CN111798057B - Charging station site selection method based on fuzzy-hierarchy profit analysis - Google Patents
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Abstract
The invention provides a charging station site selection method based on fuzzy-hierarchy profit analysis, which takes profit as a foothold to select sites, fully considers a plurality of factors influencing the construction operation of a charging station to perform fuzzy evaluation, and calculates the optimal site selection by combining historical data and experience estimated profit conditions of the operated station.
Description
Technical Field
The invention belongs to the field of data analysis and calculation, and particularly relates to a charging station site selection method based on fuzzy level profit analysis.
Background
According to industry prediction of the new energy automobile industry long-term development planning (2021-2035) (asking for opinion manuscripts), the sales of the new energy automobile in 2025 accounts for 25% of the total sales of automobiles in the current year, more than 700 ten thousand vehicles are predicted, and the holding quantity is more than 2000 ten thousand vehicles. At present, the problem of the endurance mileage of the new energy automobile exists, and the charging can be timely and rapidly carried out, so that the urgent demands of users are met, and each capital dispute invests in construction of a charging station. However, the current pile ratio still exceeds 3:1, the trouble of staggered layout of the piles exists, the overall storage of new energy automobiles is small, the average utilization rate of charging facilities is less than 10%, the recovery period of the construction project of the charging station is long, and the profit space is limited. The profitability of the charging station is improved, the charging station needs to be grabbed from the source, and scientific planning and site selection are important.
At present, most of charging station site selection schemes only consider partial influencing factors, and a site selection method has certain defects and cannot accurately and comprehensively reflect the whole situation of alternative addresses. Therefore, the comprehensiveness, accuracy and systematicness of the existing site selection method are further improved, scientific guidance is provided for construction and operation of the charging station, and investment income of the station is ensured.
Disclosure of Invention
Aiming at the problems existing in the existing charging station site selection, the invention provides a charging station site selection method based on fuzzy-level profit analysis, site selection is carried out by taking profit as a landing point, fuzzy evaluation is carried out by fully considering a plurality of factors influencing the construction operation of the charging station, and optimal site selection is calculated by combining historical data and experience estimated profit conditions of the operated station.
The invention provides a charging station site selection method based on fuzzy hierarchy profit analysis, which sequentially comprises the following steps:
step 1: constructing an alternative address library: analyzing planning, business district, traffic flow and competing information of a designated area, and constructing a plurality of alternative address libraries D by combining land availability and construction safety, wherein the alternative address libraries D are marked as D= { D 1 ,D 2 ,...D n }；
Step 2: constructing an index weight vector K;
step 3: establishing a judgment set of a judgment model, establishing a fuzzy evaluation matrix according to expert evaluation results, and finally calculating to generate a fuzzy comprehensive evaluation value;
step 4: performing fuzzy evaluation on all candidate addresses to obtain fuzzy evaluation values of the candidate addresses;
step 5: calculating the profit condition of the candidate addresses by combining the historical data and experience of the operated stations to obtain the gross profit rate of each candidate address;
step 6: and (5) carrying out weighted calculation on the fuzzy evaluation value and the gross interest rate to obtain a comprehensive evaluation value of the candidate address, and obtaining an optimal address scheme.
In order to better implement the present invention, further, the step 2 specifically operates as:
step 2.1: comprehensively considering the characteristics of a new energy automobile charging station and various influencing factors, establishing a multi-level index system, and considering the aspects of cost, planning, power grid, charging demand and competitors, wherein each aspect comprises a plurality of parameter indexes;
step 2.2: establishing a site selection analytic hierarchy process model of the charging station, wherein the second layer classification number is m, the index layer total index number is n, and the index set C= { C 1 ,C 2 ,...,C n }；
Step 2.3: constructing an analytic hierarchy process n-order judgment matrix P, wherein elements in the matrix P are represented by Cij and the reciprocal Cji thereof:
wherein,C ij as index C i Sum index C j Relative importance value, C ij The specific value of (2) is determined by expert evaluation indexes;
step 2.4: calculating index importance, and obtaining matrix maximum eigenvalue lambda according to judgment matrix P max Corresponding feature vectors; the specific solving formula is as follows:
Pζ＝λ max ζ；
step 2.5: the feature vector is normalized, the normalized value is marked as index weight, and the normalized value is expressed as index weight vector K= { K 1 ,K 2 ,....K n And the third layer index meets
In order to better implement the present invention, further, the step 2.5 calculates an index weight vector k= { K 1 ,K 2 ,....K n After the step, step 2.6 is also needed, wherein the step 2.6 is that the index weight vector K= { K 1 ,K 2 ,....K n The consistency of the two is checked, and the specific checking steps are as follows:
first, a judgment matrix random consistency ratio CR is calculated, wherein the CR is equal to the ratio of CI to RI, and the ratio of CI to RI is calculatedRI is the average consistency ratio of the judgment matrix I;
then, the size of CR is judged:
if CR is less than or equal to 0.1, continuing to perform the step 3 through consistency test;
if CR is>0.1, repeating the steps 2.4-2.6, re-evaluating the relative importance of the parameter indexes, adjusting the judgment matrix P, and finally obtaining an index weight vector K= { K meeting the consistency test 1 ,K 2 ,....K n Then step 3 is performed again.
