability, SA) Integrated Evaluation Model is established；From local Electric energy public service platform in power distribution network obtains the history inbound information of electric car l, and according to the electric car l's of acquisition History inbound information and this inbound information, comprehensive assessment obtain the value R of the SA of electric car ll；
S6: according to the history inbound information of all EV in local power distribution network, the SA comprehensive assessment based on EV in step S5 Model determines optimal SA threshold value Q*；
S7: pass through RlWith Q*Numerical relation determine the charge and discharge mode of electric car l, if meeting Rl≥Q*, then from electric energy Public service platform reads the total load information of current time local power distribution network, and the total load of local power distribution network includes conventional load The EV load of local power distribution network has been accessed with current time, and has executed step S8, if Rl< Q*Then electric car l is carried out unordered It charges and skips to step S9；
S8: charge-discharge facility executes two stages Optimized Operation strategy to electric car l；
S9: the charge and discharge plan of electric car l is uploaded to electric energy public service platform, and waits connecing for next EV Enter.
Step S5 of the present invention specifically includes the following steps:
S51. SA evaluation index is chosen, SA evaluation system is established；
Three evaluation indexes are chosen, if the initial decision matrix as composed by evaluation index is expressed as: B=(blj)n×3, In, j=1,2,3, three evaluation indexes include reverse index: the extent of deterioration b of electric car l batteryl1, positive index: EV is used Family credit rating bl2With EV reverse power supply ability bl3；
The credit rating b of EV userl2And the reverse power supply ability b of EVl3Attribute value respectively indicate it is as follows:
Wherein, X indicates the total degree of electric car l participation Optimized Operation in a certain period of time, excellent for any x-th Change scheduling, there is x ∈ { 1,2 ..., X }；Pd,l、ηdRespectively indicate the discharge power and discharging efficiency of electric car l； It respectively indicates the time of access local power distribution network and EV user's expection when electric car l x-th participates in scheduling and leaves local distribution The time of net；Indicate the practical time for leaving local power distribution network when electric car l x-th participates in scheduling；Indicate electronic vapour When vehicle l x-th participates in scheduling, the starting SOC of electric car l；Cs,lIndicate the battery capacity of electric car l；
S52. SA comprehensive estimation method is executed, specific as follows:
To each index attribute value in initial decision matrix B: bl1、bl2And bl3Carry out nondimensionalization processing；Treated Evaluating matrix is denoted as: D=(dlj)n×3, in which:
In formula, dljFor the attribute value of j-th of index of electric car l after nondimensionalization processing；For positive index bl2With bl3,Indicate the maximum value of j-th of index；For reverse index bl1,Indicate the minimum value of j-th of index；ξjIndicate bljWithDifference absolute value maximum value:
Analytic hierarchy process (AHP) (i.e. AHP), standard deviation and average value maximization approach is respectively adopted and determines evaluation index bl1、bl2 And bl3Master, objective weight wSj、wOj, AHP is a kind of subjective weighting method, and the subjective weight of three indexs is determined using AHP When, first by three evaluation index bl1、bl2And bl3As the rule layer of AHP, secondly by three index b of subjective homeostasisl1、 bl2And bl3Importance carry out the judgment matrix of construction rules layer, finally can determine that three assessments refer to by consistency check Target subjectivity weight；Standard deviation and average value maximization approach are a kind of objective weighted models, by comparing three evaluation index bl1、 bl2And bl3The variation degree of attribute value determines objective weight, and the more high then objective weight of variation degree is bigger, otherwise smaller； By three evaluation index bl1、bl2And bl3Subjective and objective weight separately constitute vector wS、wO, obtained according to the legal fusion of multiplication group Three evaluation index bl1、bl2And bl3Comprehensive weight coefficient:And then obtain the access electronic vapour of local power distribution network The value R of the SA of vehicle ll:
In step S6 of the present invention, optimal SA threshold value Q*Determination process is specific as follows:
S61: the inbound information of all EV in nearly h days: EV turn-on time, the initial SOC of EV battery is set；EV user's expection is left The time of local power distribution network and the when of leaving desired SOC are that SA threshold value extracts source, constitute set H, for any one day s, Have s ∈ { 1,2 ..., h }, setting starting optimization day: s=1；
S62: extracting the inbound information of s day all EV and the conventional load information of power distribution network, to all EV progress SA Assessment；
S63: SA threshold search section Θ is primarily determined, and siding-to-siding block length is I, i.e. SA belongs to the EV sum of region of search Θ For I, has i ∈ { 1,2 ..., I } for any EV search serial number i, initiating searches serial number: i=1 is set；
S64: setting e=1, if the schedulable ability R of electric car ee≥Θi, then obtain priority scheduling and weigh and carry out two ranks Section Optimized Operation, ΘiIndicate i-th of search value of region of search Θ；If Re< ΘiThen electric car e carries out unordered charging；It is folded The charge and discharge load of electrical automobile e is powered on to power distribution network total load, e=e+1 continues the charge and discharge mode for judging electric car；When When e >=n, i.e., the charge and discharge plan of s days all EV is completed, and calculates power distribution network total load peak-valley difference；
S65:i=i+1 goes to step S64, continues to carry out decision to the charge and discharge mode of all EV；It is as i >=I, i.e., complete At search in s days, the SA threshold value for making the smallest SA of power distribution network total load peak-valley difference be s days in the SA region of search is determined Qs；S=s+1 goes to S62；
S66: as nearly h days QsWhen solution finishes, using averaging methodDetermine optimal SA threshold value.
