CN106557872A - Many parking stall intelligent three-phase charging group charging systems and method - Google Patents
Many parking stall intelligent three-phase charging group charging systems and method Download PDFInfo
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Abstract
Many parking stall intelligent three-phase charging group's charging systems, including input/output module, communication module, control module, intelligence computation module, protection module；The control method that intelligent three-phase charging group in parking stall charges mainly includes three steps：The first step carries out data acquisition；Second step is pre-processed to data；3rd step carries out optimization processing.The power dispatching distribution of each charging inlet of present period is calculated according to intelligent dispatching algorithm, and scheduling result is sent to into corresponding each charging pile, so as to instruct control centre to be charged control.The program can be prevented effectively from the wasting of resources, improve the utilization rate of charging pile.
Description
Technical field
The present invention relates to charging electric vehicle control method, more particularly to many parking stall intelligent three-phase charging group's charge controls
Method.
Background technology
Energy-saving and emission-reduction, low-carbon environment-friendly are being advocated all in the whole world.Under this overall situation, electric automobile is used as a kind of new forms of energy vapour
Car, is progressively promoting, and on future market, accounting can more and more higher.But, for electric automobile compares traditional automobile, there is also
The limitation of itself, such as endurance are low, and this has resulted in its distance travelled than relatively limited, and the energy of battery is relatively low, work(
The problems such as rate is inadequate.Therefore, the complementary capabilities of the endurance of electric automobile, and power supply are to determine Development of Electric Vehicles quality
Key factor.
Along with the development of electric automobile, with its auxiliary facility also progressively in the middle of building, at present comparatively in than
Compared with the primary stage.In the middle of a city, electric automobile charging pile is with respect to being fewer for electric automobile share.But such as
Fruit does not adopt charging pile to be charged, and adopts household electric to charge, then charging cost time very long, speed is relatively low.It is above-mentioned this
One of two problems all cause electric automobile using not enough facilitating, and the principal element of puzzlement Development of Electric Vehicles.Due to little
Area parking lot has built maturation substantially, and each parking lot is also only equipped with a certain proportion of charging pile；And limited by power load
System, if simultaneously to will also result in total load to the unordered charging of multiple electric automobiles excessive for multiple charging piles, if will relatively simply fill
Electric module packet, can cause part charging supply idle, cause the wasting of resources.
The content of the invention
To solve the deficiencies in the prior art, the control method that a kind of many parking stall intelligent three-phase charging groups charge is proposed, this
Kind of method is every electric automobile reasonable distribution charge power and time under total load so that charge faster, it is safer.
For achieving the above object, the present invention is employed the following technical solutions：
Many parking stall intelligent three-phase charging group's charging systems, including input/output module, communication module, control module, intelligence
Can computing module, protection module；Wherein：
Input/output module：For gathering the data of the charging electric vehicle state of different periods start time, Yi Jijie
Receive the charge power allocative decision from control centre, to be charged control to each electric automobile, while by charging progress and
Institute's power purchase expense is shown on a display screen；The data of charging electric vehicle state include batteries of electric automobile capacity, current electricity
Charge volume SOC (State of Charge), the car starting layover time, the car are expected time of departure, current area electric power in pond
Information on load, including cell net capacity, cell real-time electric power load value, power pack provide peak-trough electricity when hop count and peak valley
Electricity price information；
Control module：For processing the Various types of data information that transmits by communication module, while by input module therein
The data information transfer of the different periods start time charging electric vehicle state of collection gives intelligence computation module；Control module is also
The charge power allocative decision produced after intelligence computation module optimized scheduling is passed to into each charging pile by communication module
Input/output module；
Intelligence computation module：For the different periods start time charging electric vehicle status data transmitted to control module
Information is pre-processed, then according to Optimization scheduling algorithm, calculates the power dispatching point of each schedulable charge port of present period
With scheme and it is transferred to control module；
Protection module：For protecting charging pile to be not damaged by thunderstorm weather, while also having creepage protection function concurrently；
Communication module：For information transmission is carried out between processing module.
Above-mentioned many parking stall intelligent three-phase charging group's charging systems, input/output module also include RFID reader devices,
Charge for user card punching.
