CN111284347B - State clustering coding method in charging station vehicle access control - Google Patents

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

Info

Publication number
CN111284347B
CN111284347B CN202010106439.8A CN202010106439A CN111284347B CN 111284347 B CN111284347 B CN 111284347B CN 202010106439 A CN202010106439 A CN 202010106439A CN 111284347 B CN111284347 B CN 111284347B
Authority
CN
China
Prior art keywords
charging
time
state
service
recording
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010106439.8A
Other languages
Chinese (zh)
Other versions
CN111284347A (en
Inventor
唐子昱
方道宏
赵传信
唐昊
方明星
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hefei University of Technology
Anhui Normal University
Original Assignee
Hefei University of Technology
Anhui Normal University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hefei University of Technology, Anhui Normal University filed Critical Hefei University of Technology
Priority to CN202010106439.8A priority Critical patent/CN111284347B/en
Publication of CN111284347A publication Critical patent/CN111284347A/en
Priority to JP2020206712A priority patent/JP7015428B2/en
Application granted granted Critical
Publication of CN111284347B publication Critical patent/CN111284347B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • B60L53/30—Constructional details of charging stations
    • B60L53/31—Charging columns specially adapted for electric vehicles
    • 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
    • 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
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Economics (AREA)
  • Mechanical Engineering (AREA)
  • Transportation (AREA)
  • Power Engineering (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Public Health (AREA)
  • Primary Health Care (AREA)
  • General Health & Medical Sciences (AREA)
  • Water Supply & Treatment (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