In order to better implement the present invention, further, the step 3 specifically includes the following steps:
step 3.1: establishing a judging set of a judging model, namely a set formed by various total judging results possibly made by a judging person on a judging object; evaluation was described using a fuzzy language, noted py= { Y 1 ,Y 2 ,...,Y k }；
Step 3.2: inviting a certain number of experts to aim at each index in an index layer, and aiming at the address D= { D of the alternative address library 1 ,D 2 ,...D n Respectively give an evaluation ofThe language value, according to the number of the evaluation personnel and the evaluation result, each candidate address establishes the following fuzzy evaluation matrix R:
wherein i=1, 2,. -%, n; j=1, 2,.. ij Indicating the degree of membership of the ith index element to the jth comment, wherein r is more than or equal to 0 ij ≤1；
Step 3.3: based on the fuzzy evaluation matrix R, comprehensive evaluation is performed, and an evaluation result matrix l=kr, wherein k= { K 1 ,K 2 ,....K n The index weight vector K is obtained by the step 2 analytic hierarchy process;
step 3.4: note l= (l) 1 ,l 2 ,....l n ) Wherein l i Membership degree of candidate address to comment i; calculating the judgment benefit value of the alternative address by using a weighted average method
In order to better implement the present invention, further, the step 4 specifically operates as:
performing fuzzy evaluation on all candidate addresses to obtain a fuzzy evaluation result of the candidate addresses; for each alternative address d= { D 1 ,D 2 ,...,D i ,...,D n Calculating a judgment benefit value V, and judging a result set V= { V 1 ,V 2 ,...,V i ,...,V n }, wherein V i As an alternative address D i The evaluation benefit value of (2).
In order to better implement the present invention, further, the specific operation of step 5 is as follows:
step 5.1: calculating the cost of charging station iWherein->To chargeAnnual average construction costs of station i, +.>Cost of purchase for charging station i, +.>For the line loss cost of charging station i, < >>Station operating cost for charging station i,/->Other costs for charging station i;
step 5.2, calculating the income of the charging station: revenue of the charging station is mainly calculated as S i S is then i Nd×ps×365, where ND is a daily charge amount, ps is a selling price; the electricity selling price is generally larger than the electricity purchasing price, namely ps>pj；
Step 5.3 estimating the Brix of the charging station, denoted ML i ，The gross interest rate set ml= { ML 1 ,ML 2 ,...ML n "wherein ML i As an alternative address D i Is a gross margin value of (1).
In order to better implement the present invention, further, the specific operation of step 6 is as follows: evaluation benefit value V for fuzzy evaluation of station i Hemao rate value ML i Weighting calculation is carried out to obtain comprehensive evaluation values of candidate addresses, and candidate addresses D i The comprehensive evaluation value of (2) is recorded as ZZ i ，ZZ i ＝(V i /100)×α+ML i X beta, wherein alpha, beta is the evaluation benefit value V i Hemao rate value ML i Is satisfied with α+β=1; and finally, the address with the highest comprehensive evaluation value is the optimal scheme.
Compared with the prior art, the invention has the following advantages:
(1) Taking station profit as a foothold, and considering information on charging requirements and competitors to construct a complete and comprehensive index factor set;
(2) Improving the fuzzy evaluation process, and mapping the evaluation grade through a certain relation conversion on the quantifiable data index, so that the evaluation result is more scientific;
(3) The evaluation result is obtained directly through the calculation of the index weight of the third layer and the fuzzy evaluation matrix, the evaluation calculation process of the second layer is omitted, and the calculation efficiency is improved;
(4) Obtaining values of daily charge times, single-cycle charge amounts and the like through operation big data analysis, and predicting cost, income and profit conditions of candidate charging stations by combining existing construction operation experience; and obtaining a final preferred address through fuzzy evaluation and weighted calculation of the profitability, and ensuring the operability and effectiveness of the operation station built at the winning address from the source.
