CN106408452A - Optimal configuration method for electric vehicle charging station containing multiple distributed power distribution networks - Google Patents

Abstract

The optimal configuration method of the electric vehicle charging station with the multi-distributed power distribution network comprises the following steps: 1. defining a new concept of 'fuzzy service radius of EV charging station'; 2. redistributing membership of fuzzy service radius overlapping areas among a plurality of EV charging stations; 3. constructing an environment cost function considering DG influence; 4. constructing an annual average gain function, an annual average construction cost function and a multi-objective constraint condition in an optimization model; 5. constructing an optimization objective function with the annual average pure profit maximization as a target; 6. and solving the optimization model by adopting a PSO algorithm, and searching out an optimal configuration scheme for optimizing the adaptive value of the objective function through optimization iteration.

Description

Electric automobile charging station Optimal Configuration Method containing many distributed power distribution networks

Technical field

The present invention relates to the distributing rationally of a kind of electric automobile charging station addressing constant volume containing many distributed power distribution networks, Belong to electrical engineering and technical field of new energies.

Background technology

Distributed power source (Distributed Generator, DG) based on new energy technology and electric automobile (Electric vehicle, EV) obtains broad development in countries in the world.China arranges new energy power generation technology and electric automobile For emerging strategic industries, new forms of energy DG and EV will become the Developing mainstream of Science in Future in China, and many DG and EV access power distribution network in a large number Become inexorable trend.Electric automobile charging station is as important EV auxiliary facility, if unordered access power distribution network can cause in a large number Harmonic wave and current distortion problem.Electric automobile charging station is optimized with configuration significant.

At present, distribute related achievement in research rationally to electric automobile charging station, be mostly confined to the choosing to electrically-charging equipment The research of location principle, cost and operational mode aspect.Actually, on the one hand, the tradition of power distribution network directly used by electric automobile Electric energy charges, and the actual indirect carbon emission amount producing is low unlike conventional fuel oil automobile, when many DG are incorporated to power distribution network, needs to fill Divide and consider DG factor to improve to utilization of new energy resources rate, play the real low-carbon emission reduction effect of EV；On the other hand existing study into Fruit, using deterministically charging station service radius, does not consider interpersonal Subjective difference, lack flexible mechanism and Motility.Patent achievement aspect, although the achievement related to electric automobile charging station is more, is largely focused on EV charging station The design of this body structure, such as Application No. CN201610080929.9, CN201610080099.X, CN201610023420.0 divide Do not propose a kind of intelligent charging station for electric automobile, functional charging station, anti-steam corrosion charging station；Also has one Dispensing overweights Charge Management and the control of EV charging station, such as CN201410809367.8, and CN201510976719.3 proposes respectively A kind of charging station control system considering electric network protection and a kind of charging management method based on reservation of charging；And minority with regard to The achievement that EV charging station addressing constant volume is distributed rationally, such as CN201510627316.8, CN201310404628.3, The patent of invention of CN201210391040.4 propose respectively a kind of based on a determination that the site selecting method of sex service Radius Constraint condition, A kind of planing method considering transportation network and a kind of addressing constant volume method based on two benches optimization, but its extracting method is all not It is related to fuzzy service radius concept and do not take into full account the grid-connected impact of many DG.Patent of the present invention is directed to the addressing constant volume of EV charging station Problem is studied, and takes into full account that many DG are incorporated to the prospect of power distribution network it is proposed that the fuzzy service radius new ideas of EV charging station, Propose a kind of based on fuzzy service radius, meter and DG factor, traffic flow, the quality of power supply and construction cost EV charging station The new model that addressing constant volume is distributed rationally, and pass through particle cluster algorithm (Particle Swarm Optimization, PSO) The method of optimizing iterative optimal allocation.

