CN104636825B - A kind of electric automobile planning towards urban power distribution network improves immune genetic method - Google Patents
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Abstract
The invention discloses a kind of electric automobile planning towards urban power distribution network to improve immune genetic method, should improve immune genetic method towards the electric automobile planning of urban power distribution network and comprise the following steps：Decision variable is set；Initial population produces；Genetic manipulation；Immune genetic operates；The evolution of vaccine library；Population recruitment；The adjustment of electric automobile electric charging station position；End condition.The present invention using the fixed generation of genetic algebra 200 is reached as algorithm end condition, plan calculating speed, reach Fast Convergent by the electric automobile that can be effectively improved towards urban power distribution network.
Description
Technical Field
The invention belongs to the technical field of power distribution network planning, and particularly relates to an improved immune genetic method for planning of an electric vehicle for an urban power distribution network.
Background
In recent years, the emission of carbon dioxide is continuously increased, global climate change becomes the most serious development challenge for human beings in the world and for a long time later, so that the solution of the problem of environmental pollution and the realization of energy conservation and emission reduction become important plans for national development; a large amount of harmful gas can be produced in the driving process of a traditional automobile, so that great pressure is not only caused to the environment, but also harm is produced to the health of a human body. As an effective measure for energy conservation and emission reduction, the electric automobile becomes a new direction for automobile industry development, and compared with the traditional automobile, the electric automobile has the advantages of reducing carbon dioxide emission, reducing construction of a thermal power plant, being pollution-free and the like.
With the development of the electric automobile industry, the construction of electric automobile charging facilities (charging piles and battery replacement stations) is the foundation and guarantee of the electric automobile industry development, and needs to be planned in advance and reasonably arranged; on one hand, an electric automobile charging facility mainly composed of power electronic components is easy to generate multiple harmonic waves in the operation process, so that the problem of high electric energy quality is brought to a power grid, on the other hand, the electric automobile has the dual characteristics of a power supply and a load, if an access point in the power distribution network is unreasonable, the problems of voltage, frequency and the like are brought to the stable and safe operation of a local power distribution network, and even the whole large power grid is endangered; therefore, the electric automobile location and volume fixing and reasonable planning oriented to the urban power distribution network are very necessary.
Meanwhile, the electric vehicle planning facing the urban distribution network becomes a multi-target optimization problem with large dimension and many constraint conditions due to the fact that the distribution network is complex in structure, the constraint conditions such as the load flow, the voltage, the N-1 safety and the reliability of the distribution network are more, the SOC characteristic of the electric vehicle and the access time are irregular, and if the conventional optimization calculation methods such as nonlinear planning and heuristic algorithm are adopted for solving, the time is consumed; the invention provides an improved immune genetic method for planning an electric vehicle facing an urban distribution network, which can efficiently calculate the problem of site selection and volume determination of the planning of the electric vehicle facing the urban distribution network.
Besides, along with the popularization and application of electric vehicles such as electric private cars and buses in cities, the planning, construction and implementation of electric vehicle charging and exchanging stations are important bases for guaranteeing the application achievements of the electric vehicles, and in order to solve the problem that electric vehicles with load and power supply characteristics influence safe and stable operation of a power grid, reasonable planning of the location and capacity of the urban electric vehicle charging and exchanging stations is objectively required. In the aspect of the existing location and volume fixing method for the electric vehicle charging and battery replacing station, the SOC battery characteristic of the electric vehicle is generally considered, and the randomness of the electric vehicle is ignored; in the conventional immune genetic method, the mutation characteristic is considered in fewer pairs mainly from the training of genetic operators.
Disclosure of Invention
The invention aims to provide an improved immune genetic method for planning electric vehicles facing an urban power distribution network, and aims to solve the problem that in the aspect of the conventional immune genetic method, the variation characteristic is less considered.
