CN111582670B - Electric vehicle charging station site selection and volume fixing method - Google Patents
Electric vehicle charging station site selection and volume fixing method Download PDFInfo
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Abstract
The invention relates to a site selection and volume fixing method for an electric vehicle charging station, which comprises the following steps: analyzing the charging load of the electric vehicle reduced to each node in the planning area; randomly selecting a plurality of charging stations at different positions and the number of charging piles in each charging station from alternative charging stations within the range of the number of charging stations in a preset planning area and the range of the number of charging piles in a single charging station; taking the construction cost as a target function, and taking the service range and the power as constraint conditions to construct a site selection and volume determination model of the charging station; and solving the locating and sizing model of the charging station by adopting an improved immune clone selection algorithm by combining the electric vehicle charging load reduced to each node and the randomly selected charging station so as to obtain an optimal locating and sizing scheme. Compared with the prior art, the method and the device have the advantages that the optimal locating and sizing scheme can be quickly and stably obtained by combining the service range constraint and the power constraint and improving the immune clone selection algorithm, and the problems of charging difficulty and idle charging setting are solved.
Description
Technical Field
The invention relates to the technical field of planning of electric vehicle charging stations, in particular to a site selection and volume fixing method for an electric vehicle charging station.
Background
The electric automobile is used as a green travel tool for realizing energy conservation and environmental protection by using electric energy, is rapidly developed and widely applied in China, Europe, America, Japan and other countries, and initially forms a scale. Under the trend of large-scale development of electric vehicles, the planning and construction of charging stations become an important basic link, and the scientific and reasonable charging station planning has great significance on the safety, stability and economic operation of a power grid. However, the current charging station construction has the following disadvantages:
the charging station is unreasonable in planning, so that charging difficulty and the phenomenon of idling of charging facilities coexist;
the particle swarm algorithm is mostly adopted to solve the problem of location and volume fixing of the charging station, and in the later iteration stage of the particle swarm algorithm, the convergence speed of the algorithm is slowed down because the particles tend to be uniform.
All the problems can cause unreasonable site selection and volume fixing of the charging station, and the construction cost of the charging station is easily overhigh.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a method for location selection and volume fixing of an electric vehicle charging station, which is used for optimally solving the problem of location selection and volume fixing of a charging pile and simultaneously solving the problem of coexistence of charging difficulty and the phenomenon of idle charging facilities.
The purpose of the invention can be realized by the following technical scheme: a method for locating and sizing an electric vehicle charging station comprises the following steps:
s1, analyzing the electric vehicle charging load reduced to each node in the planning area, wherein the charging load comprises fixed requirements and passing requirements;
s2, randomly selecting a plurality of charging stations at different positions from the alternative charging stations within the preset number range of the charging stations in the planning area and the number range of the charging piles in a single charging station, and randomly selecting the number of the charging piles in each charging station;
s3, taking the construction cost as a target function, taking the service range and the power as constraint conditions, and constructing a location and volume model of the charging station;
and S4, solving the locating and sizing model of the charging station by adopting an improved immune clone selection algorithm according to the electric vehicle charging load reduced to each node and the randomly selected charging stations and the number of charging piles in each charging station to obtain an optimal locating and sizing scheme.
Further, in step S1, the electric vehicle charging load reduced to the node is calculated by a monte carlo algorithm:
wherein, PwFor total charging load of the node, g is the fixed demand quantity of the node, d1For electric private car occupation, d2For electric taxis, Pw,aCharging load power, P, for the ith electric taxi of the w nodew,bCharging load power, gamma, for the w-th node, the b-th electric private carwIs the w-thAnd correcting the traffic flow of the node.