Content of the invention

The present invention will overcome existing EV charging station Optimal Configuration Method can not be effectively applicable to containing many DG power distribution network and clothes Business radius lacks the problem of flexible mechanism it is proposed that the fuzzy service radius new ideas of EV charging station are it is proposed that a kind of be based on mould Paste service radius, meter and DG factor, traffic flow, the quality of power supply and construction cost EV charging station distribute rationally new model and Go out the method containing the configuration of many DG power distribution network EV charging station addressing constant volume of optimum by PSO Algorithm for Solving, and there is preferable warp Ji property and effect of optimization.

The present invention for achieving the above object it is proposed that a kind of using PSO Algorithm for Solving, can be effectively applicable to containing many points The EV charging station Optimal Configuration Method of cloth electrical power distribution net, described many distributed power sources refer to that new energy development utilizes and is distributed Multiple distributed power sources under formula generation technology background access power distribution network, and described Optimal Configuration Method refers to meeting multiple target about The best site selection of EV charging station and capacity configuration under the conditions of bundle.Described Optimal Configuration Method, comprises the following steps：

1st, " the fuzzy service radius of EV charging station " new ideas are defined；In the interpersonal cognizance hierarchy of consideration, there is mould Under conditions of paste property, characterize the coverage effect of EV charging station service radius with a degree of membership based on fuzzy theory, make charging The position stood meets the required distance facilitating user to charge；The degree of membership of the fuzzy service radius of single EV charging station is usedTable Show, the EV car owner characterizing j-th node location selects the probability of i-th charging station service, with j-th node location and i-th Charging station apart from d_ijFor independent variable, degree of membership span is in numerical value [0,1] interval；The membership function of single EV charging station Shown in expression such as formula (1) (2)；WhereinIt is the intermediate variable for replacing, θ₁For the minimum threshold of distance of service radius, θ₂ Maximal distance threshold for service radius；When node is in the θ of charging station₁In the range of degree of membership to take KB limit be 1, work as section Point is in the θ of charging station₁～θ₂Between scope, degree of membership is in that S characteristic declines, when node exceeds the θ of charging station₂Degree of membership during scope For 0；

2nd, redistribute the degree of membership of fuzzy service radius overlapping region between multiple EV charging stations；Fill when there are multiple EV Power station, then the degree of membership being in the overlapping part in the range of their service radius max-thresholds is necessarily drawn to again be divided Join, the problem overflowed for preventing from leading to degree of membership to add up due to charging station hypotelorism, distributive operation such as formula (3) institute Show；WhereinIt is the fuzzy service radius degree of membership after redistributing；

3rd, meter and the environmental cost function of DG impact are built；In view of the intermittence of new forms of energy DG generated output, EV charges Stand and can improve utilization of new energy resources rate with DG cooperation and reduce indirect carbon emission, the environmental cost function of structure meter and DG impact is such as Shown in following formula (4) (5)；Wherein C^enRepresent total average annual environmental cost, N is the expected sum building EV charging station, α is carbon emission The expense conversion rate administered, η is the average utilization of new forms of energy DG, ε₁For the conversion rate of energy consumption and distance, ε₂Indirect for charging station Carbon emission conversion rate；D_iFor the shortest feeder line distance of i-th charging station to DG,WithIt is respectively the average annual of i-th charging station Consume electric energy and charging device quantity；τ₁For year total when number, P_DGFor generation of electricity by new energy power, P_WConvey work(for new forms of energy to electrical network Rate；

4th, build the average annual revenue function in Optimized model, build cost function and multi-objective restriction condition every year；EV fills Power optimization configuration is while calculating acquisition higher average annual income in addition it is also necessary to the restriction considering Multiple factors includes EV number Amount, traffic flow, the quality of power supply, equipment cost and construction cost；

Step 401, builds shown in EV quantity and the constraints such as formula (6) (7) of traffic flow, wherein δ^trRepresent all filling Total EV flow that power station covers,For the vehicle flowrate of j-th node location, z^evRepresent the electric automobile market in planning region Occupation rate, N is the expected quantity building EV charging station, and M is the total nodes in planning region,For minimum EV flow restriction Value；

