CN111091309A - Method for evaluating economic benefits of electric vehicle charging network operation - Google Patents

Method for evaluating economic benefits of electric vehicle charging network operation Download PDF

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CN111091309A
CN111091309A CN202010038348.5A CN202010038348A CN111091309A CN 111091309 A CN111091309 A CN 111091309A CN 202010038348 A CN202010038348 A CN 202010038348A CN 111091309 A CN111091309 A CN 111091309A
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张晶
刘敦楠
张元星
王玲湘
李涛永
刘明光
李斌
蒋林洳
刁晓虹
李康
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
North China Electric Power University
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China Electric Power Research Institute Co Ltd CEPRI
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Abstract

The invention relates to an economic benefit evaluation method for operation of an electric vehicle charging network, which comprises the following steps of ①, constructing an index system and setting an evaluation standard, ②, determining index basic weight, setting the index basic weight by using a comprehensive weighting method combining a subjective weighting method, a chromatographic analysis method and an objective evaluation method, and an entropy method, ③, determining index differentiation weight, giving time to each index, calculating corresponding time weight vectors, and using the time weight vectors as differentiation weights, ④, calculating scene probability, generating a scene total set by using a Latin hypercube sampling method, reducing and clustering scenes by using a K-means algorithm, and calculating the scene probability, ⑤, calculating an evaluation result, obtaining corresponding scores according to the index values and the evaluation standard, and calculating an adaptability evaluation result of a planning scheme by combining the scene probability and the time differentiation weights.