The control method that many parking stall intelligent three-phase charging groups charge mainly includes three steps：The first step carries out data and adopts
Collection；Second step is pre-processed to data；3rd step carries out optimization processing；Wherein
Step one：Data are acquired：
Collection present period has connected the quantity and its relevant parameter of the electric automobile of charging pile group, including electric automobile
Battery capacity, present battery are when charge volume SOC (State of Charge), car starting layover time, the car are expected to sail out of
Between；Current area electric load information, including the peak that cell net capacity, cell real-time electric power load value, power pack are provided
Hop count and peak-trough electricity electricity price information during paddy electricity；
Data below is passed to dispatching control center by charge port：Whether current charge port has vehicle to connect, current to connect
Charge port electric automobile battery charge volume (State of Charge), the electric automobile of current charge port connection
Remaining charging duration；
Define the parameter set Φ of the i-th periodiIt is as follows：
In formula, Zi,kThe charged state of the connected electric automobile of the i-th period kth charge port is represented, value is 0,1 or 2；
SOCi,kRepresent the battery charge state of the connected electric automobile of the i-th period kth charge port, span for [0,
1]；
RiRepresent the electricity price of the i-th period, unit：Unit；
MiRepresent that the i-th period electrical network can distribute to the peak power of electric automobile, unit：Kilowatt；
TkHop count when representing that the charging of the connected electric automobile of kth charge port is remaining；
Step 2：Data are pre-processed：
Charge parameter is pre-processed using slip window sampling：It is divided within 24 hours 144 window periods, each window
10 minutes periods, every scheduling in 10 minutes once, the window period time started is Ts(unit：Minute) end time be Ts+10；
In the pretreatment stage of the i-th start time period, along one window-unit of period forward slip, which slides just to represent and participates in window
The renewal of the charge information of a length of 24 hours of pretreatment；
144 time periods are divided within 24 hours one day, the section that clocks is variable i, and the span of i is 1-144；For every
Individual time period dispatching control center first carries out pretreatment and the scheduling decision of surveyed charge information, and then is produced by genetic algorithm
144 periods respective scheduling scheme, and dispatching control center reads current window H after thisiThe corresponding period charges
Corresponding parameter is simultaneously exported by scheduling scheme, all will may participate in the charge power scheduling coefficient Ψ of scheduling charge portiSend
The charge power of the different charge ports of control is adjusted to each charging pile, is made each normally participate in the charge port of Optimized Operation and is being protected
Demonstrate,prove in the case that the not super electrical network maximum of all charge port chief engineer rate of doing works can bear power and realize that day part purchases strategies are minimum, from
And realize that intraday purchases strategies are optimized；
To meet the basic vehicle need for electricity of user, scheduling system regulation at least makes SOC before chargeable duration terminates
Reach the basic charge target of a%, thus needed to do the charge port power distribution corresponding to different SOC values before Optimized Operation as
Under pretreatment：
In formula：
ti,kBy before the scheduling of the i-th period for kth charge port company electric automobile SOC reach a% needed for when hop count estimate
Measured value；
Xi,kFor electric energy of the connect electric automobile of the i-th period kth charge port needed for SOC=a% is charged to, unit：Thousand
Watt-hour；
BKThe total charge dosage of batteries of electric automobile is connect by K charge ports.
For the maximum average charge power of the i-th period all schedulable charge ports,Unit：Kilowatt；Its
In, niFor the i-th period schedulable charge port number；
Data prediction is carried out to following several situations：
Situation one：Work as SOCi,k≤ a% (i.e. ti,k≥0)
If (Tk-ti,k) >=0, then the charge port normally participate in scheduling；To optimize purchases strategies, the knot of Optimized Operation
Fruit is the charge power scheduling coefficient Ψ of the participation scheduling charge port for making the peak electricity periodiValue tend to 0, and the Ψ of paddy electricity periodi
Value tends to 1；But power can be born by electrical network maximum to be constrained, the power of each charge port typically all can not be very in the paddy electricity period
Greatly, such result is easy for causing that the paddy electricity period charges crowded and part charge port is made within the chargeable period connected
The SOC value of Rechargeable vehicle reaches a%, therefore existing to improve this congested conditions, and this Optimal Operation Model is filled the situation that meets
The Ψ of power portiSpan is set to [0.2,1] so as to have higher charging lower limit, it is easier to make SOC value reach a%.
