CN107025518A - A kind of electric automobile charging station method and device for planning - Google Patents
A kind of electric automobile charging station method and device for planning Download PDFInfo
- Publication number
- CN107025518A CN107025518A CN201710167682.9A CN201710167682A CN107025518A CN 107025518 A CN107025518 A CN 107025518A CN 201710167682 A CN201710167682 A CN 201710167682A CN 107025518 A CN107025518 A CN 107025518A
- Authority
- CN
- China
- Prior art keywords
- charging station
- formula
- represent
- electric automobile
- path
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000009826 distribution Methods 0.000 claims abstract description 90
- 230000006870 function Effects 0.000 claims description 59
- 238000004088 simulation Methods 0.000 claims description 22
- 238000010276 construction Methods 0.000 claims description 13
- 230000008901 benefit Effects 0.000 claims description 9
- 238000004422 calculation algorithm Methods 0.000 claims description 8
- 230000005611 electricity Effects 0.000 claims description 7
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 3
- 230000009471 action Effects 0.000 claims description 3
- 230000002776 aggregation Effects 0.000 claims description 3
- 238000004220 aggregation Methods 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 230000001066 destructive effect Effects 0.000 claims description 3
- 230000002045 lasting effect Effects 0.000 claims description 3
- 230000014759 maintenance of location Effects 0.000 claims description 3
- 230000007257 malfunction Effects 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000011161 development Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 9
- 238000004590 computer program Methods 0.000 description 7
- 238000012545 processing Methods 0.000 description 5
- 238000005457 optimization Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 230000006378 damage Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000009440 infrastructure construction Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
Classifications
-
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The present invention relates to a kind of electric automobile charging station method and device for planning, including：The object function and constraints of automobile user side and charging station investment operation side are built respectively；Wagon flow is simulated, the driving path of electric automobile is obtained；Determine the optimal solution of the object function.The electric automobile charging station planning of above conurbation road network and power distribution network, is of great significance for the structure and comprehensive development tool of energy internet.
Description
Technical field
The present invention relates to heating system field, and in particular to a kind of electric automobile charging station method and device for planning.
Background technology
Electric automobile is obtained because having incomparable advantage in resource scarcity and problem of environmental pollution is solved the problems, such as
Various circles of society widely pay close attention to.National policy guiding is also increasingly apparent, and electric automobile is just widelyd popularize.But it is due to continuous
The limitation of boat mileage, electric automobile can not meet long-time, remote driving requirements, and popularization receives resistance.Therefore it is big to advise
Mould, which promotes electric automobile, to be used as premise using rational charging infrastructure construction.
At present, existing numerous scholars are studied electric automobile charging station optimization planning problem both at home and abroad, are established
Many typical Mathematics Models, including the magnitudes of traffic flow that system losses and cost of investment sum minimum, charging station are intercepted and captured are mesh to the maximum
Target Model for Multi-Objective Optimization.The characteristics of someone combines charging electric vehicle facilities planning each stage proposes charging modes
Select the two benches mould in principle, flow and the model and electric automobile charging station planning of Optimized model and electrically-charging equipment planning
Type, the first stage uses clustering methodology, and the traffic information in region is converted into charge requirement cluster；Second stage utilizes optimization
Algorithm, carries out the Site Selection of electric automobile charging station under the conditions of the certain constraint of consideration and fund factor etc..It is basic herein
On, it is considered to charging station analysis of operation benefit and part throttle characteristics prediction, the number of electric automobile is simulated with the distribution situation of resident load
Amount, to put into operation to target, a year maximization charging station running income carries out the siteselecting planning of charging station as object function, is based on
The electrically-charging equipment allocation models of queueing theory, and analyze influence of the different electrically-charging equipments to load rate of grid.Comprehensive electric automobile
Operating mechanism and the changed power characteristic of electrokinetic cell construct the constant volume model of electric automobile charging station, it is determined that in power
Charging mechanism under operating mode.In addition to setting up model, it is thus proposed that a kind of new radial constraint, it is ensured that feasible solution correspondence
Programme be radial networks, to actual charging station layout optimization and corresponding power distribution network upgrading there is certain finger
Lead meaning；Also scholar carries out addressing constant volume to charging station using weighted Voronoi diagrams figure method, and considers society's effect of charging station
Benefit, important evidence is provided for the economic Journal of Sex Research of charging station.
Electric automobile, as a vehicles, is the participant of city road network, and city road network can influence electric automobile energy
Can the no trip requirements for meeting user, i.e., smoothly reach set destination；Electric automobile is also a charge-discharge facility, is to match somebody with somebody
The participant of power network, power distribution network can be influenceed by the charge-discharge characteristic of electric automobile.Therefore, the planning of electric automobile charging station is needed
Consider city road network and power distribution network.Under the background of energy internet, the electronic vapour of conurbation road network and power distribution network
Car charging station is planned, is of great significance for the structure and comprehensive development tool of energy internet.
The content of the invention
For the demand, the present invention proposes a kind of electric automobile charging station method and device for planning, constructs synthesis and examines
Consider the constraints and object function of city road network and distribution network reliability, the traveling road of electric automobile is determined using game theory
Footpath, combined charge station input-output efficiency provides the preferred scheme of charging station planning.
The purpose of the present invention is realized using following technical proposals：
A kind of electric automobile charging station planing method, methods described includes：
The object function and constraints of automobile user side and charging station investment operation side are built respectively；
Wagon flow is simulated, the driving path of electric automobile is obtained；
Determine the optimal solution of the object function.
It is preferred that, the mesh as the automobile user side is maximized using electric automobile discharge regime distribution network reliability
Scalar functions, its expression formula is：
maxE1=Edn (1)
In above formula, E1For electric automobile discharge capacity, EdnFor the scarce delivery of whole region power distribution network；Wherein,
In above formula, Edn,uRepresent load area node u scarce delivery；pjRepresent accordingly to open automatically when element j breaks down
Close the action message rate of device；λjRepresent element j annual destructive malfunction rate；tjuRepresent to cause when element j breaks down
Load area node u power off time；LjuLoad area node u average power failure load is caused during expression element j failures；I(u)
The set of all nodes in load area is represented, when a failure occurs it, causes load area node u lasting power failure.
It is preferred that, the object function as the charging station investment operation side, its table are maximized using charging station comprehensive benefit
It is up to formula：
MaxB=-Psc+Pso+Psr (4)
In above formula, B is the overall annual cost of charging station income and user cost, PscFor the annual fee of charging station construction cost
With PsoFor the annual cost of charging station running income, PsrFor the annual cost of charging station residual value when out of service.
