CN111284347A - State clustering coding method in charging station vehicle access control - Google Patents

State clustering coding method in charging station vehicle access control Download PDF

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CN111284347A
CN111284347A CN202010106439.8A CN202010106439A CN111284347A CN 111284347 A CN111284347 A CN 111284347A CN 202010106439 A CN202010106439 A CN 202010106439A CN 111284347 A CN111284347 A CN 111284347A
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CN111284347B (en
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唐子昱
方道宏
赵传信
唐昊
方明星
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Hefei University of Technology
Anhui Normal University
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Anhui Normal University
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Abstract

The invention discloses a state clustering coding method in charging station vehicle access control, which comprises the following steps: 1, an electric vehicle arrives at a charging station to provide a service request as an event, and the service request is described as the type of the arriving vehicle and the charging demand time from the current charge state to full charge; 2, taking the charging time from the current SOC to full charge of the electric vehicle as the state of the accessed charging pile, and simultaneously taking the states of all the charging piles as a combined service time demand state; discretizing the demand state of the combined service time, and arranging and coding each element of the demand state from small to large to form a clustering state of the charging pile; and 4, when the event occurs, determining whether the arriving electric vehicle is accessed to the charging station and provides charging service according to the peak regulation electricity price of the power grid and the clustering state of the charging pile. The invention can effectively reduce the state space scale of the vehicle access control of the charging station, reduce the space-time resource requirement of the access control optimization and improve the optimization efficiency.

Description

State clustering coding method in charging station vehicle access control
Technical Field
The invention belongs to the technical field of intelligent control and optimization, and particularly relates to a state clustering coding method in charging station vehicle access control.
Background
Currently, automobile production has been shifted from automobiles powered by traditional energy sources to new energy automobiles, wherein electric automobiles are the key direction of new energy automobile development, and have huge consumption potential and market. Charging pile is an important infrastructure for providing charging service for electric automobile, and with the improvement of market holding capacity of electric automobile by a wide margin, it is imperative to build a charging station to carry out centralized operation and management on the charging service of a plurality of charging piles. With the improvement of the permeability of new energy such as wind, light and electricity and the like, and due to the characteristics of randomness and intermittence, how to effectively manage and guide the electricity consumption of power users, so that the intelligence and the adaptability of power service are improved is a trend. The electric automobile is an important flexible load, and the potential of the electric power users participating in electric power market dispatching can be exerted by reasonably dispatching and controlling the charging and discharging of the electric automobile. For example, each level of scheduling center can formulate a power peak regulation plan according to the source charge prediction data and issue the power peak regulation plan through real-time power price, so that reasonable power utilization of an electric vehicle charging station is guided, and autonomous peak clipping and valley filling or peak shifting and valley filling at a user side are promoted.
Therefore, under a real-time power grid peak regulation electricity price mechanism, how to perform dynamic self-adaptive response on a charging service request of a randomly coming electric vehicle according to the real-time peak regulation electricity price of a power grid and the online service states of all charging piles in the station by using an intelligent access service system of the electric vehicle of the charging station is to control whether the charging service request is accessed to the service, so that the operation economy of the charging station is improved, the power grid peak regulation requirement is self-adapted, and the intelligent access service system is an important problem to be researched and solved. However, when the vehicle access control problem is optimized, if modeling is directly performed according to the natural physical state of the charging station, the state space scale is large, the problem of dimension disaster exists, more space-time resources are occupied, and the optimization efficiency and the control effect are influenced.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a state clustering coding method in charging station vehicle access control according to the problem characteristics, which can effectively reduce the scale of a state space, reduce the space-time computing resource requirement of access control optimization, and improve the optimization efficiency and the control effect.
