CN105809278A - Queuing theory algorithm based electric vehicle power change station's location choosing and planning method - Google Patents

Abstract

The invention is a queuing theory algorithm based electric vehicle power change station's location choosing and planning method with the object of making the design of facilities for electric vehicles in some region reasonable and energy saving. The technical solutions of the method are as follows: calculating the best number of facilities required in a power change state with the queuing theory algorithm; building an optimized model featuring the smallest operation costs and transportation costs by considering the power change state and a centralized power charging station or power transforming station as a whole research subject; and finally resorting to this model for choosing the site and planning the power change station for electric vehicles. According to the invention, the costs, transportation associated energy consumption as well as user's demand for power are also taken into full consideration so as to provide a site choosing model that will ultimately meet the requirements of urban areas. Experiments show that this method can not only work out the optimized scheme for choosing the right site for a power change station, but it can also provide access to a most optimized power transforming station, thus providing importance guidance for city governors to make proper arrangement and planning to electric vehicle power changing stations in urban areas.

Description

A kind of electric automobile charging station Site planning method based on queueing theory algorithm

Technical field

The present invention relates to one can make rational planning for the method for electric automobile charging station in city (fill, electrical changing station) addressing, belong to power transmission and distribution technical field.

Background technology

Oil is as a kind of important non-renewable energy resources, and its distribution and amount of storage affect the change of World Economics to a great extent, is therefore classified as strategic reserves resource by whole world every country already.And along with the fast development of TRANSPOWORLD industry, diversified means of transportation provides in the life for people while good guarantee, also greatly accelerate the depletion rate of petroleum-based energy, and result in the appearance of problem of energy crisis.And the atmospheric pollution that the vehicles cause in the process of moving and greenhouse effect problem, constantly test again the blowdown Governance Ability of national governments.This is wherein; the most serious with automotive emission problem again; investigation according to North Carolina, USA University Environment academy of science and State Bueau of Environmental Protection's research worker shows, the whole world is annual because air pollution death toll is more than 2,000,000 people, wherein 70% is above being subject to the impact of motor vehicle exhaust emission.Therefore, a kind of novel energy of development, emphasis improves the disposal of pollutants problem of orthodox car, becomes the top priority that world's carrier needs to face.And by merging multiple up-to-date science and technology the present age, thus the new-energy automobile risen brings brand-new developing direction to World Auto Industry.This wherein, pure electric automobile, with distinctive zero-emission, free of contamination advantage, presents the bright outlook of new Power evelopment, obtains the accreditation of Global Auto industry.But, it is in the ev industry pattern of starting stage, is inevitably subject to the restriction of some factors.And in many factors, the imperfection that complete sets of basic equipment facility (fill, electrical changing station) is built becomes the main cause that restriction electric automobile is all-round developing, Ye Shi various countries scientific research personnel's problem demanding prompt solution.

In existing list of references, the research of electric automobile infrastructure construction is had focused largely on the aspects such as the site-choosing principle of charging station, comercial operation pattern, cost of investment, these researchs, only from the angle of applicable single-population interests, fail effectively to work out the programme meeting demand in many ways.Also ignore simultaneously different regions are filled, the research of electrical changing station scale, thus causing the waste of construction cost.Therefore existing document is for the limitation studying equal various degrees of electric automobile charging station, it is impossible to the construction of electric automobile infrastructure in city of making rational planning for.

Summary of the invention

Present invention aims to the drawback of prior art, it is provided that a kind of offer is based on the electric automobile charging station Site planning method of queueing theory algorithm, to realize making rational planning for and construction of electric automobile charging station in city.

Problem of the present invention realizes with following technical proposals:

A kind of electric automobile charging station Site planning method based on queueing theory algorithm, described method calculates best facility number in electrical changing station by the method for queueing theory, using electrical changing station and concentrated charging station (transformer station) as holistic approach object, build the Optimized model being minimised as target with Total Cost Ownership and cost of transportation, finally utilize the siteselecting planning of this model realization electric automobile charging station, said method comprising the steps of:

A. the site criteria formulated according to country, in primary election city the position of qualified electrical changing station candidate site, gathers the coordinate data of transformer station in city simultaneously；

B. by the statistical yearbook in area to be planned, collect the vehicle guaranteeding organic quantity data of nearly 20 years areas to be planned, and affect the factor data of vehicles number development, multifactor grey relational grade analysis is made by these data, and then simulate the development trend of motor vehicles in following city, obtain the prediction data of vehicle guaranteeding organic quantity, draw the recoverable amount data of electric automobile in city finally by motor vehicles ratio shared by the following electric automobile of scholarly forecast；

C. area to be planned is carried out subregion division according to land character (including commercial land, residential land, industrial land), identical land used kind is divided in the same area, the electric automobile recoverable amount situation drawn in integrating step b, to close in the way of down town near zone successively decreases in proportion to Urban Marginal Areas, formulate and change electricity demand in each subregion.This step needs to consider the practical factor in planned region.Such as, according to identical land character, this city being divided into 12 pieces of zonules, wherein III, VI, VII, Ⅹ is urban central zone, and II, V, VIII, Ⅺ are neighboring area, down town, and I, IV, Ⅸ, Ⅻ is Urban Marginal Areas.Owing to this center, city is economically developed, population is relatively dense, electric automobile recoverable amount is relatively many, then urban central zone can calculate in the ratio of 6:3:1 to electric automobile recoverable amount data in Urban Marginal Areas, obtains the electric automobile recoverable amount data in each subregion.Computing formula can be designated as $\left\{\begin{array}{c}\underset{m\∈M}{\Σ}{A}_m{N}_m=N_{EV}\\ \underset{m\∈M}{\Σ}{A}_m=1\end{array}\right.,$ A in formula_mFor proportionality coefficient, N_mFor electric automobile quantity in m area, N_EVFor electric automobile total number；

D. according to changing electricity demand and the parameter of electric automobile and ruuning situation (include hundred kilometers of average current drains, day travels average mileage, average cell capacity) in each region calculated in step c, use the model of M/M/s in queueing theory to determine and best change electric facility number in the region in；

E. according to the information of soil land price in area to be planned, obtain the benchmark land price of heterogeneity land used, and the file of the electric automobile charging and conversion electric station standard formulated according to country fills under the different brackets indicated, the floor space of electrical changing station, result of calculation in integrating step d, it is determined that build the cost of land of electrical changing station in zones of different；

F. regulation constraints and parameter situation, with Total Cost Ownership and cost of transportation minimal definition site selection model formula:

$\begin{array}{cc}& \begin{array}{cc}Min& C=C_1+C_2\end{array}\\ S.t.& \\ & \underset{m\∈M}{\Σ}{z}_m=1\\ & \begin{array}{cc}{z}_{mn}\≤\underset{m}{\Σ}{x}_n& \∀m\∈M,n\∈N\end{array}\\ & \underset{n}{\Σ}{x}_n\≤1\\ & \begin{array}{cc}{x}_n\∈\{0,1\}& \∀n\∈N\end{array}\\ & \begin{array}{cc}{y}_{nk}\∈\{0,1\}& \∀n\∈N,k\∈K\end{array}\\ & \begin{array}{cc}{z}_{mn}\∈\{0,1\}& \∀m\∈M,n\∈N\end{array}\end{array}$

Wherein, C₁For operation cost, its formula is:S be in electrical changing station change electricity facility number；The use cost in soil shared by electrical changing station；e₁Cost average time (maintenance cost that comprises equipment, depreciable cost, human cost) of electricity facility is changed for separate unit；z_mFor electric automobile users all in demand region m, it is only capable of accepting the service of neighbouring site within a certain period of time；x_nFor building electrical changing station at some n place, candidate site；C₂For road transport cost, its formula is: $C_2=\mathrm{\α}\underset{m}{\Σ}\underset{n}{\Σ}{H}_m\·z_{mn}\·d_{mn}+\mathrm{\η}\underset{n}{\Σ}\underset{k}{\Σ}{y}_{ng}\·d_{ng},$ M={m} is the set of demand region, and G={g} is the set of power transformation website, and N={n} is the set of electrical changing station candidate site point, H_mFor planning region in change electricity demand, d_mnFor changing the electricity demand point distance to candidate location point, d_ngFor the distance of candidate site point to transformer station, α, η is for travelling distance cost, y_ng,z_mnVariable is selected, wherein: y for 0-1_ngRepresent if candidate site point n selects transformer station g to be concentrated charging station, be then 1, be otherwise 0；z_mnRepresent if demand region m selects in the some n place acceptance service of candidate site, be then 1, be otherwise 0；

G. establish planning year, the data collected and predict are substituted among site selection model respectively, obtain minimal overall operation cost and cost of transportation C by calculating in step a, b, c, d；By meeting the set N={n} of the electrical changing station candidate site point of this minimum cost, the set G={g} of power transformation website screens, and is labeled in the planning map of city by each site point filtered out.

The above-mentioned electric automobile charging station Site planning method based on queueing theory algorithm, uses the model of M/M/s in queueing theory to determine and best changes electric facility number s's in the region in method particularly includes:

Queue theory model is as follows:

$\left\{\begin{array}{cc}{\mathrm{\λp}}_0={\mathrm{\μp}}_1& \\ {\mathrm{\λp}}_{n-1}+(n+1){\mathrm{\μp}}_{n+1}=(\mathrm{\λ}+n\mathrm{\μ})p_n& n\≤s\\ {\mathrm{\λp}}_{n-1}+{\mathrm{s\μp}}_{n+1}=(\mathrm{\λ}+s\mathrm{\μ})p_n& n>s\end{array}\right.$

Wherein, p_nAccept to change the probability of electricity service for n electric automobile；N is the electric automobile quantity accepting to change electricity service；λ is the average arrival rate of electric automobile in electrical changing station, and computing formula isk₁For hundred kilometers of average current drains of electric automobile；k₂For the average course that electric automobile day travels；D_mIt it is the electric automobile recoverable amount in the region m of n-th electrical changing station point service；Q is electric automobile average cell capacity；T is the per day working time of electrical changing station；μ is the average service rate that electric automobile completes to change electric process, and including entering the station, time leaving from station and fill, change the electricity time, computing formula isT_i/oFor the time that electric automobile is required into and out of station；T_c/hAccept to change the average time of electricity service for electric automobile；

In system, each service indication is: change the efficiency of service of electricity facilityChange the utilization rate of electricity facilityCongestion lengthsAverage queue lengthChange residence time in electricity systemWaiting time

If filling, changing the electricity average total cost that produces within the unit interval of facility system is F (s), then

MinF (s)=e₁s+e₂L_s

Wherein, e₂Cost average time (including changing electricity consumption, energy consumption cost, trip value) for separate unit electric automobile, it is assumed that s is optimal service facility number, then have:

$\left\{\begin{array}{c}F(s-1)>F\left(s\right)\\ F(s+1)<F\left(s\right)\end{array}\right.$

Substitute into above parameter, after arrangement:Obtain the difference of adjacent two of inequality the right and left successively, it may be determined that reach the number s of facility during optimum.

The above-mentioned electric automobile charging station Site planning method based on queueing theory algorithm, the described factor affecting vehicles number development includes total output value per capita and road overall length.

The present invention is taking into full account that on the basis of electrical changing station construction cost, cost of transportation and user power utilization demand, Plan meets the site selection model of urban construction requirement.The results show, this method can not only calculate the prioritization scheme of electrical changing station addressing, moreover it is possible to draws the access scheme of optimum transformer station, electric automobile in city is filled, the planning construction of electrical changing station has important directive function.

Accompanying drawing explanation

Fig. 1 is the schematic diagram of electrical changing station and position, transformer station's primary election site in area to be planned in the embodiment of the present invention；

Fig. 2 is the total recoverable amount block diagram of motor vehicles in 1997-2014 city to be planned.Abscissa represents observation time (year), and vertical coordinate represents recoverable amount data ()；

Fig. 3 is total output value curve chart per capita in 1997-2014 city to be planned.Abscissa represents observation time (year), and vertical coordinate represents the data of per-capita gross domestic product (unit)；

Fig. 4 is 1997-2014 urban roads overall length curve chart to be planned.Abscissa represents observation time (year), and vertical coordinate represents the data of road overall length (kilometer)；

Fig. 5 uses the Grey Model method forecast analysis curve to vehicle guaranteeding organic quantity, and abscissa is the time, and vertical coordinate is for possessing value；

Fig. 6 is that the addressing of electrical changing station in city to be planned in 2025 is calculated distribution situation by site selection model；

Fig. 7 is the flow chart of the present invention.

Figure with each symbol inventory in literary composition is: for C Total Cost Ownership and cost of transportation；C₁For operation cost；S be in electrical changing station change electricity facility number；The use cost in soil shared by electrical changing station；e₁Cost average time of electricity facility is changed for separate unit；e₂Cost average time for separate unit electric automobile；z_mFor electric automobile users all in demand region m, it is only capable of accepting the service of neighbouring site within a certain period of time；x_nFor building electrical changing station at some n place, candidate site；C₂For road transport cost；M={m} is the set of demand region；G={g} is the set of power transformation website；N={n} is the set of electrical changing station candidate site point；H_mFor planning region in change electricity demand；d_mnFor changing the electricity demand point distance to candidate location point；d_ngDistance for candidate site point to transformer station；α, η are for travelling distance cost；y_ng,z_mnVariable is selected for 0-1；p_nAccept to change the probability of electricity service for n electric automobile；N is the electric automobile quantity accepting to change electricity service；λ is the average arrival rate of electric automobile, k in electrical changing station₁For hundred kilometers of average current drains of electric automobile；k₂For the average course that electric automobile day travels；D_mIt it is the electric automobile recoverable amount in the region m of n-th electrical changing station point service；Q is electric automobile average cell capacity；T is the per day working time of electrical changing station；μ is the average service rate that electric automobile completes to change electric process；T_i/oFor the time that electric automobile is required into and out of station；T_c/hAccept to change the average time of electricity service for electric automobile；ρ is the efficiency of service changing electricity facility；β is the utilization rate changing electricity facility；L_qFor congestion lengths；L_sFor average queue length；T_sFor changing residence time in electricity system；T_qFor the waiting time；F (s) is for filling, change the average total cost that electricity facility system produces within the unit interval.

Detailed description of the invention

Below in conjunction with accompanying drawing, the invention will be further described.

The present invention proposes a kind of about the New Century Planned Textbook of electric automobile charging station Site planning method in city.Consider that road transport cost and consumer change the electricity demand impact on planned position, balance demand factor in many ways, establish with Total Cost Ownership and cost of transportation minimum for objective optimization model, and the content applying queueing theory determines best facility number in electrical changing station, with this addressing prediction realizing more meeting urban construction requirement.Below in conjunction with embodiment, the present invention will be described in detail:

Step one: the position coordinates of the present embodiment primary election city 27 example electric automobile charging station to be planned, and collect the position data of 5 transformer stations in city, in conjunction with city planning map, set up abstract networks.

Step 2: collect automobile pollution data in this city 1997-2014, and influence whether vehicles number development factors data (such as total output value per capita, road overall length etc.).Make multifactor grey relational grade analysis and then the development trend of matching motor vehicles in future by these data, provide rational prediction data finally by motor vehicles ratio total shared by the following electric automobile of scholarly forecast.

Step 3, abstract networks step one set up is divided into zones of different by land character, with the level of economic development in zones of different, predicting the outcome of integrating step two, to close in the way of down town near zone successively decreases in proportion to Urban Marginal Areas, formulate and change electricity demand in each subregion.

Step 4, according to change in each region the electricity situation of demand and electric automobile actual specification conditions (as: hundred kilometers of average current drains, days travel average course, average cell capacity etc., use M/M/s model in queueing theory to determine and best change electricity facility number s in the region in, so that it is determined that the grade of electrical changing station accordingly.

Queue theory model is as follows:

$\left\{\begin{array}{cc}{\mathrm{\&lambda;p}}_0={\mathrm{\&mu;p}}_1& \\ {\mathrm{\&lambda;p}}_{n-1}+(n+1){\mathrm{\&mu;p}}_{n+1}=(\mathrm{\&lambda;}+n\mathrm{\&mu;})p_n& n\&le;s\\ {\mathrm{\&lambda;p}}_{n-1}+{\mathrm{s\&mu;p}}_{n+1}=(\mathrm{\&lambda;}+s\mathrm{\&mu;})p_n& n>s\end{array}\right.$

Wherein, p_nAccept to change the probability of electricity service for n electric automobile；S changes the facility quantity of electricity service for providing；N is the electric automobile quantity accepting to change electricity service；λ is the average arrival rate of electric automobile in electrical changing station, and computing formula is

In formula: k₁For hundred kilometers of average current drains of electric automobile；k₂For the average course that electric automobile day travels；D_mIt it is the electric automobile recoverable amount in the region m of n-th electrical changing station point service；Q is electric automobile average cell capacity；T is the per day working time of electrical changing station.

μ is the average service rate that electric automobile completes to change electric process, and including entering the station, time leaving from station and fill, change the electricity time, computing formula isIn formula: T_i/oFor the time that electric automobile is required into and out of station；T_c/hAccept to change the average time of electricity service for electric automobile.

In system, each service indication is, changes the efficiency of service of electricity facilityChange the utilization rate of electricity facilityCongestion lengthsAverage queue lengthChange residence time in electricity systemWaiting time

If filling, changing the electricity average total cost that produces within the unit interval of facility system is F (s), then

MinF (s)=e₁s+e₂L_s

Wherein, e₁Cost average time (maintenance cost that comprises equipment, depreciable cost, human cost etc.) of electricity facility is changed for separate unit.e₂Cost average time (including changing electricity consumption, energy consumption cost, trip value etc.) for separate unit electric automobile.Assume that s is optimal service facility number, then have:

$\left\{\begin{array}{c}F(s-1)>F\left(s\right)\\ F(s+1)<F\left(s\right)\end{array}\right.$

Substitute into above parameter, after arrangement:Obtain the difference of adjacent two of inequality the right and left successively, it may be determined that reach the number s of facility during optimum.

Step 5: as follows, it is stipulated that constraints, sets up site selection model formula:

$\begin{array}{cc}& \begin{array}{cc}Min& C=C_1+C_2\end{array}\\ S.t.& \\ & \underset{m\&Element;M}{\&Sigma;}{z}_m=1\\ & \begin{array}{cc}{z}_{mn}\&le;\underset{m}{\&Sigma;}{x}_n& \&ForAll;m\&Element;M,n\&Element;N\end{array}\\ & \underset{n}{\&Sigma;}{x}_n\&le;1\\ & \begin{array}{cc}{x}_n\&Element;\{0,1\}& \&ForAll;n\&Element;N\end{array}\\ & \begin{array}{cc}{y}_{nk}\&Element;\{0,1\}& \&ForAll;n\&Element;N,k\&Element;K\end{array}\\ & \begin{array}{cc}{z}_{mn}\&Element;\{0,1\}& \&ForAll;m\&Element;M,n\&Element;N\end{array}\end{array}$

Wherein, C₁For operation cost, its formula is:S be in electrical changing station change electricity facility number；The use cost in soil shared by electrical changing station.

C₂For road transport cost, its formula is:

$C_2=\mathrm{\&alpha;}\underset{m}{\&Sigma;}\underset{n}{\&Sigma;}{H}_m\&CenterDot;z_{mn}\&CenterDot;d_{mn}+\mathrm{\&eta;}\underset{n}{\&Sigma;}\underset{k}{\&Sigma;}{y}_{ng}\&CenterDot;d_{ng}$

M={m} is the set of demand region, and G={g} is the set of power transformation website, and N={n} is the set of electrical changing station candidate site point, H_mFor planning region in change electricity demand, d_mnFor changing the electricity demand point distance to candidate location point, d_ngFor the distance of candidate site point to transformer station, α, η is for travelling distance cost, y_ng,z_mnVariable is selected, wherein: y for 0-1_ngRepresent if candidate site point n selects transformer station g to be concentrated charging station, be then 1, be otherwise 0.z_mnRepresent if demand region m selects in the some n place acceptance service of candidate site, be then 1, be otherwise 0.

Step 6: establish planning year, thus using the input as model of the above-mentioned all kinds of collection data, calculating addressing and predict the outcome and filter out the site point meeting optimal result.

Interpretation

Method proposed by the invention is carried out case verification by certain provincial capital's city's data of the north by the present invention, and accompanying drawing illustrates the main experimental results of the present invention.Hereby illustrating, following experimental analysis is only demonstration, rather than the method is confined in specific application environment.

First, Fig. 1 illustrates the distribution situation of urban district plan to be planned, electrical changing station primary election site point and transformer station, it can be seen that the position coordinates chosen is uniformly distributed, and meets the addressing requirement of " electric car electric energy supply and safeguards technique specification ".

Secondly, by collection this city 1997-2014 vehicle guaranteeding organic quantity data, coordinate graph is set up.As in figure 2 it is shown, and in conjunction with the total output value per capita of Fig. 3 area to be planned and Fig. 4 area to be planned road overall length two class influence factor over the years, analysis Motor Vehicles Development trend in future.

Again, motor-driven recoverable amount data are carried out gray scale matching and prediction.Its fitting result is as shown in Figure 4, it was predicted that result is in Table 1.

Finally, utilizing collection data in full to choose with the exchange plant location that predicts the outcome and be calculated screening, and be labeled in road network map, Fig. 6 is the siteselecting planning situation of 2025, and siteselecting planning result is in Table 2.

Table 1 automobile pollution predicts the outcome

Table electric automobile charging station Optimizing Site Selection result in 22025

All experimental results of this method have all strongly suggested that introducing road transport cost and user change electricity demand factor, the method using queueing theory determines best facility number in the point of site, and the site selection model of equilibrium establishment demand in many ways is feasible, and there is very important theoretical research value and practical advice meaning.

Claims (3)

1. the electric automobile charging station Site planning method based on queueing theory algorithm, it is characterized in that, described method calculates best facility number in electrical changing station by the method for queueing theory, using electrical changing station and concentrated charging station as holistic approach object, build the Optimized model being minimised as target with Total Cost Ownership and cost of transportation, finally utilize the siteselecting planning of this model realization electric automobile charging station, said method comprising the steps of:

A. the site criteria formulated according to country, in primary election city the position of qualified electrical changing station candidate site, gathers the coordinate data of transformer station in city simultaneously；

B. by the statistical yearbook in area to be planned, collect the vehicle guaranteeding organic quantity data of nearly 20 years areas to be planned, and affect the factor data of vehicles number development, multifactor grey relational grade analysis is made by these data, and then simulate the development trend of motor vehicles in following city, obtain the prediction data of vehicle guaranteeding organic quantity, draw the recoverable amount data of electric automobile in city finally by motor vehicles ratio shared by the following electric automobile of scholarly forecast；

C. by area to be planned according to land character, include commercial land, residential land, industrial land carry out subregion division, identical land used kind is divided in the same area, the electric automobile recoverable amount situation drawn in integrating step b, to close in the way of down town near zone successively decreases in proportion to Urban Marginal Areas, formulate and change electricity demand in each subregion；

D. according to changing electricity demand and the parameter of electric automobile and ruuning situation (include hundred kilometers of average current drains, day travels average mileage, average cell capacity) in each region calculated in step c, use the model of M/M/s in queueing theory to determine and best change electric facility number in the region in；

E. according to the information of soil land price in area to be planned, obtain the benchmark land price of heterogeneity land used, and the file of the electric automobile charging and conversion electric station standard formulated according to country fills under the different brackets indicated, the floor space of electrical changing station, result of calculation in integrating step d, it is determined that build the cost of land of electrical changing station in zones of different；

F. regulation constraints and parameter situation, with Total Cost Ownership and cost of transportation minimal definition site selection model formula:

MinC=C₁+C₂

S.t.