If (Tk-ti,k) ＜ 0, then the charge port is improper participates in scheduling；Even if now representing the charge port always with current
Power charges and also is difficult to reach a%, therefore directly makes the Ψ values of the charge port for 1 or the Ψ that can reach most in pretreatment stage
Total charge power P of big value and remaining schedulable charge portmax≤(Mi- m Ψ G), (wherein, m is to meet (Tk-ti,k) 0 feelings of ＜
The charge port number of condition；ψ is to meet (Tk-ti,k) 0 situation charge ports of ＜ scheduling coefficient, G is charge port peak power output, and k is
Comprising it is all can normal consistency charge port numbering array), while the numbering of the connected charge port of this kind of Rechargeable vehicle no longer will be joined
With scheduling；TkHop count when representing that the charging of kth charge port is remaining, Tk=td-tn(wherein, tdWhen representing the pick-up of user's setting
Segment number, tnRepresent present period number)；
Situation two：Work as SOCi,K ＞ a% (i.e. ti,k＜ 0) when, the charge port normally participate in scheduling；Now being similarly improves paddy
The crowded phenomenon of electricity, also sets lower limit to the span of Ψ, and now in view of the charged of the connected electric automobile of the charge port
State is larger, in order to reduce charging cost as far as possible, should not fill excessive electricity in peak electricity, therefore the span of Ψ is set to [0.1,1]；
In order to ensure carrying out in order for charging scheduling, if variable Zi,kRecord the electric automobile connected by each charge port
Charged state；Before starting scheduling every time, need to update the real-time status of the variable, update method is as follows：
State 1：Electric automobile in all firm connections is disposed as schedulable state；
State 2：All electric automobiles for having connected but being also not up to charging requirement in this period are set to schedulable shape
State；
State 3：All electric automobiles for reaching user's setting charging finishing time are set as non-scheduling state；
State 4：All electric car charging interfaces in charge fault state are set to non-scheduling state；
State 5：The charge port of all not connected electric automobiles arranges non-scheduling state；
State 6：All electric automobiles being full of are disposed as non-scheduling state；
For above-mentioned 6 kinds of different states, following setting is done：
All charge ports in non-scheduling state, Zi,k=0 and kth charge port filling in the i-th period is directly set
Electrical power Pi,k=0；It is all in schedulable state and improper participation scheduling charge port, Zi,k=1；It is all in schedulable
State and normally can participate in dispatch charge port, Zi,k=2 and record its numbering；Due to charge port numbering k in subsequent step simultaneously
Differ and be set to one group of continuous number, realize during the computing such as cumulative, producing trouble, therefore for the convenience of modeling, being incorporated herein can be just
Charge port numbering array a (x) of scheduling is participated in often to all Zi,k=2 charge port enters line renumbering, realizes its numbering k and a
The correspondence of (x)；
Step 3：Optimization processing：
Define the object function of Optimized Operation：
In formula, C is charging station in intraday electricity cost, unit：Unit；
Ei,a(x)Electric energy, unit are exported for a (x) number charge port of the i-th period：Kilowatt hour；
N is the charge port number that normally can participate in dispatching；
I-th period was scheduled to the power P of the individual charge ports of a (x)i,a(x)Determination：
Pi,a(x)=Ψi,a(x)* G (formula 4)
In formula, the unit of Pi, a (x)：Kilowatt；
The electric energy E of the i-th charge port of period a (x) number outputi,a(x)Determination
Ei,a(x)=Pi,a(x)* Δ t (formula 5)
In formula, Δ t is the optimizing scheduling cycle.
Constraints：
In order to ensure power grid security, the gross output of the normal each charge port for participating in Optimized Operation should be less than for the i-th period
Electrical network can distribute to peak power M of the normal electric automobile for participating in schedulingiThe charge port total work of Ψ values has been set with pretreatment
The difference of rate,
Meet following formula requirement：
In above formula, the i-th period electrical network can distribute to peak power M of the normal electric automobile for participating in schedulingiFor cell electricity
Network capacity amount S deducts the i-th period of cell resident living power utility power Yi, and YiValue is obtained according to the actual electricity consumption situation of statistics cell.
Car requirement is used in order to meet the basic of user, the electric automobile for participating in scheduling is divided by its SOC value in day part
It is scheduled for two classes, even if the first kind is SOC≤a% and also is difficult to reach the electronic vapour of a% with the charging of current power always
Car, such electric automobile are less due to electricity itself, should meet user with timely charging in scheduling and use substantially car demand priority,
And its current available maximum charge power is preferentially given in pretreatment stage；Equations of The Second Kind vehicle is due to when electricity itself or charging
Between it is more sufficient, then be necessary by participating in based on the charging Optimized Operation of genetic algorithm to make electricity cost optimum.
Many parking stall intelligent three-phase charging group's charging system control centres calculate present period according to intelligent dispatching algorithm
Each charging inlet power dispatching distribution, and scheduling result is sent to into corresponding each charging pile, so as to instruct control
Center is charged control.The program can be prevented effectively from the wasting of resources, improve the utilization rate of charging pile.
Description of the drawings
Fig. 1 is data prediction flow chart of the present invention.
Fig. 2 is the three-phase alternating current intelligent charge group controller connection diagram of 10 parking stalls.
Cell load distribution curve under Fig. 3 Peak-valley TOU power prices.
Cell electricity consumption total load distribution curve under Fig. 4 Peak-valley TOU power prices.
Electricity cost comparison diagram before and after Fig. 5 Optimized Operations.
It is labeled as in figure：1 community resident household electricity load curve, 2 is bent without electric automobile power load during Optimized Operation
Line, electric power curves used for electric vehicle after 3 Optimized Operations, 4 without electric automobile during Optimized Operation and residential electricity consumption total load curve,
Electric automobile and residential electricity consumption total load curve after 5 Optimized Operations.