Further, the charging station construction cost annual cost is determined by formula (5)：
In above formula, mjQuantity is configured for charging station j charger, a is the unit price of charger, bjFixed for charging station j is thrown
Rate are used, and r is discount rate, and z is the operation time limit；
The charging station running income annual cost is determined by formula (6)：
In above formula, NtFor the cycle of simulation, unit h；A is charging unit price；QmaxAnd Qi,j,kThe respectively maximum of electric automobile
Kth time period is located at charging station j electric automobile i residual electric quantity in electricity and simulation cycle；Ci,j,kSentence for the charging of the car
Disconnected mark；
The charging station residual value annual cost is determined by formula (7)：
In above formula, ε is conversion factor of the charging station residual value for investment construction cost.
It is preferred that, the constraints includes Road Network Reliability constraint and distribution network reliability constraint.
Further, determine that the Road Network Reliability is constrained by formula (7)：
In above formula, ErnFor the missing distance of whole region road network, Ern,kRepresent that user produces in the case where selecting section k
Missing distance.
In above formula, piRepresent that section i is destroyed after person's attack, user selects the probability of another section k in the same area；qi
Represent that section i is destroyed the probability of person's attack；RikRepresent that section i is destroyed after person's attack, user selects the section k travelings production
Raw actual travel is apart from RrealAnd operating range R difference needed for selecting section k as expected, shown in its expression formula such as formula (10)：
Further, the distribution network reliability constraint includes charging station capacity-constrained, the bound of node voltage amplitude
Constraint, the constraint of feeder line maximum current and charging station access point capacity-constrained；Wherein,
Shown in the charging station capacity constraints such as formula (11)：
In above formula, j ' is distribution network load node；K is the load bus that charging station is accessed in power distribution network；JjFor charging
The j that stands supplies load aggregation；KjFor the load bus set in charging station j supply districts as charging station access point；SjFor charging station
J capacity；e(Sj) it is charging station j load factors；For power factor；Pj’The burden with power put for power distribution network in j '；PkFor k
Point access charging station capacity；
The bound of the node voltage amplitude is constrained as shown in formula (12)：
Vi' min≤Vi'≤Vi' max, i'=1,2 ..., M (12)
In above formula, ViFor power distribution network node i ' voltage magnitude；Vi' maxAnd Vi' minRespectively the node voltage amplitude it is upper,
Lower limit；M is the interstitial content of power distribution network；
The feeder line maximum current constraint is as shown in formula (13)：
|Ii'j'|≤Ii'j'max, i', j'=1,2 ..., M (13)
In above formula, Ii'j'And Ii'j'maxFeeder line i'j ' electric current and the maximum current for allowing to flow through respectively in power distribution network；
Shown in the charging station access point capacity-constrained such as formula (14)：
PCj'j≤P'jmax (14)
In above formula, PCjj’For the charging station j of access j ' points maximum charge power, Pj’maxFor distribution network load node j ' most
It is big to allow access power.
It is preferred that, obtaining the driving path of the electric automobile includes：
A. Selection Strategy k, Game Simulation is carried out based on theory of games to Road Network Reliability, determines vehicle flowrate distribution and electronic
User vehicle traffic route；
B. tactful k selection probability is modified, and return to step a, obtain new vehicle flowrate and be distributed and electric automobile
The driving path of user's traffic route, i.e. electric automobile.
Further, the determination vehicle flowrate distribution and automobile user traffic route include：
Automobile user a operating range is determined by formula (15)：
In above formula, DaFor operating range, k represents the strategy chosen in self-defined policy library, Da(k) represent in tactful k bars
Operating range under part；Pal’Represent that automobile user a selects path l ' probability；
Game Simulation is carried out to automobile user, obtaining the minimum value expression of game is：
In above formula, S represents self-defined policy library, qkAutomobile user selection strategy k probability is represented,
Represent the driving path of electric automobile after selection strategy k, dlk(x) unit operating range of the electric automobile in road network is represented, its
Expression formula is：
In above formula, βuAnd γuRepresent the retardation coefficient on the u of section, clRepresent the respective path l in the case of Selection Strategy k
Vehicle flowrate capacity；When road network is not destroyed person's attack, clIt is identical with normal road capacity, after being attacked by the saboteur, clWith
Capacity after decay is identical；LthlkRepresent the actual range between l and k；
In above formula, hl’Represent the special bus flow on the l ' of path, all’The selected probability of path l is represented, when path l quilts
During selection, all’=1；When other paths are chosen, all’=0.
Further, the selection probability to tactful k be modified including：
Determine that saboteur attacks the probability of roadway by formula (19)：
In above formula, θk,rRepresent in the case of automobile user Selection Strategy k, saboteur's attack roadway r's is general
Rate, σ is correction factor, and h is wagon flow moment matrix h0In element, s represents the interdependent node after selection strategy k, αkFor in electronic vapour
Vehicle flowrate equalizing coefficient after automobile-used family selection strategy k, represent saboteur road network is attacked after influence degree；
Determined by formula (19) from path l ' to path l：
In above formula,Represent under the conditions of tactful k, from path l ' to the vehicle flowrate equalizing coefficient of path l parts, Pal’Table
Show that automobile user a selects path l ' probability, gl’kRepresent in the cost function in the case of tactful k on the l ' of path, gll'
Represent the cost function on path l ' to path l, hl’Represent the special bus flow on the l ' of path, qkFor Selection Strategy k probability；
θkRepresent it is determined that in the case of vehicle flowrate, saboteur preferentially chooses wherein maximum θK, max (r)Corresponding city road
The probability that net position is destroyed；
After section is attacked by saboteur, pass through selection probability q of the formula (21) to tactful kkIt is modified, is corrected
Strategy afterwards chooses probability：
It is preferred that, the optimal solution for determining object function includes：Electric automobile is solved using multi-objective Algorithm NSGA-II
User side and the object function of charging station investment operation side, and the individual in Pareto forward positions is ranked up using TOPSIS,
Determine the optimal solution of object function；Specifically include：
A. road network information and distribution information are inputted；
B. t=0 is made, primary iteration population A is randomly generated；
C. the parent population under the constraint of generation distribution network reliability, determines that charging station charging pile quantity and charging station institute are in place
The road-net node put, performs wagon flow simulation, eliminates the individual for being unsatisfactory for distribution network reliability constraint, true by formula (1) and formula (4)
Set the goal function, and cross and variation generation progeny population B；
D. the individual for being unsatisfactory for distribution network reliability constraint is eliminated, determines that charging station charging pile quantity and charging station institute are in place
The road-net node put, performs wagon flow simulation, eliminates the individual for being unsatisfactory for distribution network reliability constraint, merges population A, B and passes through
Formula (1) and formula (4) determine object function, and non-dominated ranking is carried out according to elite retention strategy, choose top n individual generation filial generation
Population, and judge whether iterations reaches the upper limit, if not up to, making t=t+1, return to step b；If iterations has reached
The upper limit, then terminate operation；Wherein, t represents iterations.