$\underset{m\&Element;M}{\&Sigma;}{z}_m=1$

$\begin{array}{cc}{z}_{mn}\&le;\underset{m}{\&Sigma;}{x}_n& \&ForAll;m\&Element;M,n\&Element;N\end{array}$

$\underset{n}{\&Sigma;}{x}_n\&le;1$

x_n∈{0,1}

y_nk∈{0,1}

z_mn∈{0,1}

Wherein, C₁For operation cost, its formula is:S be in electrical changing station change electricity facility number；The use cost in soil shared by electrical changing station；e₁Cost average time (maintenance cost that comprises equipment, depreciable cost, human cost) of electricity facility is changed for separate unit；z_mFor electric automobile users all in demand region m, it is only capable of accepting the service of neighbouring site within a certain period of time；x_nFor building electrical changing station at some n place, candidate site；C₂For road transport cost, its formula is:M={m} is the set of demand region, and G={g} is the set of power transformation website, and N={n} is the set of electrical changing station candidate site point, H_mFor planning region in change electricity demand, d_mnFor changing the electricity demand point distance to candidate location point, d_ngFor the distance of candidate site point to transformer station, α, η is for travelling distance cost, y_ng,z_mnVariable is selected, wherein: y for 0-1_ngRepresent if candidate site point n selects transformer station g to be concentrated charging station, be then 1, be otherwise 0；z_mnRepresent if demand region m selects in the some n place acceptance service of candidate site, be then 1, be otherwise 0；

G. establish planning year, namely determine the electrical changing station siteselecting planning time, calculated the recoverable amount situation of this year motor vehicles with this by matching forecast model, thus the recoverable amount situation of electric automobile in this region can be determined by scholarly forecast ratio data；The data collected in step a, b, c, d and predict are substituted among site selection model respectively, obtains minimal overall operation cost and cost of transportation C by calculating；By meeting the set N={n} of the electrical changing station candidate site point of this minimum cost, the set G={g} of power transformation website screens, and is labeled in the planning map of city by each site point filtered out.

2. the electric automobile charging station Site planning method based on queueing theory algorithm according to claim 1, is characterized in that, uses the model of M/M/s in queueing theory to determine and best changes electric facility number s's in the region in method particularly includes:

Queue theory model is as follows:

$\left\{\begin{array}{cc}{\mathrm{\&lambda;p}}_0={\mathrm{\&mu;p}}_1& \\ {\mathrm{\&lambda;p}}_{n-1}+(n+1){\mathrm{\&mu;p}}_{n+1}=(\mathrm{\&lambda;}+n\mathrm{\&mu;})p_n& n\&le;s\\ {\mathrm{\&lambda;p}}_{n-1}+{\mathrm{s\&mu;p}}_{n+1}=(\mathrm{\&lambda;}+s\mathrm{\&mu;})p_n& n>s\end{array}\right.$

Wherein, p_nAccept to change the probability of electricity service for n electric automobile；N is the electric automobile quantity accepting to change electricity service；λ is the average arrival rate of electric automobile in electrical changing station, and computing formula isk₁For hundred kilometers of average current drains of electric automobile；k₂For the average course that electric automobile day travels；D_mIt it is the electric automobile recoverable amount in the region m of n-th electrical changing station point service；Q is electric automobile average cell capacity；T is the per day working time of electrical changing station；μ is the average service rate that electric automobile completes to change electric process, and including entering the station, time leaving from station and fill, change the electricity time, computing formula isT_i/oFor the time that electric automobile is required into and out of station；T_c/hAccept to change the average time of electricity service for electric automobile；

In system, each service indication is: change the efficiency of service of electricity facilityChange the utilization rate of electricity facilityCongestion lengthsAverage queue lengthChange residence time in electricity systemWaiting time

If filling, changing the electricity average total cost that produces within the unit interval of facility system is F (s), then

MinF (s)=e₁s+e₂L_s

Wherein, e₂For separate unit electric automobile cost average time, include changing electricity consumption, energy consumption cost, trip value, it is assumed that s is optimal service facility number, then have:

$\left\{\begin{array}{c}F(s-1)>F\left(s\right)\\ F(s+1)<F\left(s\right)\end{array}\right.;$

Substitute into above parameter, after arrangement:Obtain the difference of adjacent two of inequality the right and left successively, it may be determined that reach the number s of facility during optimum.

3. the electric automobile charging station Site planning method based on queueing theory algorithm according to claim 2, is characterized in that, the described factor affecting vehicles number development includes total output value per capita and road overall length.