In step S8 of the present invention, two stages Optimized Operation policy enforcement procedure is specific as follows:
S81. determine optimized variable: the purpose of two stages Optimized Operation strategy is to formulate optimal charge and discharge for electric car l Plan:If the duration T of electric car l access local power distribution networksy,l= Tout,l-Tin,l, Tsy,lThe period set for being included is set as Tm,l, and set that the length is Vl,WithRespectively Indicate electric car l the of lasting accessWithThe charge-discharge electric power of period；Then two stages Optimized Operation strategy Optimized variable, that is, electric car l in k ∈ Tm,lThe charge and discharge power P of periodl(k), has continuously adjustable characteristic, and full Foot-Pd,l≤Pl(k)≤Pc,l, Pc,lIndicate the specified charge power of electric car l；
S82. the target of first stage optimization is to minimize the charge and discharge cost of electric car l user Wherein, ccd,l|(ηc=ηd=1) it indicates not considering charge and discharge efficiency When ideal charge and discharge expense, ηcIndicate the charge efficiency of electric car l, cbat,lIndicate the loss conversion of electric car l battery Cost, closs,lThe energy loss expense as caused by charge and discharge efficiency of expression, respectively indicates are as follows:
Wherein, pri (k) indicates the electricity price information of k period；Indicate the cell degradation cost as caused by discharging,Table Show and the cost depletions caused by battery are fluctuated as charge-discharge electric power；cbIndicate the acquisition cost of per unit battery capacity, cLIndicate electricity Replace expense, B in pondclIndicate the circulating battery number in the case where the depth of discharge value of the battery of electric car l is DOD, Edis,lIt indicates Calculate total discharge capacity of the battery of electric car l in time span；Electric car l is respectively indicated to hold Continued access enterPeriod,The charge-discharge electric power of period；εfIndicate the battery loss cost coefficient of electric car l； eloss,l(k) the energy loss amount of the battery k period of electric car l under energy flow angle is indicated；
When carrying out first stage optimization, constraint condition in need of consideration has: the state-of-charge constraint of electric car l；EV The charge requirement of user constrains；The constraint of transformer maximum load；Electric car l accesses the duration constraint of power distribution network；By The charge and discharge plan so that electric car l when EV user's charge and discharge cost minimization can be obtained in the optimization of first stage；
S83. the target of second stage optimization is to minimize distribution network load to fluctuate variance:Wherein, Lbas(k) the distribution base load of k period is indicated, When indicating electric car l access power distribution network, the EV cluster load of formulation is completed in charge and discharge plan,Power distribution network average load after indicating electric car l access；
When second stage optimizes, in addition to needing to consider constraint condition when first stage optimization, it is also necessary to consider EV user Charge and discharge cost constraint, i.e. the charge and discharge cost of EV user of the EV user when second stage optimizes be not greater than the first stage Charge and discharge cost when optimization；Optimum results based on the first stage can reach supply and demand two by the optimization of second stage The final charge and discharge plan of electric car l is obtained under the premise of the collaboration optimization of side.
The present invention proposes a kind of event driven for single EV in a few days on the basis of " schedulable ability " concept Priority scheduling method.The comprehensive vehicle history charging behavior of this method and current inbound information, establish the schedulable capability analysis mould of EV Type；It is accustomed in conjunction with EV user with vehicle, with the minimum index of distribution total load peak-valley difference, determines the schedulable ability optimal threshold of EV； Finally according to the numerical relation of new networking EV schedulable capability evaluation result and optimal threshold, judge that electric car is adjusted whether there is or not preferential Degree power, so that it is determined that the charge and discharge mode of each networking EV.
Networking of the present invention refers to accessing the local power distribution network.
Conventional load refers in local power distribution network: other loads in local power distribution network other than electric car.
