CN107506844A - A kind of new Optimal Deployment Method of electric automobile charging station - Google Patents
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Abstract
The invention discloses a kind of new Optimal Deployment Method of electric automobile charging station, carries out solving electric automobile charging station planning problem by introducing firefly optimized algorithm, so as to obtain the optimal solution of this problem.The present invention is broadly divided into three steps：The ordinary circumstance of electric automobile charging station addressing is analyzed, considers the running situation under the comprehensive benefit cost lowest term of charging station and user cost, the minimum Integrated Optimization Model of structure user's charging station operation cost；Programming evaluation is carried out to obtained Integrated Optimization Model using firefly optimized algorithm；The reliability of firefly optimized algorithm is verified by analog simulation.Constructed electric automobile charging station planning problem is non-convex, non-linear, combinatorial optimization problem, is solved using this method, can obtain optimal solution, without being absorbed in local optimum, simple and practical, good economy performance, has good adaptability.
Description
Technical field
The invention belongs to intelligent grid field, is related to a kind of electric automobile charging station optimum programming layout method.
Background technology
The worsening of global environment and being becoming tight day for petroleum resources so that electric automobile is as novel energy traffic
Instrument increasingly attracts attention, and its development prospect is wide, the electrically-charging equipment such as charging station, charging pile and battery altering station, is hair
Important supporting infrastructure necessary to opening up electric automobile.At the early-stage, complete charging network is built in other electrically-charging equipments
In the case of also without being formed, a collection of public charging station is established rapidly, can produce good demonstration effect and demonstration effect,
And promote the popularization as early as possible of electric automobile.With electric automobile fast development, the charging station to match therewith is just turning into a kind of new
Emerging industry, electric automobile charging station industry development have a high potential.In Japan, Dong electricity companies establish close with each automobile vendor
Partnership, plan to establish charging station successively near the public places such as supermarket parking lot, convenience store and post office；And in method
State Paris, provided with hundreds of charging stations, all important parking lots are designed with charging station, configure the plug special of charging electric vehicle.
How rational deployment is carried out to electric automobile charging station, it had both been facilitated charging electric vehicle, person charges, and saves construction cost again
Have become urban planning authority, traffic department and power supply enterprise's issues that need special attention.
Therefore, it is necessary to which a kind of new Optimal Deployment Method of electric automobile charging station is to solve the above problems.
The content of the invention
The present invention is for defect present in prior art, there is provided a kind of new optimization layout side of electric automobile charging station
Method.
In order to solve the above technical problems, technology used by the new Optimal Deployment Method of electric automobile charging station of the present invention
Scheme is：
A kind of new Optimal Deployment Method of electric automobile charging station, comprises the following steps：
Step 1: the minimum Integrated Optimization Model of user-charging station operation cost is established in the addressing to charging station；
Step 2: carrying out programming evaluation to obtained mathematical modeling by firefly optimized algorithm, user-charging station is obtained
The optimal solution of the minimum Integrated Optimization Model of operation cost.
Further, the minimum Integrated Optimization Model of user-charging station operation cost is represented by following formula in step 1：
In formula：L for built charging station quantity；C1iFor the construction cost that charging station i is annual；C2iAnnual for charging station i
Device line is lost and operation expense；C3iIt is expense caused by automobile user charges in charging station i every year；C4i
Charging station is gone to charge the expense that consumes by annual user；
Wherein, construction cost C annual charging station i1iRepresented by following formula：
In formula：niTo build charging pile quantity to be mounted needed for charging station i；D be its complete equipment unit price, fiTo fill
The construction cost of power station i infrastructure；W is charging station L projected life, and r is discount rate；
Device line loss and operation expense annual charging station i is represented by following formula：
C2i=ni·t·365·p1(Ca+Cb·λ+Cc·λ)+(ni·d+fi)·α
In formula：T is the charging station daily average charge time, and λ is that the charging pile to be charged simultaneously accounts for all charging piles
Ratio, p1For the electricity consumption unit price of charging station, Ca、Cb、CcThe attrition, line loss, charging of equipment component are represented respectively
Loss is converted to the loss value on single charging pile；
Automobile user is represented in expense caused by charging station i chargings by following formula every year：
C3i=p2·Qi·365
In formula：p2For the charging unit price of user, QiFor the averagely daily charge requirements of charging station i；
Annual user goes to the charged mathematic(al) representation of consumed expense of charging station to be：
In formula：M is the quantity of charging station i charge point；sijDistance for demand point j to charging station i；qjFor charging daily
The automobile quantity to charge is needed at demand point j；H is the traveling distance of electric automobile mean unit electricity；V is the flat of electric automobile
Equal travel speed；The average travel time that g is user is worth.