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 clustering coding method in charging station vehicle access control, which is characterized in that the 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 GDA0002894226270000021
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 GDA0002894226270000022
Then the m < th > istThe arrival event of the electric vehicle is recorded as
Figure GDA0002894226270000023
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 GDA0002894226270000024
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 GDA0002894226270000025
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 GDA0002894226270000026
Recording the state of the charging station service system at the moment t as st＝{CtPR (t) }, mtArrival event of electric vehicle
Figure GDA0002894226270000027
Taking the occurrence time t as a decision time, and recording the event expansion state of the decision time as
Figure GDA0002894226270000028
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 D ═ 0,1, where "0" denotes refuseAbsolute service, "1" indicates access to service, D indicates action set;
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 [0, T of a time variable T]Discretizing into Z intervals, wherein the Z interval is marked as [ tz,tz+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 GDA0002894226270000031
Discretizing the time constant gamma into U intervals by using the time constant sigma, and recording the U-th interval as [ t [ [ t ]u,tu+1) U-0, 1,2, …, U-2, with the U-1 th interval [ t [ ]U-1,tU) 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 GDA00028942262700000321
Its charge demand time until full is recorded as
Figure GDA0002894226270000032
Recording the service time demand state of the jth charging pile as
Figure GDA0002894226270000033
The demand state of the joint service time of J charging piles at the moment t is recorded as
Figure GDA0002894226270000034
Step 4, the service time demand state of the jth charging pile
Figure GDA0002894226270000035
Discretization if
Figure GDA0002894226270000036
Then the corresponding discretization service time demand state is recorded
Figure GDA0002894226270000037
Is encoded as ujI.e. by
Figure GDA0002894226270000038
the discretization joint service time demand states of J charging piles at the moment t are recorded as
Figure GDA0002894226270000039
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 GDA00028942262700000310
uj＝0；
step 5, setting the mth time point of ttArrival event of electric vehicle
Figure GDA00028942262700000311
Charging station service System status at time of occurrence
Figure GDA00028942262700000312
And is
Figure GDA00028942262700000313
The corresponding charging demand time is recorded as
Figure GDA00028942262700000314
The event expansion status at time t is recorded as
Figure GDA00028942262700000315
Recording the charging demand time
Figure GDA00028942262700000316
Corresponding discretized service time demand status is recorded
Figure GDA00028942262700000317
Step 6, supposing the nth decision time
Figure GDA00028942262700000318
The nth decision time TnEvent extended state of
Figure GDA00028942262700000319
Discretizing to obtain corresponding discretization event expansion state
Figure GDA00028942262700000320
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 GDA0002894226270000041
The peak shaving electricity price is only corresponding to the kth peak shaving electricity price PRk；
Step 7, mixing
Figure GDA0002894226270000042
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 GDA0002894226270000043
Namely, it is
Figure GDA0002894226270000044
Figure GDA0002894226270000045
Representing the cluster state of the charging station, recording the cluster stateThe set of states is recorded as
Figure GDA0002894226270000046
And share the number of states
Figure GDA0002894226270000047
A plurality of; the extended clustering status of the discretized event at the nth decision moment is recorded as
Figure GDA0002894226270000048
Wherein the content of the first and second substances,
Figure GDA0002894226270000049
to represent
Figure GDA00028942262700000410
The value at the nth decision time; recording discretized events extends the clustering state space into
Figure GDA00028942262700000411
Having a total number of states of
Figure GDA00028942262700000412
Step 8, defining a charging station vehicle access control strategy v as the discretization event extended clustering state space
Figure GDA00028942262700000413
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 GDA00028942262700000414
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 week of the dayThe period and the corresponding total duration are T, the peak-shaving electrovalence state of the power grid at any time T under the total duration T is recorded 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 GDA0002894226270000051
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 GDA0002894226270000052
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 GDA0002894226270000053
Recording the service state of the jth charging pile as
Figure GDA0002894226270000054
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 GDA0002894226270000055
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 GDA0002894226270000056
Will arrive at an event
Figure GDA0002894226270000057
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 GDA0002894226270000058
Recording an event expansion state space as S;
note the nth event
Figure GDA0002894226270000061
The moment of occurrence is decision moment TnI.e. T ═ TnCorresponding grid price peak-to-peak hours
Figure GDA0002894226270000062
Namely, it is
Figure GDA0002894226270000063
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 indicates that all charging piles are busy, thenan≡0；
Encoding all possible discretization event extension states to order
Figure GDA0002894226270000064
Represents the state of the spreading of the s-th discretization event, and
Figure GDA0002894226270000065
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 change the change interval [0, T of the time variable T]Discretizing into Z intervals, wherein the Z interval is marked as [ tz,tz+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 [0, 1 ] of all the electric vehicle batteries SOC]Discretizing into Y intervals, and recording the Y interval as [ theta ]y，θy+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 GDA0002894226270000066
Is recorded as
Figure GDA0002894226270000067
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 GDA0002894226270000068
Wherein the content of the first and second substances,
Figure GDA0002894226270000069
is CSj(t) corresponding discretization states, and
Figure GDA00028942262700000610
Figure GDA00028942262700000611
is that
Figure GDA00028942262700000612
The total number of states of the discretized states of (M X Y)J；
Step 4, supposing the nth decision time
Figure GDA00028942262700000613
Expanding the event of decision time to state
Figure GDA00028942262700000614
Discretizing to obtain corresponding discretization event expanding state
Figure GDA00028942262700000615
Wherein the subscript "n" indicates the corresponding nth decision time TnA numerical or discretized value of; m isnIt is shown
Figure GDA0002894226270000071
Figure GDA0002894226270000072
Is CtThe corresponding discretized charging pile is in a combined state,
Figure GDA0002894226270000073
is CSj(t) corresponding discretization states, and
Figure GDA0002894226270000074
recording discretized events extends the state space into
Figure GDA0002894226270000075
Namely, it is
Figure GDA0002894226270000076
The total number of states is Z x (M X Y)J×M×U；
Step 5, transition probability matrix
Figure GDA0002894226270000077
The probability transition under the condition of exponential lingering time can be deduced; distribution function matrix
Figure GDA0002894226270000078
All transfers are exponential distributed and uncontrolled; cost function matrix
Figure GDA0002894226270000079
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, using a smaller time constant sigma to change the change interval [0, T of the time variable T]Discretizing into Z intervals, wherein the Z interval is marked as [ tz,tz+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 GDA00028942262700000710
Discretizing the time constant gamma into U intervals by using the time constant sigma, and recording the U-th interval as [ t [ [ t ]u,tu+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 GDA00028942262700000711
Its charge demand time until full is recorded as
Figure GDA00028942262700000712