Step 402, builds the average annual revenue function based on EV flow, as shown in formula (8)；Wherein B^prFor all charging station years All incomes, λ₁The average consumption charged every time for each EV, λ₂The average power cost charging every time for EV, β₁For electric automobile The annual average time charging in charging station, β₂It is the operating cost conversion factor including personnel's wage and maintenance cost；

B^pr=β₁δ^tr(λ₁-λ₂)(1-β₂) (8)

Step 403, builds shown in the quality of power supply constraints such as formula (9-11) ensureing power supply reliability；V in formula_jFor The voltage magnitude of j node,WithIt is respectively its voltage magnitude lower limit and the upper limit；I_hFor the electric current of h article of circuit,For its maximum current limit value, H^LineFor feeder line sum；For the charge power of i-th charging station,For planning region Domain allows the peak power of the EV charging load of access；

Step 404, builds the constraints including cost of land, equipment cost and capital construction cost and builds generation every year Valency function, as shown in formula (12-14)；Subscript i of wherein variable represents corresponding i-th charging station,For land area,For The soil unit price of present position,For charging device quantity,For distribution transformer quantity,Other bases for charging station Construction cost；C_ΣBuild cost, T for average annual^yeFor the object run time limit, C^evFor charging device unit price, C^trDistribution transformer list Valency, λ₄Charge probability for EV maximum simultaneously；λ₅The ratio of electric power can be provided for multiple charging devices for single distribution transformer；

5th, build the optimization object function turning to target with average annual net profit maximum, as shown in formula (15)；Target letter in formula Number represents the average annual net profit of maximization, and the value of f is the adaptive value of optimization process, by average annual yield B^prDeduct average annual construction generation Valency C_ΣWith average annual environmental cost C^enObtain；

Max f=B^pr-C_Σ-C^en(15)

6th, adopt PSO Algorithm for Solving Optimized model, through optimizing iteration, the adaptive value searching out optimization object function is optimal Configuration scheme；

Step 601, population initializes；Generate primary population, the initial position of particle is random in planning region Assignment, initial velocity random assignment in its threshold range of particle；

Step 602, adaptive value calculates；The current position coordinates of population are mapped, is belonged to closest EV charging station both candidate nodes, calculate the adaptive value of each particle by formula (15)；

Step 603, more new individual extreme value and global extremum；For the particle meeting inequality constraints condition, if wherein depositing Once the optimal location finding better than this particle in the adaptive value of particle, then update its individual extreme value；If current global optimum Particle is better than the global extremum that up to the present they search, then update global extremum；

Step 604, the coordinate of each particle and speed are divided into horizontally and vertically two groups of data processings, by formula (16-19) Carry out particle state iteration update, and limit particle rapidity max-thresholds be v^max；Wherein w is inertia weight；c₁And c₂ For accelerated factor；r₁And r₂For the random number between [0,1]；The implication of upper and lower mark k, n, γ is n-th respectively during kth time iteration The γ particle of dimension, subscript k+1 represents next iteration,WithRepresent its horizontal and vertical coordinate respectively,With Represent its horizontal and vertical speed respectively,WithRepresent the horizontal and vertical coordinate of individual extreme value respectively,WithPoint Not Biao Shi global extremum horizontal and vertical coordinate；

Step 605, if iterationses reach maximum, enters step 606；Otherwise return to step 602 circulate operation；

Step 606, exports optimum results；By the particle decoding of optimal solution, export the EV charging station addressing position of its mapping And capacity configuration, the preferred plan distributed rationally as EV charging station.