The invention is realized in such a way that an electric automobile planning improved immune genetic method facing an urban distribution network comprises the following steps:
step one, decision variable setting:
decision variable X = { C; s is composed of two vectors, the values of all vector elements are 0 or 1, and the structure is shown as the following formula;
C＝{C 11 ,C 12 ,C 21 ,C 22 ,...,C n1 ,C n2 } (1)；
S＝{S 1 ,S 2 ,...,S m }
in the formula: n is the feasible installation position number of all electric automobile charging and exchanging stations in the power distribution system, and m is the feasible installation position number of the intelligent switch; c i1 、C i2 (i =1,2, \ 8230;, n) respectively identifying whether an electric vehicle charging station unit with stored energy and an electric vehicle pile type with a power generation unit which can be provided by receiving dispatching are installed at the ith feasible position of the electric vehicle charging station; s. the i (i =1,2, \8230;, m) identifies whether the ith feasible position of the intelligent switch is true or notInstalling a switch device; the types of the electric automobile connected to the power grid are more than two (a battery is charged and replaced and an electric automobile charging pile is charged), and the binary digits of the identification type are correspondingly increased; allowing each node to access multiple electric vehicle charging and battery replacing stations, then C ij The binary digits representing whether the electric automobile charging and replacing power station is installed are increased to represent the number of accessed units;
step two, generating an initial population:
randomly sampling according to the formula (1) to generate an initial value on each gene position in the population antibody;
by increasing the value of P in the formula (2), the number of the character '1' at the gene position representing whether the device is installed or not in the antibody character string is reduced, and C is sampled i1 、S i When the gene value of the position is obtained, the value of P is 0.9; at sample C i2 When taking value, P takes value as 0.5; the size of the initial population is selected to be 100;
step three, genetic manipulation:
as shown in formula (3); the vector distance concentration value calculated by the fitness value is determined by the selection probability determined by the affinity and the concentration, and the calculation formulas of the affinity and the concentration are respectively shown as a formula (4) and a formula (5);
the selection probability considering the concentration and affinity is:
in the formula: alpha is a weight coefficient for adjusting the selection probability; p fi And P di The selection probabilities determined by the affinity and concentration of antibody i, respectively;
calculation of affinity: there are N antibodies in the population, where the affinity of antibody i is f i Then P is fi A proportional selection strategy can be employed to obtain:
concentration calculation when antibody concentration is expressed as a vector distance, P di The calculation is as follows:
the crossing operation is in the form of two-point crossing with a crossing probability P c Setting the value to be 0.9, and adopting a dynamic variation rate in population variation operation, wherein the dynamic variation rate is shown as a formula (6);
PM g ＝PM min +(PM max -PM min )·r g (6)；
in the formula: PM (particulate matter) g The variation rate of the antibody of the population of the g-th generation, PM min And PM max The minimum value and the maximum value of the variation rate are respectively; r is a contraction factor, taken as 0.99 in the analysis;
step four, immune genetic manipulation:
adopting a self-adaptive vaccine extraction mechanism in an improved immune genetic algorithm; firstly, extracting 10 optimal antibodies appearing until the current generation number by iteration to form a vaccine library, and updating the vaccine library before the next iteration is started; secondly, extracting a vaccine antibody from the 10 optimal antibodies, wherein the symbol on each gene position of the vaccine antibody is the symbol with the maximum occurrence probability on the corresponding gene position in the 10 optimal antibodies; according to a certain inoculation probability P v Selecting a new antibody formed by genetic operation for vaccination, and comparing the affinity of the antibody and the antigen before and after vaccination; the inoculation operation can improve the adaptability of the antibody, and then the inoculation is accepted, otherwise, the inoculation is abandoned; probability of immunization P v ＝0.7；
Step five, evolution of a vaccine library:
in the process of using a clonal genetic algorithm to advance the evolution of a vaccine library, the size of a clonal population is set to be 100, and the clone number of each antibody is calculated by adopting an equation (7); calculation of gene mutationUsing a fixed rate of variation P in the seed m ＝0.1；
The meaning of formula (7) is: let n antibodies be present in the starting population, i.e. A (k) = { a = 1 ,a 2 ,…,a n H, wherein the affinity of antibody i is noted as f (a) i ). The volume of the cloned population A' (k) was set to N c (taking N as a rule) c >, n), the number of replications of each antibody q during cloning i As shown in the formula
Where the operator [ x ] represents the largest integer not greater than x.