Specific embodiment
Referring to the drawings, be verify present invention method validity, with Hangzhou residential area underground parking be
Example carries out simulation analysis.Hangzhou cell underground parking garage has 200 parking stalls, and wherein resident has 100, electric automobile vehicle.
Now plan provides charging service for cell electric vehicle master with building charging pile in 100 parking stalls wherein.
As the cell is old cell, power grid construction backwardness, net capacity are limited.According to cell current electric grid load feelings
Condition, the capacity that can at most distribute to charging electric vehicle are 300KW.And at present according to national standard, the appearance of alternating-current charging pile
Measure as 7KW, that is to say, that the cell can only at most meet 42 alternating-current charging piles while working with rated power, it is impossible to meet
100 charging piles are while normal work.This problem is solved, simplest method is exactly that cell electrical network is transformed, but
This method input cost is high, and long construction period, while affecting cell normal electricity consumption demand.
Below with technical scheme in the case where cell electrical network is not changed solving this problem.
First, the solution of employing is that many parking stall intelligent three-phase charging groups charge.
The cell only needs to the three-phase alternating current intelligent charge group controller for installing 10 10 parking stalls, you can in cell electrical network not
In the case of doing any change, it is that charging pile is installed in 100 parking stalls；And not only cell will not be caused at peak times of power consumption
Electricity consumption overloads, and electric automobile is charged when can also be arranged in paddy electricity as far as possible, so as to improve the economy of whole cell electricity consumption
Property, reduce Rechargeable vehicle car owner charging cost.
One intelligent charge group controller S1-S10, wherein intelligent charge group controller S1, S4, S7, S10 connect three-phase circuit
In A phases, intelligent charge group controller S2, S5, S8 are connect in three-phase circuit B phases, intelligent charge group controller S3, S6, S9 connect three-phase
In circuit C phases, balance can three-phase circuit.Current cell load total capacity is limited to 500KW, can at most distribute to electronic
The capacity that automobile charges is 300KW, and the control control centre of many parking stall intelligent three-phase charging groups can each charge port of Real-Time Scheduling
Power output, so as to avoid affecting the normal electricity consumption of community resident.If 100 electric automobiles of cell are required for charging, electronic
Automobile charge user only need to be input into the information of oneself Vehicular charging on the control panel of charging pile, such as when vehicle starting is berthed
Between, vehicle is expected the time of departure, and charging pile will transfer information to dispatching control center by communication module, wherein scheduling control
Center processed includes control module and intelligence computation module, carries out comprehensive tune according to the period all charging electric vehicle information
Degree.
According to the electricity rate table of cell, when electric automobile is 0.8680 yuan/kilowatt hour in the electricity price that peak time charges, low
The electricity price that paddy period charges is 0.5880 yuan/kilowatt hour.If dispatching control center is dispatched buses filling for electric automobile in low ebb
Electricity, then can save many electricity charge spendings.When dispatching control center receives the charge request information of electric automobile, adjust
Degree control centre dispatches electric automobile and exists according to charge informations such as current schedulable charge port quantity, connected electric automobile SOC
The charge power of the period.
Specific implementation method：144 window periods were divided into by 1440 minutes one day, each window period 10 minutes, often
Every scheduling in 10 minutes once, represented for the i-th period with i, the span of i is [1,144].
Following table is 0 point to 1 point of Time segments division：
Divide each period in 24 hours, each when segment number and interval conversion mode of concrete period be：Define the period
Number be i, the period start time be Ts(unit：Minute) end time be Ts+ 10,10 refer to 10 minutes.(Ts+ 10)/10 is TsExtremely
TsWhen segment number corresponding to+10, such as 3: 20 corresponding TsFor 200 minutes, corresponding to which when segment number be i=(200+10)/
10=21.In these periods, period 1-48 and 133-144 are the paddy electricity periods, and period 49-132 is the peak electricity period.
Cell has 100 parking stalls and is available for charging electric vehicle, and each parking stall is specially equipped with a charge port, now uses k tables
Show each charge port numbering, then the span of k is [1,100].