Recording the service time demand state of the jth charging pile
Figure GDA00028942262700000713
The demand state of the joint service time of J charging piles at the moment t is recorded as
Figure GDA00028942262700000714
Step 4, the service time demand state of the jth charging pile
Figure GDA00028942262700000715
Discretization if
Figure GDA00028942262700000716
Then the corresponding discretization service time demand state is recorded
Figure GDA00028942262700000717
Is encoded as ujI.e. by
Figure GDA00028942262700000718
the discretization joint service time demand states of J charging piles at the moment t are recorded as
Figure GDA0002894226270000081
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 GDA0002894226270000082
step 5, setting the mth time point of ttArrival event of electric vehicle
Figure GDA0002894226270000083
Charging station service System status at time of occurrence
Figure GDA0002894226270000084
And is
Figure GDA0002894226270000085
The corresponding charging demand time is recorded as
Figure GDA0002894226270000086
The event expansion status at time t is recorded as
Figure GDA0002894226270000087
Recording the charging demand time
Figure GDA0002894226270000088
Corresponding discretized service time demand status is recorded
Figure GDA0002894226270000089
Step 6, supposing the nth decision time
Figure GDA00028942262700000810
The event at the decision time is extended to the state
Figure GDA00028942262700000811
Discretizing to obtain corresponding discretization event expansion state
Figure GDA00028942262700000812
Wherein "n" indicates the corresponding nth decision time TnA numerical or discretized value of; m isnIt is shown
Figure GDA00028942262700000813
The total number of states is Z × UJ×M×U；
Step 7, mixing
Figure GDA00028942262700000814
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 GDA00028942262700000815
Namely, it is
Figure GDA00028942262700000816
Figure GDA00028942262700000817
Namely the cluster state of the charging station, and the set of the cluster state is recorded as
Figure GDA00028942262700000818
Then the new set
Figure GDA00028942262700000819
Common elements
Figure GDA00028942262700000820
A plurality of; the extended clustering status of the discretized event at the nth decision moment is recorded as
Figure GDA00028942262700000821
Wherein the content of the first and second substances,
Figure GDA00028942262700000822
to represent
Figure GDA00028942262700000823
The value at the nth decision time; discretized event extended clusteringThe state space is
Figure GDA00028942262700000824
Having a total number of states of
Figure GDA00028942262700000825
Step 8, defining a charging station vehicle access control strategy v as a discretization event extended clustering state space
Figure GDA00028942262700000826
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 GDA00028942262700000827
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 GDA00028942262700000828
Corresponding discretized states
Figure GDA00028942262700000829
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 GDA0002894226270000091
Step 8.4, mixing
Figure GDA0002894226270000092
The middle element goes from small to bigThe rows are sequenced to obtain the clustering state of the charging station
Figure GDA0002894226270000093
And is marked as
Figure GDA0002894226270000094
Obtaining discretized event extended cluster states
Figure GDA0002894226270000095
Step 8.5, extending the clustering state according to the discretization event of the nth decision moment
Figure GDA0002894226270000096
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 clustering coding method in charging station vehicle access control is characterized in that the 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 phiPR，0≤t≤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 FDA0002894226260000011
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 FDA0002894226260000012
Then the m < th > istThe arrival event of the electric vehicle is recorded as
Figure FDA0002894226260000013
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 FDA0002894226260000014
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 FDA0002894226260000015
to representJ th charging pile CS at 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 FDA0002894226260000016
Recording the state of the charging station service system at the moment t as st＝{CtPR (t) }, mtArrival event of electric vehicle
Figure FDA0002894226260000017
Taking the occurrence time t as a decision time, and recording the event expansion state of the decision time as
Figure FDA0002894226260000018
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 [0, T of a time variable T]Discretizing into Z intervals, wherein the Z interval is marked as [ tz,tz+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 FDA0002894226260000021
Will be time-shifted by said time constant σThe constant gamma is discretized into U intervals, and the U-th interval is marked as [ tu,tu+1) U-0, 1,2, …, U-2, with the U-1 th interval [ t [ ]U-1,tU) 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 FDA0002894226260000022
Its charge demand time until full is recorded as
Figure FDA0002894226260000023
Recording the service time demand state of the jth charging pile as
Figure FDA0002894226260000024
The demand state of the joint service time of J charging piles at the moment t is recorded as
Figure FDA0002894226260000025
Step 4, the service time demand state of the jth charging pile
Figure FDA0002894226260000026
Discretization if
Figure FDA0002894226260000027
Then the corresponding discretization service time demand state is recorded
Figure FDA0002894226260000028
Is encoded as ujI.e. by
Figure FDA0002894226260000029
the discretization joint service time demand states of J charging piles at the moment t are recorded as
Figure FDA00028942262600000210
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 FDA00028942262600000211
uj＝0；
step 5, setting the mth time point of ttArrival event of electric vehicle
Figure FDA00028942262600000212
Charging station service System status at time of occurrence
Figure FDA00028942262600000213
And is
Figure FDA00028942262600000214
The corresponding charging demand time is recorded as
Figure FDA00028942262600000215
The event expansion status at time t is recorded as
Figure FDA00028942262600000216
Recording the charging demand time
Figure FDA00028942262600000217
Corresponding discretized service time demand status is recorded
Figure FDA00028942262600000218
Step 6, supposing the nth decision time
Figure FDA00028942262600000219
zn0,1,2, …, Z-1, the nth decision time T is setnEvent extended state of
Figure FDA0002894226260000031
Discretizing to obtain corresponding discretization event expansion state
Figure FDA0002894226260000032
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 FDA0002894226260000033
The peak shaving electricity price is only corresponding to the kth peak shaving electricity price PRk；
Step 7, mixing
Figure FDA0002894226260000034
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 FDA0002894226260000035
Namely, it is
Figure FDA0002894226260000036
Figure FDA0002894226260000037
Representing the clustering state of the charging station, recording the clustering state set as
Figure FDA0002894226260000038
And share the number of states
Figure FDA0002894226260000039
A plurality of; the extended clustering status of the discretized event at the nth decision moment is recorded as
Figure FDA00028942262600000310
Wherein the content of the first and second substances,
Figure FDA00028942262600000311
to represent
Figure FDA00028942262600000312
The value at the nth decision time; recording discretized events extends the clustering state space into
Figure FDA00028942262600000313
Having a total number of states of
Figure FDA00028942262600000314
Step 8, defining a charging station vehicle access control strategy v as the discretization event extended clustering state space
Figure FDA00028942262600000315
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 FDA00028942262600000316
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.
CN202010106439.8A 2020-02-21 2020-02-21 State clustering coding method in charging station vehicle access control Active CN111284347B (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN202010106439.8A CN111284347B (en) 2020-02-21 2020-02-21 State clustering coding method in charging station vehicle access control
JP2020206712A JP7015428B2 (en) 2020-02-21 2020-12-14 State clustering coding method in vehicle connection control of charging station