Step six, population updating:
after the immunization operation, 5 antibodies with highest affinity in a vaccine library are used for replacing the same number of individuals with the worst affinity in the population; when no better solution is searched in 10 continuous generations, randomly generating 80% antibodies with poor affinity in the population;
step seven, adjusting the position of the electric automobile charging and replacing station:
in the population evolution process, the type selection and the site selection of an intelligent switch (a control device for accessing the electric vehicle into a power grid) and an electric vehicle charging and exchanging station are random, and a dynamic adjustment strategy is adopted for the final position of the electric vehicle charging and exchanging station in the process of carrying out reliability calculation on the power distribution network by an improved immune genetic algorithm;
step eight, termination condition:
the algorithm adopts 200 generations reaching a fixed genetic algebra as an algorithm termination condition.
Further, the method for planning and improving the immune heredity of the electric vehicle facing the urban distribution network comprises the following steps:
defining a forming range of an electric vehicle charging and replacing station according to the switch configuration condition of each antibody;
determining feasible nodes accessed by the electric vehicle charging and battery replacing station;
determining whether the electric vehicle charging and replacing station is positioned outside the power distribution network according to the configuration condition of the electric vehicle charging and replacing station in the antibody;
and randomly adjusting the electric automobile charging and replacing power station to a feasible node.
The improved immune genetic method for planning the electric vehicle facing the urban distribution network can effectively improve the planning calculation speed of the electric vehicle facing the urban distribution network and achieve rapid convergence.
Drawings
Fig. 1 is a schematic diagram of a planning result of an electric vehicle charging and battery replacing station system in case 1 according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a planning result of an electric vehicle charging and battery replacing station system in case 2 according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The application of the principles of the present invention will be further described with reference to the accompanying drawings and specific embodiments.
The electric automobile planning improvement immune genetic method for the urban distribution network comprises the following steps:
step one, decision variable setting:
decision variable X = { C; s is composed of two vectors, the values of all vector elements are 0 or 1, and the structure is shown as the following formula;
C＝{C 11 ,C 12 ,C 21 ,C 22 ,...,C n1 ,C n2 } (1)；
S＝{S 1 ,S 2 ,...,S m }
in the formula: n is the number of feasible installation positions of all electric automobile charging and exchanging stations in the power distribution system, and m isThe number of feasible installation positions of the intelligent switch; c i1 、C i2 (i =1,2, \ 8230;, n) respectively identifying whether an electric vehicle charging station unit with stored energy and an electric vehicle pile type with a power generation unit which can be provided by receiving dispatching are installed at the ith feasible position of the electric vehicle charging station; s i (i =1,2, \8230;, m) identifying whether the ith feasible location of the intelligent switch actually installs the switching device; the types of the electric automobile connected to the power grid are more than two (a battery is charged and replaced and an electric automobile charging pile is charged), and the binary digit number of the identification type is correspondingly increased; allowing each node to access a plurality of electric automobile charging and replacing stations, then C ij The binary digits representing whether the electric automobile charging and replacing power station is installed are increased to represent the number of accessed units;
step two, generating an initial population:
randomly sampling according to the formula (1) to generate an initial value at each gene position in the population antibody;
by increasing the value of P in the formula (2), the number of the character '1' at the gene position representing whether the device is installed or not in the antibody character string is reduced, and C is sampled i1 、S i When the gene value of the position is obtained, the value of P is 0.9; at sample C i2 When taking value, P takes value as 0.5; selecting the size of the initial population as 100;
step three, genetic manipulation:
as shown in formula (3); the vector distance concentration value calculated by the fitness value is determined by the selection probability determined by the affinity and the concentration, and the calculation formulas of the affinity and the concentration are respectively shown as a formula (4) and a formula (5);
