CN110443406A - A kind of hierarchical reconfiguration planning method of electric car electrically-charging equipment and distributed generation resource - Google Patents
A kind of hierarchical reconfiguration planning method of electric car electrically-charging equipment and distributed generation resource Download PDFInfo
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Abstract
The invention discloses the hierarchical reconfiguration planning methods of a kind of electric car electrically-charging equipment and distributed generation resource.This method has gradually solved the optimal location and capacity of the various electrically-charging equipments of electric car and distributed generation resource, has greatly reduced the complexity of optimization problem by the method for hierarchical planning.
Description
Technical field
The present invention relates to the planning problem of electric car electrically-charging equipment and distributed generation resource in urban power distribution network, especially relate to
And a kind of hierarchical reconfiguration planning method of electric car electrically-charging equipment and distributed generation resource.
Background technique
With the rapid development of economy, the problem of scarcity of resources and environmental pollution, is increasingly severe.Renewable new energy is such as
Wind-powered electricity generation, photovoltaic power generation etc. and electric car are because it has the characteristics that energy conservation and environmental protection is becoming the main hair of energy industry
Open up direction.With the increasingly raising of the technical level of renewable power supply and electric car, and the policy support of country, at present
There are the renewable power supply and electric car investment demonstrating running of certain scale, industrialization and commercialized mode are also gradually complete
It is kind.With the increase of popularity, renewable power supply and electric car charging will bring great influence to power grid.Cause
How this, plan the electrically-charging equipment of distributed type renewable power supply and electric car in city, is that must solve at present
A problem.
Summary of the invention
For the deficiency of existing electric car electrically-charging equipment and distributed generation resource planing method, it is an object of the invention to
Propose the hierarchical reconfiguration planning method of a kind of electric car electrically-charging equipment and distributed generation resource.
The purpose of the present invention is what is realized by following technological means: a kind of electric car electrically-charging equipment and distributed generation resource
Hierarchical reconfiguration planning method, method includes the following steps:
Step (1), using existing electric car trip survey data obtain electric vehicle rapid charging probability and
The diurnal variation curve of the probability that charges at a slow speed in residential quarter and shopping centre；
Step (2) goes out force data according to existing distributed generation resource, acquires distribution using K-means clustering algorithm
Several typical sunrise force curves of power supply；
Step (3) obtains the typical diurnal variation curve of power distribution network basic load according to historical data；
The placement address of step (4), electric car charging station at a slow speed is that each of city residential quarter and business are write
Electrical nodes are matched in traffic where building；
Step (5), it is general according at a slow speed charging of the electric car obtained in step (1) in residential quarter and commercial office building
The diurnal variation curve of rate is chosen at the maximum value for the probability that charges in residential quarter and commercial office building, and the maximum value is multiplied by the house
The electric car ownership of cell or commercial office building can be obtained the charging pile quantity of electric car charging station at a slow speed；
Step (6), counts the electric car quantity of each node in urban traffic network, and calculating fills all electric cars
The position of the smallest electric vehicle rapid charging station of the sum of electric operating range；
Step (7) calculates each charging station institute according to the position of electric vehicle rapid charging station obtained in step (6)
The electric car total quantity for needing to service, further according to the electric vehicle rapid charging load diurnal variation curve obtained in step (1),
Further calculate the number of the charging pile for making electric vehicle rapid charging station obtain day maximum profit and waiting position；
Step (8), based on electric car obtained in step (4)-(7) at a slow speed the position of charging station and quick charge station and
The distributed generation resource typical case's power curve obtained in capacity and step (3) further solves optimal problem, obtains distributed
The optimal location and capacity of power supply.
Further, the several of distributed generation resource are calculated using K-means clustering algorithm in MATLAB in the step (2)
The typical sunrise force curve of kind.