Further, wherein, charge requirement Q averagely daily charging station iiIt is calculated by following formula：
Wherein, Z is the quantity of charging station i coverages interior nodes, if having at node z, u bar roads are coupled, qiFor T
Charge requirement in period at z nodes,T is the daily charging interval,zeRepresent in t
The traffic flow density on the e articles road that moment is connected with node z, y are the average size of electric automobile, and μ is to be needed in wagon flow
Ratio shared by the electric automobile to be charged, ztRepresent traffic flow densities of the node z in t.
Further, charging pile quantity n to be mounted needed for charging station i is builtiIt is calculated by following formula：
In formula：QiFor the averagely daily charge requirements of charging station i, θ is charging station i charging capacity nargin；P is single electricity
The charge power of stake；η is the charge efficiency of charging pile, and t is the charging station daily average charge time, and λ is to be charged simultaneously
Charging pile account for the ratios of all charging piles.
Further, the constraints of the Integrated Optimization Model is：
Dmin≤dij≤Dmax
In formula：MiGathering for alternative site, N is the quantity of alternative charging station site, wherein, hzFor 0 or 1 variable, represent
The relations of distribution between charging station and user, work as hzWhen=1, represent that node z is chosen as site of charging, otherwise hz=0, represent section
Point z is not chosen as site of charging；dijFor the distance between charging station i and j, DminAnd DmaxThe minimum between charging station is represented respectively
Spacing and maximum spacing.
Further, carrying out programming evaluation to obtained mathematical modeling by firefly optimized algorithm in step 2) includes
Following steps：
(1) initial value, is assigned to each firefly, the fluorescein of every firefly is identical, is l0, perceive simultaneously
Radius is also identical, is r0；
(2) its position in Optimized model search space, is assigned to each firefly i in a random way, wherein,
I=1,2..., n；
(3) fluorescein renewal, is calculated according to following formula：
li(t)=(1- ρ) li(t-1)+γJ(xi(t))
In formula：J(xi(t)) for every firefly i in t position xi(t) target function value corresponding to；li(t) t is represented
The fluorescein concentration of i-th firefly of moment；ρ is fluorescein volatility coefficient；γ is fluorescein enhancer；
The firefly i for the t times iteration that above formula is obtained position xi(t) the desired value J (x corresponding toi(t)) it is converted into
Required fluorescein value li(t)；
(4), every firefly is with its distance in sensing rangeWherein,r0To perceive
Radius, and be constant given in advance, selection forms a set N better than the individual of itselfi(t), referred to as neighborhood collection；
(5), firefly i is to Ni(t) Probability p of the firefly j movements inij(t) it is calculated by following formula：
Wherein, Ni(t) it is neighborhood collection：Wherein,r0For firefly
The perception radius of fireworm individual；
(6) firefly i moving direction, is determined, passes through the Probability p of step (5)ij(t) roulette selection firefly, is utilized
J, following formula is recycled to be updated its position：
Wherein, wherein, s is moving step length；
(7), the perception radius is adjusted according to following formula：
In formula：r0For initial the perception radius, β represents neighborhood rate of change；ntRepresent neighbours' threshold values；|Ni(t) | represent neighbours' collection
Close Ni(t) number of element number, i.e. neighbours；
(8), repeat step (4)-(7), until obtaining the optimal solution of Integrated Optimization Model, and output result.
The solution analysis that firefly optimized algorithm carries out model is introduced, the optimal solution of problem can be obtained, so that will not
It is absorbed in local optimum.