Define the parameter set Φ of the i-th periodiIt is as follows：
In formula, Zi,kThe charged state of the connected electric automobile of the i-th period kth charge port is represented, value is 0,1 or 2；
SOCi,kRepresent the battery charge state of the connected electric automobile of the i-th period kth charge port, span for [0,
1]；
RiRepresent the electricity price of the i-th period, unit：Unit；
MiRepresent that the i-th period electrical network can distribute to the peak power of electric automobile, unit：Kilowatt；
TkHop count when representing that the charging of the connected electric automobile of kth charge port is remaining；
Charge parameter is pre-processed using slip window sampling：It is divided within 24 hours 144 window periods, each window
10 minutes periods, every scheduling in 10 minutes once, the window period time started is Ts(unit：Minute) end time be Ts+10。
In the pretreatment stage of the i-th start time period, window can be along one window-unit of period forward slip, its slip ginseng of just represent
With the renewal of the charge information of a length of 24 hours of pretreatment；
144 time periods are divided within 24 hours one day, the section that clocks is variable i, and the span of i is 1-144；For every
Individual time period dispatching control center can first carry out pretreatment and the scheduling decision of surveyed charge information, and then be produced by genetic algorithm
Raw 144 periods respective scheduling scheme, and dispatching control center can read current window H after thisiThe corresponding period
Charging scheduling scheme by corresponding output parameter, i.e., all charge power scheduling coefficient Ψ that may participate in scheduling charge portiSend out
Give each charging pile to adjust the charge power of the different charge ports of control, make each charge port that can normally participate in Optimized Operation exist
Ensure that the not super electrical network maximum of all charge port chief engineer rate of doing works realizes that day part purchases strategies are minimum in the case of can bearing power,
So as to realize that intraday purchases strategies are optimized；
To meet the basic vehicle need for electricity of user, scheduling system regulation at least makes SOC before chargeable duration terminates
60% basic charge target is reached, therefore needed to do the charge port power distribution corresponding to different SOC values before Optimized Operation
Following pretreatment：
In formula：
ti,kBy before the scheduling of the i-th period for kth charge port company electric automobile SOC up to 60% needed for when hop count
Estimated value；
Xi,kFor electric energy of the connect electric automobile of the i-th period kth charge port needed for SOC=60% is charged to, unit：
Kilowatt hour；
BKThe total charge dosage of batteries of electric automobile is connect by K charge ports.
For the maximum average charge power of the i-th period all schedulable charge ports,Unit：Kilowatt；Its
In, niFor the i-th period schedulable charge port number；
Data prediction is carried out to following several situations：
Situation one：Work as SOCi,k(t when≤60%i,k>=0),
If (Tk-ti , k) >=0, then the charge port normally participate in scheduling；To optimize purchases strategies, the knot of Optimized Operation
Fruit is that the value of the charge power scheduling coefficient Ψ i of the participation scheduling charge port for making the peak electricity period tends to 0, and the paddy electricity period
ΨiValue tends to 1；But power can be born by electrical network maximum to be constrained, the power of each charge port typically all could not in the paddy electricity period
Very big, such result is easy for causing the paddy electricity period to charge crowded and make part charge port make institute within the chargeable period
Even the SOC value of Rechargeable vehicle reaches 60%, therefore existing to improve this congested conditions, and this Optimal Operation Model is by the situation that meets
The Ψ of charge portiSpan is set to [0.2,1] so as to have higher charging lower limit, it is easier to make SOC value reach 60%.
If (Tk-ti,k) ＜ 0, then the charge port is improper participates in scheduling；Even if now representing the charge port always with current
Power charges and also is difficult to reach 60%, therefore directly makes the Ψ values of the charge port for 1 or the Ψ that can reach in pretreatment stage
Total charge power P of maximum and remaining schedulable charge portmax≤(Mi- m Ψ G), (wherein, m is to meet (Tk-ti,k) ＜ 0
The charge port number of situation；ψ is to meet (Tk-ti,k) 0 situation charge ports of ＜ scheduling coefficient, G be charge port peak power output, k
Be comprising it is all can normal consistency charge port numbering array), while the numbering of the connected charge port of this kind of Rechargeable vehicle will no longer
Participate in scheduling；TkHop count when representing that the charging of kth charge port is remaining, Tk=td-tn(wherein, tdRepresent the pick-up of user's setting
When segment number, tnRepresent present period number)；
Situation two：Work as SOCi,k(t during ＞ 60%i,k＜ 0) when, the charge port normally participate in scheduling；Now it is similarly and changes
The crowded phenomenon of kind paddy electricity, also sets lower limit to the span of Ψ, and now in view of the connected electric automobile of the charge port
State-of-charge is larger, in order to reduce charging cost as far as possible, should not fill excessive electricity in peak electricity, therefore the span of Ψ is set to
[0.1,1]；
In order to ensure carrying out in order for charging scheduling, if variable Zi,kRecord the electric automobile connected by each charge port
Charged state；Before starting scheduling every time, need to update the real-time status of the variable, update method is as follows：
State 1：Electric automobile in all firm connections is disposed as schedulable state；
State 2：All electric automobiles for having connected but being also not up to charging requirement in this period are set to schedulable shape
State；
State 3：All electric automobiles for reaching user's setting charging finishing time are set as non-scheduling state；
State 4：All electric car charging interfaces in charge fault state are set to non-scheduling state；
State 5：The charge port of all not connected electric automobiles arranges non-scheduling state；
State 6：All electric automobiles being full of are disposed as non-scheduling state；
For above-mentioned 6 kinds of different states, following setting is done：
All charge ports in non-scheduling state, Zi,k=0 and kth charge port filling in the i-th period is directly set
Electrical power Pi,k=0；It is all in schedulable state and improper participation scheduling charge port, Zi,k=1；It is all in schedulable
State and normally can participate in dispatch charge port, Zi,k=2 and record its numbering；And and can normally participate in the charge port volume of scheduling
Number group a (x) is to all Zi,k=2 charge port enters line renumbering, realizes that its numbering k is corresponding with a (x).For example：It is adjustable
The charge port numbering of degree is 1,5,10,17,19, then a (x)=[1,5,10,17,19], i.e. a (1)=1, a (2)=5, a (3)=
10, a (4)=17, a (5)=19.