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FIG. 1 is a hierarchical model of the present invention;
FIG. 2 is a mapping relationship of index evaluation according to the present invention.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it should be understood that the described embodiments are only some embodiments of the present invention, but not all embodiments, and therefore should not be considered as limiting the scope of protection. All other embodiments, which are obtained by a worker of ordinary skill in the art without creative efforts, are within the protection scope of the present invention based on the embodiments of the present invention.
Example 1:
the embodiment proposes an example based on measurement and calculation of an addressing scheme of a certain area, as shown in fig. 1 and fig. 2, specifically the steps are as follows:
step 1: and analyzing business district, planning and competing information of a certain designated area, and constructing an alternative address library by combining land availability.
Step 2: and analyzing various factors influencing the site selection of the new energy charging station, and constructing a differential index system. And establishing a weight set corresponding to each influence factor according to the analytic hierarchy process, reflecting the importance degree of each factor, and enabling the weights to meet normalization conditions and pass consistency verification.
Step 3: and comprehensively evaluating by using a fuzzy evaluation method. And establishing a judgment set of the judgment model, establishing a fuzzy evaluation matrix according to expert evaluation results, converting and mapping the index easy to be quantified into an evaluation grade, and finally calculating according to quantitative and qualitative evaluation results to generate a fuzzy comprehensive evaluation value.
Step 4: and carrying out fuzzy evaluation on all the candidate addresses to obtain fuzzy evaluation values of the candidate addresses.
Step 5: and calculating the profit condition of the candidate addresses by combining the historical data and experience of the operated stations to obtain the gross profit rate of each candidate address.
Step 6: and (5) carrying out weighted calculation on the fuzzy evaluation value and the gross interest rate to obtain a comprehensive evaluation value of the candidate address, and obtaining an optimal address scheme.
Working principle: the invention provides a new energy automobile charging station site selection method comprehensively considering profitability and fuzzy evaluation of various indexes, an index system is built around core requirements such as station profitability, user satisfaction and the like, the weight of an evaluation index is calculated through a hierarchical analysis method, quantitative and qualitative comprehensive evaluation is carried out through a fuzzy evaluation method, and finally cost, income and profitability of candidate charging stations are estimated through operation big data analysis and finally the final optimal address is obtained according to weighted calculation.
Example 2:
the present invention on the basis of the above-described embodiment 1, in order to better realize the present invention, further,
the step 1 specifically comprises the following steps:
analyzing planning, business district, traffic flow and competing information of a certain designated area, and constructing an alternative address library by combining land availability, construction safety and the like, wherein the alternative address library is marked as D= { D 1 ,D 2 ,...D n 3 alternative addresses in the area to be planned, then d= { D 1 ,D 2 ,D 3 }。
The step 2 specifically comprises the following steps:
step 2.1 comprehensively considers the characteristics and various influencing factors of the new energy automobile charging station, and establishes a multi-level index system shown in figure 1. The method mainly comprehensively considers five aspects of cost, planning, power grid, charging requirement and competitors, wherein each aspect comprises a plurality of indexes, and 18 indexes are considered in the five aspects.
Step 2.2: establishing an intelligent site selection analytic hierarchy process model of the charging station, wherein the classification number m of the second layer is 5, the total index number n of the third layer, namely the index layer, is 18, and the index set C= { C 1 ,C 2 ,....,C 5 }。
Step 2.3: constructing an analytic hierarchy process n-order judgment matrix P, wherein elements in the matrix P are represented by numbers 1-9 and the inverse thereof,
wherein,C ij as index C i Sum index C j Relative importance value, C ij The specific value of (2) is determined by expert evaluation indexes;
step 2.4: calculating index importance, and combining Pζ=λ according to the judgment matrix P max Zeta, finding out the maximum eigenvalue lambda of the matrix max Corresponding feature vectors;
step 2.5: the feature vector is normalized, the normalized value is marked as index weight, and the index weight vector of the third layer is marked as K= { K 1 ,K 2 ,....K 18 Third layer index meets
Step 2.6: performing consistency test, calculating a judgment matrix random consistency ratio CR,wherein the method comprises the steps ofRI is the average consistency ratio of the judgment matrix I, and if CR is less than or equal to 0.1, the step 3 is completed through consistency test; if CR is>0.1, re-evaluating the relative importance of the parameter indexes, adjusting the judgment matrix P, and repeating the steps 2.4-2.6 to finally obtain an index weight vector K= { K meeting the consistency test 1 ,K 2 ,....K 18 Then step 3 is performed again.