Further, the objective function in step S3 is specifically:
wherein f is the construction cost of the charging station, SiIs the area of the ith charging station candidate, Cland,iUnit price of land for i-th alternative charging station, yiNumber of charging piles for ith alternative charging station, CeFor the price of a single charging pile, ZiOther fixed costs for the ith alternative charging station;
the constraint conditions are specifically as follows:
and (3) service range constraint:
|Bi∩Br|/|Bi|≤θ i≠r,|Bi|≠0
where n is the number of alternative charging stations, Bi、BrSet of service areas for the ith and the r-th charging stations, respectively, BtotalThe method comprises the following steps that a set of planning areas is represented by theta, wherein the preset value of the contact ratio of two charging stations is represented by theta, and the contact ratio is the ratio of the number of the same nodes in the service ranges of the two charging stations to the total number of nodes in the service ranges of the charging stations;
xiis a variable from 0 to 1 when xiWhen the charging station is 1, the charging station indicates that the ith alternative charging station is selected as the newly-built charging station; when x isiWhen the charging station is 0, the charging station indicates that the ith alternative charging station is not selected as the newly-built charging station;
and (3) power constraint:
Pch,i≤Pcs,i·xi
wherein, Pch,iFor the charging demand in the service range of the i-th alternative charging station, PtotalFor load power in the region, P, other than charging power for electric vehiclesGThe total power of the substations in the region is planned.
Further, the step S4 specifically includes the following steps:
s41, screening charging stations and the number of corresponding charging piles meeting constraint conditions according to the randomly selected charging stations and the number of charging piles in each charging station based on service range constraint and power constraint;
s42, calculating by adopting an improved immune clone selection algorithm to obtain the optimal value of the construction cost of the charging station by combining the construction cost objective function, the charging stations meeting the constraint conditions and the corresponding number of the charging piles;
and S43, outputting the corresponding charging station position and the number of the charging piles in the charging station according to the optimal value of the construction cost of the charging station, and obtaining the optimal site selection and volume fixing scheme.
Further, the step S41 is specifically to determine whether the service range of the randomly selected charging stations covers all nodes in the area and the contact ratio between each charging station does not exceed the contact ratio preset value based on the service range constraint;
based on the power constraint, whether the random charging stations meet the charging loads of all electric vehicles in the service range and whether the power of the transformer substation in the planned area meets the total load requirement is judged.
Further, the service range of the charging station in step S41 is specifically:
Fcs,i＝Pcs,i/(Dev-cs·Co-cs)
wherein f iscs,iIn order to normalize the attraction of the ith charging station to the electric vehicle, if fcs,iIf f is greater than 0, it means that the node is in the service range of the charging stationcs,i< 0, meaning that the node is not within the range of the charging station, Fcs,iAttraction of the ith charging station to the electric vehicle, Pcs,iRated capacity for the ith charging station, Dev-csThe shortest distance of the node to the charging station, Co-csRepresents the cost of the starting point reaching the charging station, and is equal to the product of the charge quantity required by the starting point to the charging station and the electricity price, sigma (F)cs,i) For charging station attraction force Fcs,iStandard deviation of (1), Fcs,averAttraction to electric vehicle for charging station Fcs,iThe greater the attraction of the charging station, the greater the likelihood of node preference.
Further, the step S42 specifically includes the following steps:
s421, combining the construction cost objective function, and respectively calculating construction cost values corresponding to a plurality of charging stations meeting the constraint condition by adopting an improved immune clone selection algorithm;
and S422, selecting the minimum value from the plurality of construction cost values as the optimal construction cost value.
Further, the specific calculation process of the improved immune clone selection algorithm is as follows:
t1, initializing parameters;
t2, respectively calculating the affinity between the antibodies and the antigens and the affinity between the antibodies to generate a memory set M and a memory set P', wherein the antibodies correspond to charging stations meeting constraint conditions and the number of corresponding charging piles, and the antigens correspond to a construction cost objective function;
t3, selecting M optimal antibodies from the memory set M, and performing cloning operation and mutation operation to obtain a new population containing NcA random solution;
and T4, judging whether the preset maximum iteration number is reached or not, if so, outputting the optimal antibody, namely the optimal value of the construction cost, if not, combining the new population with the old memory set, updating the memory set M and the set P ', then randomly generating d new antibodies to replace the original antibodies in the P', and returning to the step T2.