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010106439.8A CN111284347B (en) 2020-02-21 2020-02-21 State clustering coding method in charging station vehicle access control

Publications (2)

Publication Number Publication Date
CN111284347A CN111284347A (en) 2020-06-16
CN111284347B true CN111284347B (en) 2021-06-08

Family

ID=71019089

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010106439.8A Active CN111284347B (en) 2020-02-21 2020-02-21 State clustering coding method in charging station vehicle access control

Country Status (2)

Country Link
JP (1) JP7015428B2 (en)
CN (1) CN111284347B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
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
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

Family Cites Families (8)

* 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
CN104064829B (en) * 2013-11-28 2016-04-06 清华大学 A kind of electric automobile real time charging method based on electric automobile cluster Charge Prediction
JP2015141465A (en) * 2014-01-27 2015-08-03 株式会社日立製作所 Management device and method of electric vehicle
CN105024432B (en) * 2015-07-30 2017-12-29 浙江工业大学 A kind of electric automobile discharge and recharge Optimization Scheduling based on virtual electricity price
CN106712061B (en) * 2016-05-16 2019-02-01 浙江工业大学 A kind of in a few days priority scheduling method based on the schedulable ability of electric car
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
CN107749629B (en) * 2017-10-27 2021-06-18 深圳供电局有限公司 Charging pile access control method based on charging station load real-time scheduling
CN110509788B (en) * 2019-08-21 2022-12-02 三峡大学 Electric automobile group combination optimization charging and discharging method with deep peak regulation

Also Published As

Publication number Publication date
JP7015428B2 (en) 2022-02-03
CN111284347A (en) 2020-06-16
JP2021136859A (en) 2021-09-13

Similar Documents

Publication Publication Date Title
CN111284347B (en) State clustering coding method in charging station vehicle access control
CN108494034B (en) Calculation method for charging load distribution of electric automobile in power distribution network
CN110774929A (en) Real-time control strategy and optimization method for orderly charging of electric automobile
CN111027842B (en) Electric vehicle charging and new energy power generation cooperative scheduling method and device
CN110428165A (en) The electric car charging schedule method of reservation and queuing is taken into account in a kind of charging station
CN105760949A (en) Optimizing configuration method for amount of chargers of electromobile charging station
CN101836190A (en) Method, system and computer program for distributing a plurality of jobs to a plurality of computers
CN106650993A (en) Markov decision process-based dynamic resource optimization method
CN114734855A (en) Charging power scheduling method and device for matrix type flexible charging pile
CN112865082A (en) Virtual power plant day-ahead scheduling method for aggregating multiple types of electric vehicles
CN113580997B (en) Three-degree scheduling charging method based on user habit
CN112836287B (en) Electric automobile resource flexibility prediction method based on neural network
CN117353346A (en) Demand response method and device for communication base station and power system
Wu et al. A Q-learning method for scheduling shared EVs under uncertain user demand and wind power supply
Yuan et al. Source: Towards solar-uncertainty-aware e-taxi coordination under dynamic passenger mobility
CN113381406B (en) Electric vehicle charging and discharging control method, device, equipment and storage medium
CN115514020A (en) Cross-region power scheduling method and device, computer equipment and storage medium
Zhang et al. Coordinated dispatch of the wind-thermal power system by optimizing electric vehicle charging
CN113486504B (en) Battery management control method based on scheduling cost
CN110991931B (en) Charging station cooperative optimization control method based on double-center Q learning
CN112968962B (en) Cloud platform storage resource scheduling method based on distributed computer cluster architecture
Lee et al. Fast scheduling policy for electric vehicle charging stations in smart transportation
CN112258737A (en) Charging system and method for charging pile of community
CN114683908A (en) Battery replacement capability evaluation method and system for battery replacement station, electronic device and medium
Chang et al. Bi‐level scheduling of large‐scale electric vehicles based on the generation side and the distribution side

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant