CN110276517B - Electric vehicle charging station site selection method based on MOPSO algorithm - Google Patents
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Abstract
The invention discloses an electric vehicle charging station site selection method based on MOPSO, which comprises the following steps: s1, constructing a charging station site selection constraint multi-objective optimization model with the charging station building cost, the time of a user going to and fro a charging station and the population number of a service range of the charging station as objective functions and with the charging station capacity constraint and the charging station voltage offset as constraint conditions; s2, designing a multi-target particle swarm optimization algorithm based on a competition and teaching mechanism to solve, constructing an elite population through the competition mechanism, and learning other individuals from the elite individuals through the teaching mechanism to generate a filial generation population; s3, dynamically processing the two types of complex constraint conditions by adopting a self-adaptive constraint processing technology, and selecting an excellent feasible solution to enter a next generation population; s4, judging whether the current iteration times reach the maximum iteration times, and if so, outputting an optimal and feasible address selection scheme solution set; otherwise, executing the step S2, and finally obtaining the optimal feasible scheme of the electric vehicle charging station under different requirements of site selection.
Description
Technical Field
The invention relates to the technical field of electric vehicle charging stations, in particular to a charging station site selection method based on an MOPSO algorithm.
Background
Along with the rapid development of global economy, the problems of energy shortage, environmental pollution and the like become more serious. The green logistics aims at reducing environmental pollution and resource consumption, realizes economic sustainable development and is advocated by governments and international organizations of all countries. Electric vehicles gradually become important new energy vehicles in green logistics distribution due to the obvious advantages of cleanness and energy conservation. Among them, the site selection layout of the charging facility has a significant influence on the development of the electric vehicle. The improper construction of the charging facilities can lead to the charging station being idle or too crowded, which causes the conditions of poor user experience, resource waste and the like, thereby affecting the popularization and use of the electric automobile. In addition, improper location of the electric vehicle charging station can have many negative effects on the local public power grid, such as voltage deviation, power loss, and the like.
Due to the characteristics of the electric automobile, a user has a large demand for supplementing electric energy in a short time, and the electric automobile can quickly go to a charging station for supplementing electric energy when the electric energy is insufficient; and when charging, need to guarantee that user's wait charging time is the shortest to improve user's use experience to electric automobile. In summary, the planning and construction of the charging station involves various factors, such as the station construction cost of the charging station and the benefit of the user of the electric vehicle.
Disclosure of Invention
Based on the technical problems in the background art, the invention provides an electric vehicle charging station address selecting method based on an MOPSO algorithm;
the invention provides an electric vehicle charging station address selecting method based on an MOPSO algorithm, which comprises the following steps:
s1, according to relevant data of charging stations in a planning area, constructing a charging station site selection constraint multi-objective optimization model which takes the station construction cost of the charging station, the time of a user for going to and fro the charging station and the population number of the service range of the charging station as objective functions and takes charging station capacity constraint and charging station voltage offset as constraint conditions;
s2, solving the multi-target optimization model by adopting a multi-target particle swarm optimization algorithm based on a competition and teaching mechanism, selecting an elite population in the current population through the competition mechanism, and learning other individuals from the elite population through the teaching mechanism to generate an offspring population;
s3, dynamically processing the complex constraint conditions by adopting a self-adaptive constraint processing technology, and selecting excellent feasible solutions to enter next generation of population;
s4, judging whether the current iteration times reach the maximum iteration times or not, and outputting an optimal addressing scheme solution set if the judgment result is yes; otherwise, step S2 is performed.
Preferably, step S1 specifically includes:
s11, acquiring data of the charged power station in the planning area, wherein the data of the charged power station in the planning area comprises the following steps: the average daily charging probability of the electric automobile, the land price of the planning area, the average running speed of the electric automobile in the planning area and the population number covered by the service range of the charging station in the planning area; setting the maximum iteration times;
s12, constructing a charging station site selection constraint multi-objective optimization model:
MINB cost ＝N L P L +N C P C ，
wherein, B cost For the cost of building a station, N L And P L Respectively land area and unit price, N C And P C Respectively the number and unit price of the charging piles, X i Indicating whether a charging station is to be set up at this location, X i =1 construction, X i =0 denotes no construction, pop i Number of population covered for charging station, U cost For the user round trip time, N P Wherein N is the set of charging stations for electric vehicles, P is the probability of charging for electric vehicles, d ij The linear distance from the demand point j to the charging station i is shown, and v is the average speed of the electric vehicle running in the area;
s13, respectively calculating an objective function value and a constraint violation value of each individual according to the daily average charging probability of the electric vehicle, the land price of the planning area, the average running speed of the electric vehicle in the planning area and the population number covered by the service range of the charging station in the planning area, wherein the constraint values are a charging station capacity constraint and a voltage offset constraint,
charging station capacity constraint W max ：
Wherein X i Indicating whether a charging station is to be set up at this location, X i =1 for construction, X i =0 denotes no construction, W i Maximum capacity of charging stations that can be built for location i;
voltage offset constraint a:
wherein, U i Is the voltage at position i, U N A is the maximum voltage deviation allowed for the rated voltage of the distribution network.