Further, the affinity between the antibody and the antigen is specifically:
wherein A is1(j) The affinity between the jth antibody and the antigen, C is a penalty factor which is generally a large integer, err (j) is a constraint condition after the jth antibody is normalized, and for each constraint condition, 1 is satisfied, and 0 is not satisfied;
the affinity between the antibodies is specifically as follows:
wherein A is2(j) Is the affinity between antibodies of the jth antibody, m is the number of cloned antibodies, i.e., m optimal antibodies, P 'is the number of uncloned antibodies, i.e., the set P', H is the attribute number of antibodies, antij(h) Is the h attribute of the j antibody, antiξ(h) Is the h attribute of the ξ antibody, ρj,ξDenotes the Euclidean distance between the two antibodies.
Further, the mutation operation is specifically a polynomial mutation specification operation:
δ1＝(Vh-Lh)/(Uh-Lh)
δ2＝(Uh-Vh)/(Uh-Lh)
η>0
wherein u is [0-1 ]]Eta is distribution index, VhIs the h-position, U, of an antibodyhIs a VhUpper limit of (1), LhIs a VhLower limit of (D), δ1And delta2Are all polynomial variation parameters;
the random number in the new population is specifically:
wherein, NcFor the number of antibodies after cloning, β is the amplification factor, N is the total number of antibodies, and round () is a rounding function.
Compared with the prior art, the invention has the following advantages:
according to the method, all the charging loads of the electric automobile can be fully considered by combining the fixed requirements and the passing-by requirements of the nodes, so that the charging load analysis of the nodes is more reasonable, and the accuracy of the subsequent construction of the suspension constant volume model is facilitated.
The invention provides a service range constraint condition based on node service range calculation, can effectively improve the idle condition of the charging pile, improves the node coverage rate of the charging station and avoids the problem of difficult charging or idle charging pile.
By means of the improved immune clone selection algorithm, the convergence speed of the algorithm is improved, the stability of the solving process is guaranteed, and meanwhile, the construction cost is used as a target function, so that the locating and sizing scheme of the charging station with the lowest construction cost can be obtained quickly and stably.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of an IEEE33 node power distribution network in an embodiment;
FIG. 3 illustrates an embodiment of node charging loads;
FIG. 4 is a schematic diagram of the improved immune clonal selection algorithm of the present invention;
fig. 5 is a schematic diagram of the number of solving iterations in the embodiment.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Examples
As shown in fig. 1, a method for locating and sizing an electric vehicle charging station includes the following steps:
s1, analyzing the charging load reduced to each node in the planning area, wherein the charging load comprises fixed requirements and road passing requirements;
s2, randomly selecting a plurality of charging stations at different positions from the alternative charging stations within the preset number range of the charging stations in the planning area and the number range of the charging piles in a single charging station, and randomly selecting the number of the charging piles in each charging station;
s3, taking the construction cost as a target function, taking the service range and the power as constraint conditions, and constructing a location and volume model of the charging station;
and S4, solving the location and volume determination model of the charging station by adopting an improved immune clone selection algorithm according to the charging load reduced to each node and the charging stations selected randomly and the number of charging piles in each charging station to obtain an optimal location and volume determination scheme.
In the embodiment, the IEEE33 node is taken as an example to perform location determination and capacity determination on the electric vehicle charging station, as shown in fig. 2. In fig. 2, there are 3 substations and 8 alternative charging stations, where the number of charging piles in each charging station is constrained to be 5-18, the power of a single charging pile is 40kW, the total power of the substation is 3MW, and the data on the line represents the distance between two nodes. The IEEE33 node is divided into residential and business areas, where the business area includes nodes 5-7 and nodes 26-29, and the other nodes are residential areas. And a constant-power charging mode is adopted, and the charging power requirements of various electric automobiles are obtained by referring to the rule obtained by national travel survey and statistical analysis in the United states. A power curve is obtained with 1min as a sampling interval, and a load demand curve is obtained with 15min as a discrete time interval, which is used as sample data, as shown in fig. 3.
Randomly generating antibodies, calculating the minimum construction cost f, setting the number of populations to be 50, arranging the iteration times to be 50 as the Immune clone Selection Algorithm can quickly reach convergence, arranging the variation probability to be inversely proportional to the affinity between the antigen and the antibody, improving ICSA (Immune clone Selection Algorithm) to sort according to the affinity between the antibody and the antigen, selecting 10 populations with the top ranking in each iteration process to carry out clone variation operation, and participating in the next iteration process, wherein the specific Algorithm process is shown in figure 4.
The method of the present invention is applied to the embodiment, and the specific process is as follows:
1. analyzing the electric automobile charging load reduced to each node in the planning area, wherein the electric automobile charging load comprises fixed requirements and passing requirements of the nodes;
2. determining the shortest distance from each node to each alternative charging station by using a floyd algorithm;
3. determining the number range of charging stations in a planning area and the number range of charging piles in each charging station;
4. randomly selecting a plurality of charging stations and the number of charging piles of each charging station from the alternative charging stations;
5. determining a service range of each charging station in consideration of a relationship among the capacity, the location and the service range of the charging station;
6. judging whether the service range of the charging station covers all nodes in the planning area or not, wherein the coincidence degree of the service range between each charging station is not high;
7. judging whether each charging station meets the charging load in the service range or not and whether the power of the transformer substation in the planning area meets the total load requirement or not;
8. calculating the construction cost of the charging station meeting the required site selection and volume fixing scheme;
9. calculating an optimal value of the construction cost by using an immune clone selection algorithm: putting M current optimal solutions into a memory set M, and reserving P' better solutions in the rest solutions to obtain seedsGroup P, cloning and mutating the memory set M to obtain NcRandom solutions are calculated, the construction cost of the random solutions is calculated, the random solutions of the memory sets M and Nc are selected to replace the old memory sets, P' better solutions are reserved in all the remaining solutions, and the set P is updated;
10. and judging whether the iteration times are finished, if the iteration is not finished, continuing to perform the steps 4-9, and if the iteration is finished, outputting the optimal construction cost and the optimal addressing and sizing scheme.
In step 1, the charging load is analyzed by using a monte carlo algorithm and combining the node demand and the passing demand:
in the formula: pwThe total charging load of the node is g, the fixed demand quantity of the node is g, and the electric private car occupation ratio is d1D is the ratio of the electric taxi2，Pw,aCharging load power, P, for the ith electric taxi of the w nodew,bCharging load power, gamma, for the w-th node, the b-th electric private carwThe traffic flow correction coefficient is the w-th node;
in step 5, a charging station service range model is obtained by utilizing a gravitational field theory and a reachability theory:
Fcs,i＝Pcs,i/(Dev-cs·Co-cs)
in the formula: fc,iRepresents the attraction of the ith charging station to the electric vehicle, Pc,iIndicating the rated capacity of the i-th charging station, Dev-cIndicating the distance of the electric vehicle to the charging station, Co-cRepresenting the cost of the starting point reaching the charging station, and representing the cost by the product of the charge quantity and the electricity price required by the starting point reaching the charging station;
the attraction of each charging station will also vary due to the total power of the charging stations and the distance from the electric vehicle, and in order to ensure the comparability between the data, Z-Score normalization is introduced to standardize the data, i.e. the Z-Score normalization is performed
Wherein the content of the first and second substances,
in the formula: sigma (F)cs,i) Indicating a charging station attraction force Fcs,iStandard deviation of (1), Fcs,averShowing the attraction of the charging station to the electric vehicle Fcs,iThe greater the attraction of the charging station, the greater the probability of preference of the electric vehicle;
in step 9, because the immune clone selection algorithm has the defects of slow convergence rate and instability of the algorithm, the invention improves the immune clone selection algorithm and provides a method for calculating the affinity between antibodies, namely:
wherein, the first and the second end of the pipe are connected with each other,
in the formula: a. the2(j) M is the number of cloned antibodies, P' is the number of uncloned antibodies, H is the number of attributes of the antibodies, antij(h) Is the h attribute of the j antibody, antiξ(h) Is the h attribute of the ξ antibody, ρj,ξRepresents the euclidean distance between the two antibodies;
the cloning operator based on the affinity between antibodies includes:
in the formula: n is a radical ofcThe number of the cloned antibodies, beta is an amplification factor, N is the total number of the antibodies, and round () is a rounding function;
the mutation operation of the immune clonal selection algorithm is normalized by polynomial mutation, namely:
wherein u is [0-1 ]]A random number in between; eta is distribution index, eta>0；δ1＝(Vh-Lh)/(Uh-Lh)，δ2＝(Uh-Vh)/(Uh-Lh)；
The calculation formula of the construction cost of the charging station is as follows:
in the formula: f is the construction cost of the charging station, SiArea of the ith charging station candidate; cland,iUnit price of land for i-th alternative charging station, yiThe charging pile number of the ith alternative charging station; ceThe price for a single charging pile; ziOther fixed costs for the ith charging station candidate.
The service range constraints of the charging station are:
in the service range model, when an electric vehicle is between two nodes, the distance between the electric vehicle and the nearest node is ignored, that is, assuming that all electric vehicles are on the nearest node, the service range of the charging station is the node set served by the electric vehicle, and the contact ratio is represented by the ratio of the number of the same nodes in the service ranges of the two charging stations to the total number of nodes in the service range of the charging station:
|Bi∩Br|/|Bi|≤θ i≠r,|Bi|≠0
in the formula: n is the number of alternative charging stations, Bi、BrSet of service areas for the ith and the r-th charging stations, respectively, BtotalFor the set of planned areas, θ represents the overlap ratio of two charging stations, xiIs a variable from 0 to 1 when xiWhen the charging station is 1, the charging station indicates that the ith alternative charging station is selected as the newly-built charging station; when x isiWhen the charging station is 0, the charging station indicates that the ith alternative charging station is not selected as the newly-built charging station;
the power constraints are:
Pch,i≤Pcs,i·xi
in the formula: p isch,iFor the charging demand in the service range of the i-th alternative charging station, PtotalFor load power in the region, P, other than charging power for electric vehiclesGThe total power of the substations in the region is planned.
Finally, the minimum construction cost of the embodiment of the present embodiment is 1723 ten thousand yuan, 4 charging stations are constructed in the planned area, wherein the number of charging piles of each charging station is at most 10, and at least 5, and the solution iteration result of the algorithm is shown in fig. 5.
The invention provides a scientific and reasonable charging station site selection and volume fixing planning method, aiming at reducing the construction cost of a charging station and minimizing the construction cost of the charging station, the method can carry out site selection and volume fixing planning of the charging station from multiple angles according to the charging load characteristics of each node in a planning area, the position and the capacity of the charging station are planned on the basis of ensuring that the power of a power distribution network in the planning area is not exceeded, the charging requirement of an electric automobile and the coverage rate and the contact ratio of the charging station are considered, and the planning result is more scientific and reasonable.
Claims (5)
1. A location and volume selecting method for an electric vehicle charging station is characterized by comprising the following steps:
s1, analyzing the electric vehicle charging load reduced to each node in the planning area, wherein the charging load comprises fixed requirements and passing requirements;
s2, randomly selecting a plurality of charging stations at different positions from the alternative charging stations within the preset planning area and the number of charging piles in a single charging station, and randomly selecting the number of the charging piles in each charging station;
s3, taking the construction cost as a target function, taking the service range and the power as constraint conditions, and constructing a location and volume model of the charging station;
s4, solving the locating and sizing model of the charging station by adopting an improved immune clone selection algorithm by combining the electric vehicle charging load reduced to each node and the charging stations selected randomly and the number of charging piles in each charging station to obtain an optimal locating and sizing scheme;
the step S1 is specifically to calculate the electric vehicle charging load reduced to the node by the monte carlo algorithm:
wherein, PwFor total charging load of the node, g is the fixed demand quantity of the node, d1For electric private car, d2For electric taxis, Pw,aCharging load power, P, for the ith electric taxi of the w nodew,bCharging load power, gamma, for the w-th node, the b-th electric private carwA traffic flow correction coefficient for the w-th node;
the objective function in step S3 is specifically:
wherein f is the construction cost of the charging station, SiIs the area of the ith charging station candidate, Cland,iPrice of land for i-th alternative charging station, yiNumber of charging piles for ith alternative charging station, CeFor the price of a single charging pile, ZiOther fixed costs for the ith alternative charging station;
the constraint conditions are specifically as follows:
and (3) service range constraint:
|Bi∩Br|/|Bi|≤θi≠r,|Bi|≠0
where n is the number of alternative charging stations, Bi、BrSet of service areas for the ith and the r-th charging stations, respectively, BtotalThe method comprises the following steps that a set of planning areas is represented by theta, wherein the preset value of the contact ratio of two charging stations is represented by theta, and the contact ratio is the ratio of the number of the same nodes in the service ranges of the two charging stations to the total number of nodes in the service ranges of the charging stations;
xiis a variable from 0 to 1 when xiWhen the charging station is 1, the charging station indicates that the ith alternative charging station is selected as the newly-built charging station; when x isiWhen the charging station is 0, the charging station indicates that the ith alternative charging station is not selected as the newly-built charging station;
and (3) power constraint:
Pch,i≤Pcs,i·xi
wherein, Pch,iFor the charging demand in the service range of the i-th alternative charging station, PtotalFor load power in the region, P, other than charging power for electric vehiclesGPlanning the total power of the transformer substation in the area;
the specific calculation process of the improved immune clone selection algorithm is as follows:
t1, initializing parameters;
t2, respectively calculating the affinity between the antibodies and the antigens and the affinity between the antibodies to generate a memory set M and a memory set P', wherein the antibodies correspond to charging stations meeting constraint conditions and the number of corresponding charging piles, and the antigens correspond to a construction cost objective function;
t3, selecting M optimal antibodies from the memory set M, and performing cloning operation and mutation operation to obtain a new population containing NcA random solution;
t4, judging whether the preset maximum iteration number is reached or not, if so, outputting an optimal antibody, namely the optimal value of the construction cost, if not, combining the new population with the old memory set, updating the memory set M and the set P ', then randomly generating d new antibodies to replace the original antibodies in the P', and returning to the step T2;
the affinity between the antibody and the antigen is specifically as follows:
wherein A is1(j) The affinity between the jth antibody and the antigen, C is a penalty factor, err (j) is a constraint condition after the jth antibody is normalized, and for each constraint condition, the affinity is 1 if the affinity is met, and the affinity is 0 if the affinity is not met;
the affinity between the antibodies is specifically as follows:
wherein A is2(j) The affinity between the antibodies of the jth antibody, m is the number of cloned antibodies, i.e., m optimal antibodies, and P' is notThe number of cloned antibodies, i.e., the set P', H is the number of antibody attributes, antij(h) Is the h attribute of the j antibody, antiξ(h) Is the h attribute of the ξ antibody, ρj,ξRepresents the euclidean distance between the two antibodies;
the mutation operation is specifically a polynomial mutation specification operation:
δ1＝(Vh-Lh)/(Uh-Lh)
δ2＝(Uh-Vh)/(Uh-Lh)
η>0
wherein u is [0-1 ]]Eta is distribution index, VhIs the h position, U, of an antibodyhIs a VhUpper limit of (1), LhIs a VhLower limit of (d), δ1And delta2Are all polynomial variant parameters;
the random number in the new population is specifically:
wherein N iscFor the number of antibodies after cloning, β is the amplification factor, N is the total number of antibodies, and round () is a rounding function.
2. The electric vehicle charging station site selection and sizing method as claimed in claim 1, wherein the step S4 specifically comprises the following steps:
s41, screening charging stations and the number of corresponding charging piles meeting constraint conditions according to the randomly selected charging stations and the number of charging piles in each charging station based on service range constraint and power constraint;
s42, calculating by adopting an improved immune clone selection algorithm to obtain an optimal value of the construction cost of the charging station by combining the objective function of the construction cost, the charging stations meeting the constraint conditions and the corresponding quantity of the charging piles;
and S43, outputting the corresponding charging station position and the number of the charging piles in the charging station according to the optimal value of the construction cost of the charging station, and obtaining the optimal site selection and volume fixing scheme.
3. The method according to claim 2, wherein the step S41 is specifically performed based on a service range constraint to determine whether the service range of the randomly selected charging stations covers all nodes in the area, and the contact ratio between each charging station does not exceed a contact ratio preset value;
and based on the power constraint, judging whether the random charging stations meet the charging loads of all nodes in the service range and whether the power of the transformer substation in the planned area meets the total load requirement.
