CN110378724A - A kind of charging station addressing constant volume strategy considering the transfer of user's charge requirement - Google Patents
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Abstract
Present invention is generally directed to users to charge behavior randomness and charging behavior to grid power quality influence, based on electric car history driving trace data and charging pile construction operation maintenance cost service profit model, according to electric car mileage and objective geogen, addressing constant volume is carried out to electric automobile charging station, maximize charging service quotient profit and reduces the influence to grid power quality.Due to electric car charging moment it is difficult to predict, the behavior that will not charge to single motor automobile of the invention is predicted, is formulated optimal addressing constant volume scheme using history traval trace, is reached user's charging convenience, service provider's profit, the stable collaboration optimization of operation of power networks.
Description
Technical field
The present invention relates to a kind of charging station addressing constant volume strategies of consideration user's charge requirement transfer, fill for electric car
Power station addressing constant volume planning, to coordinate and optimize charging service provider profit and grid power quality.
Background technique
Electric car is compared to orthodox car and has the features such as zero-emission, noise is small, environmentally friendly, energy efficiency
Height, at present quantity rapid development.But the bottle that limitation electric car increases is had become as the construction of the charging station of mating infrastructure
Neck, presently, there are power quality is influenced, installation limitation is more, the few problem of profit small number.Simultaneously because allocation plan is unreasonable,
The problems such as there are part charging piles nobody shows any interest in, and the part charging pile waiting time is too long.Reasonable electric automobile charging station addressing
Constant volume planning can allow power grid, user, service provider to reach collaboration win-win relationship, promote electric car large-scale development.
The research of the constant volume of electric automobile charging station addressing at present is numerous, mainly with construction cost, user's cost, user's stroke limit
System and grid net loss construct objective function, and a few studies are related to battery life, variation problem.It is related to charging pile choosing
When the constant volume problem of location, when a large amount of unordered access charging piles of electric car, the load in region is increased, and influences operation of power networks.But
Research at present is not concerned about user's charge requirement when in face of charging pile queuing situation, according to the direction of traffic of oneself itself, fills
Electricity demanding has different transition probabilities.This algorithm from charging service provider get a profit angle, it is also considered that grid power quality
Problem considers that user's charge requirement shifts the influence for charging station addressing constant volume, comprehensive to determine planning strategy.
Summary of the invention
The present invention mainly travels GPS data according to electric car history and charging pile builds operation maintenance cost service and is full of
Based on sharp mode, being associated between user demand transfer and wheelpath is considered, according to electric car mileage and objectively
Environmental factor is managed, addressing constant volume is carried out to electric automobile charging station, maximize charging service quotient profit and is reduced to power grid electricity
The influence of energy quality.This method process includes the following steps, as shown in Figure 1:
1, a kind of charging station addressing constant volume strategy for considering the transfer of user's charge requirement, which is characterized in that this method includes
Following steps:
Step 1 carries out piecemeal to survey region using dimension promise drawing method, considers that real region land used charging station can be constructive
It is selected, determines that charging station builds candidate point set using electric car mileage and charging station service range；
Step 2, according to vehicle number, website free time charging pile, the data such as queue length are reached, determined according to queueing theory theory
Charge requirement can be met, if not being able to satisfy, the transfer of user's charge requirement or loss；
Step 3 travels GPS data according to electric car history, electric automobile during traveling track is analyzed, according to electric car rail
Adjoining distance between mark and candidate point set establishes user's charge requirement metastasis model；
Step 4, the distribution network Load flow calculation according to the survey region calculate power quality index, are taken according to charging station
Business profit, construction operation cost and step 4 network loss loss establishes objective function, with user's charge transfers model, electric network swim,
Power grid access power, charging pile construction space build Optimized model, concentrate in candidate point and carry out addressing constant volume, make as constraint
With genetic algorithm solving model.