Preferably, step S2 specifically includes:
s21, performing non-dominated sorting on all population individuals on all target functions, dividing all individuals into non-dominated sorting levels, and constructing an elite population with the population size of n according to the non-dominated sorting levels, wherein n is 1/5 of the population size;
s22, randomly selecting two elite individuals from the elite population, enabling the two elite individuals to compete, selecting a teacher of the current individual, and then learning the teacher by the individuals to generate offspring individuals;
s23, judging whether the number of the filial generation individuals reaches a preset number or not, and combining the filial generation individuals into a filial generation population if the judgment result is yes; otherwise, step S22 is executed.
Preferably, in step S22, the two elite individuals compete, specifically:
by the formula p w ＝max(c 1 ,c 2 )，
Preferably, in step S22, the individual learns from a teacher, specifically:
through formula v' i ＝r 1 v i +r 2 (p w -p i )+(1-r 2 )(p l -p i )，p′ i ＝p i +v′ i Performing individual learning to a teacher, wherein r 1 、r 2 Is a random number, and r 1 ，r 2 ∈(0，1)，v′ i Is the value of velocity after update of the i-th individual, p' i Updated position, p, for the ith individual l The individuals who failed the competition.
Preferably, in step S3, the dynamically processing the complex constraint condition by using the adaptive constraint processing technique specifically includes:
construction of constraint violation L of charging station capacity and voltage excursions cap And L V The total constraint violation for a solution is: CV = ω cap ·L cap +ω V ·L V Wherein, ω is cap And ω V Weights, ω, representing charging station capacity and voltage excursion constraints, respectively cap +ω V ＝1；
Performing self-adaptive evaluation on the feasible solution and the infeasible solution according to different evolution stages, wherein the expression of the dynamically changed infeasible threshold eta:
η＝CV mean ×e -FR×(g/G)
wherein CV is mean The population average constraint violation degree is obtained, G is the current iteration number, and G is the maximum iteration number; feasible solution ratio FR = N F /NP，N F NP is the population size for feasible solution number.
When the charging station site selection is carried out, the benefits of an electric automobile company, an electric power company and a user are considered at the same time, the site construction cost, the user waiting time and the service range population are taken as objective functions of the method, the charging station capacity constraint and the voltage deviation constraint are taken as constraint functions, the MOPSO algorithm based on competition and teaching mechanisms is adopted to simultaneously optimize multiple targets of the charging station site selection, and the two complex constraints are processed by combining the self-adaptive constraint processing technology, so that the optimal feasible scheme of the electric automobile charging station site selection under different requirements is obtained.
Drawings
FIG. 1 is a schematic flow chart of an electric vehicle charging station location method based on an MOPSO algorithm according to the present invention;
FIG. 2 is a population distribution plot of utility and planning zones within a planning zone in an embodiment of the present invention;
fig. 3 is a schematic view of a selected charging station when 3 charging stations are built in an embodiment of the present invention;
FIG. 4 is a schematic diagram of selected charging stations when 4 charging stations are constructed in accordance with an embodiment of the present invention;
fig. 5 is a schematic diagram of a charging station selected when 5 charging stations are built according to an embodiment of the present invention.
Detailed Description
Referring to fig. 1 to 5, the charging station address selecting method based on the MOPSO algorithm provided by the invention includes:
step S1, according to relevant data of charging stations in a planning area, a charging station site selection constraint multi-objective optimization model which takes charging station building cost, time of a user for going to and fro and charging station service range population as objective functions and takes charging station capacity constraint and charging station voltage offset as constraint conditions is constructed.