4. The electric vehicle charging station site selection and sizing method according to claim 3, wherein the service range of the charging station in the step S41 is specifically as follows:
Fcs,i＝Pcs,i/(Dev-cs·Co-cs)
wherein, fcs,iIf f, standardizing the attraction of the ith charging station to the electric vehiclecs,iIf f is greater than 0, it means that the node is in the service range of the charging stationcs,i< 0, meaning that the node is not within the range of the charging station, Fcs,iAttraction of the ith charging station to the electric vehicle point, Pcs,iRated capacity for the ith charging station, Dev-csThe shortest distance of the node to the charging station, Co-csRepresents the cost of the starting point reaching the charging station, and is equal to the product of the charge quantity required by the starting point to the charging station and the electricity price, sigma (F)cs,i) For charging station attraction force Fcs,iStandard deviation of (1), Fcs,averAttraction to electric vehicle for charging station Fcs,iThe greater the attraction of the charging station, the greater the likelihood of node preference.
5. The electric vehicle charging station site selection and sizing method as claimed in claim 2, wherein the step S42 specifically comprises the following steps:
s421, combining the construction cost objective function, and respectively calculating construction cost values corresponding to a plurality of charging stations meeting the constraint condition by adopting an improved immune clone selection algorithm;
s422, the minimum value is selected from the plurality of construction cost values as the optimum construction cost value.
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| CN112468984B (en) * | 2020-11-04 | 2023-02-10 | 国网上海市电力公司 | Method for selecting address of power wireless private network base station and related equipment |
| CN113011652A (en) * | 2021-03-18 | 2021-06-22 | 山东交通学院 | Site selection optimization method and system for electric vehicle charging station |
| CN113378079B (en) * | 2021-04-07 | 2023-04-07 | 山东交通学院 | Improved immune algorithm-based electric vehicle charging station site selection method and system |
| CN113139694B (en) * | 2021-05-11 | 2022-12-20 | 北京理工大学前沿技术研究院 | Hydrogenation station site selection method and system |
| CN113642757B (en) * | 2021-06-01 | 2024-02-27 | 北京慧辰资道资讯股份有限公司 | Method and system for planning construction of charging pile of Internet of things based on artificial intelligence |
| CN113971484A (en) * | 2021-10-20 | 2022-01-25 | 朱珈汛 | Planning method of electric vehicle charging station based on CRITIC method and non-cooperative game |
| CN115115268B (en) * | 2022-07-22 | 2023-04-18 | 东南大学溧阳研究院 | Electric vehicle charging pile capacity planning method based on circuit electric coupling and low-carbon constraint |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2505421A2 (en) * | 2011-03-30 | 2012-10-03 | General Electric Company | System and method for optimal load planning of electric vehicle charging |
| CN108460487A (en) * | 2018-03-07 | 2018-08-28 | 国网江苏省电力有限公司无锡供电分公司 | Electric vehicle rapid charging station Optimizing Site Selection constant volume method based on APSO algorithms |
| WO2019109084A1 (en) * | 2017-12-01 | 2019-06-06 | California Institute Of Technology | Optimization framework and methods for adaptive ev charging |
| CN110276517A (en) * | 2019-05-10 | 2019-09-24 | 安徽大学 | A kind of electric automobile charging station site selecting method based on MOPSO algorithm |
| CN110705864A (en) * | 2019-09-27 | 2020-01-17 | 广东电网有限责任公司 | Site selection and volume fixing method for charging station |
-
2020
- 2020-04-21 CN CN202010317420.8A patent/CN111582670B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2505421A2 (en) * | 2011-03-30 | 2012-10-03 | General Electric Company | System and method for optimal load planning of electric vehicle charging |
| WO2019109084A1 (en) * | 2017-12-01 | 2019-06-06 | California Institute Of Technology | Optimization framework and methods for adaptive ev charging |
| CN108460487A (en) * | 2018-03-07 | 2018-08-28 | 国网江苏省电力有限公司无锡供电分公司 | Electric vehicle rapid charging station Optimizing Site Selection constant volume method based on APSO algorithms |
| CN110276517A (en) * | 2019-05-10 | 2019-09-24 | 安徽大学 | A kind of electric automobile charging station site selecting method based on MOPSO algorithm |
| CN110705864A (en) * | 2019-09-27 | 2020-01-17 | 广东电网有限责任公司 | Site selection and volume fixing method for charging station |
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