The invention adopts the following technical scheme for solving the technical problems:
the invention relates to a state coding method in charging station vehicle access control, which is characterized in that the state coding method is applied to a charging station service system which is provided with J charging piles and provides paid charging service for M electric vehicles which arrive randomly, each charging pile can meet the charging power requirements of the M electric vehicles, and one charging pile only provides charging service for one electric vehicle at a time;
recording the J charging piles as CS respectively1,CS2,…,CSj,…,CSJAnd the charging power requirement of the M electric vehicles is recorded as P1,P2,…,Pm,…,PMWherein CS isjIndicates the jth charging pile, PmRepresenting the charging power requirement of the mth electric vehicle;
the battery capacity of the mth electric vehicle is recorded as Em，m＝1,2,…,M；
Recording the total time of a day as T, and making the peak-shaving electricity price of the power grid a function of time T as PR (T), and PR (T) epsilon phiPRT is more than or equal to 0 and less than or equal to T; supposing that the peak regulation electricity price of the power grid is issued according to the dispatching instruction period, K is the maximum period number of one day, and T is orderedkPeak-shaving power rate PR for the kthkWhen the electricity price is issued, the electricity price sequence of peak regulation is recorded as { (tau)k,PRk)|k＝0,1,2,…,K-1,τ00, wherein,
Figure BDA0002388606250000021
ΦPRis a limited electricity price state space; let τ beK＝T；PR(t)＝PRk，τk≤t＜τk+1；
Noting that the charge service price of a charge station service system is a function PR of time tev(t)；
Suppose that there is m at time ttThe electric vehicle randomly arrives at the electric station to apply for charging service, order the mthtThe current state of charge of the battery of the electric vehicle is SOCmt(t), then the m-thtThe arrival event of the electric vehicle is recorded as
Figure BDA0002388606250000022
Recording the joint state of the J charging piles at the moment t as Ct＝(CS1(t),CS2(t),…,CSj(t),…,CSJ(t)), wherein
Figure BDA0002388606250000023
Representing the actual service state of the jth charging pile; m isj(t) indicates the jth charging pile CS at time tjKind of electric vehicle being serviced, mj(t) ═ 0 denotes the jth charging pile CS at time tjNo vehicle access, mj(t) is equal to {1,2, …, M } and represents the jth charging pile CS at the time tjCharging one electric vehicle in {1,2, …, M };
Figure BDA0002388606250000027
represents the jth charging pile CS at the time tjM < th > of being servedj(t) the current state of charge of the batteries of the electric vehicles; when m isj(t) 0
Figure BDA0002388606250000024
Recording the state of the charging station service system at the moment t as st＝{CtPR (t) }, mtArrival event of electric vehicle
Figure BDA0002388606250000025
Taking the occurrence time t as a decision time, and recording the event expansion state of the decision time as
Figure BDA0002388606250000026
Recording whether the charging station service system is connected to the electric vehicle or not and provides charging service as action a and recording the nth decision time TnIs anAnd a is anE.g., {0,1}, where "0" denotes denial of service, "1" denotes access to service, and D denotes a set of actions;
the state clustering coding method in the charging station vehicle access control comprises the following steps:
step 1, using a time constant sigma to change the change interval [0T ] of a time variable T]Discretizing into Z intervals, wherein the Z interval is marked as [ tztz+1) I.e. tz+1＝tz+ σ, Z is 0,1,2, …, Z-1, and let t0＝τ0When it is 0, note tZ＝τKT; recording the discretization state of the time variable t as z;
step 2, supposing that the m-th vehicle is from the SOCmThe charging is started at 0, and the charging time required until full charging is Γm＝Em/(ρm·Pm) Where ρ ismIndicating the charging efficiency, order
Figure BDA0002388606250000031
Discretizing the time constant gamma into U intervals by using the time constant sigma, and recording the U-th interval as [ t [ [ t ]utu+1) U-0, 1,2, …, U-2, with the U-1 th interval [ t [ ]U-1tU) And t is0＝0，tU＝Γ；
Step 3, if the j th charging pile CS is in the moment tjElectric vehicle m receiving charging servicej(t) a battery state of charge of
Figure BDA0002388606250000032
Its charge demand time until full is recorded as
Figure BDA0002388606250000033
Recording the service time demand state of the jth charging pile as
Figure BDA0002388606250000034
The demand state of the joint service time of J charging piles at the moment t is recorded as
Figure BDA0002388606250000035