It is preferred that, described device includes：
First builds module, object function and its constraints for building automobile user side；
Second builds module, object function and its constraints for building charging station investment operation side；
Analog module, is simulated to wagon flow, obtains vehicle flowrate distribution and automobile user traffic route；
The optimal solution of the object function is determined using multi-objective Algorithm NSGA-II.
Compared with immediate prior art, beneficial effects of the present invention are：
The present invention is directed to current electric automobile as the important participant of city road network and charge-discharge facility, but its electronic vapour
The problem of planning of car charging station fails to consider city road network and power distribution network.The present invention proposes a kind of electric automobile charging station
Method and device for planning, constructs the object function and constraints for considering city road network and distribution network reliability first；
Its object function specifically includes the object function of automobile user side and the object function of charging station investment operation side, electronic vapour
The object function emphasis of car user side considers the reliability of urban power distribution network, and the object function of charging station investment operation side is then led
Consider construction cost, running income, residual value of charging station etc..Power distribution network must take into consideration the appearance of power distribution network as electrically-charging equipment
Amount limitation and the constraint of safe operation, can improve the reliability of power distribution network as electric discharge facility, reduce and lack delivery.Next is adopted
The driving path of electric automobile is determined with game theory, and combined charge station input-output efficiency determines optimal solution, is derived from filling
The preferred scheme of power scheme, with high efficiency and practicality.
Brief description of the drawings
Fig. 1 is total method flow diagram that the present invention is provided；
Fig. 2 is the method flow diagram that the multi-objective Algorithm NSGA-II that the present invention is provided determines object function optimal solution；
Fig. 3 is somewhere city road network and power distribution network simulation master drawing in the embodiment of the present invention.
Embodiment
The embodiment to the present invention elaborates below in conjunction with the accompanying drawings.
Current electric automobile charging station planning fails to consider city road network and power distribution network, but electric automobile is used as one
The individual vehicles, are the participants of city road network, and can city road network can influence electric automobile meet the trip requirements of user, i.e.,
Set destination can smoothly be reached；Electric automobile is also a charge-discharge facility, is the participant of power distribution network, and power distribution network can be by
Charge-discharge characteristic to electric automobile influences.
Therefore, the planning of electric automobile charging station needs to consider city road network and power distribution network.In energy internet
Under background, the electric automobile charging station planning of conurbation road network and power distribution network is sent out for the structure of energy internet with comprehensive
Exhibition tool is of great significance.In view of the above-mentioned problems, the present invention proposes a kind of electric automobile charging station planing method, such as Fig. 1
Shown, its method includes：
1st, the object function and constraints of automobile user side and charging station investment operation side are built respectively；
Object function as the automobile user side is maximized using electric automobile discharge regime distribution network reliability,
Its expression formula is：
maxE1=Edn (1)
In above formula, E1For electric automobile discharge capacity, EdnFor the scarce delivery of whole region power distribution network；Wherein,
In above formula, Edn,uRepresent load area node u scarce delivery；pjRepresent accordingly to open automatically when element j breaks down
Close the action message rate of device；λjRepresent element j annual destructive malfunction rate；tjuRepresent to cause when element j breaks down
Load area node u power off time；LjuLoad area node u average power failure load is caused during expression element j failures；I(u)
The set of all nodes in load area is represented, when a failure occurs it, causes load area node u lasting power failure.
Object function as the charging station investment operation side is maximized using charging station comprehensive benefit, its expression formula is：
MaxB=-Psc+Pso+Psr (4)
In above formula, B is the overall annual cost of charging station income and user cost, PscFor the annual fee of charging station construction cost
With PsoFor the annual cost of charging station running income, PsrFor the annual cost of charging station residual value when out of service.
The charging station construction cost annual cost is determined by formula (5)：
In above formula, mjQuantity is configured for charging station j charger, a is the unit price of charger, bjFixed for charging station j is thrown
Rate are used, and r is discount rate, and z is the operation time limit；
The charging station running income annual cost is determined by formula (6)：
In above formula, NtFor the cycle of simulation, unit h；A is charging unit price；QmaxAnd Qi,j,kThe respectively maximum of electric automobile
Kth time period is located at charging station j electric automobile i residual electric quantity in electricity and simulation cycle；Ci,j,kSentence for the charging of the car
Disconnected mark；
The charging station residual value annual cost is determined by formula (7)：
In above formula, ε is conversion factor of the charging station residual value for investment construction cost.
The constraints includes Road Network Reliability constraint and distribution network reliability constraint.
Determine that the Road Network Reliability is constrained by formula (7)：
In above formula, ErnFor the missing distance of whole region road network, Ern,kRepresent that user produces in the case where selecting section k
Missing distance.
In above formula, piRepresent that section i is destroyed after person's attack, user selects the probability of another section k in the same area；qi
Represent that section i is destroyed the probability of person's attack；RikRepresent that section i is destroyed after person's attack, user selects the section k travelings production
Raw actual travel is apart from RrealAnd operating range R difference needed for selecting section k as expected, shown in its expression formula such as formula (10)：
Distribution network reliability constraint includes charging station capacity-constrained, the constraint of the bound of node voltage amplitude, feeder line maximum
Restriction of current and charging station access point capacity-constrained；Wherein,
Shown in charging station capacity constraints such as formula (11)：
In above formula, j ' is distribution network load node；K is the load bus that charging station is accessed in power distribution network；JjFor charging
The j that stands supplies load aggregation；KjFor the load bus set in charging station j supply districts as charging station access point；SjFor charging station
J capacity；e(Sj) it is charging station j load factors；For power factor；Pj’The burden with power put for power distribution network in j '；PkFor k points
Access charging station capacity；
The bound of the node voltage amplitude is constrained as shown in formula (12)：
Vi' min≤Vi'≤Vi' max, i'=1,2 ..., M (12)
In above formula, ViFor power distribution network node i ' voltage magnitude；Vi' maxAnd Vi' minRespectively the node voltage amplitude it is upper,
Lower limit；M is the interstitial content of power distribution network；
The feeder line maximum current constraint is as shown in formula (13)：
|Ii'j'|≤Ii'j'max, i', j'=1,2 ..., M (13)
In above formula, Ii'j'And Ii'j'maxFeeder line i'j ' electric current and the maximum current for allowing to flow through respectively in power distribution network；
Shown in the charging station access point capacity-constrained such as formula (14)：
PCjj'≤Pj'max (14)
In above formula, PCjj’For the charging station j of access j ' points maximum charge power, Pj’maxFor distribution network load node j ' most
It is big to allow access power.