The function of electric energy public service platform describes: in the local distribution, public service platform is collected in local power distribution network Total load information, on the one hand, issue real-time total load information for the EV charge-discharge facility into the local power distribution network, it is another Aspect, the EV load for receiving each EV charge-discharge facility are integrated, so as to real-time update total load information.
The beneficial effects of the present invention are:
1, process is simple, and EV user only need to input charge information, EV battery information in the client of charge-discharge facility, To learn the schedulable capability evaluation value of EV.
2, it can preferably go out the vehicle for being more suitable for participating in priority scheduling, meet user's charge requirement and distribution transformer appearance Under the premise of amount limitation, reduce the peak-valley difference of Distribution Network Load Data, optimization Distribution Network Load Data fluctuation, while reduce the equal charge and discharge of user's vehicle at This.
3, practicability is stronger, which is based on event driven service mechanism, compared to dispatching a few days ago, in face of trip Situation has more to be of practical significance when single EV of larger randomness.
Detailed description of the invention
Fig. 1 is the day total load curve graph under different charge and discharge modes
Fig. 2 is the schedulable capability evaluation result figure of electric car
Specific embodiment
A kind of in a few days priority scheduling method based on the schedulable ability of electric car, the in a few days priority scheduling method towards EV charge-discharge facility cluster in local power distribution network is followed on current point in time and is networked with event driven decision service mechanism EV, to networking on current point in time, the charge and discharge mode of EV of (i.e. described local power distribution network) carries out decision, this method packet Include following steps:
S1: one day continuous time for 24 hours was subjected to sliding-model control, J period is divided into, for any kth time period, has K ∈ { 1,2 ..., J }, and the when a length of Δ t of kth time period, draw conventional load curve in local power distribution network, formulate and use towards EV The charge and discharge electricity price at family；
S2: setting the sum of the EV of pre-access local power distribution network as n, when there is new electric car l access, and l ∈ 1, 2 ..., n }, the relevant information of electric car l: turn-on time T is obtained by charge-discharge facilityin,l, the initial charged shape of EV battery State S0,l, SOC indicates the ratio of battery remaining power and battery capacity, therefore has 0≤S0,l≤1；
S3:EV user inputs expected time departure Tout,lAnd desired state-of-charge S when leavingE,l, and have 0≤SE,l≤ 1；
S4: if the duration of electric car l access local power distribution network, which is greater than, charges to S for the battery of electric car lE,l Required most grows in short-term, thens follow the steps S5, and EV user is otherwise allowed independently to choose whether to be ready to modify Tout,lAnd SE,lIf EV User agree to execute modification then skips to step S3, if EV user refuse execute modification if not to the charge and discharge mode of the EV user into Row decision；
S5: the schedulable ability integration assessment models of electric car are established；It is flat from the electric energy public service in local power distribution network Platform obtains the history inbound information of electric car l, and according to the history inbound information of electric car l of acquisition and entering for this Net information, comprehensive assessment obtain the value R of the SA of electric car ll；
S6: according to the history inbound information of all EV in local power distribution network, the SA comprehensive assessment based on EV in step S5 Model determines optimal SA threshold value Q*；
S7: pass through RlWith Q*Numerical relation determine the charge and discharge mode of electric car l, if meeting Rl≥Q*, then from electric energy Public service platform reads the total load information of current time local power distribution network, and the total load of local power distribution network includes conventional load The EV load of local power distribution network has been accessed with current time, and has executed step S8, if Rl< Q*Then electric car l is carried out unordered It charges and skips to step S9；
S8: charge-discharge facility executes two stages Optimized Operation strategy to electric car l；
S9: the charge and discharge plan of electric car l is uploaded to electric energy public service platform, and waits the access of next EV.