Further, in the step (6), calculating makes all electric car charging traveling the smallest electronic vapour of sum of the distance
The position of vehicle quick charge station, specific as follows:
Optimization aim are as follows:
Constraint condition is:
Wherein, D is all electric cars charging traveling sum of the distance, nLIt is transport node sum,It is transport node l
On electric car quantity,It is distance of the transport node l to nearest electric vehicle rapid charging station；disminIt is electronic vapour
The distance that vehicle least residue electricity can travel.
Further, in the step (7), the charging pile for making electric vehicle rapid charging station obtain day maximum profit is calculated
It is specific as follows with the number of waiting position:
Optimization aim are as follows:
Constraint condition is: 1≤s≤smax、1≤w≤wmax
Wherein, E (t)=cpB-(cqQ+crR+ccMc+cwMw), it is the annual return of electric vehicle rapid charging station；It is the year fixed cost of electric vehicle rapid charging station；R=λ PN, it is electric car
The charging electric automobile quantity being rejected in quick charge station；Be in electric vehicle rapid charging station
The electric car quantity of queuing；B=s ρ (1-PN), it is the electric automobile charging pile to work in electric vehicle rapid charging station
Quantity；It is the probability for having n vehicle in electric vehicle rapid charging station；It is the probability that electric vehicle rapid charging station does not have vehicle to be charged；It is the service intensity of electric vehicle rapid charging station；Mc=s-B is idle charging pile in electric vehicle rapid charging station
Quantity, Mw=w-Q；It is the quantity that idle pending bit is set in electric vehicle rapid charging station；S, w are that electric car is quick respectively
The number of charging pile and waiting position in charging station；λ, μ are the automobile arrival rate of electric vehicle rapid charging station respectively and fill
Electric stake efficiency of service；N=s+w is the total capacity of electric vehicle rapid charging station；R is equipment depreciation rate；M is that equipment uses year
Limit；ic,iwIt is the unit price of charging pile and waiting position respectively；IfIt is the fixed cost of electric vehicle rapid charging station；cp cq cr
cc cwIt is charging profit, waiting cost, refusal cost, charging pile cost of idleness, waiting position cost of idleness respectively；smax, wmax
It is the maximum quantity of charging pile and waiting position respectively.
Further, specific step is as follows for the step (8):
Solve optimal problemIt can be obtained distributed generation resource
Optimal location and capacity；
Wherein,It is power distribution network one day the sum of active power loss；Power distribution network one day the sum of reactive power loss；
It is power distribution network one day the sum of voltage fluctuation range；σ1σ2σ3It is weight factor, and σ1+σ2+σ3=1；PLt,QLt,VDtIt is respectively
In the original power distribution network of no access electric car charging and distributed generation resource in the active loss of t moment, reactive loss and electricity
Pressure fluctuation；Nh,NL,NBBe the number of distributed generation resource typical case's sunrise force curve, in power distribution network branch number and interstitial content；
Rij,XijIt is resistance and the reactance of branch i-j；pij,h,t,qij,h,tIt is branch i-j in t moment and h-th of distributed generation resource typical day
Active power and reactive power under power curve；Vi,h,tIt is node i in t moment and h-th of distributed generation resource typical case's daily output
Voltage under curve；
Constraint condition has:
Wherein, SilIt is the pertinency factor of node i Yu branch l,It is that substation is distributed in t moment and h-th
Active power and reactive power under power supply typical case's sunrise force curve,It is that distributed generation resource is active in i-node t moment
Power and reactive power, Iij,h,tIt is electric current of the branch i-j under t moment and h-th of distributed generation resource typical case's sunrise force curve,It is charging wattful power of the electric car under i-node t moment and h-th of distributed generation resource typical case's sunrise force curve
Rate and reactive power,It is that power distribution network basic load is bent in i-node t moment and h-th of distributed generation resource typical case daily output
Charging active power and reactive power under line,It is the permeability upper limit of distributed generation resource,It is the maximum of node i
Distributed electrical source powerIt is under distributed generation resource i-node t moment and h-th of distributed generation resource typical case's sunrise force curve
Power factor.