Beneficial effects of the present invention are mainly manifested in：1st, " the fuzzy service radius of EV charging station " new ideas are defined, and Propose the redistribution method of fuzzy service radius overlapping region degree of membership between multiple EV charging stations；2nd, construct a kind of base In fuzzy service radius, meter and DG factor, traffic flow, the quality of power supply and construction cost EV charging station addressing constant volume optimize The new model of configuration；3rd, propose one kind and pass through PSO Algorithm for Solving Optimized model, draw containing the configuration of many DG power distribution network EV charging station The method of optimal case；4th, carried model and method can seek average annual net income while taking into account environmental cost and construction cost Profit maximizes, and can provide technical support for the joint development of new energy power generation technology and ev industry.

Brief description

Fig. 1 is the general frame of the inventive method.

Fig. 2 is the function curve diagram of fuzzy service radius degree of membership.

Fig. 3 is the topology diagram of the IEEE 34 node power distribution net containing four DG.

Fig. 4 presses, for new forms of energy DG, the average power curve figure dividing for 24 hours.

Fig. 5 is the adaptive optimal control value convergence process figure in PSO optimizing iterative process.

Specific embodiment

With reference to embodiment and accompanying drawing, the present invention is described in further detail, but embodiments of the present invention do not limit In this.In embodiment, a kind of the general frame of the electric automobile charging station Optimal Configuration Method containing many distributed power distribution networks is such as Shown in accompanying drawing 1, comprise the following steps：

1st, " the fuzzy service radius of EV charging station " new ideas are defined；In the interpersonal cognizance hierarchy of consideration, there is mould Under conditions of paste property, characterize the coverage effect of EV charging station service radius with a degree of membership based on fuzzy theory, make charging The position stood meets the required distance facilitating user to charge；The degree of membership of the fuzzy service radius of single EV charging station is usedTable Show, the EV car owner characterizing j-th node location selects the probability of i-th charging station service, with j-th node location and i-th Charging station apart from d_ijFor independent variable, degree of membership span is in numerical value [0,1] interval；The membership function of single EV charging station Shown in expression such as formula (1) (2)；The characteristic curve of fuzzy service radius is as shown in Figure 2；

2nd, redistribute the degree of membership of fuzzy service radius overlapping region between multiple EV charging stations；Fill when there are multiple EV Power station, then the degree of membership being in the overlapping part in the range of their service radius max-thresholds is necessarily drawn to again be divided Join, the problem overflowed for preventing from leading to degree of membership to add up due to charging station hypotelorism, distributive operation such as formula (3) institute Show；

3rd, meter and the environmental cost function of DG impact are built；In view of the intermittence of new forms of energy DG generated output, EV charges Stand and can improve utilization of new energy resources rate with DG cooperation and reduce indirect carbon emission, the environmental cost function of structure meter and DG impact is such as Shown in following formula (4) (5)；

4th, build the average annual revenue function in Optimized model, build cost function and multi-objective restriction condition every year；EV fills Power optimization configuration is while calculating acquisition higher average annual income in addition it is also necessary to the restriction considering Multiple factors includes EV number Amount, traffic flow, the quality of power supply, equipment cost and construction cost；

Step 401, builds shown in EV quantity and the constraints such as formula (6) (7) of traffic flow；

Step 402, builds the average annual revenue function based on EV flow, as shown in formula (8)；

Step 403, builds shown in the quality of power supply constraints such as formula (9-11) ensureing power supply reliability；

Step 404, builds the constraints including cost of land, equipment cost and capital construction cost and builds generation every year Valency function, as shown in formula (12-14)；

5th, build the optimization object function turning to target with average annual net profit maximum, as shown in formula (15)；

6th, adopt PSO Algorithm for Solving Optimized model, through optimizing iteration, the adaptive value searching out optimization object function is optimal Configuration scheme；

Step 601, population initializes；Generate primary population, the initial position of particle is random in planning region Assignment, initial velocity random assignment in its threshold range of particle；

Step 602, adaptive value calculates；The current position coordinates of population are mapped, is belonged to closest EV charging station both candidate nodes, calculate the adaptive value of each particle by formula (15)；