The method specifically comprises the following steps:
s11, acquiring data of the charged power station in the planning area, wherein the data of the charged power station in the planning area comprises the following steps: the daily average charging probability of the electric automobile, the land price of the planning area, the running average speed of the electric automobile in the planning area and the population number covered by the service range of the charging station in the planning area; setting the maximum iteration times;
s12, constructing a charging station constraint multi-objective optimization model:
MINB cost ＝N L P L +N C P C ，
wherein, B cost For the cost of building a station, N L And P L Respectively land area and unit price, N C And P C Respectively the number and unit price of the charging piles, X i Indicating whether a charging station is to be set up at this location, X i =1 construction, X i =0 denotes non-construction, pop i Number of population covered for charging station, U cost For the user round trip time, N P Wherein N is the set of charging stations for electric vehicles, P is the probability of charging for electric vehicles, d ij The linear distance from the demand point j to the charging station i is shown, and v is the average speed of the electric vehicle running in the area;
s13, respectively calculating an objective function value and a constraint violation value of each individual according to the daily average charging probability of the electric vehicle, the land price of a planning area, the running average speed of the electric vehicle in the planning area and the population number covered by the service range of the charging station in the planning area, wherein the constraint values are a capacity constraint and a voltage offset constraint of the charging station,
charging station capacity constraint W max ：
Wherein X i Indicating whether a charging station is to be set up at this location, X i =1 for construction, X i =0 denotes no construction, W i Maximum capacity of charging stations that can be built for location i;
voltage offset constraint a:
wherein, U i Is the voltage at position i, U N A is the maximum voltage deviation allowed for the rated voltage of the distribution network.
In the specific scheme, a Multi-objective Particle Swarm Optimization (MOPSO) is adopted to solve the constructed charging station site selection constraint Multi-objective Optimization problem, and a high-quality offspring population generation method Based on a competition Mechanism (Competitive Mechanism) and a Teaching Mechanism (Teaching-Learning-Based Optimization) is designed by combining the problem characteristics;
the objective function is set by taking into account the cost of building the station, the population number in the coverage area of the station, and the round trip time of the user to the electric vehicle charging station, i.e. optimizing the cost of building the station and the round trip time of the user to minimize them, and the mathematical model is as follows:
MINB cost ＝N L P L +N C P C ，
considering charging station capacity constraint and voltage offset constraint when setting up the constraint function, the total charge capacity of charging station should be able to satisfy all the charging demands in planning region, and the joining of charging station can lead to the loss increase of distribution network to can change distribution network's voltage distribution, and then influence the electric energy quality to the user supply, therefore, voltage offset should restrict within a certain range:
charging station capacity constraint W max ：
Voltage offset constraint a:
and S2, selecting an elite population in the population, and learning the elite population from individuals and generating a filial generation population.
The method specifically comprises the following steps:
s21, performing non-dominant sorting on all population individuals on all target functions, dividing all the individuals into non-dominant sorting levels, and constructing an elite population with the population size of n according to the non-dominant sorting levels, wherein n is 1/5 of the population size;
s22, randomly selecting two elite individuals from the elite population, enabling the two elite individuals to compete, selecting a teacher of the current individual, and then learning the teacher by the individuals to generate offspring individuals;
wherein, the competition of the two elite individuals is as follows:
by the formula p w ＝max(c 1 ,c 2 )，
wherein the individual learns from a teacher, specifically:
through formula v' i ＝r 1 v i +r 2 (p w -p i )+(1-r 2 )(p l -p i )，p′ i ＝p i +v′ i Performing individual learning to a teacher, wherein r 1 、r 2 Is a random number, and r 1 ，r 2 ∈(0，1)，v i Is the value of velocity after update of the i-th individual, p' i Updated position, p, for the ith individual l The individuals who fail to compete.
S23, judging whether the number of the filial generation individuals reaches a preset number or not, and combining the filial generation individuals into a filial generation population if the judgment result is yes; otherwise, step S22 is executed.
In the specific scheme, the MOPSO algorithm is improved, the mode that the PSO algorithm selects the local optimal individual is improved, and the convergence speed of the algorithm is increased; meanwhile, a teaching mechanism is further introduced to obtain a Pareto solution set with rich diversity, n elite individuals are selected by carrying out non-dominated sorting on the population, wherein n is 1/5 of the size of the population, two individuals are selected from the n elite individuals randomly for competition, teachers of the individuals are selected for each individual respectively, and the competition formula is as follows:
p w ＝max(c 1 ,c 2 )，
meanwhile, the learning mechanism of the population individual to the teacher is as follows:
v′ i ＝r 1 v i +r 2 (p w -p i )+(1-r 2 )(p l -p i )，p′ i ＝p i +v′ i 。
and S3, dynamically processing the complex constraint conditions by adopting a self-adaptive constraint processing technology, and selecting excellent feasible solutions to enter the next generation of population.