The step 3 specifically comprises the following steps:
step 3.1: and establishing a judgment set of the judgment model, namely a set formed by various total judgment results possibly made by the judgment object by the judgment person. Evaluation was described using a fuzzy language, noted py= { Y 1 ,Y 2 ,...,Y k "very bad (VP)", "bad (P)", "general (F)", "good (G)", and "Very Good (VG)" 5 grades. The corresponding evaluation set of the evaluation description is V= { V 1 ,v 2 ,...,v k }，v i (i=1, 2,., k) is the evaluation value corresponding to the i-th fuzzy evaluation, and is respectively recorded as 0, 40, 60, 80, 100.
Step 3.2: inviting 10 experts to address d= { D for the candidate address library for each index in the index layer 1 ,D 2 ,D 3 Respectively giving comment values, obtaining an expert evaluation result table in table 1 according to the number of evaluation personnel and the evaluation result, and establishing a fuzzy evaluation matrix R as follows for each alternative address:
wherein i=1, 2,. -%, n; j=1, 2,.. ij Indicating the degree of membership of the ith index element to the jth comment, wherein r is more than or equal to 0 ij ≤1。
In the expert evaluation process, for index factors which are easy to quantify, a relation conversion mapping process is performed according to index values, and the values are mapped to different evaluationsAnd the grade enables expert evaluation to be more scientific. If the average daily traffic flow data index value of the electric automobile in a certain month is marked as L, the minimum daily traffic flow in the range of the site selection area is marked as L min The maximum daily traffic flow in the range of the address selection zone is recorded as L max Corresponding to 5 levels of evaluation, we will interval [ L ] min ,L max ]Evenly divided into 5 cells connected in sequence, as shown in fig. 2, the average daily flow L of the electric automobile falls into the output value mu of the ith zone i The larger the traffic flow, the higher the corresponding rating, while all experts in the expert rating table select the i rating.
Candidate address D i The number information of the surrounding competitors can be obtained by investigation and is marked as JZ i The total charging station number of the area to be planned is JZ l Is provided withThe number of existing charging stations is large, so that the existing charging stations can be simply considered to be in competition, and future operation is difficult to gain. The equal proportion of 0 to 1 is divided into 5 intervals, and 5 grades, namely 5, 4, 3, 2 and 1 are sequentially corresponding from small to large. Number of pairs in JZ i /JZ l And when the expert evaluation table is in a certain interval, the grade number of the corresponding mapping of the interval can be obtained, and all the experts in the expert evaluation table select the grade. The expert evaluation table format obtained is shown in table 1 below:
table 1 expert evaluation results table step 3.3: based on the fuzzy evaluation matrix R, comprehensive evaluation is performed, and an evaluation result matrix l=kr, wherein k= { K 1 ,K 2 ,....K n And the index weight vector K is obtained by the step 2 analytic hierarchy process.
Step 3.4: step 3.4: note l= (l) 1 ,l 2 ,....l n ) Wherein n is 5,l i Membership to comment i is the candidate address. In this example, l= (0.604,0.255,0.126,0.015,0) was obtained. By weighted averagingCalculating the judgment benefit value of the alternative addressIn this example v=100×0.604+80×0.255+60×0.126+40×0.015+0×0= 88.96.
The step 4 specifically comprises the following steps:
and performing fuzzy evaluation on all the candidate addresses to obtain a fuzzy evaluation result of the candidate addresses. For each alternative address d= { D 1 ,D 2 ,D 3 And respectively calculating the judgment benefit value V, wherein the specific values are 88.96, 82.30 and 75.51.
The step 5 specifically comprises the following steps: and calculating the profit condition of the candidate addresses by combining the historical data and experience of the operated stations to obtain the gross profit rate of each candidate address.
Step 5.1 calculates the cost of the charging station. The method comprises the following steps:
cost of charging station iWherein->Annual average construction costs for charging station i, < >>Cost of purchase for charging station i, +.>For the line loss cost of charging station i, < >>Station operating cost for charging station i,/->Other costs for charging station i.
The annual average construction cost of the charging station i is:wherein m is i And a is the area and unit price of the candidate station, t i And b is the number and unit price of distribution transformers, g i And c is the number and unit price of the charging piles, g is the capital cost, r 0 For return on investment, m is the target operating life.