Step 603, more new individual extreme value and global extremum；For the particle meeting inequality constraints condition, if wherein depositing Once the optimal location finding better than this particle in the adaptive value of particle, then update its individual extreme value；If current global optimum Particle is better than the global extremum that up to the present they search, then update global extremum；

Step 604, the coordinate of each particle and speed are divided into horizontal and vertical two groups of data processings, press formula (16- respectively 19) carry out particle state iteration update, and limit particle rapidity max-thresholds be v^max；

Step 605, if iterationses reach maximum, enters step 606；Otherwise return to step 602 circulate operation；

Step 606, exports optimum results；By the particle decoding of optimal solution, export the EV charging station addressing position of its mapping And capacity configuration, the preferred plan distributed rationally as EV charging station.

Below with IEEE 34 node power distribution net as embodiment, further illustrate the operating process of the present invention, add four DG It is being incorporated to the topological structure of power distribution network, using DG₁～DG₄Four DG of name, their grid-connected position is as shown in Figure 3.Four new forms of energy DG power generation characteristics, wherein press the average power curve dividing for 24 hours as shown in Figure 4.Planning region gross area 2110.29km², West and east span 146.65km, north and south span 14.39km；Use MATLAB/simulink modeling and simulating, in constraints in step 4 Basic data, including each node Power system load data as shown in table 1, special bus flow, capital construction cost, soil class Type, land price data are as shown in table 2.

Table 1 Power system load data

The each node data of table 2

In this example, in reference standard file《Electric car electric energy is for giving safeguards technique specification：Charging station》Basis On, the parameter in carried Optimized model and threshold value are reasonably arranged, wherein dimensionless and with " -- " mark, as table 3 institute Show.

The related parameters optimization table of table 3

For the node power distribution net of IEEE 34 shown in Fig. 3, fuzzy service radius degree of membership is set up based on step 1 and step 2 Computational methods, recycle step 3-4 sets up the constraints that EV charging station is distributed rationally in mathematical model, sets according to table 3 parameter Put the optimization object function carrying out calculation procedure 5 as adaptive value, and changed using the PSO optimizing that MATLAB software carries out step 6 For process, solving-optimizing allocation models.Wherein population parameter setting：Scale chooses 50, and accelerated factor is c₁=c₂=1.2, speed Degree limits v^max=600, maximum iteration time 200 times.In iterative process global extremum adaptive value convergence process as shown in figure 5, Effect after optimization improves 41.25 ten thousand yuan/year with respect to initial configuration.After reaching maximum iteration time, distributed rationally It is { 808,818,852,844,860 } that result is mapped to system node；It is as shown in table 4 that the output of optimal allocation optimizes content.

Table 4 exports optimal result

Sample calculation analysis show, institute of the present invention extracting method effectively can be optimized to EV charging station in containing many DG power distribution network Configuration, makes optimal allocation result have flexible mechanism based on the fuzzy service radius degree of membership of definition, also can take into account new forms of energy DG Utilization rate, and there is preferable economy and effect of optimization.

As described above, just can preferably realize the present invention, above-described embodiment is only the exemplary embodiments of the present invention, not uses To limit the practical range of the present invention, i.e. all impartial changes made according to present invention and modification, all will for right of the present invention Scope required for protection is asked to be covered.

Claims (1)

1. contain the electric automobile charging station Optimal Configuration Method of many distributed power distribution networks, comprise the steps：