the selection probability considering the concentration and affinity is:
in the formula: alpha is a weight coefficient for adjusting the selection probability; p fi And P di The selection probabilities determined by the affinity and concentration of antibody i, respectively;
calculation of affinity: there are N antibodies in the population, where the affinity of antibody i is f i Then P is fi A proportional selection strategy can be employed to obtain:
concentration calculation when antibody concentration is expressed as a vector distance, P di The calculation is as follows:
the crossing operation is in the form of two-point crossing with a crossing probability P c Setting the value to be 0.9, and adopting a dynamic variation rate in population variation operation, wherein the dynamic variation rate is shown as a formula (6);
PM g ＝PM min +(PM max -PM min )·r g (6)；
in the formula: PM (particulate matter) g The variation rate of the antibody of the population of the g generation, PM min And PM max The minimum value and the maximum value of the variation rate are respectively; r is a contraction factor, taken as 0.99 in the analysis;
step four, immune genetic manipulation:
adopting a self-adaptive vaccine extraction mechanism in an improved immune genetic algorithm; firstly, extracting 10 optimal antibodies appearing until the current generation number by iteration to form a vaccine library, and updating the vaccine library before the next iteration is started; secondly, extracting a vaccine antibody from the 10 optimal antibodies, wherein the symbol on each gene position of the vaccine antibody is the symbol with the maximum occurrence probability on the corresponding gene position in the 10 optimal antibodies; according to a certain inoculation probability P v Selecting a new antibody formed by genetic operation for vaccination, and comparing the affinity of the antibody and the antigen before and after vaccination; the inoculation operation can improve the adaptability of the antibody and then the inoculation is carried outAnd (4) seed planting, otherwise, abandoning inoculation; probability of immunization P v ＝0.7；
Step five, evolution of a vaccine library:
in the process of using a clonal genetic algorithm to advance the evolution of a vaccine library, the size of a clonal population is set to be 100, and the clone number of each antibody is calculated by adopting an equation (7); using a fixed mutation rate P in the genetic mutation operator m ＝0.1；
The meaning of formula (7) is: let n antibodies in the starting population, i.e., A (k) = { a = 1 ,a 2 ,…,a n In which the affinity of antibody i is noted as f (a) i ). The volume of the cloned population A' (k) was set to N c (in general, take N c >, n), the number of replications of each antibody q during cloning i As shown in the formula
Where the operator [ x ] represents the largest integer not greater than x.
Step six, population updating:
after the immunization operation, 5 antibodies with highest affinity in a vaccine library are used for replacing the same number of individuals with the worst affinity in the population; when no better solution is searched in 10 continuous generations, randomly generating 80% antibodies with poor affinity in the population;
step seven, adjusting the position of the electric automobile charging and replacing station:
in the population evolution process, the type selection and the site selection of an intelligent switch (a control device for accessing the electric vehicle into a power grid) and an electric vehicle charging and exchanging station are random, and a dynamic adjustment strategy is adopted for the final position of the electric vehicle charging and exchanging station in the process of carrying out reliability calculation on the power distribution network by an improved immune genetic algorithm;
step eight, termination condition:
the algorithm adopts the condition of reaching the fixed genetic algebra for 200 generations as the termination condition of the algorithm.
The specific embodiment of the invention:
the RBTS-BUS6 system is taken as an example for analysis, the minimum deviation index of system reliability is taken as a target, the system investment budget and the equipment installation quantity are taken as constraints for carrying out electric vehicle charging and battery replacement station optimization configuration on the complex power distribution system, information such as the length of a line in the network, the fault rate and repair or replacement time of elements, the number of users of load points and the size of loads can be obtained from relevant documents, and equipment cost data adopted in the process of calculating the economy is shown in a table below.
Table 1 electric vehicle charging and battery-changing station equipment cost information table
Suppose the reliability requirements of each load point in the system are shown in table 2. Wherein, R, C, I respectively represent the corresponding load types as residential load, commercial load, industrial load; lambda [ alpha ] T Expected failure rate (time/year), U, for load point T Annual blackout time (hours/year) is expected for the load point.