The purchase cost of the charging station i is recorded asWherein ND represents a daily charge amount, pj represents a purchase price, L represents a daily traffic flow, CC represents a daily charge number, and CD represents a single charge amount. The average daily traffic flow is obtained through an acquisition and analysis system, and the average daily charging times and the single-cycle charging amount can be obtained through data analysis according to the already-operated charging station data and the type of the charging vehicle.
Line loss cost of charging station iAbout 5% of the cost of electricity purchase.
Station operation and maintenance cost (equipment maintenance, personnel investment, etc.) of charging station iUsually calculated as a percentage of the site construction costs, i.e. +.>Delta is a scale factor, this example delta being considered as 2%.
Other costs of charging station iSuch as marketing activities, hydropower charges, etc., calculated as a percentage of site construction costs, i.e. +.>Epsilon is a scale factor, this example epsilon being considered as 4%.
Step 5.2 calculates the revenue for the charging station. In particular to
Revenue of the charging station is mainly calculated as S i S is then i Nd×ps×365, where ND is the daily charge amount and ps is the electricity selling price. The electricity selling price is generally larger than the electricity purchasing price, namely ps>pj。
Step 5.3 estimating the Brix of the charging station, denoted ML i ，The gross interest rate set ml= { ML 1 ,ML 2 ,...ML n "wherein ML i As an alternative address D i Is a gross margin value of (1). The bristled rate of the three candidate addresses in this example is 0.21, 0.15, 0.18, respectively.
The step 6 specifically comprises the following steps:
weighting calculation is carried out on the fuzzy evaluation value and the gross interest rate of the station to obtain the comprehensive evaluation value of the candidate address, and the candidate address D i The comprehensive evaluation value of (2) is recorded as ZZ i ，ZZ i ＝(V i /100)×α+ML i X β, where α, β are weights of the fuzzy evaluation value and the gross interest rate, satisfies α+β=1. In this example, α is 0.55 and β is 0.45. Weighted d= { D 1 ,D 2 ,D 3 The corresponding comprehensive evaluation values are 0.58, 0.52 and 0.50 respectively.
Address D with highest final overall evaluation value 1 Is the optimal scheme.
Other portions of this embodiment are the same as those of embodiment 1 described above, and thus will not be described again.
The foregoing description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and any simple modification, equivalent variation, etc. of the above embodiment according to the technical matter of the present invention fall within the scope of the present invention.
Claims (6)
1. The charging station site selection method based on fuzzy level profit analysis is characterized by comprising the following steps in sequence:
step 1: constructing an alternative address library: analyzing planning, business district, traffic flow and bidding information of the designated area,combining land availability and construction safety, constructing a plurality of alternative address libraries D, and recording as D= { D 1 ,D 2 ,...D n }；
Step 2: constructing an index weight vector K;
step 3: establishing a judgment set of a judgment model, establishing a fuzzy evaluation matrix according to expert evaluation results, and finally calculating to generate a fuzzy comprehensive evaluation value;
step 4: performing fuzzy evaluation on all candidate addresses to obtain fuzzy evaluation values of the candidate addresses;
step 5: calculating the profit condition of the candidate addresses by combining the historical data and experience of the operated stations to obtain the gross profit rate of each candidate address;
step 6: performing weighted calculation on the fuzzy evaluation value and the gross interest rate to obtain a comprehensive evaluation value of the candidate address, and obtaining an optimal address scheme;
the specific operation of the step 2 is as follows:
step 2.1: comprehensively considering the characteristics of a new energy automobile charging station and various influencing factors, establishing a multi-level index system, and considering the aspects of cost, planning, power grid, charging demand and competitors, wherein each aspect comprises a plurality of parameter indexes;
step 2.2: establishing a site selection analytic hierarchy process model of the charging site, wherein the second layer classification number of the site selection analytic hierarchy process model of the charging site is m, the third layer total index number of the site selection analytic hierarchy process model of the charging site is n, and the index set C= { C 1 ,C 2 ,...,C n }；
Step 2.3: constructing an analytic hierarchy process n-order judgment matrix P, wherein elements in the matrix P are represented by Cij and the reciprocal Cji thereof:
wherein,C ij as index C i Sum index C j Relative importance courseDegree value C ij The specific value of (2) is determined by expert evaluation indexes;
step 2.4: calculating index importance, and obtaining matrix maximum eigenvalue lambda according to judgment matrix P max A corresponding feature vector ζ; the specific solving formula is as follows:
Pζ＝λ max ζ；
step 2.5: the feature vector is normalized, the normalized value is marked as index weight, and the normalized value is expressed as index weight vector K= { K 1 ,K 2 ,....K n And the index of the third layer meets
2. The method for addressing a charging station based on fuzzy-hierarchy profit analysis according to claim 1, wherein the step 2.5 calculates an index weight vector k= { K 1 ,K 2 ,....K n After the step, step 2.6 is also needed, wherein the step 2.6 is that the index weight vector K= { K 1 ,K 2 ,....K n The consistency of the two is checked, and the specific checking steps are as follows:
first, a judgment matrix random consistency ratio CR is calculated, wherein the CR is equal to the ratio of CI to RI, and the ratio of CI to RI is calculatedRI is the average consistency ratio of the judgment matrix I;
then, the size of CR is judged:
if CR is less than or equal to 0.1, continuing to perform the step 3 through consistency test;
if CR is>0.1, re-evaluating the relative importance of the parameter indexes, adjusting the judgment matrix P, and repeating the steps of the steps 2.4-2.6 to finally obtain an index weight vector K= { K meeting the consistency test 1 ,K 2 ,....K n Step 3 was performed.