Step 1, definition " the fuzzy service radius of EV charging station " new ideas；In the interpersonal cognizance hierarchy of consideration, there is mould Under conditions of paste property, characterize the coverage effect of EV charging station service radius with a degree of membership based on fuzzy theory, make charging The position stood meets the required distance facilitating user to charge；The degree of membership of the fuzzy service radius of single EV charging station is usedTable Show, the EV car owner characterizing j-th node location selects the probability of i-th charging station service, with j-th node location and i-th Charging station apart from d_ijFor independent variable, degree of membership span is in numerical value [0,1] interval；The membership function of single EV charging station Shown in expression such as formula (1) (2)；WhereinIt is the intermediate variable for replacing, θ₁For the minimum threshold of distance of service radius, θ₂ Maximal distance threshold for service radius；When node is in the θ of charging station₁In the range of degree of membership to take KB limit be 1, work as section Point is in the θ of charging station₁～θ₂Between scope, degree of membership is in that S characteristic declines, when node exceeds the θ of charging station₂Degree of membership during scope For 0；

${\widehat{\mathrm{\&mu;}}}_{ij}^{cd}=\left\{\begin{array}{cc}1,& d_{ij}\&le;{\mathrm{\&theta;}}_1\\ \frac{e^{-d_{ij}^{*}}}{1+e^{-d_{ij}^{*}}},& {\mathrm{\&theta;}}_1<d_{ij}<{\mathrm{\&theta;}}_2\\ 0,& {\mathrm{\&theta;}}_2\&le;d_{ij}\end{array}\right.---\left(1\right)$

$d_{ij}^{*}=8(d_{ij}-{\mathrm{\&theta;}}_1)/({\mathrm{\&theta;}}_2-{\mathrm{\&theta;}}_1)-4---\left(2\right)$

Step 2, redistribute between multiple EV charging stations the degree of membership of fuzzy service radius overlapping region；Fill when there are multiple EV Power station, then the degree of membership being in the overlapping part in the range of their service radius max-thresholds is necessarily drawn to again be divided Join, the problem overflowed for preventing from leading to degree of membership to add up due to charging station hypotelorism, distributive operation such as formula (3) institute Show；WhereinIt is the fuzzy service radius degree of membership after redistributing；

${\mathrm{\&mu;}}_{ij}^{cd}=\left\{\begin{array}{cc}{\widehat{\mathrm{\&mu;}}}_{ij}^{cd}/\underset{i=1}{\overset{N}{\&Sigma;}}{\widehat{\mathrm{\&mu;}}}_{ij}^{cd},& \underset{i=1}{\overset{N}{\&Sigma;}}{\widehat{\mathrm{\&mu;}}}_{ij}^{cd}>1\\ {\widehat{\mathrm{\&mu;}}}_{ij}^{cd},& \underset{i=1}{\overset{N}{\&Sigma;}}{\widehat{\mathrm{\&mu;}}}_{ij}^{cd}\&le;1\end{array}\right.---\left(3\right)$

The environmental cost function of step 3, structure meter and DG impact；In view of the intermittence of new forms of energy DG generated output, EV charges Stand and can improve utilization of new energy resources rate with DG cooperation and reduce indirect carbon emission, the environmental cost function of structure meter and DG impact is such as Shown in following formula (4), (5)；Wherein C^enRepresent total average annual environmental cost, N is the expected sum building EV charging station, α arranges for carbon Put the expense conversion rate of improvement, η is the average utilization of new forms of energy DG, ε₁For the conversion rate of energy consumption and distance, ε₂For between charging station Connect carbon emission conversion rate；D_iFor the shortest feeder line distance of i-th charging station to DG,WithIt is respectively i-th charging station Average annual consumption electric energy and charging device quantity；τ₁For year total when number, P_DGFor generation of electricity by new energy power, P_WDefeated to electrical network for new forms of energy Send power；

$C^{en}=\underset{i=1}{\overset{N}{\&Sigma;}}\mathrm{\&alpha;}(1-\mathrm{\&eta;})({\mathrm{\&epsiv;}}_1{D}_i{N}_i^{ev}{\mathrm{\&tau;}}_1+{\mathrm{\&epsiv;}}_2{E}_i^{cd})---\left(4\right)$