Beneficial effects of the present invention are as follows: it is various gradually to have solved electric car by the method for hierarchical planning by the present invention
The optimal location and capacity of electrically-charging equipment and distributed generation resource, can solve in optimal city electric car at a slow speed charging station,
The position and capacity of quick charge station and distributed generation resource, while the complexity of optimization problem can also be significantly simplified, shorten
Solve the time.
Detailed description of the invention
Fig. 1: distribution net topology schematic diagram.
Fig. 2: transportation network topology schematic diagram.
Fig. 3: electric car charging Load Probability figure.
Fig. 4: distributed generation resource typical case's daily output curve graph.
Fig. 5: typical day load curve figure.
Fig. 6: the location drawing of electric vehicle rapid charging station.
Specific embodiment
Below using the transportation network of the power distribution network of certain 53 node of IEEE and certain 25 node as example, detailed calculation is given
Method description, by it is a series of experiments have shown that the method proposed validity.
The power distribution network of 53 node of IEEE is as shown in Figure 1, the transportation network of 25 nodes is as shown in Figure 2；Wherein, transport node
3,5,10,11,13 residential quarters are connected with, transport node 1,4,16,19,22 is connected with commercial office building；Power distribution network and traffic
The connection of net is as shown in table 1；Car ownership is 1000, and the electronic vapour automobile of each residential quarters and commercial office building
Quantity is identical.
Table 1
Step (1), using existing electric car trip survey data obtain electric vehicle rapid charging probability and
The diurnal variation curve of the probability that charges at a slow speed in residential quarter and shopping centre, as shown in Figure 3；
Step (2) goes out force data according to existing distributed generation resource, K-means clustering algorithm is utilized in MATLAB
Function calculates several typical sunrise force curves of distributed generation resource, and data go out force data using the blower of Zhejiang Province's wind power plant,
After normalization calculates, 4 kinds of typical sunrise force curves can be summarized as using K-means function in MATLAB, as shown in Figure 4；
Step (3) obtains the typical diurnal variation curve of normalized power distribution network basic load, such as Fig. 5 according to historical data
It is shown；
The placement address of step (4), electric car charging station at a slow speed is that each of city residential quarter and business are write
Electrical nodes are matched in traffic where building, i.e., in 3,5,10,11,13 Placement Dwellings area of transport node charging pile at a slow speed, in traffic section
Point 1,4,16,19,22 disposes business district charging pile at a slow speed；
Step (5), it is general according at a slow speed charging of the electric car obtained in step (1) in residential quarter and commercial office building
The diurnal variation curve of rate, be chosen in residential quarter and commercial office building charge probability maximum value, i.e., 0.410 and 0.245, this is most
Big value multiplied by the residential quarters or the electric car ownership of commercial office building, i.e. 1000/5=200, can be obtained residential quarter and
The charging pile quantity of the electric car of business district charging station at a slow speed is respectively 82 and 49；
Step (6), counts the electric car quantity of each node in urban traffic network, and calculating fills all electric cars
The position of the smallest electric vehicle rapid charging station of the sum of electric operating range, optimization aim are as follows:Wherein, D is all electric cars charging traveling sum of the distance, nLIt is transport node sum,It is the electric car quantity on transport node l,Transport node l to nearest electric vehicle rapid charging station away from
From can be calculated by Floyd algorithm；The constraint condition of the optimization problem is:Wherein, disminIt is
The distance that electric car least residue electricity can travel；Solving the above optimization problem can obtain: electric vehicle rapid charging station
Position is in transport node 2,17 and 22, as shown in Figure 6；
Step (7) calculates each charging station institute according to the position of electric vehicle rapid charging station obtained in step (6)
The electric car total quantity for needing to service, further according to the electric vehicle rapid charging load diurnal variation curve obtained in step (1),
Further calculate the number of the charging pile for making electric vehicle rapid charging station obtain day maximum profit and waiting position；Optimization aim
Are as follows:Wherein, E (t)=cpB-(cqQ+crR+ccMc+cwMw), it is the year of electric vehicle rapid charging station