3. The method for locating a charging station based on fuzzy-hierarchy profit analysis according to claim 2, wherein the step 3 comprises the following steps:
step 3.1: establishing a judging set of a judging model, namely a set formed by various total judging results possibly made by a judging person on a judging object; evaluation was described using a fuzzy language, noted py= { Y 1 ,Y 2 ,...,Y k }；
Step 3.2: inviting a certain number of experts to aim at each index in an index layer, and aiming at the address D= { D of the alternative address library 1 ,D 2 ,...D n Respectively giving comment values, and establishing the following fuzzy evaluation matrix R for each candidate address according to the number of evaluation personnel and the evaluation result:
wherein i=1, 2,. -%, n; j=1, 2,.. ij Indicating the degree of membership of the ith index element to the jth comment, wherein r is more than or equal to 0 ij ≤1；
Step 3.3: based on the fuzzy evaluation matrix R, comprehensive evaluation is performed, and an evaluation result matrix l=kr, wherein k= { K 1 ,K 2 ,....K n The index weight vector K is obtained by the step 2 analytic hierarchy process;
step 3.4: note l= (l) 1 ,l 2 ,....l n ) Wherein l i Membership degree of candidate address to comment i; calculating the judgment benefit value of the alternative address by using a weighted average methodWherein v is i And the evaluation benefit value is the evaluation benefit value of the alternative address.
4. A method of charging station addressing based on fuzzy-level profitability analysis as set forth in claim 3, wherein said step 4 is operative to:
performing fuzzy evaluation on all candidate addresses to obtain a fuzzy evaluation result of the candidate addresses; for each alternativeAddress d= { D 1 ,D 2 ,...,D i ,...,D n Calculating a judgment benefit value V, and judging a result set V= { V 1 ,V 2 ,...,V i ,...,V n }, wherein V i As an alternative address D i The evaluation benefit value of (2).
5. The method for locating a charging station based on fuzzy-level profit analysis according to claim 4, wherein the specific operations of step 5 are as follows:
step 5.1: calculating the cost of charging station iWherein->Annual average construction costs for charging station i, < >>Cost of purchase for charging station i, +.>For the line loss cost of charging station i, < >>Station operating cost for charging station i,/->Other costs for charging station i;
step 5.2, calculating the income of the charging station: revenue of the charging station is mainly calculated as S i S is then i Nd×ps×365, where ND is a daily charge amount, ps is a selling price; the electricity selling price is generally larger than the electricity purchasing price pj, namely ps>pj；
Step 5.3 estimating the Brix of the charging station, denoted ML i ，The gross interest rate set ml= { ML 1 ,ML 2 ,...,ML i ,...,ML n "wherein ML i As an alternative address D i Is a gross margin value of (1).
6. The method for locating a charging station based on fuzzy-level profit analysis according to claim 5, wherein the specific operations in step 6 are as follows: evaluation benefit value V for fuzzy evaluation of station i Hemao rate value ML i Weighting calculation is carried out to obtain comprehensive evaluation values of candidate addresses, and candidate addresses D i The comprehensive evaluation value of (2) is recorded as ZZ i ，ZZ i ＝(V i /100)×α+ML i X beta, wherein alpha, beta is the evaluation benefit value V i Hemao rate value ML i Is satisfied with α+β=1; and finally, the address with the highest comprehensive evaluation value is the optimal scheme.
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