Description

Method for evaluating economic benefits of electric vehicle charging network operation
Technical Field
The invention relates to an economic benefit evaluation method for operation of an electric automobile charging network.
Background
In recent years, the electric automobiles develop rapidly, and the occupation ratio of the electric automobiles is higher and is more and more widely distributed. The corresponding problem of endurance charging of the electric vehicle becomes an important issue concerned by people. Meanwhile, for an electric vehicle charging network operator, in addition to ensuring the quality of service of customers, enterprises need to perform economic evaluation on the charging network to ensure that the enterprises obtain reasonable return on the investment and operation of the charging network, and then plan a next decision. Therefore, the economic benefit evaluation system of the electric vehicle charging network provided by the patent has important significance for electric vehicle operation.
Aiming at the situations that related research conditions for evaluating the economical efficiency of the charging network are lacked at present and the application scenes of related index systems are different, the invention provides an electric vehicle charging network economic benefit evaluation system for the first time aiming at the investment and operation of the electric vehicle charging network; the benefit and cost calculation mode of the electric vehicle charging network is pointed out in a targeted manner, and the electric vehicle charging network is converted into an index system by using a standardized method; a method for setting the basic weight of the index by a comprehensive weighting method combining a subjective weighting method, a chromatographic analysis method, an objective evaluation method and an entropy value method is provided, and differential weight is set by the index surplus time degree; a scene total set is generated by adopting a Latin hypercube sampling method, and the scene occurrence probability is innovatively calculated; and finally, calculating through a correlation standard to obtain an adaptability evaluation result.
Disclosure of Invention
Aiming at the problem of economic benefit evaluation of electric vehicle charging network operation, the invention provides an actual and reliable economic benefit evaluation method of electric vehicle charging network operation.
An electric vehicle charging network operation economic benefit evaluation method comprises the following steps:
①, constructing an index system and setting an evaluation standard;
step ②, determining the basic weight of the index, namely setting the basic weight of the index by utilizing a comprehensive weighting method combining a subjective weighting method-chromatographic analysis method and an objective evaluation method-entropy method;
step ③, determining index differentiation weight, namely endowing each index with time, calculating corresponding time weight vector as differentiation weight;
④, calculating scene probability, namely generating a scene total set by using a Latin hypercube sampling method, performing clustering reduction on the scene by using a K-means algorithm, and calculating the scene probability;
and ⑤, calculating an evaluation result, namely obtaining a corresponding score according to the index value and the evaluation standard, calculating an adaptive evaluation result of the planning scheme by combining scene probability and time differentiation weight, and firstly, constructing an index system and setting the evaluation standard, wherein the index system comprises a charging network builder economic benefit index and an auxiliary service economic benefit index.
(1) Charging network builder economic benefit index
1) Annual construction investment cost of charging station
Figure BDA0002366826530000021
In the formula, eiThe number of the transformers configured in the charging station i; a is the unit price of the transformer; m isiThe number of chargers configured for the charging station i; b is the unit price of the charger; liThe length of a medium-voltage line connected to a power distribution network for a charging station; c. ClThe unit cost of the medium voltage line; omegaiThe capital cost for charging station i; r is0The current rate is the current rate; z is the operating life.
2) Operating and maintenance costs of charging stations
Generally, each cost value is not clear, the annual operation maintenance cost can be considered to be calculated according to the percentage of initial investment, and if the scale factor is η, the annual operation maintenance cost of the charging station i is η
C2i＝(eia+mib+lici+ωi)η (2)
3) Network loss annual cost of charging station
Figure BDA0002366826530000022
In the formula, CFeAnd CCuThe iron and copper losses of the transformer are respectively; cLConverting the line loss in the charging station into the loss value of each charger; cDThe charging loss of a single charger; k is a radical oftThe charging time is the synchronous rate of a plurality of chargers in the charging station; t isvAveraging the effective charging time per day for the charging station; and p is the charging price.
4) Charging service revenue
From charging facility operationFrom the perspective of charging service income I from facility usage fee I1And charging electric charge I2And subsidies obtained from government I3Earning income from 3 channels
I＝I1+I2+I3(4)
5) Charging station revenue
For power supply enterprises, the fundamental goal of building electric vehicle charging stations is revenue, i.e., economy of operation. The site selection and the location of the electric vehicle charging station are influenced by regional load distribution (charging demand), and the initial construction cost, the annual operation cost and the power distribution system loss caused by the access of a power grid of the charging station can be determined.
Figure BDA0002366826530000031
In the formula, F is the economy of system operation and consists of a revenue function and a cost function; r is an annual economic benefit function as a revenue function of the charging station; cost function from initial construction cost
Figure BDA0002366826530000032
Annual operating costs
Figure BDA0002366826530000033
And the network loss cost of the power distribution network system
Figure BDA0002366826530000034
Composition is carried out; n is the number of charging stations.
(2) Auxiliary service economic benefit index
1) Avoiding investment costs of power generation and transmission equipment