Step 4, the service time demand state of the jth charging pile
Figure BDA0002388606250000036
Discretization if
Figure BDA0002388606250000037
Then the corresponding discretization service time demand state is recorded
Figure BDA0002388606250000038
Is encoded as ujI.e. by
Figure BDA0002388606250000039
the discretization joint service time demand states of J charging piles at the moment t are recorded as
Figure BDA00023886062500000310
The aggregate of all possible discretization combined service time demand states of the charging pile is recorded as gamma, and the total number of the states is UJ(ii) a When m isjWhen (t) is 0, the reaction is carried out,
Figure BDA00023886062500000311
uj＝0；
step 5, setting the mth time point of ttArrival event of electric vehicle
Figure BDA00023886062500000312
Charging station service System status at time of occurrence
Figure BDA00023886062500000313
And SOCmt(t) the corresponding charge demand time is recorded as
Figure BDA00023886062500000314
The event expansion status at time t is recorded as
Figure BDA00023886062500000315
Recording the charging demand time
Figure BDA00023886062500000316
Corresponding discretized service time demand status is recorded
Figure BDA00023886062500000317
Step 6, supposing the nth decision time
Figure BDA00023886062500000318
z n0,1,2, …, Z-1, the nth decision time T is setnEvent extended state of
Figure BDA00023886062500000319
Discretizing to obtain corresponding discretization event expansion state
Figure BDA00023886062500000320
Where n denotes the corresponding nth decision time TnA numerical or discretized value of; the total number of states is Z × UJX M x U; if it is
Figure BDA0002388606250000041
The peak shaving electricity price is only corresponding to the kth peak shaving electricity price PRk；
Step 7, mixing
Figure BDA0002388606250000042
Element u in (1)1,u2,…,uj,…,uJSorting according to the sequence from small to large, and recording the ascending operation as theta, and then ordering
Figure BDA0002388606250000043
Namely, it is
Figure BDA0002388606250000044
Figure BDA0002388606250000045
Representing the clustering state of the charging station, recording the clustering state set as
Figure BDA0002388606250000046
And share the number of states
Figure BDA0002388606250000047
A plurality of; the extended clustering status of the discretized event at the nth decision moment is recorded as
Figure BDA0002388606250000048
Wherein the content of the first and second substances,
Figure BDA0002388606250000049
to represent
Figure BDA00023886062500000410
The value at the nth decision time; recording discretized events extends the clustering state space into
Figure BDA00023886062500000411
Having a total number of states of
Figure BDA00023886062500000412
Step 8, defining a charging station vehicle access control strategy v as the discretization event extended clustering state spaceWorkshop
Figure BDA00023886062500000413
A mapping to said action set D, thereby obtaining an access control policy table; when the nth vehicle arrival event occurs, observing and calculating to obtain the discretization event extended clustering state of the system
Figure BDA00023886062500000414
And selects a corresponding access control action a from the access control policy tablenIf anIf 0, refusing vehicle access, if anAnd (1) immediately connecting the arriving vehicle to any idle charging pile for charging.
Compared with the prior art, the invention has the beneficial effects that:
1. the charging method and the charging system firstly take the charging demand time of each vehicle which is charging from the current SOC to full charge as the state of the charging pile for providing the charging service, namely the service time demand state or the residual charging time state, and can more directly reflect the service state change condition of the charging pile compared with the traditional method which takes the current state of charge of the battery of the electric vehicle as the state of the charging pile;
2. discretizing the joint service time demand states of all the charging piles to form discretized joint service time demand states, and sequencing the discretized joint service time demand states from small to large to form a clustering state of the charging station, so that the scale of the discretized event expanded clustering state is effectively reduced, and the optimization efficiency and the execution effect of vehicle access control are improved;
3. the state clustering coding method provided by the invention is also suitable for other situations relating to the charging and discharging problems of the electric vehicle.
Drawings
Fig. 1 is a schematic diagram of a charging station service system according to the present invention.