Step S5 of the present invention specifically includes the following steps:
S51. SA evaluation index is chosen, SA evaluation system is established；
Three evaluation indexes are chosen, if the initial decision matrix as composed by evaluation index is expressed as: B=(blj)n×3, In, j=1,2,3, three evaluation indexes include reverse index: the extent of deterioration b of electric car l batteryl1, positive index: EV is used Family credit rating bl2With EV reverse power supply ability bl3；
The credit rating b of EV userl2And the reverse power supply ability b of EVl3Attribute value respectively indicate it is as follows:
Wherein, X indicates the total degree of electric car l participation Optimized Operation in a certain period of time, excellent for any x-th Change scheduling, there is x ∈ { 1,2 ..., X }；Pd,l、ηdRespectively indicate the discharge power and discharging efficiency of electric car l； It respectively indicates the time of access local power distribution network and EV user's expection when electric car l x-th participates in scheduling and leaves local distribution The time of net；Indicate the practical time for leaving local power distribution network when electric car l x-th participates in scheduling；Indicate electronic vapour When vehicle l x-th participates in scheduling, the starting SOC of electric car l；Cs,lIndicate the battery capacity of electric car l；
S52. SA comprehensive estimation method is executed, specific as follows:
To each index attribute value in initial decision matrix B: bl1、bl2And bl3Carry out nondimensionalization processing；Treated Evaluating matrix is denoted as: D=(dlj)n×3, in which:
In formula, dljFor the attribute value of j-th of index of electric car l after nondimensionalization processing；For positive index bl2With bl3,Indicate the maximum value of j-th of index；For reverse index bl1,Indicate the minimum value of j-th of index；ξjIndicate bljWith Difference absolute value maximum value:
Analytic hierarchy process (AHP) (i.e. AHP), standard deviation and average value maximization approach is respectively adopted and determines evaluation index bl1、bl2 And bl3Master, objective weight wSj、wOj, AHP is a kind of subjective weighting method, and the subjective weight of three indexs is determined using AHP When, first by three evaluation index bl1、bl2And bl3As the rule layer of AHP, secondly by three index b of subjective homeostasisl1、 bl2And bl3Importance carry out the judgment matrix of construction rules layer, finally can determine that three assessments refer to by consistency check Target subjectivity weight；Standard deviation and average value maximization approach are a kind of objective weighted models, by comparing three evaluation index bl1、 bl2And bl3The variation degree of attribute value determines objective weight, and the more high then objective weight of variation degree is bigger, otherwise smaller； By three evaluation index bl1、bl2And bl3Subjective and objective weight separately constitute vector wS、wO, obtained according to the legal fusion of multiplication group Three evaluation index bl1、bl2And bl3Comprehensive weight coefficient:And then obtain the access electronic vapour of local power distribution network The value R of the SA of vehicle ll:
In step S6 of the present invention, optimal SA threshold value Q*Determination process is specific as follows:
S61: the inbound information of all EV in nearly h days: EV turn-on time, the initial SOC of EV battery is set；EV user's expection is left The time of local power distribution network and the when of leaving desired SOC are that SA threshold value extracts source, constitute set H, for any one day s, Have s ∈ { 1,2 ..., h }, setting starting optimization day: s=1；
S62: extracting the inbound information of s day all EV and the conventional load information of power distribution network, to all EV progress SA assessment；
S63: SA threshold search section Θ is primarily determined, and siding-to-siding block length is I, i.e. SA belongs to the EV sum of region of search Θ For I, has i ∈ { 1,2 ..., I } for any EV search serial number i, initiating searches serial number: i=1 is set；
S64: setting e=1, if the schedulable ability R of electric car ee≥Θi, then obtain priority scheduling and weigh and carry out two ranks Section Optimized Operation, ΘiIndicate i-th of search value of region of search Θ；If Re< ΘiThen electric car e carries out unordered charging；It is folded The charge and discharge load of electrical automobile e is powered on to power distribution network total load, e=e+1 continues the charge and discharge mode for judging electric car；When When e >=n, i.e., the charge and discharge plan of s days all EV is completed, and calculates power distribution network total load peak-valley difference；
S65:i=i+1 goes to step S64, continues to carry out decision to the charge and discharge mode of all EV；It is as i >=I, i.e., complete At search in s days, the SA threshold value for making the smallest SA of power distribution network total load peak-valley difference be s days in the SA region of search is determined Qs；S=s+1 goes to S62；
S66: when Qs solution in nearly h days finishes, using averaging methodDetermine optimal SA threshold value.