The object function of Optimized Operation：Use car requirement, this model to participate in the electronic of scheduling to meet the basic of user
Automobile is divided into two classes in the SOC value of day part by which and is scheduled, even if the first kind is for SOC≤60% and always with current power
Charging also is difficult to reach 60% electric automobile, and such electric automobile is less due to electricity itself, should be filling in time in scheduling
Electricity meets user and uses substantially car demand, and preferentially provides it with maximum charge power as far as possible in pretreatment stage；Equations of The Second Kind,
Due to vehicle as electricity itself or charging interval are more sufficient, then it is necessary to adjust by the charging optimization participated in based on genetic algorithm
Spend to make electricity cost optimum.
To sum up, a (x) in following modeling only includes Equations of The Second Kind electric automobile.
The object function of Optimized Operation：
In formula, C is charging station in intraday electricity cost, unit：Unit.
Ei,a(x)Electricity is exported for a (x) number charge port of the i-th period；
N is the charge port number that normally can participate in dispatching.
I-th period was scheduled to the power P of the individual charge ports of a (x)i,a(x)Determination
Pi,a(x)=Ψi,a(x)* G (formula 4)
In formula, the unit of Pi, a (x)：Kilowatt；
The electric energy E of the i-th charge port of period a (x) number outputi,a(x)Determination
Ei,a(x)=Pi,a(x) * Δ t (formula 5)
In formula, unit：Kilowatt hour；Δ t be the optimizing scheduling cycle, here
Constraints
In order to ensure power grid security, the gross output of the normal each charge port for participating in Optimized Operation should be less than for the i-th period
Electrical network can distribute to peak power M of the normal electric automobile for participating in schedulingiThe charge port total work of Ψ values has been set with pretreatment
The difference of rate.
In formula, the i-th period electrical network can distribute to peak power M of the normal electric automobile for participating in schedulingi=cell electrical network
Capacity S- the i-th period of cell resident living power utility power Yi；YiValue is obtained according to the actual electricity consumption situation of statistics cell.
The simulation result of the embodiment of the present invention as illustrated, respectively community resident power load, without electricity during Optimized Operation
The curve map of charging electric vehicle load after electrical automobile charging load and Optimized Operation.
From Fig. 3, we can see that the peak period of the household electricity for carrying out resident 19:00 to 22:Between 00, without optimization
The peak period of charging electric vehicle is scheduling to 18:The peak period of 00 to the second day morning and residential electricity consumption overlaps, and increases electrical network
Load, and charging electric vehicle peak period is the peak period that residential electricity consumption can be both avoided during paddy electricity after Optimized Operation,
The electricity charge can also be saved.
As can be seen from Figure 4 come, before and after Optimized Operation, charging electric vehicle and resident living power utility power are dropped from 668KW
To 500KW, paddy electricity is fully used after Optimized Operation, plays a part of peak load shifting, to be solved and add peak on the peak without the need for charging
Problem.Electrical network peak load can be exceeded without residential electricity consumption during Optimized Operation, Electrical Safety after Optimized Operation, is can ensure that.
As can be seen from Figure 5 paddy electricity is made full use of to charge after Optimized Operation, first block diagram is residential electricity consumption expense,
Middle block diagram is charging electric vehicle and residential electricity consumption expense after Optimized Operation, and rightmost block diagram is without Optimized Operation
When charging electric vehicle and residential electricity consumption expense.It can be seen that, the cost of charging after optimized scheduling, is significantly reduced, from original
3648.232 yuan be reduced to 2669.814 yuan, significantly reduce 27% electricity cost.