TABLE 2 load point reliability requirement information Table
| Load point | Type (B) | λ T | U T | Load point | Type (B) | λ T | U T |
| LP1 | R | 0.15 | 1 | LP21 | R | 0.05 | 2.6 |
| LP2 | R | 0.23 | 2 | LP22 | R | 0.65 | 1.7 |
| LP3 | R | 0.06 | 1.25 | LP23 | R | 0.16 | 0.5 |
| LP4 | R | 0.01 | 3 | LP24 | R | 0.02 | 0.7 |
| LP5 | R | 0.34 | 2.5 | LP25 | R | 0.6 | 1.4 |
| LP6 | R | 1.5 | 3 | LP26 | R | 0.03 | 0.08 |
| LP7 | R | 1 | 2.5 | LP27 | R | 0.2 | 2.6 |
| LP8 | R | 0.06 | 1.4 | LP28 | R | 0.55 | 2.1 |
| LP9 | R | 0.45 | 1.6 | LP29 | R | 0.64 | 2 |
| LP10 | R | 0.36 | 2.4 | LP30 | R | 1.05 | 1.5 |
| LP11 | R | 0.003 | 0.01 | LP31 | R | 2 | 3 |
| LP12 | R | 0.04 | 0.5 | LP32 | R | 0.2 | 0.5 |
| LP13 | R | 0.21 | 1.9 | LP33 | R | 0.08 | 0.8 |
| LP14 | C | 0.002 | 0.5 | LP34 | R | 0.14 | 1.44 |
| LP15 | I | 0.0001 | 0.05 | LP35 | R | 0.05 | 1.9 |
| LP16 | I | 0.0001 | 0.03 | LP36 | R | 0.4 | 1.1 |
| LP17 | C | 0.002 | 0.08 | LP37 | R | 0.55 | 1.6 |
| LP18 | R | 1.25 | 2.4 | LP38 | R | 0.12 | 2.1 |
| LP19 | R | 0.34 | 18 | LP39 | R | 0.22 | 5 |
| LP20 | R | 0.05 | 3 | LP40 | R | 0.32 | 0.22 |
The electric vehicle charging and battery replacing station optimization planning is performed under two constraint conditions shown in table 3, and the result is shown in table 4.
TABLE 3 constraint situation table
| Situation(s) | N DZmax | N Dzmax | C max |
| Case 1 | 5 | 5 | 300 |
| Case 2 | 10 | 10 | 150 |
Wherein N is DZmax Representing the maximum access point number of the electric vehicle charging and replacing station; n, N Dzmax Maximum access points representing electric automobile charging pile
Table 4 electric vehicle charging and battery replacing station planning result
Fig. 1 and fig. 2 show visual results of electric vehicle charging and battery replacing station planning in two situations.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (2)
1. The method for planning, improving and immunogenetics of the electric vehicles facing the urban distribution network is characterized by comprising the following steps of:
step one, setting decision variables:
decision variable X = { C; s is composed of two vectors, the values of all vector elements are 0 or 1, and the structure is shown as the following formula;
C＝{C 11 ,C 12 ,C 21 ,C 22 ,...,C n1 ,C n2 }(1)；
S＝{S 1 ,S 2 ,...,S m }
in the formula: n is the feasible installation position number of all electric automobile charging and exchanging stations in the power distribution system, and m is the feasible installation position number of the intelligent switch; c i1 、C i2 (i =1,2, \8230;, n) respectively identifying whether an electric automobile power exchanging station unit with energy storage is installed at the ith feasible position of the electric automobile power charging and exchanging station and the type of an electric automobile pile which can provide a power generating unit by receiving dispatching; s i (i =1,2, \8230;, m) identifying whether the ith feasible location of the intelligent switch actually installs the switching device; the binary digit number of the identification type is correspondingly increased; allowing each node to access a plurality of electric automobile charging and replacing stations, then C ij The binary digits representing whether the electric automobile charging and replacing power station is installed are increased to represent the number of accessed units;
step two, generating an initial population:
randomly sampling according to the formula (1) to generate an initial value on each gene position in the population antibody;
by increasing the value of P in the formula (2), the number of characters 1 in the gene position representing whether the device is installed or not in the antibody character string is reduced, and C is sampled i1 、S i When the gene value of the position is obtained, the value of P is 0.9; at sample C i2 When taking value, P takes value as 0.5; the size of the initial population is selected to be 100;
step three, genetic manipulation:
as shown in formula (3); the vector distance concentration value calculated by the fitness value is determined by the selection probability determined by the affinity and the concentration, and the calculation formulas of the affinity and the concentration are respectively shown as a formula (4) and a formula (5);
the selection probability considering the concentration and affinity is:
in the formula: alpha is a weight coefficient for adjusting the selection probability; p is fi And P di The selection probabilities determined by the affinity and concentration of antibody i, respectively;
calculation of affinity: assuming that there are N antibodies in the population, wherein the affinity of antibody i is f i Then P is fi Obtaining by using a proportional selection strategy:
concentration calculation when antibody concentration is expressed as a vector distance, P di The calculation is as follows:
the crossing operation is in the form of two-point crossing with a crossing probability P c Setting the value to be 0.9, and adopting a dynamic variation rate in population variation operation, wherein the dynamic variation rate is shown as a formula (6);
PM g ＝PM min +(PM max -PM min )·r g (6)；
in the formula: PM (particulate matter) g The variation rate of the antibody of the population of the g-th generation, PM min And PM max The minimum value and the maximum value of the variation rate are respectively; r is a contraction factor, taken as 0.99 in the analysis;
step four, immune genetic manipulation:
adopting a self-adaptive vaccine extraction mechanism in an improved immune genetic algorithm; firstly, extracting 10 optimal antibodies appearing until the current generation number by iteration to form a vaccine library, and updating the vaccine library before the next iteration is started; secondly, extracting a vaccine antibody from the 10 optimal antibodies, wherein the symbol on each gene position of the vaccine antibody is the symbol with the maximum occurrence probability on the corresponding gene position in the 10 optimal antibodies; according to the inoculation probability P v Selecting a new antibody formed by genetic operation for vaccination, and comparing the affinity of the antibody and the antigen before and after vaccination; the inoculation operation can improve the adaptability of the antibody, and then the inoculation is accepted, otherwise, the inoculation is abandoned; probability of immunization P v ＝0.7；
Step five, evolution of a vaccine library:
in the process of using a clonal genetic algorithm to advance the evolution of a vaccine library, the size of a clonal population is set to be 100, and the clone number of each antibody is calculated by adopting an equation (7); using a fixed mutation rate P in the genetic mutation operator m ＝0.1；
The meaning of formula (7) is: let n antibodies be present in the starting population, i.e. A (k) = { a = 1 ,a 2 ,…,a n In which the affinity of antibody i is noted as f (a) i ) The volume of the cloned population A' (k) is set to N c Taking Nc>, n, the number of replications of each antibody q during the cloning process i As shown in the formula:
wherein the operator [ ] represents taking the largest integer;
step six, population updating:
after the immunization operation, 5 antibodies with highest affinity in a vaccine library are used for replacing the same number of individuals with the worst affinity in the population; when no better solution is searched in 10 continuous generations, randomly generating 80% antibodies with poor affinity in the population;
step seven, adjusting the position of the electric automobile charging and replacing station:
in the population evolution process, the type selection and the site selection of a control device of an electric vehicle access power grid and an electric vehicle charging and replacing station are random, and a dynamic adjustment strategy is adopted for the final position of the electric vehicle charging and replacing station in the process of carrying out reliability calculation on a power distribution network by an improved immune genetic algorithm;
step eight, termination condition:
the algorithm adopts 200 generations reaching a fixed genetic algebra as an algorithm termination condition.
2. The method for planning and improving the immune heredity of the electric vehicles facing the urban power distribution network according to claim 1, wherein the method for planning and improving the electric vehicles facing the urban power distribution network comprises the following steps:
defining a forming range of an electric automobile charging and replacing station according to the switch configuration condition of each antibody;
determining a feasible node for accessing the electric automobile charging and battery replacing station;
determining whether the electric vehicle is positioned outside the power distribution network according to the configuration condition of the electric vehicle charging and replacing station in the antibody;
and randomly adjusting the electric vehicle charging and replacing power station to a feasible node.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN103136585A (en) * | 2013-02-06 | 2013-06-05 | 天津大学 | Weighting Voronoi diagram substation planning method based on chaotic and genetic strategy |
| CN103699950A (en) * | 2013-09-07 | 2014-04-02 | 国家电网公司 | Electric vehicle charging station planning method considering traffic network flow |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103078152A (en) * | 2012-10-16 | 2013-05-01 | 华北电力大学 | Intelligent charging method for centralized charging station |
| CN103136585A (en) * | 2013-02-06 | 2013-06-05 | 天津大学 | Weighting Voronoi diagram substation planning method based on chaotic and genetic strategy |
| CN103699950A (en) * | 2013-09-07 | 2014-04-02 | 国家电网公司 | Electric vehicle charging station planning method considering traffic network flow |
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