The method specifically comprises the following steps:
s31, constructing constraint violations of the charging station capacity and the voltage deviation as L respectively cap And L V The total constraint violation for a solution is:
CV＝ω cap ·L cap +ω V ·L V
wherein, ω is cap And omega V Weights, ω, representing charging station capacity and voltage excursion constraints, respectively cap +ω V ＝1；
Updating weight value, initial time, setting omega cap ＝ω V =1/2, which indicates that the importance of the two constraint conditions is the same, and then in an iteration process, the weight of the constraint condition is updated based on a constraint violation degree average value, wherein a larger constraint violation degree average value indicates that the population under the constraint is farther from a feasible state, and the corresponding weight needs to be increased to provide more computing resources; conversely, the smaller the constraint average value is, the closer the population individual under the constraint is to the feasible state, the corresponding weight can be reduced, and thus the importance degree of the two constraint conditions can be adaptively adjusted.
S32, carrying out self-adaptive evaluation on the feasible solution and the infeasible solution according to different evolution stages, and designing a dynamically-changed infeasible threshold eta, wherein the expression is as follows:
η＝CV mean ×e -FR×(g/G)
wherein, CV is mean The number of the current iterations is G, and the maximum iterations is G; feasible solution ratio FR = N F /NP，N F NP is the population size for feasible solution number.
S4, judging whether the current iteration times reach the maximum iteration times or not, and outputting an optimal addressing scheme solution set when the judgment result is yes; otherwise, step S2 is performed.
In the specific scheme, judging whether the current iteration number reaches the maximum iteration number, if so, stopping the algorithm, and outputting an optimal feasible site selection scheme solution set, otherwise, turning to the step 2, wherein the embodiment adopts a competition mechanism to accelerate the convergence speed; and a teaching mechanism is adopted to increase the diversity of Pareto solution sets.
Example (b):
taking the electric vehicle charging station plan in a certain region as an example, the city has a 10-node road network shown in fig. 2;
according to the data statistics of the China low price detection network, the average level of the current commercial land price is 7000 yuan; the selling price of one charging pile is 20000 yuan; the single-day charging probability of the electric automobile is 0.05, and the average running speed in the planning area is 40km/h; the available charging stations have 4 types, the maximum capacity is 0.1MW,0.2MW,0.3MW and 0.4MW respectively, and the maximum allowable offset alpha of the voltage is set to 10 percent.
By comparing columns in table 1, it can be obtained that as the number of the charging stations is increased, the station building cost is gradually increased, the number of covered people in a service range is increased, the average queuing time is shortened, and the satisfaction degree of electric vehicle users is higher, but as the number of the charging stations is increased, the total capacity required by the charging stations is increased, and the voltage deviation is overlarge, so that huge pressure is caused on the operation of a power distribution network, the quality of electric energy distribution is influenced, and the economic operation of a power grid is not facilitated, and actually, when the number of the charging stations is 4, the station building cost is increased to the minimum extent, the number of covered people in the service range is increased to the maximum extent, and the average queuing time is reduced to the maximum extent; when the number of the charging stations is increased to 5, the voltage deviation and the total capacity of the charging stations are increased, and in order to ensure that the obtained optimal solution can meet the constraint condition, the construction selection range of the charging stations is reduced to a certain extent, and finally the number of people in the service range of the selected charging stations is less than that of the situation that the number of the charging stations is 4, so that the charging stations can best meet the benefits of an electric automobile formula, an electric power company and a user when the number of the charging stations is 4.
TABLE 1 optimal target situation under different site selection numbers
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered as the technical solutions and the inventive concepts of the present invention within the technical scope of the present invention.