In step S8 of the present invention, two stages Optimized Operation policy enforcement procedure is specific as follows:
S81. determine optimized variable: the purpose of two stages Optimized Operation strategy is to formulate optimal charge and discharge for electric car l Plan:If the duration T of electric car l access local power distribution networksy,l=Tout,l- Tin,l, Tsy,lThe period set for being included is set as Tm,l, and set that the length is Vl,WithRespectively indicate electricity Electrical automobile l in lasting accessWithThe charge-discharge electric power of period；The then optimization of two stages Optimized Operation strategy Variable, that is, electric car l is in k ∈ Tm,lThe charge and discharge power P of periodl(k), have continuously adjustable characteristic, and satisfaction-Pd,l≤ Pl(k)≤Pc,l, Pc,lIndicate the specified charge power of electric car l；
S82. the target of first stage optimization is to minimize the charge and discharge cost of electric car l user Wherein, ccd,l|(ηc=ηd=1) it indicates not considering charge and discharge efficiency When ideal charge and discharge expense, ηcIndicate the charge efficiency of electric car l, cbat,lIndicate the loss conversion of electric car l battery Cost, closs,lThe energy loss expense as caused by charge and discharge efficiency of expression, respectively indicates are as follows:
Wherein, pri (k) indicates the electricity price information of k period；Indicate the cell degradation cost as caused by discharging, It indicates to fluctuate the cost depletions caused by battery as charge-discharge electric power；cbIndicate the acquisition cost of per unit battery capacity, cLIt indicates Battery replacement expense, BclIndicate the circulating battery number in the case where the depth of discharge value of the battery of electric car l is DOD, Edis,lTable Show the total discharge capacity for calculating the battery of electric car l in time span；Electric car l is respectively indicated to exist Persistently accessedPeriod,The charge-discharge electric power of period；εfIndicate the battery loss cost coefficient of electric car l； eloss,l(k) the energy loss amount of the battery k period of electric car l under energy flow angle is indicated；
When carrying out first stage optimization, constraint condition in need of consideration has: the state-of-charge constraint of electric car l；EV The charge requirement of user constrains；The constraint of transformer maximum load；Electric car l accesses the duration constraint of power distribution network；By The charge and discharge plan so that electric car l when EV user's charge and discharge cost minimization can be obtained in the optimization of first stage；
S83. the target of second stage optimization is to minimize distribution network load to fluctuate variance:Wherein, Lbas(k) the distribution base load of k period is indicated,It indicates When electric car l accesses power distribution network, the EV cluster load of formulation is completed in charge and discharge plan, Power distribution network average load after indicating electric car l access；
When second stage optimizes, in addition to needing to consider constraint condition when first stage optimization, it is also necessary to consider EV user Charge and discharge cost constraint, i.e. the charge and discharge cost of EV user of the EV user when second stage optimizes be not greater than the first stage Charge and discharge cost when optimization；Optimum results based on the first stage can reach supply and demand two by the optimization of second stage The final charge and discharge plan of electric car l is obtained under the premise of the collaboration optimization of side.The total load packet of certain residential block local power distribution network Include conventional load and electric car cluster load.Residential block access distribution transformer capacity be 750kVA, efficiency 0.95, if Calculating time span is that for 24 hours, time interval Δ t is 0.5h.If the electric car scale of the local power distribution network service is 60.Electricity The relative parameters setting and part throttle characteristics of electrical automobile are as shown in table 1.
1 parameter setting of table
In table 1, rdIndicate EV daily travel, it is assumed that EV only fills primary electricity daily, and is charged to desired state-of-charge SE,lAfterwards Start second to go on a journey, user is insufficient for next day charge requirement S in battery capacityE,lWhen start to charge.Define S0,l= (SE,l-rd/Ra), wherein RaThe mileage after being charged to expectation state is charged to for EV.SEV,max、SEV,minRespectively indicate EV The upper and lower limit of battery SOC, Tout,l~N (8.92,3.242) indicate Tout,lMeet normal distribution.
By the charging behavior of Monte Carlo simulation EV, sampling obtains in a few days the inbound information of interior 60 electric cars for 24 hours With day charge requirement data.Using YALMIP and Cplex to the two stages obtained according to step S81~S83 in Matlab Optimized Operation strategy is modeled, is solved, and is superimposed Shuffle Mode, all orderly mode, theoretical priority scheduling mode, reality respectively EV cluster load and conventional load under priority scheduling mode obtain the day total load curve of corresponding local power distribution network, such as Fig. 1 institute Show.
Four kinds of charge and discharge modes are described as follows:
Shuffle Mode: the EV charge-discharge facility in local power distribution network provides lasting invariable power charging clothes for the EV of access Business, the charge mode until EV user leaves or reach EV user's charge requirement；
Whole orderly modes: the two stages that the EV charge-discharge facility in local power distribution network is proposed based on step S81~S83 are excellent Change the charge mode that scheduling strategy carries out orderly charge and discharge control to 60 networking EV；
Theoretical priority scheduling mode: using the charging of the theoretical priority scheduling of priority scheduling method in day of the present invention Mode；The inbound information and charge requirement data of 60 EV in simulating sun for 24 hours is mentioned according to analogue data according to the present invention In a few days priority scheduling method determines the charge and discharge system of 60 EV.
Practical priority scheduling mode: using the charging of the practical priority scheduling of priority scheduling method in day of the present invention Mode；When EV is sequentially ingressed into local power distribution network in chronological order, it is based on event driven Optimization Mechanism, in accordance with the present invention In a few days priority scheduling method determine the charge and discharge system of current point in time networking EV, until 60 EV are all accessed.
The related data information of the total load of local power distribution network day under four kinds of modes is counted, as shown in table 2.