2nd, city road network, power distribution network and the three of user are connected by electric automobile, wagon flow is simulated, obtained
The driving path of electric automobile；
The necessity that wagon flow simulation is done to electric automobile is：The traveling of electric automobile benefits from family subjective desire influence
Flow behavior of the electricity on road network.Path selection and charge requirement, which judge two parts, to be considered to wagon flow simulation.Wherein, it is electronic
The driving path of automobile should combine game theory thought, introduce " city saboteur " concept, and complicated city road network reliability is carried out
Game Simulation is so as to obtain the driving path of electric automobile.And charge requirement judge refer to travel paths set in advance by
In the case that saboteur attacks, user must change travel route and be possible to arrive at, but be due to electric automobile electricity
The limitation of pond characteristic, after road network is attacked by saboteur, will appear from following two situations：
1) although user's one way operating range increases, it need not still be charged in way home, backward distribution of simply going home
The discharge capacity of net has been reduced；
2) user needs to be charged in way home, but does not have charging station in way home, causes user not return smoothly
Family.
When there is second of situation, user produces charge requirement, in order to smoothly complete traveling process, it is necessary to set up charging
Device.In view of the convenience of charging, it is considered to build charging station in road-net node.
And then after saboteur is attacked a certain road-net node, for certain charging station construction scheme, it is necessary to count
Electric automobile missing distance and the charging station comprehensive benefit in the regional city road network are calculated, and examines power distribution network under the program
Reliability.
The consequence that saboteur causes is so that electric automobile is maximized in the missing distance for running over journey generation, electric automobile
User and city saboteur are at war with game；It mainly solves the information how automobile user relies on city road network to provide
Carry out the problem of optimal driving path is selected.Generally, automobile user all can the minimum path of alternative costs.
The driving path for obtaining electric automobile specifically includes following steps：
A. Selection Strategy k is concentrated in Existing policies, Game Simulation is carried out to Road Network Reliability based on theory of games, car is determined
Flow distribution and automobile user traffic route；Wherein it is determined that vehicle flowrate distribution and automobile user traffic route include：
Automobile user a operating range is determined by formula (15)：
In above formula, DaFor operating range, k represents the strategy chosen in self-defined policy library, Da(k) represent in tactful k bars
Operating range under part；Pal’Represent that automobile user a selects path l ' probability；
Game Simulation is carried out to automobile user, obtaining the minimum value expression of game is：
In above formula, S represents self-defined policy library, qkAutomobile user selection strategy k probability is represented,
Represent the driving path of electric automobile after selection strategy k, dlk(x) unit operating range of the electric automobile in road network is represented, its
Expression formula is：
In above formula, βuAnd γuRepresent the retardation coefficient on the u of section, clRepresent the respective path l in the case of Selection Strategy k
Vehicle flowrate capacity；When road network is not destroyed person's attack, clIt is identical with normal road capacity, after being attacked by the saboteur, clWith
Capacity after decay is identical；LthlkRepresent the actual range between l and k；
In above formula, hl’Represent the special bus flow on the l ' of path, all’The selected probability of path l is represented, when path l quilts
During selection, all’=1；When other paths are chosen, all’=0.
B. tactful k selection probability is modified, and return to step a, obtain new vehicle flowrate and be distributed and electric automobile
The driving path of user's traffic route, i.e. electric automobile.Game in the step is mainly for saboteur, it is assumed that saboteur is attacking
The automobile user in road network can be caused to change driving path during hitting road-net node, but do not control electronic vapour directly
Automobile-used family is distributed, selection causes maximum damage to automobile user to the specifically chosen of route by the step a vehicle flowrates obtained
The node of mistake.
The probability of the node of saboteur's selection destruction determines that is, saboteur is more prone to by the vehicle flowrate in city road network
In region of the attack vehicle flow-rate ratio compared with concentration.
Tactful k selection probability is modified including：
Determine that saboteur attacks the probability of roadway by formula (19)：
In above formula, θk,rRepresent in the case of automobile user Selection Strategy k, saboteur's attack roadway r's is general
Rate, σ is correction factor, and h is wagon flow moment matrix h0In element, s represents the interdependent node after selection strategy k, αkFor in electronic vapour
Vehicle flowrate equalizing coefficient after automobile-used family selection strategy k, represent saboteur road network is attacked after influence degree；
Determined by formula (19) from path l ' to path l：
In above formula,Represent under the conditions of tactful k, from path l ' to the vehicle flowrate equalizing coefficient of path l parts, Pal’Table
Show that automobile user a selects path l ' probability, gl’kRepresent in the cost function in the case of tactful k on the l ' of path, gll'
Represent the cost function on path l ' to path l, hl’Represent the special bus flow on the l ' of path, qkFor Selection Strategy k probability；
θkRepresent it is determined that in the case of vehicle flowrate, saboteur preferentially chooses wherein maximum θK, max (r)Corresponding city road
The probability that net position is destroyed；
After section is attacked by saboteur, pass through selection probability q of the formula (21) to tactful kkIt is modified, is corrected
Strategy afterwards chooses probability：
3rd, the optimal solution of the object function is determined using multi-objective Algorithm NSGA-II, as shown in Figure 2.
Determining the optimal solution of object function includes：Automobile user side is solved with filling using multi-objective Algorithm NSGA-II
The object function of power station investment operation side, and the individual in Pareto forward positions is ranked up using TOPSIS, determine target letter
Several optimal solutions；Concretely comprise the following steps：
A. road network information and distribution information are inputted；
B. t=0 is made, primary iteration population A is randomly generated；
C. the parent population under the constraint of generation distribution network reliability, determines that charging station charging pile quantity and charging station institute are in place
The road-net node put, performs wagon flow simulation, eliminates the individual for being unsatisfactory for distribution network reliability constraint, true by formula (1) and formula (4)
Set the goal function, and cross and variation generation progeny population B；
D. the individual for being unsatisfactory for distribution network reliability constraint is eliminated, determines that charging station charging pile quantity and charging station institute are in place
The road-net node put, performs wagon flow simulation, eliminates the individual for being unsatisfactory for distribution network reliability constraint, merges population A, B and passes through
Formula (1) and formula (4) determine object function, and non-dominated ranking is carried out according to elite retention strategy, choose top n individual generation filial generation
Population, and judge whether iterations reaches the upper limit, if not up to, making t=t+1, return to step b；If iterations has reached
The upper limit, then terminate operation；Wherein, t represents iterations.
To sum up, the above method constructs the object function for considering city road network and distribution network reliability and constraint first
Condition；Its object function specifically includes the object function of automobile user side and the object function of charging station investment operation side,
The reliability of the object function emphasis consideration urban power distribution network of automobile user side, and the target letter of charging station investment operation side
It is several, mainly consider construction cost, running income, residual value of charging station etc..Power distribution network must take into consideration distribution as electrically-charging equipment
The capacity limit of net and the constraint of safe operation, can improve the reliability of power distribution network as electric discharge facility, reduce and lack delivery.