Further, in addition to step 3, the reliability that analog simulation verifies firefly optimized algorithm is passed through.By right
The instantiation of the electric automobile operation in somewhere carries out l-G simulation test, the results showed that using firefly optimized algorithm to being established
Social synthesis's benefit-cost model solved, can rapidly converge to optimal solution.Meet under conditions of cost minimization
Practical operation situation, effectively solve electric automobile charging station addressing optimization problem.
Beneficial effect：The new Optimal Deployment Method of electric automobile charging station of the present invention：The mathematical modeling established is not only
The earnings target of charging station is considered, comprehensive comprehensive analysis has also been carried out to user cost, it is minimum to establish comprehensive social cost
Optimal site plan model.Using firefly optimized algorithm thought, make the more efficient of solution, can rapidly converge to most
Excellent solution, the problem of effectively avoiding being absorbed in local optimum..
Brief description of the drawings
Fig. 1 is a kind of new Optimal Deployment Method flow chart of electric automobile charging station；
Fig. 2 is programme flow chart corresponding to output integrated social cost minimum；
Fig. 3 is firefly optimized algorithm convergence curve figure；
Fig. 4 is the charging station addressing distribution map being calculated using firefly optimized algorithm.
In Fig. 1,1 is establishes social synthesis's benefit minimum cost model, and 2 be to solve to calculate using firefly optimized algorithm, and 3
To verify firefly optimized algorithm by Case Simulation.
Embodiment
Below in conjunction with the accompanying drawings and specific embodiment, the present invention is furture elucidated, it should be understood that these embodiments are merely to illustrate
The present invention rather than limitation the scope of the present invention, after the present invention has been read, those skilled in the art are each to the present invention's
The modification of the kind equivalent form of value falls within the application appended claims limited range.
The realization of the present invention is specifically divided into following three steps：
Step 1: the corresponding relation between charging station and user enters as constraints to charging station optimum programming model
Row is comprehensive, fully analyzes, and establishes the minimum optimal site plan model of comprehensive social cost.
Solved, asked Step 2: being directed to the mathematical modeling established in step 1 using firefly optimized algorithm
Inscribe optimal solution.
Step 3: carrying out simulation analysis using example of calculation, checking is laid out using the new optimization of firefly optimized algorithm
The validity and correctness of method.
Further, in step 1, mathematical modeling is initially set up.The wherein involved electric motorcar charging station siting of station
Problem can be described as, in the case where meeting apart from the upper limit and electrical demand, L place being selected in alternative site, with reasonable
Scale set up charging station, provide power supply for the automobile user of surrounding so that selecting the charging electric vehicle established
Cost of standing (including building cost early stage and actual operation cost) is minimum.The present invention considers on the basis of charging station cost model
User cost, structure social synthesis benefit-cost model.Consider the Equilibrium of Interests between charging station and user, built with this
Model objective function, its mathematic(al) representation are as follows：
In formula：L for built charging station quantity；C1iFor the construction cost that charging station i is annual；C2iAnnual for charging station i
Device line is lost and operation expense；C3iIt is expense caused by automobile user charges in charging station i every year；C4i
Charging station is gone to charge the expense that consumes by annual user.
Construction cost mathematic(al) representation annual charging station i is as follows：
In formula：niTo build charging pile quantity to be mounted needed for charging station i；D be its complete equipment unit price, fiTo fill
The construction cost of power station i infrastructure；W is charging station L projected life, and r is discount rate.
The abrasion of charging station annual device line loss and operation expense mainly including equipment component etc. disappears
Consumption, line loss, charge loss, maintenance running cost etc..Because maintenance running cost is typically uncertain, can be accounted for according to it just
The certain ratio calculation of investment that begins show that it is a to make ratio, then device line loss and operation maintenance annual charging station i
Cost can represent as follows：
C2i=ni·t·365·p1(Ca+Cb·λ+Cc·λ)+(ni·d+fi)·α
\*MERGEFORMAT (3)
In formula：T is the charging station daily average charge time, and λ is that the charging pile to be charged simultaneously accounts for all charging piles
Ratio, p1For the electricity consumption unit price of charging station, Ca、Cb、CcThe attrition of equipment component etc., line loss are represented respectively, are filled
Electrical loss is converted to the loss value on single charging pile.
