CN110276517A - A kind of electric automobile charging station site selecting method based on MOPSO algorithm - Google Patents
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Abstract
The invention discloses a kind of electric automobile charging station site selecting method based on MOPSO, include: S1, construct and cost, user's round-trip charging station time and charging station service range population are built a station as objective function using charging station, constrains Model for Multi-Objective Optimization using charging station capacity-constrained and charging station variation as the charging station addressing of constraint condition；S2, design are solved based on the multi-objective particle of competition and teaching and learning mechanics, construct elite population by competition mechanism, other individuals learn to generate progeny population to elite individual by teaching and learning mechanics；S3, dynamic processing is carried out to two class Complex Constraints conditions using adaptive constrain-handling technique, and outstanding feasible solution is selected to enter next-generation population；S4, judge whether current iteration number reaches maximum number of iterations, if so, the optimal feasible addressing scheme disaggregation of output；Otherwise, step S2 is executed, in this way, finally obtaining the optimal feasible program under electric automobile charging station addressing different demands.
Description
Technical field
The present invention relates to electric automobile charging station technical field more particularly to a kind of charging station choosings based on MOPSO algorithm
Location method.
Background technique
Along with the high speed development of global economy, the problems such as energy shortage, environmental pollution, is more serious.Environlnental logistics are to drop
Low environmental pollution, reduction resource consumption are target, realize sustainable economic development, are advocated by national governments and international organization
It leads.Electric car gradually becomes the new energy vehicles important in environlnental logistics dispatching because of the significant advantage of clean energy-saving.Its
In, the Layout of electrically-charging equipment has significant impact to the development of electric car.Electrically-charging equipment construction is improper, will lead to charging
Situations such as standing idle or excessively crowded, causing poor user experience, the wasting of resources, to influence the popularization of electric car and make
With.In addition, electric automobile charging station addressing is improper also to bring many negative effects to local public electric wire net, as voltage deviation,
Power loss etc..
Due to the characteristic of electric car, user is larger for the demand of electric energy supplement in the short time, needs to guarantee electronic vapour
Vehicle can go to rapidly charging station to carry out electricity supplement when the power is insufficient；And in charging, need to guarantee user's
Wait the charging time most short, to improve user to the usage experience of electric car.In conclusion the planning construction of charging station relates to
And many factors, it should consider the cost of building a station of charging station, consider the interests of automobile user again.
Summary of the invention
Technical problems based on background technology, the invention proposes a kind of electric cars based on MOPSO algorithm to fill
Power station site selecting method；
A kind of electric automobile charging station site selecting method based on MOPSO algorithm proposed by the present invention, comprising:
S1, according to charging station related data in planning region, building is built a station cost, user's round-trip charging station time with charging station
It is objective function with charging station service range population, using charging station capacity-constrained and charging station variation as constraint condition
Charging station addressing constrains Model for Multi-Objective Optimization；
S2, using based on competition and teaching and learning mechanics multi-objective particle to this Model for Multi-Objective Optimization into
Row solves, and chooses the elite population in current population by competition mechanism, other individuals are by teaching and learning mechanics into elite population
Individual study, generates progeny population；
S3, dynamic processing is carried out to Complex Constraints condition using adaptive constrain-handling technique, and selected outstanding feasible
Solution enters next-generation population；
S4, judge whether current iteration number reaches maximum number of iterations, when the judgment result is yes, export optimal addressing
Scheme disaggregation；Otherwise, step S2 is executed.
Preferably, step S1 is specifically included:
S11, charging station data in planning region are obtained, charging station data include: that electric car is per day in the planning region
It charges probability, planning region land price, electric automobile during traveling average speed in planning region, charging station service range covering in planning region
Population；Maximum number of iterations is set；
S12, building charging station addressing constrain Model for Multi-Objective Optimization:
MINBcost=NLPL+NCPC,
Wherein, BcostFor cost of building a station, NLAnd PLRespectively land area and unit price, NCAnd PCThe respectively number of charging pile
And unit price, XiIt indicates whether to build charging station, X in this positioni=1 indicates construction, Xi=0 indicates not build, PopiIt is covered for charging station
The population of lid, UcostFor user's two-way time, NPMiddle N is electric automobile charging station set, and P is the probability of electric car charging,
dijIt is the average speed that electric car travels in this region for demand point j to the linear distance of charging station i, v；
S13, be averaged speed according to electric automobile during traveling in the per day charging probability of electric car, planning region land price, planning region
The population of charging station service range covering calculates separately the target function value of each individual in degree, planning region and constraint is disobeyed
Converse value, the binding occurrence are that charging station capacity-constrained and variation constrain,
Charging station capacity-constrained Wmax:Wherein XiIt indicates whether to build charging station, X in this positioni=1 table
Show construction, Xi=0 indicates not build, WiFor the maximum capacity of the position i charging station that can be built；
Variation constrains a:Wherein, UiFor the voltage of position i, UNFor the voltage rating of distribution network,
A is that the maximum voltage allowed deviates.