Claims (5)
1. A charging station address selection method based on an MOPSO algorithm is characterized by comprising the following steps:
s1, according to relevant data of charging stations in a planning area, constructing a charging station site selection constraint multi-objective optimization model which takes the station building cost of the charging station, the time of a user for going to and fro the charging station and the population number of the service range of the charging station as objective functions and takes charging station capacity constraint and charging station voltage offset as constraint conditions;
s2, solving the multi-target optimization model by adopting a multi-target particle swarm optimization algorithm based on a competition and teaching mechanism, selecting the elite population in the current population through the competition mechanism, and learning other individuals from the elite population through the teaching mechanism to generate offspring populations;
s3, dynamically processing the complex constraint conditions by adopting a self-adaptive constraint processing technology, and selecting excellent feasible solutions to enter next generation of population;
s4, judging whether the current iteration times reach the maximum iteration times or not, and outputting an optimal addressing scheme solution set if the judgment result is yes; otherwise, executing step S2;
step S1, specifically comprising:
s11, obtaining relevant data of the charging station in the planning area, wherein the relevant data of the charging station in the planning area comprises the following steps: the average daily charging probability of the electric automobile, the land price of the planning area, the average running speed of the electric automobile in the planning area and the population number covered by the service range of the charging station in the planning area; setting the maximum iteration times;
s12, constructing a charging station site selection constraint multi-objective optimization model:
MINB cost ＝N L P L +N C P C ，
wherein, B cost For the cost of building a station, N L And P L Respectively land area and unit price, N C And P C Respectively the number and unit price of the charging piles, X i Indicating whether a charging station is to be set up at this location, X i =1 for construction, X i =0 denotes non-construction, pop i Number of population covered for charging station, U cost For the user round trip time, N P Wherein N is the set of charging stations for electric vehicles, P is the probability of charging for electric vehicles, d ij The linear distance from the demand point j to the charging station i is shown, and v is the average speed of the electric vehicle running in the area;
s13, respectively calculating an objective function value and a constraint violation value of each individual according to the daily average charging probability of the electric vehicle, the land price of a planning area, the running average speed of the electric vehicle in the planning area and the population number covered by the service range of the charging station in the planning area, wherein the constraint violation values are a capacity constraint of the charging station and a voltage offset constraint,
charging method and apparatusStation capacity constraint W max ：
Wherein X i Indicating whether a charging station is to be set up at this location, X i =1 for construction, X i =0 denotes no construction, W i Maximum capacity of charging stations that can be built for location i;
voltage offset constraint a:
wherein, U i Is the voltage at position i, U N A is the maximum voltage deviation allowed for the rated voltage of the distribution network.
2. The MOPSO algorithm-based charging station addressing method according to claim 1, wherein the step S2 specifically comprises:
s21, performing non-dominant sorting on all population individuals on all target functions, dividing all the individuals into non-dominant sorting levels, and constructing an elite population with the population size of n according to the non-dominant sorting levels, wherein n is 1/5 of the population size;
s22, randomly selecting two elite individuals from the elite population, enabling the two elite individuals to compete, selecting a teacher of the current individual, and then learning the teacher by the individual to generate offspring individuals;
s23, judging whether the number of the filial generation individuals reaches a preset number or not, and combining the filial generation individuals into a filial generation population if the judgment result is yes; otherwise, step S22 is executed.
3. The MOPSO algorithm-based charging station addressing method of claim 2, wherein in step S22, the two elite individuals compete, specifically:
by the formula p w ＝max(c 1 ,c 2 )，
4. The MOPSO algorithm-based charging station addressing method of claim 3, wherein in step S22, said individual learns from a teacher, specifically:
through formula v' i ＝r 1 v i +r 2 (p w -p i )+(1-r 2 )(p l -p i )，p′ i ＝p i +v′ i Performing individual learning to a teacher, wherein r 1 、r 2 Is a random number, and r 1 ，r 2 ∈(0，1)，v′ i Updated speed value, p 'for the ith individual' i Updated position, p, for the ith individual l For individuals who have failed competition, v i Speed of the i-th individual, p i Is the ith individual.
5. The charging station addressing method based on the MOPSO algorithm according to claim 1, wherein in step S3, the dynamically processing the complex constraint condition by using the adaptive constraint processing technique specifically comprises:
construction of a violation of constraints L for charging station capacity and voltage excursions cap And L V The total constraint violation for a solution is: CV = ω cap ·L cap +ω V ·L V Wherein, ω is cap And ω V Weights, ω, representing charging station capacity and voltage offset constraints, respectively cap +ω V ＝1；
Performing self-adaptive evaluation on the feasible solution and the infeasible solution according to different evolution stages, wherein the expression of the dynamically changed infeasible degree threshold eta:
η＝CV mean ×e -FR×(g/G)
wherein CV is mean The population average constraint violation value is obtained, G is the current iteration number, and G is the maximum iteration number; feasible solution ratio FR = N F /NP，N F For feasible solution number, NP is population size, CV j The constraint violation for the jth individual.
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