Secondly the driving path of electric automobile is determined using game theory, and combined charge station input-output efficiency determines optimal solution, thus
The preferred scheme of charging station planning is obtained, with high efficiency and practicality.
Embodiment：The method provided based on the present invention, cooks up somewhere city road network and power distribution network mould as shown in Figure 3
Intend master drawing.
Dotted line represents the 10kV circuits in power distribution network in Fig. 3, and solid line represents urban road.A and B represent electric automobile in region
The traveling departure place of user, region C represents that corresponding urban road is long between the traveling destination of automobile user, Area Node
Degree is shown in Table 1.
It is assumed that the electric automobile in the region is evenly distributed on A to C and B to C paths, average daily charging vehicle is
100, the battery capacity of each car is 57kWh, and 120km can be travelled (it is assumed that batteries of electric automobile is electric in the state of full electricity
0) bottom line of amount is, calculated for 1 year by 365 days, and charging unit price is 0.8 yuan/kWh, and discount rate is 6%, and service life is 10
Year.
Each node builds maintenance cost, original scarce delivery and constraints and is shown in Table 2.
Corresponding link length between the Area Node of table 1
Each node of table 2 builds maintenance cost, original scarce delivery and constraints
Node set forth below is that electric automobile can be supported smoothly to return to the node in home after installing additional, and calculating obtains each
What node built that electric automobile discharge regime after charging station lacks the change of delivery, comprehensive benefit and each constraints meets feelings
Condition.
Result of calculation is shown in Table 3, wherein " ----", which represents, meets constraints, the representative for writing numerical value exactly is unsatisfactory for constraining bar
Part.
Each node correspondence object function and constraints that table 3 can build charging station meet situation
In table 3, the construction charging station of node 13,18, which is unsatisfactory for constraint, can not improve scarce delivery；Node 17, which is built, to be filled
Power station meets constraints but can not improve scarce delivery；Node 11,15, which builds charging station, can improve scarce delivery but discontented
Foot about condition；Node 12,14,16,19, which builds charging station and meets to constrain, can also improve scarce delivery, it is also contemplated that building charging station
Year input-output efficiency is maximum.Because being multiple objective function, these individuals are ranked up using TOPSIS, ideality by
Small sequence is arrived greatly:16,12,19,14.Therefore optimal node 16 is selected to build charging station.
Based on same inventive concept, the present invention also proposes a kind of electric automobile charging station device for planning, including：
First builds module, object function and its constraints for building automobile user side；
Second builds module, object function and its constraints for building charging station investment operation side；
Analog module, is simulated to wagon flow, obtains vehicle flowrate distribution and automobile user traffic route.
The proposition of the device solves the electric automobile charging station planning problem for considering city road network and power distribution network.Power distribution network
The capacity limit of power distribution network and the constraint of safe operation are must take into consideration as electrically-charging equipment, distribution can be improved as electric discharge facility
The reliability of net, reduces and lacks delivery.Secondly the driving path of electric automobile is determined, and combined charge station input-output efficiency is true
Determine optimal solution, the preferred scheme of charging station planning is derived from, with high efficiency and practicality.For the structure of energy internet
It is of great significance with comprehensive development tool.
It should be understood by those skilled in the art that, embodiments herein can be provided as method, system or computer program
Product.Therefore, the application can be using the reality in terms of complete hardware embodiment, complete software embodiment or combination software and hardware
Apply the form of example.Moreover, the application can be used in one or more computers for wherein including computer usable program code
The computer program production that usable storage medium is implemented on (including but is not limited to magnetic disk storage, CD-ROM, optical memory etc.)
The form of product.
The application is the flow with reference to method, equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram are described.It should be understood that can be by every first-class in computer program instructions implementation process figure and/or block diagram
Journey and/or the flow in square frame and flow chart and/or block diagram and/or the combination of square frame.These computer programs can be provided
The processor of all-purpose computer, special-purpose computer, Embedded Processor or other programmable data processing devices is instructed to produce
A raw machine so that produced by the instruction of computer or the computing device of other programmable data processing devices for real
The device for the function of being specified in present one flow of flow chart or one square frame of multiple flows and/or block diagram or multiple square frames.
These computer program instructions, which may be alternatively stored in, can guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works so that the instruction being stored in the computer-readable memory, which is produced, to be included referring to
Make the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one square frame of block diagram or
The function of being specified in multiple square frames.
These computer program instructions can be also loaded into computer or other programmable data processing devices so that in meter
Series of operation steps is performed on calculation machine or other programmable devices to produce computer implemented processing, thus in computer or
The instruction performed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in individual square frame or multiple square frames.
Finally it should be noted that：The above embodiments are merely illustrative of the technical scheme of the present invention and are not intended to be limiting thereof, to the greatest extent
The present invention is described in detail with reference to above-described embodiment for pipe, those of ordinary skills in the art should understand that：Still
The embodiment of the present invention can be modified or equivalent substitution, and without departing from any of spirit and scope of the invention
Modification or equivalent substitution, it all should cover within the claims of the present invention.
Claims (12)
1. a kind of electric automobile charging station planing method, it is characterised in that methods described includes：
The object function and constraints of automobile user side and charging station investment operation side are built respectively；
Wagon flow is simulated, the driving path of electric automobile is obtained；
Determine the optimal solution of the object function.
2. the method as described in claim 1, it is characterised in that be turned into electric automobile discharge regime distribution network reliability maximum
For the object function of the automobile user side, its expression formula is：
max E1=Edn (1)
In above formula, E1For electric automobile discharge capacity, EdnFor the scarce delivery of whole region power distribution network；Wherein,
In above formula, Edn,uRepresent load area node u scarce delivery；pjRepresent corresponding automatic switch dress when element j breaks down
The action message rate put；λjRepresent element j annual destructive malfunction rate；tjuRepresent to cause load when element j breaks down
Area Node u power off time；LjuLoad area node u average power failure load is caused during expression element j failures；I (u) is represented
The set of all nodes in load area, when a failure occurs it, causes load area node u lasting power failure.
3. the method as described in claim 1, it is characterised in that maximized and thrown as the charging station using charging station comprehensive benefit
The object function of operator is provided, its expression formula is：
Max B=-Psc+Pso+Psr (4)
In above formula, B is the overall annual cost of charging station income and user cost, PscFor the annual cost of charging station construction cost, Pso
For the annual cost of charging station running income, PsrFor the annual cost of charging station residual value when out of service.
4. method as claimed in claim 3, it is characterised in that the charging station construction cost annual cost is determined by formula (5)：
In above formula, mjQuantity is configured for charging station j charger, a is the unit price of charger, bjFor charging station j fixed investment expense
With r is discount rate, and z is the operation time limit；
The charging station running income annual cost is determined by formula (6)：
In above formula, NtFor the cycle of simulation, unit h；A is charging unit price；QmaxAnd Qi,j,kThe respectively maximum electricity of electric automobile
It is located at charging station j electric automobile i residual electric quantity with kth time period in simulation cycle；Ci,j,kJudge mark for the charging of the car
Know；
The charging station residual value annual cost is determined by formula (7)：
In above formula, ε is conversion factor of the charging station residual value for investment construction cost.