Profit,It is the year fixed cost of electric vehicle rapid charging station, R=λ PN, it is electronic
The charging electric automobile quantity being rejected in automobile quick charge station,It is in electric vehicle rapid charging station
The electric car quantity being lined up, B=s ρ (1-PN), it is that the electric car to work in electric vehicle rapid charging station fills
Electric stake quantity,It is the probability for having n vehicle in electric vehicle rapid charging station,It is the probability that electric vehicle rapid charging station does not have vehicle to be charged,It is the service intensity of electric vehicle rapid charging station, Mc=s-B is idle charging pile in electric vehicle rapid charging station
Quantity, Mw=w-Q is the quantity that idle pending bit is set in electric vehicle rapid charging station, and s, w are that electric car is quick respectively
The number of charging pile and waiting position in charging station, λ, μ are the automobile arrival rate of electric vehicle rapid charging station respectively and fill
Electric stake efficiency of service, N=s+w are the total capacities of electric vehicle rapid charging station, and r is equipment depreciation rate, and m is that equipment uses year
Limit, ic,iwIt is the unit price of charging pile and waiting position, I respectivelyfIt is the fixed cost of electric vehicle rapid charging station, cp cq cr
cc cwIt is charging profit, waiting cost, refusal cost, charging pile cost of idleness, waiting position cost of idleness respectively；The problem
Constraint condition is: 1≤s≤smax、1≤w≤wmax, wherein smax, wmaxIt is the maximum quantity of charging pile and waiting position respectively；
Assuming that cp=5 ($), cq=1 ($), cr=2 ($), cc=0.5 ($), cw=0.05 ($), smax=40and wmax=40, r=
0.08, m=10, ic=2000 ($), iw=8140 ($), If=163000 ($), being computed can obtain, and transport node 2 quickly fills
14 charging piles, 7 waiting positions are arranged in power station；20 charging piles, 8 pending bits are arranged in the quick charge station of transport node 17
It sets；15 charging piles, 7 waiting positions are arranged in the quick charge station of transport node 22；
Step (8), based on electric car obtained in step (4)-(7) at a slow speed the position of charging station and quick charge station and
The distributed generation resource typical case's power curve obtained in capacity and step (3), further solves optimal problemIt can be obtained the optimal location and capacity of distributed generation resource.Wherein,It is power distribution network one day the sum of active power loss,Match
Power grid one day the sum of reactive power loss,It is power distribution network voltage fluctuation in one day
The sum of amplitude, σ1σ2σ3It is weight factor, and σ1+σ2+σ3=1, PLt,QLt,VDtIt is to charge in no access electric car respectively
Original power distribution network with distributed generation resource is in the active loss of t moment, reactive loss and voltage fluctuation, Nh,NL,NBIt is distributed
The number and interstitial content of branch, Ri in the number of power supply typical case's sunrise force curve, power distribution networkj,XijBe branch i-j resistance and
Reactance, pij,h,t,qij,h,tBe active power of the branch i-j under t moment and h-th of distributed generation resource typical case's sunrise force curve and
Reactive power, Vi,h,tIt is voltage of the node i under t moment and h-th of distributed generation resource typical case's sunrise force curve；The optimization problem
Constraint condition have: 0.95pu≤Vi,h,t≤
1.05pu、|Iij,h,t|≤1.0pu、Wherein, SilNode i with
The pertinency factor of branch l,It is substation's having under t moment and h-th of distributed generation resource typical case's sunrise force curve
Function power and reactive power,It is distributed generation resource in i-node t moment active power and reactive power, Iij,h,tIt is branch
Electric current of the road i-j under t moment and h-th of distributed generation resource typical case's sunrise force curve,It is electric car in i-node
Charging active power and reactive power under t moment and h-th of distributed generation resource typical case's sunrise force curve,It is to match
Charging active power and nothing of the power grid basic load under i-node t moment and h-th of distributed generation resource typical case's sunrise force curve
Function power,It is the permeability upper limit of distributed generation resource,It is the maximum distribution formula power of node i,It is distribution
Power factor under formula power supply i-node t moment and h-th of distributed generation resource typical case's sunrise force curve；It is computed, optimal distribution
The addressing constant volume of formula power supply is as shown in table 2:
The above description is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all utilizations
Equivalent process transformation made by description of the invention and accompanying drawing content is applied directly or indirectly in other relevant technology necks
Domain is included within the scope of the present invention.