Process supplementary explanation:
Segmented areas center is that candidate establishes charging station website in the step 1, for an overlay model, the electronic vapour in the region
Vehicle preferentially goes to one's respective area charging station to charge.Electric car mileage and charging station service range should meet following constraint:
Wherein, rcsIndicate that charging station services maximum distance, daveIndicate that electric car is averaged mileage, dcsExpression two is adjacent
The distance between charging station.
Determine that charging station can satisfy single queue Multiple server stations parallel model of demand in the step 2 are as follows:
Wherein PnN electric car is indicated in the steady state probability of service area, λ indicates electric car arriving amt, μ table
Showing that single charging pile service rate, c indicate that charging pile quantity can be used at that time, N is queue queue's overall length, and ρ indicates charging pile utilization rate,It indicates that the i.e. queue queue of charging pile blocking is full up, is not able to satisfy the probability of more charge requirements.
Electric car history driving GPS data should be the vehicle GPS data for including time point in the step 3, consider
Vehicle driving direction, the statement of power demand metastasis model are as follows:
pii,t=1-Pi,t B
In the step 4, grid net loss index is calculated using power distribution network distflow tide model, power transmission network is in transmission & distribution
Power loss caused by electric process (network loss * electricity price) CLOSS:
Wherein EtFor electricity price, Ploss is that network loss loses electricity.
Charging station operation cost CO:
Wherein C1,iThe cost of charging station, C are built for i-node2,iFor the operation cost of each charging pile, herein
C1,i、C2,iIt has all shared equally to one day cost, Nd is candidate point set node total number.
Service the income C that electric car obtainsI:
In summary, Optimized model are as follows:
M α x C=CI-CO-CLOSS
S.t.:
0.95≤Vi,t≤1.05
Wherein, PB i,tFor the probability of i-node obstruction, O (i) indicates the adjacent node set of i,It builds and charges for i-node
The upper limit of stake quantity.ViTo be limited for grid side voltage in the per unit value of t moment i-node voltage.
Invention effect
Profit is able to maintain for charging station service provider, so that more charging piles are promoted, the charging clothes provided
Business.For power grid, such charging strategy can guarantee power quality shadow of the unordered charging for power grid of electric car
Minimum is rung, limitation is built hence for charging station and reduces.It, can in view of the transfer of user's charge requirement meets user's driving rule
Reduce user's charging cost, improves user satisfaction.To sum up, user's charging convenience, service provider's profit, power grid can be reached
Stable collaboration optimization, improves social benefit.
Accompanying drawing content
Fig. 1 is charging pile addressing constant volume algorithm flow.
Table 1 is charging pile addressing constant volume optimum results, other do not point out that node does not build charging pile.
Fig. 2 is the spatial distribution of charging pile construction distribution and electric car quantity.
Fig. 3 is that charging pile is got a profit under Different Strategies model.
Fig. 4 is charging pile service vehicle number under Different Strategies model.
Specific embodiment
Illustrate algorithm calculation process combined with specific embodiments below:
Step 1 carries out piecemeal to survey region using dimension promise drawing method, considers that real region land used charging station can be constructive
It is selected, determines that charging station builds candidate point set using electric car mileage and charging station service range；
Step 2, according to vehicle number, website free time charging pile, the data such as queue length are reached, determined according to queueing theory theory
Charge requirement can be met, if not being able to satisfy, the transfer of user's charge requirement or loss；
Step 3 travels GPS data according to electric car history, electric automobile during traveling track is analyzed, according to electric car rail
Adjoining distance between mark and candidate point set establishes user's charge requirement metastasis model；
Step 4, the distribution network Load flow calculation according to the survey region calculate power quality index, are taken according to charging station
Business profit, construction operation cost and step 4 network loss loss establishes objective function, with user's charge transfers model, electric network swim,
Power grid access power, charging pile construction space build Optimized model, concentrate in candidate point and carry out addressing constant volume, make as constraint
With genetic algorithm solving model.