Preferably, step S2 is specifically included:
S21, all population at individual are carried out on all objective functions with non-dominated ranking, and non-branch is divided to all individuals
With rank, the elite population that Population Size is n is constructed according to non-dominated ranking grade, wherein n is the 1/5 of Population Size；
S22, two elite individuals are selected from elite population at random, two elite individuals is enabled to be at war with, selected current
The teacher of individual, then individual learns to teacher, generates offspring individual；
S23, judge whether offspring individual number reaches preset quantity, when the judgment result is yes, offspring individual is merged
At progeny population；Otherwise, step S22 is executed.
Preferably, in step S22, described two elite individuals are at war with, specifically:
Pass through formula pw=max (c1,c2),
Make two
A elite individual is at war with, wherein pwFor victor, e is the ratio between current iteration number and maximum number of iterations, e ∈ [0,1], θ
For the angle between selected elite individual and selected population, fx1、fx2And fxiRespectively selected two elite individuals, selected population
The sum of all objective functions of individual, CV1And CV2The constraint violation value of respectively selected elite individual.
Preferably, in step S22, the individual learns to teacher, specifically:
Pass through formula v 'i=r1vi+r2(pw-pi)+(1-r2)(pl-pi), p 'i=pi+v′iIndividual is carried out to learn to teacher,
Wherein, r1、r2For random number, and r1, r2∈ (0,1), v 'iFor i-th of updated velocity amplitude of individual, p 'iMore for i-th of individual
Position after new, plFor the individual of competition failure.
It is preferably, described that dynamic processing is carried out to Complex Constraints condition using adaptive constrain-handling technique in step S3,
It specifically includes:
Construct the constraint violation degree L of charging station capacity and variationcapAnd LV, total constraint violation degree of a solution are as follows: CV
=ωcap·Lcap+ωV·LV, wherein ωcapAnd ωVRespectively indicate the weight of charging station capacity and variation constraint, ωcap+
ωV=1；
Adaptive critic is carried out to feasible solution and infeasible solution according to different phylogenetic scales, wherein dynamic change is not
The expression formula of Feasible degree threshold value η:
η=CVmean×e-FR×(g/G)
Wherein, CVmeanFor kind of a group mean constraint violation degree, g is current iteration number, and G is maximum number of iterations；Feasible solution
Ratio FR=NF/ NP, NFFor feasible solution number, NP is population scale.
The present invention considers three aspect of Vehicules Electr Sas Soc D., Utilities Electric Co. and user when carrying out charging station addressing
Interests, using cost of building a station, period of reservation of number and service range population as the objective function of this method, with charging station capacity
Constraint and variation are constrained to constraint function, more to charging station addressing using the MOPSO algorithm based on competition and teaching and learning mechanics
A target carries out while optimizing, and combining adaptive constrain-handling technique handles two kinds of Complex Constraints, obtains electronic vapour
Optimal feasible program under vehicle charging station addressing different demands.
Detailed description of the invention
Fig. 1 is a kind of process signal of electric automobile charging station site selecting method based on MOPSO algorithm proposed by the present invention
Figure；
Fig. 2 is the population distribution figure of charging station and planning region in planning region in the embodiment of the present invention；
Fig. 3 is the schematic diagram of selected charging station when building 3 charging stations in the embodiment of the present invention；
Fig. 4 is the schematic diagram of selected charging station when building 4 charging stations in the embodiment of the present invention；
Fig. 5 is the schematic diagram of selected charging station when building 5 charging stations in the embodiment of the present invention.
Specific embodiment
Referring to figs. 1 to Fig. 5, a kind of charging station site selecting method based on MOPSO algorithm proposed by the present invention, comprising:
Step S1, according to charging station related data in planning region, building is built a station cost, the round-trip charging station of user with charging station
Time and charging station service range population are objective function, are constraint item with charging station capacity-constrained and charging station variation
The charging station addressing of part constrains Model for Multi-Objective Optimization.