Detailed Description
In this embodiment, as shown in fig. 1, a state clustering coding method in charging station vehicle access control is applied to a charging station service system composed of J charging piles 1, M electric vehicles 2 arriving at random, a power grid peak shaving electricity price plan 3, and an access control center 4, and each charging pile can adaptively meet the charging power requirements of the M electric vehicles;
recording the jth charging pile as CSjAnd only one electric vehicle is provided with charging service at a time; thereby recording J charging piles as CS1,CS2,…,CSj,…,CSJ，j＝1,2,…,J；
Recording the charging power requirement of the mth electric vehicle as PmAnd the total battery capacity is EmThe configuration of the electric vehicle determines; thereby recording the charging power requirement of M electric vehicles as P1,P2,…,Pm,…,PM，m＝1,2,…,M；
Let K be the maximum number of cycles per day and the corresponding total time length be T, record the peak-shaving electricity price state of the power grid at any time T under the total time length T as PR (T) element/kilowatt hour, and PR (T) element is phiPR，0≤t≤T，ΦPRIs a limited electricity price state space; suppose that the peak regulation electricity price of the power grid is issued according to the dispatching instruction period and taukPeak-shaving power rate PR for the kthkThe time of delivery, the price is maintained to the next peak-shaving electricity price delivery time tauk+1To that end, i.e. PRt＝PRk，τk≤t＜τk+1K is 0,1,2, …, K-1 and τ00; the sequence of peak-shaving electrovalence is recorded as { (τ)k,PRk)k＝0,1,2,…,K-1,τ00 }; wherein the content of the first and second substances,
Figure BDA0002388606250000051
and let τ beK＝T；
The charging station provides paid charging service, and the price of charging service of the charging station is a function of time t and is recorded as PRev(t) yuan/kwh;
setting the battery SOC at the time t as
Figure BDA0002388606250000052
M oftThe event that the electric vehicle randomly arrives at the electric station to apply for the charging service is recorded as the arrival event
Figure BDA0002388606250000053
Recording the service state of the jth charging pile as
Figure BDA0002388606250000054
Thereby recording the joint state of J charging piles at the moment t as Ct＝(CS1(t),CS2(t),…,CSj(t),…,CSJ(t)); when t is assumed to be 0, all charging piles are empty; m isj(t) indicates the jth charging pile CS at time tjClass of electric vehicle being serviced, if mj(t) ═ 0 denotes the jth charging pile CS at time tjNo vehicle access, if mj(t) is equal to {1,2, …, M } and represents the jth charging pile CS at the time tjCharging one electric vehicle in {1,2, …, M };
Figure BDA0002388606250000055
represents the jth charging pile CS at the time tjM < th > of being servedj(t) kinds of electric vehicle's battery SOC, and mj(t) 0
Figure BDA0002388606250000056
Will arrive at an event
Figure BDA0002388606250000057
The charging station service system status at the time of occurrence is recorded as st＝{CtPR (t) }, since the peak-shaving power rate PR (t) is a function of time t, the extension state of the notepad is
Figure BDA0002388606250000058
Recording an event expansion state space as S;
note the nth event
Figure BDA0002388606250000061
The moment of occurrence is decision moment TnI.e. T ═ TnCorresponding grid price peak-to-peak hours
Figure BDA0002388606250000062
knE {0,1, …, K-1}, i.e.
Figure BDA0002388606250000063
Defining the system decision time as the arrival time of any electric vehicle, namely the event occurrence time;
taking whether the charging station service system is connected to the electric vehicle or not and providing the charging service as a control action a, and recording the nth decision time TnIs anAnd a is anE.g., {0,1}, where "0" denotes denial of service, "1" denotes access to service, and D denotes a set of actions; at any decision time TnIf m isj(t) ≠ 0, J ≠ 1,2, …, J, which means that all charging posts are busy, then an≡0；
Encoding all possible discretization event extension states to order
Figure BDA0002388606250000064
Represents the state of the spreading of the s-th discretization event, and
Figure BDA0002388606250000065
s is 1,2, …, S; coding the spreading state of all possible discretization events with busy charging piles into the last discretization event and recording the state number as Sb；
At the nth decision time TnIf anIf the number of the electric vehicles reaches 1, the electric vehicles are connected to any idle charging pile and immediately charged; leaving the charging station immediately assuming one electric vehicle is full;
the original state coding method in the charging station vehicle access control comprises the following steps:
step 1, using a smaller time constant sigma to divide the change interval [0T ] of a time variable T]Discretizing into Z intervals, wherein the Z interval is marked as [ tztz+1) Z is 0,1,2, …, Z-1, and let t0＝τ0When it is 0, note tZ＝τKT; recording the discretization state code of the continuous time variable t as z;
step 2, using a smaller constant delta to divide the change interval of all the electric vehicle batteries SOC [ 01]Discretizing into Y intervals, and recording the Y interval as [ theta ]yθy+1) Y is 0,1,2, …, Y-1, and let θ0＝0，θY1 is ═ 1; then note the mth time of ttCurrent state of charge of battery of electric vehicle
Figure BDA0002388606250000066
Is recorded as
Figure BDA0002388606250000067
The state code is marked as y;
step 3, combining the J charging piles at the moment ttDiscretizing, recording the discretization combined state of the charging pile as
Figure BDA0002388606250000068
Wherein the content of the first and second substances,
Figure BDA0002388606250000069
is CSj(t) corresponding discretization states, and
Figure BDA00023886062500000610
is that
Figure BDA00023886062500000611
The total number of states of the discretized states of (M X Y)J；
Step 4, supposing the nth decision time
Figure BDA00023886062500000612
z n0,1,2, …, Z-1, expand the event at the decision time to a state
Figure BDA00023886062500000613
Discretizing to obtain corresponding discretization event expanding state
Figure BDA00023886062500000614
Wherein, the lower partThe symbol "n" indicates the corresponding nth decision time TnA numerical or discretized value of; m isnIt is shown
Figure BDA0002388606250000071
Figure BDA0002388606250000072
Is CtThe corresponding discretized charging pile is in a combined state,
Figure BDA0002388606250000073
is CSj(t) corresponding discretization states, and
Figure BDA0002388606250000074
recording discretized events extends the state space into
Figure BDA0002388606250000075
Namely, it is
Figure BDA0002388606250000076
The total number of states is Z x (M X Y)J×M×U；
Step 5, transition probability matrix
Figure BDA0002388606250000077
The probability transition under the condition of exponential lingering time can be deduced; distribution function matrix
Figure BDA0002388606250000078
All transfers are exponential distributed and uncontrolled; cost function matrix
Figure BDA0002388606250000079
It should also be deduced that if the cost rate is the continuous time MDP, if the cost rate is the one-step transfer cost, the discrete time process is the discrete time process; the Q matrix can be calculated, and the equivalent A matrix can be calculated; numerical calculations may be performed.
The state clustering coding method in the charging station vehicle access control comprises the following steps:
step 1, use oneThe smaller time constant sigma is the change interval [0T ] of the time variable T]Discretizing into Z intervals, wherein the Z interval is marked as [ tztz+1) I.e. tz+1＝tz+ σ, Z is 0,1,2, …, Z-1, and let t0＝τ0When it is 0, note tZ＝τKT; recording the discretization state of the continuous time variable t as z;
step 2, supposing that the m-th vehicle is from the SOCmThe charging is started at 0, and the charging time required until full charging is Γm＝Em/(ρm·Pm) Where ρ ismIndicating the charging efficiency, order
Figure BDA00023886062500000710
Discretizing the time constant gamma into U intervals by using the time constant sigma, and recording the U-th interval as [ t [ [ t ]utu+1) I.e. tu+1＝tu+ σ, U ═ 0,1,2, …, U-2, and t0＝0，tU≤Γ；
Step 3, if one charging pile is placed at jth charging pile CS at moment tjElectric vehicle m receiving charging servicej(t) the current state of charge of the battery is
Figure BDA00023886062500000711
Its charge demand time until full is recorded as
Figure BDA00023886062500000712
Recording the service time demand state of the jth charging pile
Figure BDA00023886062500000713
The demand state of the joint service time of J charging piles at the moment t is recorded as
Figure BDA00023886062500000714
Step 4, the service time demand state of the jth charging pile
Figure BDA00023886062500000715
Discretization if
Figure BDA00023886062500000716
Then the corresponding discretization service time demand state is recorded
Figure BDA00023886062500000717
Is encoded as ujI.e. by
Figure BDA0002388606250000081
the discretization joint service time demand states of J charging piles at the moment t are recorded as
Figure BDA0002388606250000082
Recording the aggregate of all possible discretization combined service time requirement states of the charging pile as gamma, wherein the total state number is U multiplied by J; m isjWhen (t) is 0, the reaction is carried out,
Figure BDA0002388606250000083
step 5, setting the mth time point of ttArrival event of electric vehicle
Figure BDA0002388606250000084
Charging station service System status at time of occurrence
Figure BDA0002388606250000085
And is
Figure BDA0002388606250000086
The corresponding charging demand time is recorded as
Figure BDA0002388606250000087