Automobile user every year charging station i charging caused by expense mathematic(al) representation be：
C3i=p2·Qi·365\*MERGEFORMAT (4)
In formula：p2For the charging unit price of user, QiFor the averagely daily charge requirements of charging station i.
Annual user goes to charging station the consumed expense that charged not only to go to charging station to be charged including user
Wear and tear expense, include the time value of user's charging process.Therefore, annual user goes to charging station to be charged and consumed
The mathematic(al) representation of expense be：
In formula：M is the quantity of charging station i charge point；sijDistance for demand point j to charging station i；qjFor charging daily
The automobile quantity to charge is needed at demand point j；H is the traveling distance of electric automobile mean unit electricity；V is the flat of electric automobile
Equal travel speed；The average travel time that g is user is worth, and can be estimated according to the average income of resident at charge requirement point
Go out.
The charge requirement of electric automobile can be drawn by the magnitude of traffic flow estimation on road.If have at node z u bars road with
It is connected, zeRepresent the traffic flow density on the e articles road that t is connected with node z, then node z is in t
Traffic flow density is：
Calculating the traffic flow density in a section should take a certain moment to be flowed in or out from the same direction in the section
The vehicle flowrate of the node.Charge requirement so in T time section at golden z nodes is：
In formula：Y is the average size of electric automobile, and μ is that the ratio shared by the electric automobile that charges is needed in wagon flow.If
Charging station i has Z node in coverage, then charge requirements of the charging station i in T time be：
So in order to meet the charge requirement of all electric automobiles in charging station i service ranges, it should the charging pile of installation
Quantity niMathematic(al) representation it is as follows：
In formula：θ is charging station i charging capacity nargin；P is the charge power of single electric stake；η is that the charging of charging pile is imitated
Rate.
The constraints of above-mentioned model is：
Dmin≤dij≤Dmax\*MERGEFORMAT (12)
In formula：MiGather for alternative site.The quantity that formula (10) defines alternative charging station site is N, wherein hzFor 0-1
Variable, the relations of distribution between charging station and user are represented, work as hzWhen=1, represent that node z is chosen as site of charging, otherwise hz=
0.D in formula (12)ijFor the distance between charging station i and j, meet the maximum and minimum spacing of two charging stations, ensure that charging
Stand planning reasonable service range.
Further, in step 2, firefly optimized algorithm is introduced, optimal solution can be rapidly converged to, is found optimal
Change layout method.It is the glowworm swarm algorithm that designs, basic basic assumption by simulating the group behavior of fire fly luminescence：Firefly
Fireworm luminosity is relevant with their current positions, and position is better, and the brightness sent is higher.Therefore, now it have it is bigger
Attraction Degree, so as to attract in the range of it brightness not as its other fireflies draw close to it, and they with it is relative
Brightness is inversely proportional with Attraction Degree and distance, and this point can have Shuai Minus by the propagation of light and be explained to award.In the reality of algorithm
In existing, the desired value of Utilizing question weighs the quality of the position of artificial firefly, and uses iterative submodule plan firefly
Scouting flight is moved, and with this construction algorithm, is progressively moved to the optimal solution of problem, is reached optimization purpose.
GSO algorithms are that scale is randomly generated in the search space of Optimized model as n artificial fireflies, and to every
Firefly assigns certain fluorescein li.The decision-making radius of every fireflySize determine its decision-making
Domain, and be with influencing each other by fluorescence come mutual transmission information.The size of firefly luciferin depends on its position pair
The desired value answered, desired value is excellent, then has bigger fluorescein；Firefly is more bright also to be illustrated at this firefly more
Good position, at this moment desired value corresponding to it is better, on the contrary then poor.Influence decision domain radius is the number of firefly in field
Amount, firefly is low density, increases decision domain radius；Density is high, reduces radius.The purpose is to can also be searched for when density is low
More neighbours.After algorithm calculates, artificial firefly assembles on several positions mostly.Algorithm has just started initialization and has been to confer to often
The fluorescein of firefly is identical, is l0, while the perception radius is also identical r0。
Algorithm mainly includes following important step：
(1) fluorescein updates
li(t)=(1- ρ) li(t-1)+γJ(xi(t))
\*MERGEFORMAT (13)
In formula：J(xi(t)) for every firefly i in t position xi(t) target function value corresponding to；li(t) t is represented
The fluorescein concentration of i-th firefly of moment；ρ is fluorescein volatility coefficient；γ is fluorescein enhancer.