Claims (3)
1. the intelligent three-phase charging group's charging system of parking stall more than, including input/output module, communication module, control module, intelligence
Computing module, protection module；Wherein：
Input/output module：For gathering the data of the charging electric vehicle state of different periods start time, and receive
From the charge power allocative decision of control centre, to be charged control to each electric automobile, while by charging progress and being purchased
The electricity charge are shown on a display screen；The data of charging electric vehicle state have included batteries of electric automobile capacity, present battery
Charge volume SOC (State of Charge), the car starting layover time, the car are expected time of departure, current area electric load
Information, including cell net capacity, cell real-time electric power load value, power pack provide peak-trough electricity when hop count and peak-trough electricity electricity
Valency information；
Control module：For processing the Various types of data information transmitted by communication module, while input module therein is gathered
The data information transfer of different periods start time charging electric vehicle state give intelligence computation module；Control module is also by intelligence
The charge power allocative decision produced after the optimized scheduling of energy computing module passes to the input of each charging pile by communication module
Output module；
Intelligence computation module：For the different periods start time charging electric vehicle status data information transmitted to control module
Pre-processed, then according to Optimization scheduling algorithm, calculated the power dispatching distribution side of each schedulable charge port of present period
Case is simultaneously transferred to control module；
Protection module：For protecting charging pile to be not damaged by thunderstorm weather, while also having creepage protection function concurrently；
Communication module：For information transmission is carried out between processing module.
2. many parking stall intelligent three-phase charging group's charging systems as claimed in claim 1, input/output module also include RFID
Reader device, charges for user card punching.
3. the control method that the intelligent three-phase charging of parking stall more than group charges mainly includes three steps：The first step carries out data and adopts
Collection；Second step is pre-processed to data；3rd step carries out optimization processing；Wherein
Step one：Data are acquired：
Collection present period has connected the quantity and its relevant parameter of the electric automobile of charging pile group, including batteries of electric automobile
Charge volume SOC (State of Charge), the car starting layover time, the car are expected the time of departure for capacity, present battery；
Current area electric load information, including the peak-trough electricity that cell net capacity, cell real-time electric power load value, power pack are provided
When hop count and peak-trough electricity electricity price information；
Data below is passed to dispatching control center by charge port：Whether current charge port has vehicle to connect, and what is currently connected fills
The battery of the electric automobile of power port charge volume (State of Charge), the residue of the electric automobile of current charge port connection
Charging duration；
Define the parameter set Φ of the i-th periodiIt is as follows：
In formula, Zi,kThe charged state of the connected electric automobile of the i-th period kth charge port is represented, value is 0,1 or 2；
SOCi,kThe battery charge state of the connected electric automobile of the i-th period kth charge port is represented, span is [0,1]；
RiRepresent the electricity price of the i-th period, unit：Unit；
MiRepresent that the i-th period electrical network can distribute to the peak power of electric automobile, unit：Kilowatt；
Hop count when Tk represents that the charging of the connected electric automobile of kth charge port is remaining；
Step 2：Data are pre-processed：
Charge parameter is pre-processed using slip window sampling：It is divided within 24 hours 144 window periods, each window period
10 minutes, every scheduling in 10 minutes once, the window period time started was Ts(unit：Minute) end time be Ts+10；I-th
The pretreatment stage of start time period, along one window-unit of period forward slip, which slides and participates in pre- locating window
The renewal of the charge information of a length of 24 hours of reason；
144 time periods are divided within 24 hours one day, the section that clocks is variable i, and the span of i is 1-144；During for each
Between section dispatching control center first carry out pretreatment and the scheduling decision of surveyed charge information, and then produce 144 by genetic algorithm
Individual period respective scheduling scheme, and dispatching control center reads current window H after thisiThe corresponding period charges and dispatches
Corresponding parameter is simultaneously exported by scheme, all will may participate in the charge power scheduling coefficient Ψ of scheduling charge portiIt is sent to each
Charging pile controls the charge power of different charge ports to adjust, and makes each normally participate in the charge port of Optimized Operation and is ensureing institute
There is the not super electrical network maximum of the charge port chief engineer rate of doing work to realize that day part purchases strategies are minimum in the case of can bearing power, so as to reality
Existing intraday purchases strategies are optimized；
To meet the basic vehicle need for electricity of user, scheduling system regulation at least reaches SOC before chargeable duration terminates
The basic charge target of a%, therefore needed to do following to the charge port power distribution corresponding to different SOC values before Optimized Operation
Pretreatment：
In formula：
ti,kBy before the scheduling of the i-th period for kth charge port company electric automobile SOC reach a% needed for when hop count estimation
Value；