This step specifically includes:
S11, charging station data in planning region are obtained, charging station data include: that electric car is per day in the planning region
It charges probability, planning region land price, electric automobile during traveling average speed in planning region, charging station service range covering in planning region
Population；Maximum number of iterations is set；
S12, building charging station constrain Model for Multi-Objective Optimization:
MINBcost=NLPL+NCPC,
Wherein, BcostFor cost of building a station, NLAnd PLRespectively land area and unit price, NCAnd PCThe respectively number of charging pile
And unit price, XiIt indicates whether to build charging station, X in this positioni=1 indicates construction, Xi=0 indicates not build, PopiIt is covered for charging station
The population of lid, UcostFor user's two-way time, NPMiddle N is electric automobile charging station set, and P is the probability of electric car charging,
dijIt is the average speed that electric car travels in this region for demand point j to the linear distance of charging station i, v；
S13, be averaged speed according to electric automobile during traveling in the per day charging probability of electric car, planning region land price, planning region
The population of charging station service range covering calculates separately the target function value of each individual in degree, planning region and constraint is disobeyed
Converse value, the binding occurrence are that charging station capacity-constrained and variation constrain,
Charging station capacity-constrained Wmax:Wherein XiIt indicates whether to build charging station, X in this positioni=1 table
Show construction, Xi=0 indicates not build, WiFor the maximum capacity of the position i charging station that can be built；
Variation constrains a:Wherein, UiFor the voltage of position i, UNFor the voltage rating of distribution network,
A is that the maximum voltage allowed deviates.
In concrete scheme, using multi-objective particle (Multi-objective Particle Swarm
Optimization, MOPSO) the charging station addressing of building constraint multi-objective optimization question is solved, in conjunction with problem characteristic,
Design is based on competition mechanism (Competitive Mechanism) and teaching and learning mechanics (Teaching-Learning-Based
Optimization Mechanism) high-quality progeny population generation method；
The required expense spent of building a station is considered in setting objective function, the population in charging station coverage area,
And the case where user's round-trip electric automobile charging station, i.e. optimization are built a station cost and user's two-way time, them is made to reach minimum,
Its mathematical model is as follows:
MINBcost=NLPL+NCPC,It is examined when constraint function is arranged
Consider charging station capacity-constrained and variation constraint, total charging capacity of charging station should be able to meet all chargings of planning region
Demand, the addition of charging station, the loss that will lead to distribution network increases, and can change the voltage's distribiuting of distribution network, Jin Erying
The power quality supplied user is rung, therefore, variation should limit within limits:
Charging station capacity-constrained Wmax:Variation constrains a:
Step S2, chooses the elite population in population, and individual learns to elite population individual and generates progeny population.
This step specifically includes:
S21, all population at individual are carried out on all objective functions with non-dominated ranking, and non-branch is divided to all individuals
With rank, the elite population that Population Size is n is constructed according to non-dominated ranking grade, wherein n is the 1/5 of Population Size；
S22, two elite individuals are selected from elite population at random, two elite individuals is enabled to be at war with, selected current
The teacher of individual, then individual learns to teacher, generates offspring individual；
Wherein, described two elite individuals are at war with, specifically:
Pass through formula pw=max (c1,c2),
Make two
A elite individual is at war with, wherein pwFor victor, e is the ratio between current iteration number and maximum number of iterations, e ∈ [0,1], θ
For the angle between selected elite individual and selected individual, fx1、fx2And fxiRespectively selected two elite individuals, selected population
The sum of all objective functions of individual, CV1And CV2The constraint violation degree of respectively selected two elite individuals；
Wherein, the individual learns to teacher, specifically:
Pass through formula v 'i=r1vi+r2(pw-pi)+(1-r2)(pl-pi), p 'i=pi+v′iIndividual is carried out to learn to teacher,
Wherein, r1、r2For random number, and r1, r2∈ (0,1), viFor i-th of updated velocity amplitude of individual, p 'iMore for i-th of individual
Position after new, plFor the individual of competition failure.
S23, judge whether offspring individual number reaches preset quantity, when the judgment result is yes, offspring individual is merged
At progeny population；Otherwise, step S22 is executed.
In concrete scheme, the MOPSO algorithm improvement mode of PSO algorithm picks local optimum individuals is improved, accelerates to calculate
The convergence rate of method；Simultaneously be further introduced into teaching and learning mechanics, to obtain diversity Pareto disaggregation abundant, by by population into
Row non-dominated ranking, to pick out n elite individual, wherein n is the 1/5 of Population Size, and by individual from this n elite
In arbitrarily select two individuals and be at war with, respectively each individual selects their teacher, competes formula are as follows:
pw=max (c1,c2),
Make two
A elite individual is at war with；
Meanwhile the study mechanism that population at individual learns to teacher are as follows:
v′i=r1vi+r2(pw-pi)+(1-r2)(pl-pi), p 'i=pi+v′i。
Step S3 carries out dynamic processing to Complex Constraints condition using adaptive constrain-handling technique, and selects outstanding
Feasible solution enters next-generation population.