(2) probability selection
Neighborhood collection N is shifted in selectioni(t) individual j probability is p inij(t)：
Wherein, Ni(t) it is neighborhood collection：
In above formula,r0For the perception radius of firefly individual.
(3) location updating
Wherein, s is moving step length.
(4) dynamic decision domain radius updates
In formula：β represents neighborhood rate of change；ntRepresent neighbours' threshold values (neighbours' number of control firefly)；|Ni(t) | represent
Neighborhood Ni(t) element number (i.e. the numbers of neighbours).
The step of glowworm swarm algorithm performs is as follows：
(1) program starts, and initial value is assigned to each parameter.
(2) it is assigned in Optimized model search space to each firefly i (i=1,2..., n) in a random way
Position.
(3) the firefly i for the t times iteration that wushu (13) obtains position xi(t) the desired value J (x corresponding toi(t)) turn
Turn to the fluorescein value l required for subsequent calculationsi(t)。
(4) every firefly is less than it in sensing range with its distancer0It is given in advance
Constant, claim the perception radius) in the range of, selection forms a set N better than the individual of itselfi(t), referred to as neighborhood collection.
(5) firefly i is to Ni(t) Probability p of the firefly j movements inij(t) it is calculated by formula (14).
(6) firefly i moving directions are determined, pass through the Probability p of previous stepij(t), using roulette selection firefly j, then
Its position is updated using formula (15).
(7) adjust according to formula (16), update the perception radius.
(8) judge whether to meet termination condition, perform (9) if meeting, if not then turn (4).
(9) output result, EP (end of program).
Further, in step 3, by taking the electric automobile running situation in somewhere as an example, this area totally 33 electronic vapour is gathered
The coordinate at automobile-used family, because each electric automobile parameter has difference, its unit distance consumption electricity is also different, by being built
Vertical model understands that charging station plan model is multi peak value model, and different fortune is had when the quantity difference of charging station
Row result:When the quantity increase of charging station, the capacity of each charging station reduces, and corresponding operating cost reduces, but separate unit
The cost of charger is gradually increasing again；On the contrary, during the negligible amounts of charging station, the cost of separate unit charger is again gradual
Decline, it is seen then that the project study of this charging station, it should obtain balance under cost and demand, obtain planning optimal solution.
Assuming that the ratio μ shared by the electric automobile for needing to charge in wagon flow is 12%, average size y is 45kWh, single
The charge power P of charging pile is 90kW, and charging capacity nargin θ is 20%, and the charge efficiency η of charging pile is 0.95, electricity price
Calculated according to 0.6 yuan/kWh, the average overall travel speed v of electric automobile is 30km/h, is estimated according to user's income in the region
The average travel time value g for calculating user is 20 yuan/h.And customer location and the average hundred kilometers of distances consumption of each electric automobile
Electricity kiIt is shown in Table 1.
With reference to site optimum programming model and glowworm swarm algorithm, the parameter for setting algorithm is：Population scale is 100, memory
Storage capacity is 10, iterations 100, crossover probability 0.5, mutation probability 0.3, and Diversity parameter is set to 0.98.
From glowworm swarm algorithm searching process it can be seen from the figure that, GSO can reach poised state iterating to or so 20 generations,
Efficiency comparison is high.Optimum programming knot of the charging pile quantity of charging station coordinates, quantity and each charging station under model analysis
Fruit is shown in Table 2.And charging station operation and the cost calculation of user the results are shown in Table shown in 3.