Xi,kFor electric energy of the connect electric automobile of the i-th period kth charge port needed for SOC=a% is charged to, unit：Kilowatt
When；
BKThe total charge dosage of batteries of electric automobile is connect by K charge ports；
For the maximum average charge power of the i-th period all schedulable charge ports,Unit：Kilowatt；Wherein, ni
For the i-th period schedulable charge port number；
Data prediction is carried out to following several situations：
Situation one：Work as SOCi,k≤ a% (i.e. ti,k≥0)
If (Tk-ti,k) >=0, then the charge port normally participate in scheduling；To optimize purchases strategies, the result of Optimized Operation is to make
The charge power scheduling coefficient Ψ of the participation scheduling charge port of peak electricity periodiValue tend to 0, and the Ψ of paddy electricity periodiValue becomes
In 1；But power can be born by electrical network maximum to be constrained, the power of each charge port typically all can not be very big in the paddy electricity period, so
Result be easy for causing the paddy electricity period to charge crowded and make part charge port that connected charging vapour cannot be made within the chargeable period
The SOC value of car reaches a%, therefore existing to improve this congested conditions, and this Optimal Operation Model is by the charge port of the situation that meets
ΨiSpan is set to [0.2,1] so as to have higher charging lower limit, it is easier to make SOC value reach a%；
If (Tk-ti,k) ＜ 0, then the charge port is improper participates in scheduling；Even if now representing the charge port always with current power
Charging also is difficult to reach a%, therefore it is 1 or the maximum of the Ψ that can be reached directly to make the Ψ values of the charge port in pretreatment stage
And total charge power P of remaining schedulable charge portmax≤(Mi- m Ψ G), (wherein, m is to meet (Tk-ti,k) 0 situations of ＜
Charge port number；ψ is to meet (Tk-ti,k) 0 situation charge ports of ＜ scheduling coefficient, G be charge port peak power output, k be comprising
It is all can normal consistency charge port numbering array), while the numbering of the connected charge port of this kind of Rechargeable vehicle will be no longer participate in tune
Degree；TkHop count when representing that the charging of kth charge port is remaining, Tk=td-tn(wherein, tdSegment number when representing the pick-up of user's setting,
tnRepresent present period number)；
Situation two：Work as SOCi,k＞ a% (i.e. ti,k＜ 0) when, the charge port normally participate in scheduling；Now being similarly improves paddy electricity and gathers around
Crowded phenomenon, also sets lower limit to the span of Ψ, and now considers the state-of-charge of the connected electric automobile of the charge port
It is larger, in order to reduce charging cost as far as possible, excessive electricity should not be filled in peak electricity, therefore the span of Ψ is set to [0.1,1]；
In order to ensure carrying out in order for charging scheduling, if variable Zi,kRecord the charging of the electric automobile connected by each charge port
State；Before starting scheduling every time, need to update the real-time status of the variable, update method is as follows：
State 1：Electric automobile in all firm connections is disposed as schedulable state；
State 2：All electric automobiles for having connected but being also not up to charging requirement in this period are set to schedulable state；
State 3：All electric automobiles for reaching user's setting charging finishing time are set as non-scheduling state；
State 4：All electric car charging interfaces in charge fault state are set to non-scheduling state；
State 5：The charge port of all not connected electric automobiles arranges non-scheduling state；
State 6：All electric automobiles being full of are disposed as non-scheduling state；
For above-mentioned 6 kinds of different states, following setting is done：
All charge ports in non-scheduling state, Zi,k=0 and directly arrange kth charge port the i-th period charging work(
Rate Pi,k=0；It is all in schedulable state and improper participation scheduling charge port, Zi,k=1；It is all in schedulable state
And can normally participate in the charge port dispatched, Zi,k=2 and record its numbering；Due to charge port numbering k in subsequent step and differ
It is set to one group of continuous number, realizes during the computing such as cumulative, producing trouble, therefore for the convenience of modeling, be incorporated herein and normally can join
With charge port numbering array a (x) dispatched to all Zi,k=2 charge port enters line renumbering, realizes its numbering k and a (x)
Correspondence；
Step 3：Optimization processing：
Define the object function of Optimized Operation：
In formula, C is charging station in intraday electricity cost, unit：Unit；
Ei,a(x)Electric energy, unit are exported for a (x) number charge port of the i-th period：Kilowatt hour；
N is the charge port number that normally can participate in dispatching；
I-th period was scheduled to the power P of the individual charge ports of a (x)i,a(x)Determination：
Pi,a(x)=Ψi,a(x)* G (formula 4)
In formula, the unit of Pi, a (x)：Kilowatt；
The electric energy E of the i-th charge port of period a (x) number outputi,a(x)Determination
Ei,a(x)=Pi,a(x)* Δ t (formula 5)
In formula, Δ t is the optimizing scheduling cycle；
Constraints：
In order to ensure power grid security, the gross output of the normal each charge port for participating in Optimized Operation should be less than the i-th period electrical network
Peak power M of the normal electric automobile for participating in scheduling can be distributed toiWith pretreatment set Ψ values charge port general power it
Difference, that is, meet following formula requirement：
In above formula, the i-th period electrical network can distribute to peak power M of the normal electric automobile for participating in schedulingiFor cell net capacity
S deducts the i-th period of cell resident living power utility power Yi, and YiValue is obtained according to the actual electricity consumption situation of statistics cell.
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Application publication date: 20170405 |