This step specifically includes:
The constraint violation degree of S31, construction charging station capacity and variation are respectively LcapAnd LV, total constraint of a solution
Violation degree are as follows:
CV=ωcap·Lcap+ωV·LV
Wherein, ωcapAnd ωVRespectively indicate the weight of charging station capacity and variation constraint, ωcap+ωV=1；
Weight: initial time is updated, ω is setcap=ωV=1/2, indicate that both constraint condition importance are identical, it
Afterwards in an iterative process, the weight of constraint condition is updated based on constraint violation degree average value, wherein certain class constraint violation degree is average
Value shows that more greatly this constrains lower population at individual farther out from able state, need to increase corresponding weight value to give more computing resources；Instead
It, constraint average value is smaller to show that this constrains lower population at individual closer to able state, can reduce corresponding weight, thus can
The significance level of both constraint conditions is adaptively adjusted.
S32, feasible solution and infeasible solution are adaptively evaluated according to different phylogenetic scales, designs a dynamic
The infeasibility degree threshold value η of variation, expression formula are as follows:
η=CVmean×e-FR×(g/G)
Wherein, CVmeanFor kind of a group mean constraint violation degree, g is current iteration number, and G is maximum number of iterations；Feasible solution
Ratio FR=NF/ NP, NFFor feasible solution number, NP is population scale.
Step S4, judges whether current iteration number reaches maximum number of iterations, when the judgment result is yes, exports optimal
Addressing scheme disaggregation；Otherwise, step S2 is executed.
In concrete scheme, judge whether current iteration number has reached maximum number of iterations, if reaching, algorithm stops
Only, optimal feasible addressing scheme disaggregation is exported, otherwise goes to step 2, present embodiment accelerates convergence rate using competition mechanism；
Increase the diversity of Pareto disaggregation using teaching and learning mechanics.
Embodiment:
By taking the planning of somewhere electric automobile charging station as an example, which has 10 node road network shown in Fig. 2；
According to the data statistics of the Chinese net of detection at a low price, the average level of current commercialization land price is 7000 yuan；One charging
The price of stake is 20000 yuan；The odd-numbered day charging probability of electric car is 0.05, and the average overall travel speed in planning region is 40km/
h；Alternative charging station shares 4 classes, and maximum capacity is respectively 0.1MW, 0.2MW, 0.3MW, 0.4MW, and voltage is maximum allowable
Offset α is set as 10%.
It is compared by the file to table 1, it can be deduced that with increasing for construction charging station number, cost of building a station also can
It gradually increases, service range covers number and becomes more, and average queuing time shortens, and automobile user satisfaction is higher, still
As construction charging station number increases, total capacity needed for charging station increases, and variation also can be excessive, this will run power distribution network
Huge pressure is caused, electrical power distribution quality is influenced, is unfavorable for the economical operation of power grid, in fact, when construction charging station number
When being 4, cost of building a station increasing degree is minimum, and service range covering number increases at most, and average queuing time reduces most；
When charging station number increases to 5, since its variation and charging station total capacity can all increase, in order to what is guaranteed
Optimal solution can satisfy constraint condition, and the construction selection range of charging station will reduce to a certain extent, eventually lead to its choosing
The service range number for the charging station selected is fewer than building the case where charging station number is 4, therefore from the point of view of comprehensively considering, build charging station
When number is 4, it can best meet the interests of three aspect of electric car formula, Utilities Electric Co. and user.
Optimal objective situation under the different addressing quantity of table 1
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Anyone skilled in the art in the technical scope disclosed by the present invention, according to the technique and scheme of the present invention and its
Inventive concept is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (6)
1. a kind of charging station site selecting method based on MOPSO algorithm characterized by comprising
S1, according to charging station related data in planning region, building is built a station with charging station cost, user's round-trip charging station time and fills
Power station service range population is objective function, using charging station capacity-constrained and charging station variation as the charging of constraint condition
Addressing of standing constrains Model for Multi-Objective Optimization；
S2, this Model for Multi-Objective Optimization is asked using the multi-objective particle based on competition and teaching and learning mechanics
Solution, chooses the elite population in current population by competition mechanism, other individuals are individual into elite population by teaching and learning mechanics
Study generates progeny population；
S3, using adaptive constrain-handling technique to Complex Constraints condition carry out dynamic processing, and select outstanding feasible solution into
Enter next-generation population；
S4, judge whether current iteration number reaches maximum number of iterations, when the judgment result is yes, export optimal addressing scheme
Disaggregation；Otherwise, step S2 is executed.