The customer location of table 1 and electric automobile parameter
The charging station coordinates of table 2 and quantity optimum programming result
The charging station of table 3 and user cost
| C1 | C2 | C3 | C4 | C | |
| GSO | 345.74 | 328.55 | 1763.60 | 76.43 | 2514.32 |
It can be obtained from the result of table 2, table 3, glowworm swarm algorithm can rapidly converge to optimal solution, in the condition of cost minimization
Under meet practical operation situation, can effectively solve electric automobile addressing optimization problem.
Claims (7)
- A kind of 1. new Optimal Deployment Method of electric automobile charging station, it is characterised in that：Comprise the following steps：Step 1: the minimum Integrated Optimization Model of user-charging station operation cost is established in the addressing to charging station；Step 2: carrying out programming evaluation to obtained mathematical modeling by firefly optimized algorithm, user-charging station operation is obtained The optimal solution of the Integrated Optimization Model of cost minimization.
- 2. the new Optimal Deployment Method of electric automobile charging station as claimed in claim 1, it is characterised in that used in step 1 The minimum Integrated Optimization Model of family-charging station operation cost is represented by following formula：<mrow> <mi>min</mi> <mi> </mi> <mi>C</mi> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>L</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mrow> <mn>1</mn> <mi>i</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mn>2</mn> <mi>i</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mn>3</mn> <mi>i</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>C</mi> <mrow> <mn>4</mn> <mi>i</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow>In formula：L for built charging station quantity；C1iFor the construction cost that charging station i is annual；C2iFor the equipment that charging station i is annual Line loss and operation expense；C3iIt is expense caused by automobile user charges in charging station i every year；C4iTo be every Year user goes to charging station to be charged consumed expense；Wherein, construction cost C annual charging station i1iRepresented by following formula：<mrow> <msub> <mi>C</mi> <mrow> <mn>1</mn> <mi>i</mi> </mrow> </msub> <mo>=</mo> <mrow> <mo>(</mo> <msub> <mi>n</mi> <mi>i</mi> </msub> <mo>&times;</mo> <mi>d</mi> <mo>+</mo> <msub> <mi>f</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mfrac> <mrow> <mi>r</mi> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <mi>r</mi> <mo>)</mo> </mrow> <mi>w</mi> </msup> </mrow> <mrow> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <mi>r</mi> <mo>)</mo> </mrow> <mi>w</mi> </msup> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> </mrow>In formula：niTo build charging pile quantity to be mounted needed for charging station i；D be its complete equipment unit price, fiFor charging station i The construction cost of infrastructure；W is charging station L projected life, and r is discount rate；Device line loss and operation expense annual charging station i is represented by following formula：C2i=ni·t·365·p1(Ca+Cb·λ+Cc·λ)+(ni·d+fi)·αIn formula：T is the charging station daily average charge time, and λ is the ratio that the charging pile to be charged simultaneously accounts for all charging piles Rate, p1For the electricity consumption unit price of charging station, Ca、Cb、CcThe attrition, line loss, charge loss of equipment component are represented respectively Convert the loss value on single charging pile；Automobile user is represented in expense caused by charging station i chargings by following formula every year：C3i=p2·Qi·365In formula：p2For the charging unit price of user, QiFor the averagely daily charge requirements of charging station i；Annual user goes to the charged mathematic(al) representation of consumed expense of charging station to be：<mrow> <msub> <mi>C</mi> <mrow> <mn>4</mn> <mi>i</mi> </mrow> </msub> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>M</mi> </munderover> <msub> <mi>s</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <msub> <mi>q</mi> <mi>j</mi> </msub> <mrow> <mo>(</mo> <mfrac> <msub> <mi>p</mi> <mn>2</mn> </msub> <mi>h</mi> </mfrac> <mo>+</mo> <mfrac> <mi>g</mi> <mi>v</mi> </mfrac> <mo>)</mo> </mrow> <mo>&CenterDot;</mo> <mn>365</mn> </mrow>In formula：M is the quantity of charging station i charge point；sijDistance for demand point j to charging station i；qjFor daily charge requirement The automobile quantity to charge is needed at point j；H is the traveling distance of electric automobile mean unit electricity；V is the average row of electric automobile Sail speed；The average travel time that g is user is worth.
- 3. the new Optimal Deployment Method of electric automobile charging station as claimed in claim 2, it is characterised in thatWherein, charge requirement Q averagely daily charging station iiIt is calculated by following formula：<mrow> <msub> <mi>Q</mi> <mi>i</mi> </msub> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>Z</mi> </munderover> <msub> <mi>q</mi> <mi>i</mi> </msub> </mrow>Wherein, Z is the quantity of charging station i coverages interior nodes, if having at node z, u bar roads are coupled, qiFor T time section Charge requirement at interior z nodes,T is the daily charging interval,zeRepresent in t The traffic flow density on the e articles road being connected with node z, y are the average size of electric automobile, and μ is to need to fill in wagon flow Ratio shared by the electric automobile of electricity, ztRepresent traffic flow densities of the node z in t.
- 4. the new Optimal Deployment Method of electric automobile charging station as claimed in claim 3, it is characterised in that build charging station i Required charging pile quantity n to be mountediIt is calculated by following formula：<mrow> <msub> <mi>n</mi> <mi>i</mi> </msub> <mo>=</mo> <mo>&lsqb;</mo> <mfrac> <mrow> <msub> <mi>Q</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mn>1</mn> <mo>+</mo> <mi>&theta;</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mi>P</mi> <mi>&eta;</mi> <mi>t</mi> <mi>&lambda;</mi> </mrow> </mfrac> <mo>&rsqb;</mo> <mo>+</mo> <mn>1</mn> </mrow>In formula：QiFor the averagely daily charge requirements of charging station i, θ is charging station i charging capacity nargin；P is single electric stake Charge power；η is the charge efficiency of charging pile, and t is the charging station daily average charge time, and what λ was while charged fills Electric stake accounts for the ratio of all charging piles.
- 5. the new Optimal Deployment Method of electric automobile charging station as claimed in claim 2, it is characterised in that the complex optimum The constraints of model is：<mrow> <munder> <mo>&Sigma;</mo> <mrow> <mi>z</mi> <mo>&Element;</mo> <msub> <mi>M</mi> <mi>i</mi> </msub> </mrow> </munder> <msub> <mi>h</mi> <mi>i</mi> </msub> <mo>=</mo> <mi>N</mi> <mo>,</mo> <mi>z</mi> <mo>&le;</mo> <msub> <mi>M</mi> <mi>i</mi> </msub> </mrow>Dmin≤dij≤DmaxIn formula：MiGathering for alternative site, N is the quantity of alternative charging station site, wherein, hzFor 0 or 1 variable, charging is represented The relations of distribution stood between user, work as hzWhen=1, represent that node z is chosen as site of charging, otherwise hz=0, represent node z not It is chosen as site of charging；dijFor the distance between charging station i and j, DminAnd DmaxThe minimum spacing between charging station is represented respectively With maximum spacing.
- 6. the new Optimal Deployment Method of electric automobile charging station as claimed in claim 1, it is characterised in that pass through in step 2) Firefly optimized algorithm carries out programming evaluation to obtained mathematical modeling and comprised the following steps：(1) initial value, is assigned to each firefly, the fluorescein of every firefly is identical, is l0, while the perception radius And identical, it is r0；(2) its position in Optimized model search space, is assigned to each firefly i in a random way, wherein, i= 1,2...,n；(3) fluorescein renewal, is calculated according to following formula：li(t)=(1- ρ) li(t-1)+γJ(xi(t))In formula：J(xi(t)) for every firefly i in t position xi(t) target function value corresponding to；li(t) t is represented The fluorescein concentration of i-th firefly；ρ is fluorescein volatility coefficient；γ is fluorescein enhancer；The firefly i for the t times iteration that above formula is obtained position xi(t) the desired value J (x corresponding toi(t) needed for) being converted into The fluorescein value l wantedi(t)；(4), every firefly is with its distance in sensing rangeWherein,r0For the perception radius, And be constant given in advance, selection forms a set N better than the individual of itselfi(t), referred to as neighborhood collection；(5), firefly i is to Ni(t) Probability p of the firefly j movements inij(t) it is calculated by following formula：<mrow> <msub> <mi>p</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <msub> <mi>l</mi> <mi>j</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>l</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <mrow> <munder> <mo>&Sigma;</mo> <mrow> <mi>k</mi> <mo>&Element;</mo> <msub> <mi>N</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </munder> <msub> <mi>l</mi> <mi>k</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>l</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>,</mo> </mrow>Wherein, Ni(t) it is neighborhood collection：Wherein,r0For firefly The perception radius of individual；(6) firefly i moving direction, is determined, passes through the Probability p of step (5)ij(t), using roulette selection firefly j, then Its position is updated using following formula：Wherein, wherein, s is moving step length；(7), the perception radius is adjusted according to following formula：<mrow> <msubsup> <mi>r</mi> <mi>d</mi> <mi>i</mi> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>=</mo> <mi>m</mi> <mi>i</mi> <mi>n</mi> <mo>{</mo> <msub> <mi>r</mi> <mn>0</mn> </msub> <mo>,</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo>{</mo> <mn>0</mn> <mo>,</mo> <mi>r</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <mi>&beta;</mi> <mrow> <mo>(</mo> <msub> <mi>n</mi> <mi>t</mi> </msub> <mo>-</mo> <mo>|</mo> <mrow> <msub> <mi>N</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <mo>|</mo> <mo>)</mo> </mrow> <mo>}</mo> <mo>}</mo> </mrow>In formula：r0For initial the perception radius, β represents neighborhood rate of change；ntRepresent neighbours' threshold values；|Ni(t) | represent neighborhood Ni (t) number of element number, i.e. neighbours；(8), repeat step (4)-(7), until obtaining the optimal solution of Integrated Optimization Model, and output result.
- 7. the new Optimal Deployment Method of electric automobile charging station as claimed in claim 1, it is characterised in that also including step Three, the reliability of firefly optimized algorithm is verified by analog simulation.
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| CN108288110A (en) * | 2018-01-18 | 2018-07-17 | 武汉理工大学 | A kind of charging pile Optimal Deployment Method based on the true running data of electric vehicle |
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| CN108764634A (en) * | 2018-04-24 | 2018-11-06 | 河海大学 | A kind of electric automobile charging station dynamic programming method for considering charge requirement and increasing |
| CN109447410A (en) * | 2018-09-25 | 2019-03-08 | 华南理工大学 | A kind of charging station site selecting method considering urban road network traffic operating status |
| CN109636070A (en) * | 2019-01-30 | 2019-04-16 | 国网安徽省电力有限公司 | A kind of DSS of electric automobile charging station layout optimization |
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| CN110059860B (en) * | 2019-03-21 | 2021-01-12 | 华北电力大学 | Multi-objective optimization method for position layout of urban public charging station |
| CN110059860A (en) * | 2019-03-21 | 2019-07-26 | 华北电力大学 | A kind of public charging station location layout Multipurpose Optimal Method in city |
| CN110598927A (en) * | 2019-09-05 | 2019-12-20 | 国创新能源汽车能源与信息创新中心(江苏)有限公司 | Charging station optimal configuration type selection method |
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| CN113343437A (en) * | 2021-05-20 | 2021-09-03 | 国网上海市电力公司 | Electric vehicle rapid charging guiding method, system, terminal and medium |
| CN113343437B (en) * | 2021-05-20 | 2023-11-24 | 国网上海市电力公司 | Electric automobile quick charge guiding method, system, terminal and medium |
| CN113642757A (en) * | 2021-06-01 | 2021-11-12 | 北京慧辰资道资讯股份有限公司 | Internet of things charging pile construction planning method and system based on artificial intelligence |
| CN113642757B (en) * | 2021-06-01 | 2024-02-27 | 北京慧辰资道资讯股份有限公司 | Method and system for planning construction of charging pile of Internet of things based on artificial intelligence |
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