The event expansion status at time t is recorded as
Figure BDA0002388606250000088
Recording the charging demand time
Figure BDA0002388606250000089
Corresponding discretized service time demand status is recorded
Figure BDA00023886062500000810
Step 6, supposing the nth decision time
Figure BDA00023886062500000811
z n0,1,2, …, Z-1, the event at the decision time is extended to the state
Figure BDA00023886062500000812
Discretizing to obtain corresponding discretization event expansion state
Figure BDA00023886062500000813
Wherein "n" indicates the corresponding nth decision time TnA numerical or discretized value of; m isnIt is shown
Figure BDA00023886062500000814
The total number of states is Z × UJ×M×U；
Step 7, mixing
Figure BDA00023886062500000815
Element u in (1)1,u2,…,uj,…,uJSorting according to the order from small to large, and recording the sorting operation as theta, and then ordering
Figure BDA00023886062500000816
Namely, it is
Figure BDA00023886062500000817
Figure BDA00023886062500000818
Namely the cluster state of the charging station, and the set of the cluster state is recorded as
Figure BDA00023886062500000819
Then the new set
Figure BDA00023886062500000820
Common elements
Figure BDA00023886062500000821
A plurality of; the extended clustering status of the discretized event at the nth decision moment is recorded as
Figure BDA00023886062500000822
Wherein the content of the first and second substances,
Figure BDA00023886062500000823
to represent
Figure BDA00023886062500000824
The value at the nth decision time; recording discretized events extends the clustering state space into
Figure BDA00023886062500000825
Having a total number of states of
Figure BDA00023886062500000826
Step 8, defining a charging station vehicle access control strategy v as a discretization event extended clustering state space
Figure BDA00023886062500000827
A mapping to action set D, resulting in an access control policy table, the charging station vehicle access control process is as follows:
step 8.1, waiting for the vehicle arrival event to occur; when an event occurs, turning to step 8.2;
step 8.2, recording vehicle arrival events
Figure BDA00023886062500000828
The occurrence order n of the current event, the occurrence time T of the current event is recorded and calculatednCorresponding discretized time state znThe type m of arriving vehiclenAnd charging demand time thereof
Figure BDA00023886062500000829
Corresponding discretized states
Figure BDA0002388606250000091
Step 8.3, observing SOC values of vehicles connected with all charging piles of the charging station at the event occurrence moment, and calculating a combined service time demand state gamma (t) of the charging piles and a discretized combined service time demand state gamma (t) of the charging piles
Figure BDA0002388606250000092
Step 8.4, mixing
Figure BDA0002388606250000093
The elements in the charging field station are sequenced from small to large to obtain the clustering state of the charging field station
Figure BDA0002388606250000094
And is marked as
Figure BDA0002388606250000095
Obtaining discretized event extended cluster states
Figure BDA0002388606250000096
Step 8.5, extending the clustering state according to the discretization event of the nth decision moment
Figure BDA0002388606250000097
Selecting a corresponding access control action a from the access control policy tablenIf anIf 0, deny vehicle access, if an1, connecting the arriving vehicle to any idle charging pile for charging;
and 8.6, turning to step 8.1.

Claims (1)

1. A state coding method in charging station vehicle access control is characterized in that the state coding method is applied to a charging station service system which is provided with J charging piles and provides paid charging service for M electric vehicles which arrive randomly, each charging pile can meet the charging power requirements of the M electric vehicles, and one charging pile only provides charging service for one electric vehicle at a time;
recording the J charging piles as CS respectively1,CS2,…,CSj,…,CSJAnd the charging power requirement of the M electric vehicles is recorded as P1,P2,…,Pm,…,PMWherein CS isjIndicates the jth charging pile, PmRepresenting the charging power requirement of the mth electric vehicle;
the battery capacity of the mth electric vehicle is recorded as Em，m＝1,2,…,M；
Recording the total time of a day as T, and making the peak-shaving electricity price of the power grid a function of time T as PR (T), and PR (T) epsilon phiPRT is more than or equal to 0 and less than or equal to T; supposing that the peak regulation electricity price of the power grid is issued according to the dispatching instruction period, K is the maximum period number of one day, and T is orderedkPeak-shaving power rate PR for the kthkWhen the electricity price is issued, the electricity price sequence of peak regulation is recorded as { (tau)k,PRk)|k＝0,1,2,…,K-1,τ00, wherein,