Process supplementary explanation:
Segmented areas center is that candidate establishes charging station website in the step 1, for an overlay model, the electronic vapour in the region
Vehicle preferentially goes to one's respective area charging station to charge.Electric car mileage and charging station service range should meet following constraint:
Wherein, rcsIndicate that charging station services maximum distance, daveIndicate that electric car is averaged mileage, dcsExpression two is adjacent
The distance between charging station.Embodiment utilizes the method, obtains the candidate point set that capacity is 40.
Determine that charging station can satisfy single queue Multiple server stations parallel model of demand in the step 2 are as follows:
Wherein PnN electric car is indicated in the steady state probability of service area, λ indicates electric car arriving amt, μ table
Showing that single charging pile service rate, c indicate that charging pile quantity can be used at that time, N is queue queue's overall length, and ρ indicates charging pile utilization rate,It indicates that the i.e. queue queue of charging pile blocking is full up, is not able to satisfy the probability of more charge requirements.
Electric car history driving GPS data should be the vehicle GPS data for including time point in the step 3, consider
Vehicle driving direction, the statement of power demand metastasis model are as follows:
pii,t=1-Pi,t B
In the step 4, embodiment uses IEEE33 node power distribution net, calculates power grid using distflow tide model
Transmission loss index, power transmission network during power transmission and distribution caused by power loss (network loss * electricity price) CLOSS:
Wherein EtFor electricity price, Ploss is that network loss loses electricity.
Charging station operation cost CO:
Wherein C1,iThe cost of charging station, C are built for i-node2,iFor the operation cost of each charging pile, herein
C1,i、C2,iIt has all shared equally to one day cost, Nd is candidate point set node total number.
Service the income C that electric car obtainsI:
In summary, Optimized model are as follows:
M α x C=CI-CO-CLOSS
S.t.:
0.95≤Vi,t≤1.05
Wherein, PB i,tFor the probability of i-node obstruction, O (i) indicates the adjacent node set of i,It builds and charges for i-node
The upper limit of stake quantity.ViTo be limited for grid side voltage in the per unit value of t moment i-node voltage.
Table 1
| Node | 5 | 6 | 8 | 9 | 10 | 11 | 12 | 15 | 16 | 17 | 18 | 19 | 21 |
| Charging pile quantity | 12 | 5 | 8 | 12 | 3 | 10 | 10 | 6 | 29 | 28 | 7 | 28 | 21 |
| Node | 22 | 23 | 24 | 25 | 26 | 28 | 29 | 30 | 31 | 32 | 33 | 35 | 38 |
| Charging pile quantity | 8 | 9 | 20 | 5 | 12 | 27 | 11 | 7 | 13 | 11 | 19 | 8 | 5 |
Claims (1)
1. a kind of charging station addressing constant volume strategy for considering the transfer of user's charge requirement, which is characterized in that this method includes following
Step:
Step 1 carries out piecemeal to survey region using dimension promise drawing method, considers that real region land used charging station constructive can carry out
Selection determines that charging station builds candidate point set using electric car mileage and charging station service range；
Step 2, according to vehicle number, website free time charging pile, the data such as queue length are reached, can according to the judgement of queueing theory theory
Meet charge requirement, if not being able to satisfy, the transfer of user's charge requirement or loss；
Step 3, according to electric car history travel GPS data, analyze electric automobile during traveling track, according to electric car track with
And the adjoining distance between candidate point set establishes user's charge requirement metastasis model；
Step 4, the distribution network Load flow calculation according to the survey region calculate power quality index, are full of according to charging station service
Objective function is established in benefit, construction operation cost and the loss of step 4 network loss, with user's charge transfers model, electric network swim, power grid
Access power, charging pile construction space build Optimized model, concentrate in candidate point and carry out addressing constant volume, use something lost as constraint
Propagation algorithm solving model.
Process supplementary explanation:
Segmented areas center is that candidate establishes charging station website in the step 1, and for an overlay model, the region electric car is excellent
One's respective area charging station is first gone to charge.Electric car mileage and charging station service range should meet following constraint:
Wherein, rcsIndicate that charging station services maximum distance, daveIndicate that electric car is averaged mileage, dcsIndicate two adjacent chargings
The distance between stand.