The different charge and discharge mode relevant statistics of table 2
As shown in Table 2, theoretical priority scheduling mode and the day total load of local power distribution network under practical priority scheduling mode are bent Line peak-valley difference is respectively 43.98kW, 46.95kW, and load fluctuation standard deviation is respectively as follows: 10.44kW, 11.99kW, compared to unordered Mode and all orderly peak-valley difference 306.64kW, 185.14kW under modes and load fluctuation standard deviation 96.47kW, 66.51kW, local power distribution network day total load peak-valley difference and load fluctuation standard deviation decrease.
As shown in Figure 1, theoretical priority scheduling mode and local power distribution network day total load curve under practical priority scheduling mode It tends to be steady, realizes the beneficial effect for reducing local power distribution network total load peak-valley difference, optimization local distribution network load fluctuation； Compared to the day total load curve under theoretical priority scheduling mode, local power distribution network day under practical priority scheduling mode of the invention Total load curve can deviation theory priority scheduling mode, this deviation be to a certain extent by the random of in a few days EV networking behavior Caused by property.
As shown in Table 2, in terms of the optimization of the equal cost of user side vehicle, Shuffle Mode, all orderly mode and theory are excellent First and under practical mode of priority the equal cost of user side vehicle (first /) is respectively 15.28, -3.17,8.07 and 8.59；This result Show the present invention to optimize to the maximum extent with net side load condition, avoids load occur when vehicle scale is larger that " peak valley falls Set " phenomenon, the interests of part EV user are sacrificed, when so that the equal charge and discharge cost of the vehicle of EV user is compared to all orderly modes Higher but relatively low compared to Shuffle Mode, i.e., compared to Shuffle Mode, priority scheduling method is still certain in day of the present invention User side interests are optimized in degree.
Specifically, the charging behavior of EV cluster of stochastic simulation, assesses the SA of each networking EV.Unordered charging mould Formula and all orderly modes are equivalent to and SA threshold value are set as the in a few days excellent of EV cluster assessment result maximum value and minimum value respectively It first dispatches, belongs to two kinds of special circumstances of threshold value setting.By attached drawing 1 it is found that matching under unordered charge mode and all orderly modes There is " on peak plus peak " and " peak valley inversion " respectively in the load curve of power grid, it is easy to cause voltage out-of-limit problem, therefore be The safe operation for guaranteeing local power distribution network, need to set a suitable threshold value.The priority scheduling mode within day of the present invention Under, SA Integrated Evaluation Model is established according to step S51~S52 in conjunction with EV user's history inbound information, first according to step S61~ S66 determines that optimal SA threshold value is 0.53, and the SA assessed value of each new networking EV, optimal SA threshold are secondly determined according to step S51~S52 Value and the SA evaluation value information of 60 EV are as shown in Figure 2；The SA assessed value and Q of 60 EV are judged by step S7*Numerical value close It is the charge and discharge mode for determining electric car l, the final vehicle number for preferably going out to participate in priority scheduling is 25, and remaining 35 then straight It taps into unordered charge mode, to reach optimal scheduling effect.
In summary, the present invention is based on the schedulable Capacity Analysis Models of EV, by rationally screening, dispatch to participation is more suitable for Vehicle orderly controlled, can reduce peak-valley difference and the load fluctuation of Distribution Network Load Data, realize EV cluster load peak load shifting While reduce user side EV user the equal charge and discharge cost of vehicle.
Content described in this specification embodiment is only enumerating to the way of realization of inventive concept, protection of the invention Range should not be construed as being limited to the specific forms stated in the embodiments, and protection scope of the present invention also includes art technology Personnel conceive according to the present invention it is conceivable that equivalent technologies mean.