5. the method as described in claim 1, it is characterised in that the constraints includes Road Network Reliability and constrained and power distribution network
Reliability constraint.
6. method as claimed in claim 5, it is characterised in that determine that the Road Network Reliability is constrained by formula (7)：
In above formula, ErnFor the missing distance of whole region road network, Ern,kRepresent that user produces in the case where selecting section k scarce
Lose distance.
In above formula, piRepresent that section i is destroyed after person's attack, user selects the probability of another section k in the same area；qiRepresent
Section i is destroyed the probability of person's attack；RikRepresent that section i is destroyed after person's attack, user selects what the section k travelings were produced
Actual travel is apart from RrealAnd operating range R difference needed for selecting section k as expected, shown in its expression formula such as formula (10)：
7. method as claimed in claim 5, it is characterised in that the distribution network reliability constraint includes charging station capacity about
Beam, the bound constraint of node voltage amplitude, the constraint of feeder line maximum current and charging station access point capacity-constrained；Wherein,
Shown in the charging station capacity constraints such as formula (11)：
In above formula, j ' is distribution network load node；K is the load bus that charging station is accessed in power distribution network；JjFor charging station j institutes
For load aggregation；KjFor the load bus set in charging station j supply districts as charging station access point；SjFor charging station j appearance
Amount；e(Sj) it is charging station j load factors；For power factor；Pj’The burden with power put for power distribution network in j '；PkAccessed for k points
Charging station capacity；
The bound of the node voltage amplitude is constrained as shown in formula (12)：
In above formula, ViFor power distribution network node i ' voltage magnitude；Vi' maxAnd Vi' minRespectively the node voltage amplitude is upper and lower
Limit；M is the interstitial content of power distribution network；
The feeder line maximum current constraint is as shown in formula (13)：
|Ii'j'|≤Ii'j'max, i', j'=1,2 ..., M (13)
In above formula, Ii'j'And Ii'j'maxFeeder line i'j ' electric current and the maximum current for allowing to flow through respectively in power distribution network；
Shown in the charging station access point capacity-constrained such as formula (14)：
PCjj'≤Pj'max (14)
In above formula, PCjj’For the charging station j of access j ' points maximum charge power, Pj’maxPermit for distribution network load node j ' maximums
Perhaps access power.
8. the method as described in claim 1, it is characterised in that obtaining the driving path of the electric automobile includes：
A. Selection Strategy k, Game Simulation is carried out based on theory of games to Road Network Reliability, determines vehicle flowrate distribution and electric automobile
User's traffic route；
B. tactful k selection probability is modified, and return to step a, obtain new vehicle flowrate and be distributed and automobile user
The driving path of traffic route, i.e. electric automobile.
9. method as claimed in claim 8, it is characterised in that the determination vehicle flowrate distribution and automobile user roadway
Line includes：
Automobile user a operating range is determined by formula (15)：
In above formula, DaFor operating range, k represents the strategy chosen in self-defined policy library, Da(k) represent under the conditions of tactful k
Operating range；Pal’Represent that automobile user a selects path l ' probability；
Game Simulation is carried out to automobile user, obtaining the minimum value expression of game is：
In above formula, S represents self-defined policy library, qkAutomobile user selection strategy k probability is represented,Represent
The driving path of electric automobile, d after selection strategy klk(x) unit operating range of the electric automobile in road network is represented, it is expressed
Formula is：
In above formula, βuAnd γuRepresent the retardation coefficient on the u of section, clRepresent the wagon flow of the respective path l in the case of Selection Strategy k
Measure capacity；When road network is not destroyed person's attack, clIt is identical with normal road capacity, after being attacked by the saboteur, clWith decay
Capacity afterwards is identical；LthlkRepresent the actual range between l and k；
In above formula, hl’Represent the special bus flow on the l ' of path, all’The selected probability of path l is represented, when path l is chosen
When, all’=1；When other paths are chosen, all’=0.
10. method as claimed in claim 8, it is characterised in that the selection probability to tactful k be modified including：
Determine that saboteur attacks the probability of roadway by formula (19)：
In above formula, θk,rRepresent in the case of automobile user Selection Strategy k, saboteur's attack roadway r probability, σ
For correction factor, h is wagon flow moment matrix h0In element, s represents the interdependent node after selection strategy k, αkFor used for electric vehicle
Vehicle flowrate equalizing coefficient after the selection strategy k of family, represent saboteur road network is attacked after influence degree；
Determined by formula (19) from path l ' to path l：
In above formula,Represent under the conditions of tactful k, from path l ' to the vehicle flowrate equalizing coefficient of path l parts, Pal’Represent electricity
Electrical automobile user a selects path l ' probability, gl’kRepresent in the cost function in the case of tactful k on the l ' of path, gll'Represent
Cost function on path l ' to path l, hl’Represent the special bus flow on the l ' of path, qkFor Selection Strategy k probability；
θkRepresent it is determined that in the case of vehicle flowrate, saboteur preferentially chooses wherein maximum θK, max (r)Corresponding city road network position
Put the probability destroyed；
After section is attacked by saboteur, pass through selection probability q of the formula (21) to tactful kkIt is modified, obtains revised
Strategy chooses probability：
11. the method as described in claim 1-3, it is characterised in that the optimal solution of the determination object function includes：Using many
Target algorithm NSGA-II solves the object function of automobile user side and charging station investment operation side, and utilizes TOPSIS pairs
Individual in Pareto forward positions is ranked up, and determines the optimal solution of object function；Specifically include：
A. road network information and distribution information are inputted；
B. t=0 is made, primary iteration population A is randomly generated；
C. the parent population under the constraint of generation distribution network reliability, determines charging station charging pile quantity and charging station position
Road-net node, performs wagon flow simulation, eliminates the individual for being unsatisfactory for distribution network reliability constraint, mesh is determined by formula (1) and formula (4)
Scalar functions, and cross and variation generation progeny population B；
D. the individual for being unsatisfactory for distribution network reliability constraint is eliminated, charging station charging pile quantity and charging station position is determined
Road-net node, performs wagon flow simulation, eliminates the individual for being unsatisfactory for distribution network reliability constraint, merges population A, B and passes through formula (1)
Object function is determined with formula (4), non-dominated ranking is carried out according to elite retention strategy, top n individual generation progeny population is chosen,
And judge whether iterations reaches the upper limit, if not up to, making t=t+1, return to step b；If iterations reaches the upper limit,
Then terminate operation；Wherein, t represents iterations.