Determine that charging station can satisfy single queue Multiple server stations parallel model of demand in the step 2 are as follows:
Wherein PnN electric car is indicated in the steady state probability of service area, λ indicates that electric car arriving amt, μ indicate single
A charging pile service rate, c expression can use charging pile quantity at that time, and N is queue queue's overall length, and ρ indicates charging pile utilization rate,Table
Show that the i.e. queue queue of charging pile blocking is full up, is not able to satisfy the probability of more charge requirements.
Electric car history driving GPS data should be the vehicle GPS data for including time point in the step 3, consider vehicle
Direction of traffic, the statement of power demand metastasis model are as follows:
pii,t=1-Pi,t B
In the step 4, grid net loss index is calculated using power distribution network distflow tide model, power transmission network is in power transmission and distribution
Power loss caused by journey (network loss * electricity price) CLOSS:
Wherein EtFor electricity price, Ploss is that network loss loses electricity.
Charging station operation cost CO:
Wherein C1,iThe cost of charging station, C are built for i-node2,iFor the operation cost of each charging pile, C herein1,i、
C2,iIt has all shared equally to one day cost, Nd is candidate point set node total number.
Service the income C that electric car obtainsI:
In summary, Optimized model are as follows:
M α x C=CI-CO-CLOSS
0.95≤Vi,t≤1.05
Wherein, PB i,tFor the probability of i-node obstruction, O (i) indicates the adjacent node set of i,Charging pile number is built for i-node
The upper limit of amount.ViTo be limited for grid side voltage in the per unit value of t moment i-node voltage.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110991744A (en) * | 2019-12-05 | 2020-04-10 | 中国银行股份有限公司 | Transaction limit setting method and system |
| CN111047119A (en) * | 2020-01-08 | 2020-04-21 | 浙江大学 | Electric vehicle charging station dynamic pricing method for regulating and controlling power quality |
| CN111798057A (en) * | 2020-07-06 | 2020-10-20 | 四川中电启明星信息技术有限公司 | Charging station site selection method based on fuzzy level profit analysis |
| CN113435777A (en) * | 2021-07-13 | 2021-09-24 | 北京交通大学 | Planning method and system for electric operating vehicle charging station |
| CN116362523A (en) * | 2023-06-01 | 2023-06-30 | 吉林大学 | Coordinated optimization method for site selection and operation strategy of power exchange station considering temperature adaptability |
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2019
- 2019-06-19 CN CN201910534374.4A patent/CN110378724A/en active Pending
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110991744A (en) * | 2019-12-05 | 2020-04-10 | 中国银行股份有限公司 | Transaction limit setting method and system |
| CN110991744B (en) * | 2019-12-05 | 2022-07-12 | 中国银行股份有限公司 | Transaction limit setting method and system |
| CN111047119A (en) * | 2020-01-08 | 2020-04-21 | 浙江大学 | Electric vehicle charging station dynamic pricing method for regulating and controlling power quality |
| CN111047119B (en) * | 2020-01-08 | 2022-05-03 | 浙江大学 | Electric vehicle charging station dynamic pricing method for regulating and controlling power quality |
| CN111798057A (en) * | 2020-07-06 | 2020-10-20 | 四川中电启明星信息技术有限公司 | Charging station site selection method based on fuzzy level profit analysis |
| CN111798057B (en) * | 2020-07-06 | 2023-11-10 | 四川中电启明星信息技术有限公司 | Charging station site selection method based on fuzzy-hierarchy profit analysis |
| CN113435777A (en) * | 2021-07-13 | 2021-09-24 | 北京交通大学 | Planning method and system for electric operating vehicle charging station |
| CN116362523A (en) * | 2023-06-01 | 2023-06-30 | 吉林大学 | Coordinated optimization method for site selection and operation strategy of power exchange station considering temperature adaptability |
| CN116362523B (en) * | 2023-06-01 | 2023-09-26 | 吉林大学 | Coordinated optimization method for site selection and operation strategy of power exchange station considering temperature adaptability |
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