$\mathrm{\&eta;}=\frac{{\&Integral;}_0^{{\mathrm{\&tau;}}_1}{P}_{W}dt}{{\&Integral;}_0^{{\mathrm{\&tau;}}_1}{P}_{DG}dt}\&times;100\%---\left(5\right)$

Step 4, the average annual revenue function building in Optimized model, average annual construction cost function and multi-objective restriction condition；EV fills Power optimization configuration is while calculating acquisition higher average annual income in addition it is also necessary to the restriction considering Multiple factors includes EV number Amount, traffic flow, the quality of power supply, equipment cost and construction cost；

Step 401, builds shown in EV quantity and the constraints such as formula (6) (7) of traffic flow, wherein δ^trRepresent all charging stations The total EV flow covering,For the vehicle flowrate of j-th node location, z^evRepresent that the electric automobile market in planning region accounts for There is rate, N is the expected quantity building EV charging station, M is the total nodes in planning region,For minimum EV flow restriction value；

${\mathrm{\&delta;}}^{tr}=\underset{i=1}{\overset{N}{\&Sigma;}}\underset{j=1}{\overset{M}{\&Sigma;}}{z}^{ev}{\mathrm{\&mu;}}_{ij}^{cd}{\mathrm{\&delta;}}_j^{tr}---\left(6\right)$

$\underset{j=1}{\overset{M}{\&Sigma;}}{z}^{ev}{\mathrm{\&mu;}}_{ij}^{cd}{\mathrm{\&delta;}}_j^{tr}\&GreaterEqual;{\mathrm{\&delta;}}_{\min }^{tr},i=1,2...N---\left(7\right)$

Step 402, builds the average annual revenue function based on EV flow, as shown in formula (8)；Wherein B^prReceive every year for all charging stations Benefit, λ₁The average consumption charged every time for each EV, λ₂The average power cost charging every time for EV, β₁Annual for electric automobile The average time charging in charging station, β₂It is the operating cost conversion factor including personnel's wage and maintenance cost；

B^pr=β₁δ^tr(λ₁-λ₂)(1-β₂) (8)

Step 403, builds shown in the quality of power supply constraints such as formula (9-11) ensureing power supply reliability；V in formula_jSave for j-th The voltage magnitude of point,WithIt is respectively its voltage magnitude lower limit and the upper limit；I_hFor the electric current of h article of circuit,For Its maximum current limit value, H^LineFor feeder line sum；P_i ^evFor the charge power of i-th charging station,Permit for planning region Permitted the peak power of the EV charging load of access；

$V_j^{\min }\&le;V_j\&le;V_j^{max},j=1,2...M---\left(9\right)$

$\left|I_h\right|\&le;I_h^{\max },h=1,2...,H^{Line}---\left(10\right)$

$\underset{i=1}{\overset{N}{\&Sigma;}}{P}_i^{ev}\&le;P_{max}^{ev}---\left(11\right)$

Step 404, builds the constraints including cost of land, equipment cost and capital construction cost and average annual construction cost letter Number, as shown in formula (12-14)；Subscript i of wherein variable represents corresponding i-th charging station,For land area,For residing The soil unit price of position,For charging device quantity,For distribution transformer quantity,Other capital constructions for charging station Cost；C_ΣBuild cost, T for average annual^yeFor the object run time limit, C^evFor charging device unit price, C^trDistribution transformer unit price, λ₄ Charge probability for EV maximum simultaneously；λ₅The ratio of electric power can be provided for multiple charging devices for single distribution transformer；

$C_{\mathrm{\&Sigma;}}=\frac{1}{T^{ye}}\underset{i=1}{\overset{N}{\&Sigma;}}\&lsqb;S_i^{ev}(C_i^{gr}+C_i^{bu})+N_i^{ev}{C}^{ev}+N_i^{tr}{C}^{tr}\&rsqb;---\left(12\right)$