Figure FDA0002388606240000011
ΦPRis a limited electricity price state space; let τ beK＝T；PR(t)＝PRk，τk≤t＜τk+1；
Noting that the charge service price of a charge station service system is a function PR of time tev(t)；
Suppose that there is m at time ttThe electric vehicle randomly arrives at the electric station to apply for charging service, order the mthtThe current state of charge of the battery of the electric vehicle is
Figure FDA0002388606240000012
Then the m < th > istThe arrival event of the electric vehicle is recorded as
Figure FDA0002388606240000013
Recording the joint state of the J charging piles at the moment t as Ct＝(CS1(t),CS2(t),…,CSj(t),…,CSJ(t) Therein), wherein
Figure FDA0002388606240000014
Representing the actual service state of the jth charging pile; m isj(t) indicates the jth charging pile CS at time tjKind of electric vehicle being serviced, mj(t) ═ 0 denotes the jth charging pile CS at time tjNo vehicle access, mj(t) is equal to {1,2, …, M } and represents the jth charging pile CS at the time tjCharging one electric vehicle in {1,2, …, M };
Figure FDA0002388606240000015
represents the jth charging pile CS at the time tjM < th > of being servedj(t) the current state of charge of the batteries of the electric vehicles; when m isj(t) 0
Figure FDA0002388606240000016
Recording the state of the charging station service system at the moment t as st＝{CtPR (t) }, mtArrival event of electric vehicle
Figure FDA0002388606240000017
Taking the occurrence time t as a decision time, and recording the event expansion state of the decision time as
Figure FDA0002388606240000018
Recording whether the charging station service system is connected to the electric vehicle or not and provides charging service as action a and recording the nth decision time TnIs anAnd a is anE.g., {0,1}, where "0" denotes denial of service, "1" denotes access to service, and D denotes a set of actions;
the state clustering coding method in the charging station vehicle access control comprises the following steps:
step 1, using a time constant sigma to change the change interval [0T ] of a time variable T]Discretizing into Z intervals, the Z interval being recorded as[tztz+1) I.e. tz+1＝tz+ σ, Z is 0,1,2, …, Z-1, and let t0＝τ0When it is 0, note tZ＝τKT; recording the discretization state of the time variable t as z;
step 2, supposing that the m-th vehicle is from the SOCmThe charging is started at 0, and the charging time required until full charging is Γm＝Em/(ρm·Pm) Where ρ ismIndicating the charging efficiency, order
Figure FDA0002388606240000021
Discretizing the time constant gamma into U intervals by using the time constant sigma, and recording the U-th interval as [ t [ [ t ]utu+1) U-0, 1,2, …, U-2, with the U-1 th interval [ t [ ]U-1tU) And t is0＝0，tU＝Γ；
Step 3, if the j th charging pile CS is in the moment tjElectric vehicle m receiving charging servicej(t) a battery state of charge of
Figure FDA0002388606240000022
Its charge demand time until full is recorded as
Figure FDA0002388606240000023
Recording the service time demand state of the jth charging pile as
Figure FDA0002388606240000024
The demand state of the joint service time of J charging piles at the moment t is recorded as
Figure FDA0002388606240000025
Step 4, the service time demand state of the jth charging pile
Figure FDA0002388606240000026
Discretization if
Figure FDA0002388606240000027
Then the corresponding discretization service time demand state is recorded
Figure FDA0002388606240000028
Is encoded as ujI.e. by
Figure FDA0002388606240000029
the discretization joint service time demand states of J charging piles at the moment t are recorded as
Figure FDA00023886062400000210
The aggregate of all possible discretization combined service time demand states of the charging pile is recorded as gamma, and the total number of the states is UJ(ii) a When m isjWhen (t) is 0, the reaction is carried out,
Figure FDA00023886062400000211
uj＝0；
step 5, setting the mth time point of ttArrival event of electric vehicle
Figure FDA00023886062400000212
Charging station service System status at time of occurrence
Figure FDA00023886062400000213
And is
Figure FDA00023886062400000214
The corresponding charging demand time is recorded as
Figure FDA00023886062400000215
The event expansion status at time t is recorded as
Figure FDA00023886062400000216
Recording the charging demand time
Figure FDA00023886062400000217
Corresponding discretized service time demand status is recorded
Figure FDA00023886062400000218
Step 6, supposing the nth decision time
Figure FDA00023886062400000219
The nth decision time TnEvent extended state of
Figure FDA0002388606240000031
Discretizing to obtain corresponding discretization event expansion state
Figure FDA0002388606240000032
Where n denotes the corresponding nth decision time TnA numerical or discretized value of; the total number of states is Z × UJX M x U; if it is