2. the charging station site selecting method according to claim 1 based on MOPSO algorithm, which is characterized in that step S1, specifically
Include:
S11, charging station data in planning region are obtained, charging station related data includes in the planning region: electric car is per day
It charges probability, planning region land price, electric automobile during traveling average speed in planning region, charging station service range covering in planning region
Population；Maximum number of iterations is set；
S12, building charging station addressing constrain Model for Multi-Objective Optimization:
MINBcost=NLPL+NCPC,Wherein, BcostFor cost of building a station, NLWith
PLRespectively land area and unit price, NCAnd PCThe respectively number and unit price of charging pile, XiIt indicates whether to build charging in this position
It stands, Xi=1 indicates construction, Xi=0 indicates not build, PopiFor the population of charging station covering, UcostFor user's two-way time, NP
Middle N is electric automobile charging station set, and P is the probability of electric car charging, dijFor demand point j to the linear distance of charging station i,
V is the average speed that electric car travels in this region；
S13, according to electric automobile during traveling average speed in the per day charging probability of electric car, planning region land price, planning region, rule
The population that charging station service range covers in partition calculates separately the target function value and constraint violation value of each individual,
The binding occurrence is that charging station capacity-constrained and variation constrain,
Charging station capacity-constrained Wmax:Wherein XiIt indicates whether to build charging station, X in this positioni=1 indicates to build
If Xi=0 indicates not build, WiFor the maximum capacity of the position i charging station that can be built；
Variation constrains a:Wherein, UiFor the voltage of position i, UNFor the voltage rating of distribution network, a is
The maximum voltage of permission deviates.
3. the charging station site selecting method according to claim 2 based on MOPSO algorithm, which is characterized in that step S2, specifically
Include:
S21, all population at individual are carried out on all objective functions with non-dominated ranking, and non-dominant row is divided to all individuals
Sequence grade constructs the elite population that Population Size is n according to non-dominated ranking grade, and wherein n is the 1/5 of Population Size；
S22, two elite individuals are selected from elite population at random, enables two elite individuals be at war with, selects current individual
Teacher, then individual learn to teacher, generation offspring individual；
S23, judge whether offspring individual number reaches preset quantity, when the judgment result is yes, offspring individual is merged into son
For population；Otherwise, step S22 is executed.
4. the charging station site selecting method according to claim 2 based on MOPSO algorithm, which is characterized in that in step S22,
Described two elite individuals are at war with, specifically:
Pass through formula pw=max (c1,c2),
Make two essences
English individual is at war with, wherein pwFor victor, e is the ratio between current iteration number and maximum number of iterations, and e ∈ [0,1], θ are institute
Select the angle between elite individual and selected individual, fx1、fx2And fxiRespectively selected two elite individuals, selected population at individual
The sum of all objective functions, CV1And CV2The constraint violation degree of respectively selected two elite individuals.
5. the charging station site selecting method according to claim 2 based on MOPSO algorithm, which is characterized in that in step S22,
The individual learns to teacher, specifically:
Pass through formula v 'i=r1vi+r2(pw-pi)+(1-r2)(pl-pi), p 'i=pi+v′iIndividual is carried out to learn to teacher, wherein
r1、r2For random number, and r1, r2∈ (0,1), vi' it is i-th of updated velocity amplitude of individual, pi' it is after i-th of individual updates
Position, plFor the individual of competition failure.
6. the charging station site selecting method according to claim 1 based on MOPSO algorithm, which is characterized in that in step S3, institute
It states and dynamic processing is carried out to Complex Constraints condition using adaptive constrain-handling technique, specifically include:
Construct the constraint violation degree L of charging station capacity and variationcapAnd LV, total constraint violation degree of a solution are as follows: CV=
ωcap·Lcap+ωV·LV, wherein ωcapAnd ωVRespectively indicate the weight of charging station capacity and variation constraint, ωcap+
ωV=1；
Adaptive critic is carried out to feasible solution and infeasible solution according to different phylogenetic scales, wherein dynamic change it is infeasible
Spend the expression formula of threshold value η:
η=CVmean×e-FR×(g/G)
Wherein, CVmeanFor kind of a group mean constraint violation degree, g is current iteration number, and G is maximum number of iterations；Feasible solution ratio
FR=NF/ NP, NFFor feasible solution number, NP is population scale.
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