Claims (5)
1. a kind of hierarchical reconfiguration planning method of electric car electrically-charging equipment and distributed generation resource, which is characterized in that this method include with
Lower step:
Step (1) obtains electric vehicle rapid charging probability using existing electric car trip survey data and in house
The diurnal variation curve of the probability that charges at a slow speed in area and shopping centre；
Step (2) goes out force data according to existing distributed generation resource, acquires distributed generation resource using K-means clustering algorithm
Several typical sunrise force curves；
Step (3) obtains the typical diurnal variation curve of power distribution network basic load according to historical data；
The placement address of step (4), electric car charging station at a slow speed is each of city residential quarter and commercial office building institute
Traffic or match electrical nodes；
Step (5), according at a slow speed charge probability of the electric car obtained in step (1) in residential quarter and commercial office building
Diurnal variation curve is chosen at the maximum value for the probability that charges in residential quarter and commercial office building, and the maximum value is multiplied by the residential quarters
Or the electric car ownership of commercial office building, it can be obtained the charging pile quantity of electric car charging station at a slow speed；
Step (6), counts the electric car quantity of each node in urban traffic network, and calculating makes all electric car charging row
Sail the position of the smallest electric vehicle rapid charging station of sum of the distance；
Step (7) calculates required for each charging station according to the position of electric vehicle rapid charging station obtained in step (6)
The electric car total quantity of service, further according to the electric vehicle rapid charging load diurnal variation curve obtained in step (1), into one
Step calculates the number of the charging pile for making electric vehicle rapid charging station obtain day maximum profit and waiting position.
Step (8), position and appearance based on electric car obtained in step (4)-(7) charging station and quick charge station at a slow speed
The distributed generation resource typical case's power curve obtained in amount and step (3) further solves optimal problem, obtains distributed electrical
The optimal location and capacity in source.
2. the hierarchical reconfiguration planning method of a kind of electric car electrically-charging equipment and distributed generation resource according to claim 1, special
Sign is, calculates several typical sunrise of distributed generation resource in the step (2) using K-means clustering algorithm in MATLAB
Force curve.
3. the hierarchical reconfiguration planning method of a kind of electric car electrically-charging equipment and distributed generation resource according to claim 1, special
Sign is, in the step (6), calculating makes all electric car charging traveling the smallest electric vehicle rapid chargings of sum of the distance
The position stood, specific as follows:
Optimization aim are as follows:
Constraint condition is:
Wherein, D is all electric cars charging traveling sum of the distance, nLIt is transport node sum,It is on transport node l
Electric car quantity,It is distance of the transport node l to nearest electric vehicle rapid charging station；disminBe electric car most
The distance that small remaining capacity can travel.