Claims (4)

1. a kind of in a few days priority scheduling method based on the schedulable ability of electric car, which is characterized in that the in a few days preferential tune Degree method is towards electric car charge-discharge facility cluster, i.e. EV charge-discharge facility cluster in local power distribution network, with event driven Decision service mechanism follows the EV to network on current point in time, to the charge and discharge mode of the EV to network on current point in time into Row decision, method includes the following steps:
S1: one day continuous time for 24 hours was subjected to sliding-model control, J period is divided into, for any kth time period, there is k ∈ { 1,2 ..., J }, and the when a length of Δ t of kth time period, draw conventional load curve in local power distribution network, formulate towards EV user Charge and discharge electricity price；
S2: setting the sum of the EV of pre-access local power distribution network as n, when there is new electric car l access, and l ∈ 1,2 ..., N }, the relevant information of electric car l: turn-on time T is obtained by charge-discharge facilityin,l, the initial state-of-charge S of EV battery0,l, And 0≤S0,l≤1；
S3:EV user inputs expected time departure Tout,lAnd desired state-of-charge S when leavingE,l, and have 0≤SE,l≤1；
S4: if the duration of electric car l access local power distribution network, which is greater than, charges to S for the battery of electric car lE,lIt is required Most grow in short-term, then follow the steps S5, EV user otherwise allowed independently to choose whether to be ready to modify Tout,lAnd SE,lIf EV user Agree to that executing modification then skips to step S3, does not determine to the charge and discharge mode of the EV user if EV user refuses to execute modification Plan；
S5: the schedulable ability integration assessment models of electric car, i.e. SA Integrated Evaluation Model are established；Electricity out of local power distribution network Energy public service platform obtains the history inbound information of electric car l, and the history inbound information of the electric car l according to acquisition And this inbound information, comprehensive assessment obtain the value R of the SA of electric car ll；
S6: according to the history inbound information of all EV in local power distribution network, the SA Integrated Evaluation Model based on EV in step S5 Determine optimal SA threshold value Q*；
S7: pass through RlWith Q*Numerical relation determine the charge and discharge mode of electric car l, if meeting Rl≥Q*, then public from electric energy Service platform reads the total load information of current time local power distribution network, and the total load of local power distribution network includes conventional load and works as The preceding moment has accessed the EV load of local power distribution network, and executes step S8, if Rl< Q*Then electric car l carries out unordered charging And skip to step S9；
S8: charge-discharge facility executes two stages Optimized Operation strategy to electric car l；
S9: the charge and discharge plan of electric car l is uploaded to electric energy public service platform, and waits the access of next EV.
2. a kind of in a few days priority scheduling method based on the schedulable ability of electric car according to claim 1, feature Be, the step S5 specifically includes the following steps:
S51. SA evaluation index is chosen, SA evaluation system is established；
Three evaluation indexes are chosen, if the initial decision matrix as composed by evaluation index is expressed as: B=(blj)n×3, wherein j =1,2,3, three evaluation indexes include reverse index: the extent of deterioration b of electric car l batteryl1, positive index: EV user's letter Expenditure bl2With EV reverse power supply ability bl3；
The credit rating b of EV userl2And the reverse power supply ability b of EVl3Attribute value respectively indicate it is as follows:
Wherein, X indicates the total degree of electric car l participation Optimized Operation in a certain period of time, and any x-th is optimized and is adjusted Degree, there is x ∈ { 1,2 ..., X }；Pd,l、ηdRespectively indicate the discharge power and discharging efficiency of electric car l；Respectively Indicate that local power distribution network is left in the time of access local power distribution network and EV user's expection when electric car l x-th participates in scheduling Time；Indicate the practical time for leaving local power distribution network when electric car l x-th participates in scheduling；Indicate electric car l When x-th participates in scheduling, the starting state-of-charge of electric car l；Cs,lIndicate the battery capacity of electric car l；
S52. SA comprehensive estimation method is executed, specific as follows:
To each index attribute value in initial decision matrix B: bl1、bl2And bl3Carry out nondimensionalization processing；Assessment that treated Matrix is denoted as: D=(dlj)n×3, in which:
In formula, dljFor the attribute value of j-th of index of electric car l after nondimensionalization processing；For positive index bl2And bl3, Indicate the maximum value of j-th of index；For reverse index bl1,Indicate the minimum value of j-th of index；ξjIndicate bljWithDifference The maximum value of absolute value:
Analytic hierarchy process (AHP), standard deviation and average value maximization approach is respectively adopted and determines evaluation index bl1、bl2And bl3Master, Objective weight wSj、wOj, analytic hierarchy process AHP is a kind of subjective weighting method, when determining the subjective weight of three indexs using AHP, First by three evaluation index bl1、bl2And bl3As the rule layer of AHP, secondly by three index b of subjective homeostasisl1、bl2 And bl3Importance carry out the judgment matrix of construction rules layer, finally can determine three evaluation indexes by consistency check Subjective weight；Standard deviation and average value maximization approach are a kind of objective weighted models, by comparing three evaluation index bl1、bl2 And bl3The variation degree of attribute value determines objective weight, and the more high then objective weight of variation degree is bigger, otherwise smaller；By three A evaluation index bl1、bl2And bl3Subjective and objective weight separately constitute vector wS、wO, three are obtained according to the legal fusion of multiplication group Evaluation index bl1、bl2And bl3Comprehensive weight coefficient:And then obtain access local power distribution network electric car l's The value R of SAl:
3. a kind of in a few days priority scheduling method based on the schedulable ability of electric car according to claim 1, feature It is, in the step S6, optimal SA threshold value Q*Determination process is specific as follows:
S61: the inbound information of all EV in nearly h days: EV turn-on time, the initial SOC of EV battery is set；Local is left in EV user's expection The time of power distribution network and the when of leaving desired SOC are that SA threshold value extracts source, and constituting set H has s ∈ for any one day s { 1,2 ..., h }, setting starting optimization day: s=1；
S62: extracting the inbound information of s day all EV and the conventional load information of power distribution network, to all EV progress SA assessment；
S63: primarily determining SA threshold search section Θ, and siding-to-siding block length is I, i.e. the EV sum that SA belongs to region of search Θ is I, Have i ∈ { 1,2 ..., I } for any EV search serial number i, initiating searches serial number: i=1 is set；
S64: setting e=1, if the schedulable ability R of electric car ee≥Θi, then obtain that priority scheduling is weighed and to carry out two stages excellent Change scheduling, ΘiIndicate i-th of search value of region of search Θ；If Re< ΘiThen electric car e carries out unordered charging；Superposition electricity The charge and discharge load of electrical automobile e continues the charge and discharge mode for judging electric car to power distribution network total load, e=e+1；As e >=n When, i.e., the charge and discharge plan of s days all EV is completed, and power distribution network total load peak-valley difference is calculated；
S65:i=i+1 goes to step S64, continues to carry out decision to the charge and discharge mode of all EV；As i >=I, that is, complete the Search in s days determines the SA threshold value Q for making the smallest SA of power distribution network total load peak-valley difference be s days in the SA region of searchs；S= S+1 goes to S62；
S66: as nearly h days QsWhen solution finishes, using averaging methodDetermine optimal SA threshold value.
4. a kind of in a few days priority scheduling method based on the schedulable ability of electric car according to claim 1, feature It is, in the step S8, two stages Optimized Operation policy enforcement procedure is specific as follows:
S81. determine optimized variable: the purpose of two stages Optimized Operation strategy is to formulate optimal charge and discharge plan for electric car l:If the duration T of electric car l access local power distribution networksy,l=Tout,l-Tin,l, Tsy,lThe period set for being included is set as Tm,l, and set that the length is Vl,WithRespectively indicate electric car L in lasting accessWithThe charge-discharge electric power of period；Then the optimized variable of two stages Optimized Operation strategy is Electric car l is in k ∈ Tm,lThe charge and discharge power P of periodl(k), have continuously adjustable characteristic, and satisfaction-Pd,l≤Pl(k)≤ Pc,l, Pc,lIndicate the specified charge power of electric car l；
S82. the target of first stage optimization is to minimize the charge and discharge cost of electric car l user Wherein, ccd,l|(ηc=ηdWhen=1) indicating not considering charge and discharge efficiency Ideal charge and discharge expense, ηcIndicate the charge efficiency of electric car l, cbat,lIndicate that cost is converted in the loss of electric car l battery, closs,lThe energy loss expense as caused by charge and discharge efficiency of expression, respectively indicates are as follows:
Wherein, pri (k) indicates the electricity price information of k period；Indicate the cell degradation cost as caused by discharging,Indicate by Charge-discharge electric power fluctuates the cost depletions caused by battery；cbIndicate the acquisition cost of per unit battery capacity, cLIndicate that battery is set Change expense, BclIndicate the circulating battery number in the case where the depth of discharge value of the battery of electric car l is DOD, Edis,lIt indicates to calculate Total discharge capacity of the battery of electric car l in time span；Electric car l is respectively indicated in lasting access ?Period,The charge-discharge electric power of period；εfIndicate the battery loss cost coefficient of electric car l；eloss,l(k) table Show the energy loss amount of the battery k period of electric car l under energy flow angle；
When carrying out first stage optimization, constraint condition in need of consideration has: the state-of-charge constraint of electric car l；EV user Charge requirement constraint；The constraint of transformer maximum load；Electric car l accesses the duration constraint of power distribution network；By first The charge and discharge plan so that electric car l when EV user's charge and discharge cost minimization can be obtained in the optimization in stage；
S83. the target of second stage optimization is to minimize distribution network load to fluctuate variance:Wherein, Lbas(k) the distribution base load of k period is indicated,It indicates When electric car l accesses power distribution network, the EV cluster load of formulation is completed in charge and discharge plan, Power distribution network average load after indicating electric car l access；
When second stage optimizes, in addition to needing to consider constraint condition when first stage optimization, it is also necessary to consider filling for EV user Discharge cost constraint, i.e. the charge and discharge cost of EV user of the EV user when second stage optimizes is not greater than first stage optimization When charge and discharge cost；Optimum results based on the first stage can be assisted by the optimization of second stage reaching supply and demand two sides The final charge and discharge plan of electric car l is obtained under the premise of with optimization.
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