Figure FDA0002388606240000033
The peak shaving electricity price is only corresponding to the kth peak shaving electricity price PRk；
Step 7, mixing
Figure FDA0002388606240000034
Element u in (1)1,u2,…,uj,…,uJSorting according to the sequence from small to large, and recording the ascending operation as theta, and then ordering
Figure FDA0002388606240000035
Namely, it is
Figure FDA0002388606240000036
Figure FDA0002388606240000037
Representing the clustering state of the charging station, recording the clustering state set as
Figure FDA0002388606240000038
And share the number of states
Figure FDA0002388606240000039
A plurality of; the extended clustering status of the discretized event at the nth decision moment is recorded as
Figure FDA00023886062400000310
Wherein the content of the first and second substances,
Figure FDA00023886062400000311
to represent
Figure FDA00023886062400000312
The value at the nth decision time; recording discretized events extends the clustering state space into
Figure FDA00023886062400000313
Having a total number of states of
Figure FDA00023886062400000314
Step 8, defining a charging station vehicle access control strategy v as the discretization event extended clustering state space
Figure FDA00023886062400000315
A mapping to said action set D, thereby obtaining an access control policy table; when the nth vehicle arrival event occurs, observing and calculating to obtain the discretization event extended clustering state of the system
Figure FDA00023886062400000316
And selects a corresponding access control action a from the access control policy tablenIf anIf 0, refusing vehicle access, if anAnd (1) immediately connecting the arriving vehicle to any idle charging pile for charging.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113665402A (en) * 2021-07-26 2021-11-19 华南理工大学 Charging pile cluster ordered charging method based on battery charging characteristics
CN117709634A (en) * 2023-12-05 2024-03-15 江苏智融能源科技有限公司 Charging pile power resource management system and method based on big data

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114148213B (en) * 2021-11-30 2024-03-01 西安理工大学 Method and system for processing interactive coordination comprehensive control of power grid and electric vehicle power grid

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150145478A1 (en) * 2013-11-28 2015-05-28 Tsinghua University Method and device for charging electric vehicle in power system
CN105024432A (en) * 2015-07-30 2015-11-04 浙江工业大学 Electric vehicle charge-discharge optimized dispatching method based on virtual electricity price
CN106056255A (en) * 2016-06-23 2016-10-26 海南电力技术研究院 Space-time joint scheduling ordered charging method and device
CN106712061A (en) * 2016-05-16 2017-05-24 浙江工业大学 Intra-day priority scheduling method based on electric-vehicle schedulable capability
CN107749629A (en) * 2017-10-27 2018-03-02 深圳供电局有限公司 A kind of control method based on the access of charging station load Real-Time Scheduling charging pile
CN110509788A (en) * 2019-08-21 2019-11-29 三峡大学 Deepen electric car group's Combinatorial Optimization charging/discharging thereof of peak regulation

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9306412B2 (en) * 2012-03-30 2016-04-05 Toyota Jidosha Kabushiki Kaisha Optimum charging for electric-based vehicle
JP2015141465A (en) * 2014-01-27 2015-08-03 株式会社日立製作所 Management device and method of electric vehicle

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150145478A1 (en) * 2013-11-28 2015-05-28 Tsinghua University Method and device for charging electric vehicle in power system
CN105024432A (en) * 2015-07-30 2015-11-04 浙江工业大学 Electric vehicle charge-discharge optimized dispatching method based on virtual electricity price
CN106712061A (en) * 2016-05-16 2017-05-24 浙江工业大学 Intra-day priority scheduling method based on electric-vehicle schedulable capability
CN106056255A (en) * 2016-06-23 2016-10-26 海南电力技术研究院 Space-time joint scheduling ordered charging method and device
CN107749629A (en) * 2017-10-27 2018-03-02 深圳供电局有限公司 A kind of control method based on the access of charging station load Real-Time Scheduling charging pile
CN110509788A (en) * 2019-08-21 2019-11-29 三峡大学 Deepen electric car group's Combinatorial Optimization charging/discharging thereof of peak regulation

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113665402A (en) * 2021-07-26 2021-11-19 华南理工大学 Charging pile cluster ordered charging method based on battery charging characteristics
CN113665402B (en) * 2021-07-26 2023-06-20 华南理工大学 Ordered charging method for charging pile clusters based on battery charging characteristics
CN117709634A (en) * 2023-12-05 2024-03-15 江苏智融能源科技有限公司 Charging pile power resource management system and method based on big data

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