4. the hierarchical reconfiguration planning method of a kind of electric car electrically-charging equipment and distributed generation resource according to claim 3, special
Sign is, in the step (7), calculates the charging pile for making electric vehicle rapid charging station obtain day maximum profit and waiting position
Number, it is specific as follows:
Optimization aim are as follows:
Constraint condition is: 1≤s≤smax、1≤w≤wmax
Wherein, E (t)=cpB-(cqQ+crR+ccMc+cwMw), it is the annual return of electric vehicle rapid charging station；It is the year fixed cost of electric vehicle rapid charging station；R=λ PN, it is electric car
The charging electric automobile quantity being rejected in quick charge station；Be in electric vehicle rapid charging station
The electric car quantity of queuing；B=s ρ (1-PN), it is the electric automobile charging pile to work in electric vehicle rapid charging station
Quantity；It is the probability for having n vehicle in electric vehicle rapid charging station；It is the probability that electric vehicle rapid charging station does not have vehicle to be charged；It is the service intensity of electric vehicle rapid charging station；Mc=s-B is idle charging pile in electric vehicle rapid charging station
Quantity, Mw=w-Q；It is the quantity that idle pending bit is set in electric vehicle rapid charging station；S, w are that electric car is quick respectively
The number of charging pile and waiting position in charging station；λ, μ are the automobile arrival rate of electric vehicle rapid charging station respectively and fill
Electric stake efficiency of service；N=s+w is the total capacity of electric vehicle rapid charging station；R is equipment depreciation rate；M is that equipment uses year
Limit；ic,iwIt is the unit price of charging pile and waiting position respectively；IfIt is the fixed cost of electric vehicle rapid charging station；cp cq cr
cc cwIt is charging profit, waiting cost, refusal cost, charging pile cost of idleness, waiting position cost of idleness respectively；smax, wmax
It is the maximum quantity of charging pile and waiting position respectively.
5. the hierarchical reconfiguration planning method of a kind of electric car electrically-charging equipment and distributed generation resource according to claim 4, special
Sign is that specific step is as follows for the step (8):
Solve optimal problemIt can be obtained the optimal of distributed generation resource
Position and capacity；
Wherein,It is power distribution network one day the sum of active power loss；Power distribution network one day the sum of reactive power loss；
It is power distribution network one day the sum of voltage fluctuation range；σ1 σ2 σ3It is weight factor, and σ1+σ2+σ3=1；PLt,QLt,VDtRespectively
Be it is no access electric car charging and distributed generation resource original power distribution network the active loss of t moment, reactive loss, and
Voltage fluctuation；Nh,NL,NBBe the number of distributed generation resource typical case's sunrise force curve, in power distribution network branch number and number of nodes
Mesh；Rij,XijIt is resistance and the reactance of branch i-j；pij,h,t,qij,h,tIt is branch i-j in t moment and h-th of distributed generation resource allusion quotation
Active power and reactive power under type sunrise force curve；Vi,h,tIt is node i in t moment and h-th of distributed generation resource typical day
Voltage under power curve；
Constraint condition has:
Wherein, SilIt is the pertinency factor of node i Yu branch l,It is substation in t moment and h-th of distributed generation resource allusion quotation
Active power and reactive power under type sunrise force curve,Distributed generation resource in i-node t moment active power and
Reactive power, Iij,h,tIt is electric current of the branch i-j under t moment and h-th of distributed generation resource typical case's sunrise force curve,It is charging wattful power of the electric car under i-node t moment and h-th of distributed generation resource typical case's sunrise force curve
Rate and reactive power,It is power distribution network basic load in i-node t moment and h-th of distributed generation resource typical case's daily output
Charging active power and reactive power under curve,It is the permeability upper limit of distributed generation resource,Be node i most
Big distributed electrical source power,It is under distributed generation resource i-node t moment and h-th of distributed generation resource typical case's sunrise force curve
Power factor.
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| CN112350334A (en) * | 2020-11-13 | 2021-02-09 | 深圳供电局有限公司 | Power supply method of comprehensive parking lot charging facility |
| CN112519626A (en) * | 2020-11-24 | 2021-03-19 | 西安科技大学 | Charging system and method for mobile energy storage charging vehicle |
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