12. a kind of electric automobile charging station device for planning, it is characterised in that described device includes：
First builds module, object function and its constraints for building automobile user side；
Second builds module, object function and its constraints for building charging station investment operation side；
Analog module, is simulated to wagon flow, obtains vehicle flowrate distribution and automobile user traffic route；
The optimal solution of the object function is determined using multi-objective Algorithm NSGA-II.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710167682.9A CN107025518A (en) | 2017-03-20 | 2017-03-20 | A kind of electric automobile charging station method and device for planning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710167682.9A CN107025518A (en) | 2017-03-20 | 2017-03-20 | A kind of electric automobile charging station method and device for planning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107025518A true CN107025518A (en) | 2017-08-08 |
Family
ID=59525705
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710167682.9A Pending CN107025518A (en) | 2017-03-20 | 2017-03-20 | A kind of electric automobile charging station method and device for planning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107025518A (en) |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107832891A (en) * | 2017-11-15 | 2018-03-23 | 云南电网有限责任公司 | A kind of charging station planing method of city expressway mouth |
| CN108269008A (en) * | 2017-12-29 | 2018-07-10 | 天津大学 | Consider the electrically-charging equipment Method for optimized planning of user satisfaction and Distribution Network Reliability |
| CN108932561A (en) * | 2018-06-22 | 2018-12-04 | 华北电力大学 | A kind of electric car charge path selection method considering non-linear charge function |
| CN109190935A (en) * | 2018-08-15 | 2019-01-11 | 天津大学 | A kind of highway charging station planing method considering service area and car accident |
| CN109520517A (en) * | 2017-09-19 | 2019-03-26 | 中华电信股份有限公司 | Route planning method and its system |
| CN109544006A (en) * | 2018-11-23 | 2019-03-29 | 国网浙江省电力有限公司电力科学研究院 | A method of evaluation electric automobile charging station economic benefit |
| CN109670675A (en) * | 2018-11-26 | 2019-04-23 | 国网冀北电力有限公司 | Charging pile evaluation of running status method and device |
| CN109840708A (en) * | 2019-02-01 | 2019-06-04 | 国网河北省电力有限公司经济技术研究院 | A kind of planing method, system and the terminal device of charging station construction |
| CN109902860A (en) * | 2019-01-23 | 2019-06-18 | 西南交通大学 | A kind of electric automobile charging station site selecting method based on combined distance assessment |
| CN110458332A (en) * | 2019-07-18 | 2019-11-15 | 天津大学 | A kind of electric vehicle rapid charging demand dispatch method based on load space transfer |
| CN110493305A (en) * | 2019-07-05 | 2019-11-22 | 广州车行易科技股份有限公司 | A method of charge path planning is carried out according to charging station behaviour in service and distance |
| CN110751564A (en) * | 2019-09-04 | 2020-02-04 | 清华大学 | Electric vehicle charging station construction method and system based on game theory |
| CN110826753A (en) * | 2018-08-09 | 2020-02-21 | 阿里巴巴集团控股有限公司 | Resource processing method, device and equipment |
| CN112069635A (en) * | 2020-09-01 | 2020-12-11 | 上海钧正网络科技有限公司 | Battery replacement cabinet deployment method, device, medium and electronic equipment |
| CN112784491A (en) * | 2021-01-26 | 2021-05-11 | 浙江中新电力工程建设有限公司 | Urban charging grid point planning method facing high-elasticity power grid based on LSTM and IQPSO |
| CN113036790A (en) * | 2021-02-26 | 2021-06-25 | 江苏大学 | Electric vehicle driving path optimization method based on network reconstruction |
| CN113195299A (en) * | 2019-02-28 | 2021-07-30 | 宝马股份公司 | Method and system for optimizing distributed charging station efficiency and user experience |
| CN113255135A (en) * | 2021-05-28 | 2021-08-13 | 西安交通大学 | Electric automobile rapid charging load simulation method based on traffic balance |
| CN113650515A (en) * | 2021-07-07 | 2021-11-16 | 广州杰赛科技股份有限公司 | Electric vehicle charging control method and device, terminal equipment and storage medium |
| CN114757004A (en) * | 2022-04-02 | 2022-07-15 | 宁波市规划设计研究院 | Urban rail transit network planning method, system and storage medium |
| CN117429303A (en) * | 2023-10-10 | 2024-01-23 | 北京理工大学前沿技术研究院 | Electric automobile battery replacement method, system and equipment based on Internet of things |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140028254A1 (en) * | 2012-07-30 | 2014-01-30 | General Electric Company | Method and system for charging of electric vehicles |
| CN105139096A (en) * | 2015-09-28 | 2015-12-09 | 东南大学 | Two-stage optimization-based locating and sizing method for electric vehicle charging station |
-
2017
- 2017-03-20 CN CN201710167682.9A patent/CN107025518A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140028254A1 (en) * | 2012-07-30 | 2014-01-30 | General Electric Company | Method and system for charging of electric vehicles |
| CN105139096A (en) * | 2015-09-28 | 2015-12-09 | 东南大学 | Two-stage optimization-based locating and sizing method for electric vehicle charging station |
Non-Patent Citations (3)
| Title |
|---|
| 刘洪等: "基于动态车流模拟的高速公路充电站多目标优化规划", 《电力系统自动化》 * |
| 刘洪等: "考虑出租车随机行为特性及路网行程时间可靠性的充电站多目标规划", 《电网技术》 * |
| 葛少云等: "配电网和城市路网关联网络的综合可靠性分析", 《中国电机工程学报》 * |
Cited By (33)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109520517A (en) * | 2017-09-19 | 2019-03-26 | 中华电信股份有限公司 | Route planning method and its system |
| CN109520517B (en) * | 2017-09-19 | 2023-01-31 | 台湾中华电信股份有限公司 | Route planning method and system |
| CN107832891A (en) * | 2017-11-15 | 2018-03-23 | 云南电网有限责任公司 | A kind of charging station planing method of city expressway mouth |
| CN108269008A (en) * | 2017-12-29 | 2018-07-10 | 天津大学 | Consider the electrically-charging equipment Method for optimized planning of user satisfaction and Distribution Network Reliability |
| CN108269008B (en) * | 2017-12-29 | 2021-09-10 | 天津大学 | Charging facility optimization planning method considering user satisfaction and distribution network reliability |
| CN108932561A (en) * | 2018-06-22 | 2018-12-04 | 华北电力大学 | A kind of electric car charge path selection method considering non-linear charge function |
| CN108932561B (en) * | 2018-06-22 | 2021-07-30 | 华北电力大学 | Electric vehicle charging path selection method considering nonlinear charging function |
| CN110826753A (en) * | 2018-08-09 | 2020-02-21 | 阿里巴巴集团控股有限公司 | Resource processing method, device and equipment |
| CN110826753B (en) * | 2018-08-09 | 2024-04-23 | 阿里巴巴集团控股有限公司 | Resource processing method, device and equipment thereof |