$N_i^{ev}\&GreaterEqual;{\mathrm{\&lambda;}}_4\underset{j=1}{\overset{M}{\&Sigma;}}{z}^{ev}{\mathrm{\&mu;}}_{ij}^{cd}{\mathrm{\&delta;}}_j^{tr},i=1,2...N---\left(13\right)$

$N_i^{tr}\&GreaterEqual;{\mathrm{\&lambda;}}_5{N}_i^{ev},i=1,2...N---\left(14\right)$

Step 5, structure turn to the optimization object function of target with average annual net profit maximum, as shown in formula (15)；Target letter in formula Number represents the average annual net profit of maximization, and the value of f is the adaptive value of optimization process, by average annual yield B^prDeduct average annual construction generation Valency C_ΣWith average annual environmental cost C^enObtain；

Max f=B^pr-C_Σ-C^en(15)

Step 6, adopt PSO Algorithm for Solving Optimized model, through optimizing iteration, the adaptive value searching out optimization object function is optimal Configuration scheme；

Step 601, population initializes；Generation primary population, initial position random assignment in planning region of particle, The random assignment in its threshold range of the initial velocity of particle；

Step 602, adaptive value calculates；The current position coordinates of population are mapped, is belonged to closest EV Charging station both candidate nodes, are calculated the adaptive value of each particle by formula (15)；

Step 603, more new individual extreme value and global extremum；For the particle meeting inequality constraints condition, if wherein there is grain The optimal location that the adaptive value of son once found better than this particle, then update its individual extreme value；If current global optimum's particle Better than the global extremum that up to the present they search, then update global extremum；

Step 604, the coordinate of each particle and speed are divided into horizontally and vertically two groups of data processings, carry out by formula (16-19) The iteration of particle state updates, and to limit the max-thresholds of particle rapidity be v^max；Wherein w is inertia weight；c₁And c₂For adding The fast factor；r₁And r₂For the random number between [0,1]；The implication of upper and lower mark k, n, γ is in the n-th dimension respectively during kth time iteration The γ particle, subscript k+1 represents next iteration,WithRepresent its horizontal and vertical coordinate respectively,WithRespectively Represent its horizontal and vertical speed,WithRepresent the horizontal and vertical coordinate of individual extreme value respectively,WithTable respectively Show the horizontal and vertical coordinate of global extremum；

${\stackrel{\&OverBar;}{v}}_{\mathrm{\&gamma;}n}^{k+1}=w\&CenterDot;{\stackrel{\&OverBar;}{v}}_{\mathrm{\&gamma;}n}^k+c_1{r}_1({\stackrel{\&OverBar;}{p}}_{\mathrm{\&gamma;}n}^k-x_{\mathrm{\&gamma;}n}^k)+c_2{r}_2({\stackrel{\&OverBar;}{p}}_{gn}^k-x_{\mathrm{\&gamma;}n}^k)---\left(16\right)$

$v_{\mathrm{\&gamma;}n}^{k+1}=w\&CenterDot;v_{\mathrm{\&gamma;}n}^k+c_1{r}_1(p_{\mathrm{\&gamma;}n}^k-y_{\mathrm{\&gamma;}n}^k)+c_2{r}_2(p_{gn}^k-y_{\mathrm{\&gamma;}n}^k)---\left(17\right)$

$x_{\mathrm{\&gamma;}n}^{k+1}={\stackrel{\&OverBar;}{v}}_{\mathrm{\&gamma;}n}^{k+1}+x_{\mathrm{\&gamma;}n}^k---\left(18\right)$

$y_{\mathrm{\&gamma;}n}^{k+1}=v_{\mathrm{\&gamma;}n}^{k+1}+y_{\mathrm{\&gamma;}n}^k---\left(19\right)$

Step 605, if iterationses reach maximum, enters step 606；Otherwise return to step 602 circulate operation；

Step 606, exports optimum results；By the particle decoding of optimal solution, export EV charging station addressing position and the appearance of its mapping Amount configuration, the preferred plan distributed rationally as EV charging station.