| CN109190935A (en) * | 2018-08-15 | 2019-01-11 | 天津大学 | A kind of highway charging station planing method considering service area and car accident |
| CN109190935B (en) * | 2018-08-15 | 2021-08-17 | 天津大学 | Highway charging station planning method considering service area and vehicle accident |
| CN109544006A (en) * | 2018-11-23 | 2019-03-29 | 国网浙江省电力有限公司电力科学研究院 | A method of evaluation electric automobile charging station economic benefit |
| CN109670675A (en) * | 2018-11-26 | 2019-04-23 | 国网冀北电力有限公司 | Charging pile evaluation of running status method and device |
| CN109670675B (en) * | 2018-11-26 | 2023-10-31 | 国网冀北电力有限公司 | Method and device for evaluating running state of charging pile |
| CN109902860A (en) * | 2019-01-23 | 2019-06-18 | 西南交通大学 | A kind of electric automobile charging station site selecting method based on combined distance assessment |
| CN109840708A (en) * | 2019-02-01 | 2019-06-04 | 国网河北省电力有限公司经济技术研究院 | A kind of planing method, system and the terminal device of charging station construction |
| CN113195299A (en) * | 2019-02-28 | 2021-07-30 | 宝马股份公司 | Method and system for optimizing distributed charging station efficiency and user experience |
| CN113195299B (en) * | 2019-02-28 | 2024-01-16 | 宝马股份公司 | Method and system for optimizing distributed charging station efficiency and user experience |
| CN110493305A (en) * | 2019-07-05 | 2019-11-22 | 广州车行易科技股份有限公司 | A method of charge path planning is carried out according to charging station behaviour in service and distance |
| CN110458332A (en) * | 2019-07-18 | 2019-11-15 | 天津大学 | A kind of electric vehicle rapid charging demand dispatch method based on load space transfer |
| CN110751564B (en) * | 2019-09-04 | 2020-11-03 | 清华大学 | Electric vehicle charging station construction method and system based on game theory |
| CN110751564A (en) * | 2019-09-04 | 2020-02-04 | 清华大学 | Electric vehicle charging station construction method and system based on game theory |
| CN112069635B (en) * | 2020-09-01 | 2024-05-14 | 上海钧正网络科技有限公司 | Method and device for deploying battery changing cabinet, medium and electronic equipment |
| CN112069635A (en) * | 2020-09-01 | 2020-12-11 | 上海钧正网络科技有限公司 | Battery replacement cabinet deployment method, device, medium and electronic equipment |
| CN112784491A (en) * | 2021-01-26 | 2021-05-11 | 浙江中新电力工程建设有限公司 | Urban charging grid point planning method facing high-elasticity power grid based on LSTM and IQPSO |
| CN112784491B (en) * | 2021-01-26 | 2024-04-16 | 浙江中新电力工程建设有限公司 | Urban charging network planning method based on LSTM and IQPSO oriented high-elasticity power grid |
| CN113036790B (en) * | 2021-02-26 | 2023-10-10 | 江苏大学 | Optimization method of electric vehicle driving path based on network reconstruction |
| CN113036790A (en) * | 2021-02-26 | 2021-06-25 | 江苏大学 | Electric vehicle driving path optimization method based on network reconstruction |
| CN113255135A (en) * | 2021-05-28 | 2021-08-13 | 西安交通大学 | Electric automobile rapid charging load simulation method based on traffic balance |
| CN113650515A (en) * | 2021-07-07 | 2021-11-16 | 广州杰赛科技股份有限公司 | Electric vehicle charging control method and device, terminal equipment and storage medium |
| CN114757004A (en) * | 2022-04-02 | 2022-07-15 | 宁波市规划设计研究院 | Urban rail transit network planning method, system and storage medium |
| CN117429303B (en) * | 2023-10-10 | 2024-03-12 | 北京理工大学前沿技术研究院 | Electric automobile battery replacement method, system and equipment based on Internet of things |
| CN117429303A (en) * | 2023-10-10 | 2024-01-23 | 北京理工大学前沿技术研究院 | Electric automobile battery replacement method, system and equipment based on Internet of things |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107025518A (en) | A kind of electric automobile charging station method and device for planning | |
| Billinton et al. | Reliability-based transmission reinforcement planning associated with large-scale wind farms | |
| CN103034912B (en) | A kind of optimization method of distributed power source addressing constant volume | |
| CN103903073B (en) | A kind of micro-capacitance sensor Method for optimized planning containing distributed power source and energy storage and system | |
| CN106487005A (en) | A kind of Electric power network planning method considering T-D tariff | |
| CN108122068A (en) | A kind of power distribution network risk-averse retailer method and system | |
| CN105226643A (en) | Under security constraint, power system operation simulation model generates and method for solving fast | |
| CN104102956B (en) | Distribution network expansion planning method based on strategy adaption differential evolution | |
| CN106096757A (en) | Based on the microgrid energy storage addressing constant volume optimization method improving quantum genetic algorithm | |
| Gu et al. | Joint planning of electrical storage and gas storage in power-gas distribution network considering high-penetration electric vehicle and gas vehicle | |
| CN106130007A (en) | A kind of active distribution network energy storage planing method theoretical based on vulnerability | |
| CN103914734B (en) | Microgrid capacity optimization cloth location method based on improved Ant Colony System | |
| CN106296451A (en) | A kind of fault current limiter Optimizing collocation method based on genetic algorithm | |
| CN109598377B (en) | AC/DC hybrid power distribution network robust planning method based on fault constraint | |
| CN107316113A (en) | A kind of Transmission Expansion Planning in Electric method and system | |
| CN112561273B (en) | Active power distribution network renewable DG planning method based on improved PSO | |
| CN106941256A (en) | The power distribution network main transformer contact structure optimization planing method of meter and net capability and minimum contact construction cost | |
| CN111931355B (en) | Power distribution network and energy storage joint planning method considering N-1 safety criterion | |
| CN106803130A (en) | Distributed power source accesses the planing method of power distribution network | |
| CN107330573A (en) | A kind of state evaluating method and device of photovoltaic system key equipment | |
| CN109599896A (en) | A kind of 10kV power grid distributed photovoltaic maximum can open the calculation method of capacity | |
| CN110276517A (en) | A kind of electric automobile charging station site selecting method based on MOPSO algorithm | |
| CN111079972A (en) | Method, device and medium for planning reliability of active power distribution network | |
| CN105868499A (en) | Method for capacity configuration of wind-light storage electric vehicle charging station | |
| CN107425527A (en) | A kind of THE UPFC static security prevention and control method |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170808 |