The electric automobile is as portable load, thereby through charging the guide realization electric automobile and charging in order to electric automobile, can fill the millet to the electric wire netting peak clipping and bring obvious effect to it is poor to reduce load peak millet, reduces the drawback of the too much input operation of peak value moment unit, avoids equipment to open and stop the cost loss who causes.
Rgen＝CgenΔPpeak(6)
Wherein Cgen is the unit installed capacity cost, △ PpeakReduced system peak charge for the charging network.
2) Reducing capacity expansion cost of power distribution system
Through carrying out orderly management to electric automobile of electric automobile charging network, can reach the effect of avoiding or reducing distribution network dilatation cost. Under the disordered charging state, the phenomena of node voltage reduction, transformer overload, line overload and the like caused by more charging loads of the electric automobile influence the stable operation of the power distribution network, so that the problem of power distribution network capacity expansion caused by overlarge charging network load has to be solved. The problems can be effectively solved through ordered charging management, and the costs of transformer substation capacity expansion, new line construction, new transformer substation construction and the like are reduced. The expansion cost is reduced by constructing an expansion planning model with the minimum investment cost of the power distribution network system as a target.
Figure BDA0002366826530000041
Wherein, ci,aExpanding the unit capacity of the transformer substation i; m isi,aCapacity expanded for substation i; c. Ci,bUnit cost, m, required for new construction of the ith substationi,bCapacity required to be newly built for the ith substation; c. Cij,cThe unit length cost required for newly building the ij section of line; n isij,cThe number of lines, l, to be added to the ij sectionijAn increased line length is required for the ij section.
3) Reducing system network loss cost
The ordered management of the electric automobile can reduce the network loss cost of a power distribution network system and improve the benefit of the power distribution network, and a model adopting a difference comparison method is shown as follows.
Figure BDA0002366826530000042
Wherein R is the annual economic benefit brought by the improvement of the system network loss level;
Figure BDA0002366826530000043
the value of the system grid loss for the disordered charge,
Figure BDA0002366826530000044
the system network loss in the ordered management state is obtained.
4) Reducing ancillary service purchase costs
The implementation of the ordered charging strategy of the electric automobile can effectively reduce the system compliance fluctuation, thereby obviously reducing the requirement of the system on auxiliary service. Here the approximate evaluation is done with reduced spinning reserve procurement costs.
Figure BDA0002366826530000051
Wherein, CupAdjusting up the reserve capacity price; cdownTo adjust reserve capacity prices downward;
Figure BDA0002366826530000052
in order to adjust up the price of the reserve capacity,
Figure BDA0002366826530000053
the reserve capacity price is adjusted downward.
5) Auxiliary service economic benefits
The economic benefits brought by the auxiliary service provided by the electric automobile mainly comprise three aspects, frequency modulation capacity or rotary standby benefits, benefits of electric automobile reverse discharge and environmental protection subsidies. While the costs in the ancient city that provided ancillary services primarily included charging and battery depletion costs.
Figure BDA0002366826530000054
Wherein R isr(t) capacity gain for frequency modulation or spinning reserve; er(t) is the reverse discharge electric energy yield,
Figure BDA0002366826530000055
for charging and regulating the associated costs ξr(t) is an environment-friendly patch,
Figure BDA0002366826530000056
at the cost of battery depletion.
Step two: determining the basic weight of the index: setting the basic index weight by a comprehensive weighting method combining a subjective weighting method, a chromatographic analysis method, an objective evaluation method and an entropy value method;
due to different dimensions and orders of magnitude of different indexes, obvious differences exist. Therefore, a method for standardizing the indexes is needed to de-dimension the indexes, and a percentile scoring method is adopted to convert the normalized indexes into the indexes. The max-min normalization function is mainly used. Wherein x isijIs the initialized value of the j index of the ith area. The forward index is normalized by the formula (4-140), the reverse index is normalized by the formula (4-141), and the moderate index is normalized by the formula (4-142).
Figure BDA0002366826530000061
Figure BDA0002366826530000062
Figure BDA0002366826530000063
Step three: determining the index differentiation weight: giving time degrees to each index, calculating corresponding time weight vectors, and taking the time weight vectors as differentiation weights;
the weight calculation is carried out on the constructed index system, the weighting method combining subjectivity and objectivity is adopted, the defect of strong subjectivity caused by the subjective experience of a decision maker on the weight is avoided, and the entropy weight method can carry out objective weighting on the index according to the change degree of the index. The time weight and scene probability technology provided by the above sections is used for carrying out differentiation weight design, so that the problems of different importance degrees of indexes to time sequences and different index numerical values under different scenes are effectively solved. The weight calculation model is shown in fig. 1. 1) AHP analytic hierarchy process
AHP is a decision-making method for layering and mathematics of complex problems by constructing a hierarchical structure model and using less quantitative information after deeply analyzing the intrinsic relation of indexes. The expert compares every two indexes under the same index, determines the importance degree (namely weight) of the indexes in the level relative to the indexes in the upper layer, and then synthesizes the index weights layer by layer to obtain the comprehensive weight of the indexes at the bottommost layer relative to the indexes at the highest layer.
a. Structural judgment matrix
AHP constructs the judging matrix according to the index level separately, as to each index subordinate to the previous level, construct the judging matrix by pairwise comparison method, until the last level. The specific judgment matrix is as follows:
A＝{aij}n×n(4-118)
in the formula, aijThe decision matrix a obtained by this method, which characterizes the relative importance of the indices i and j (i, j ═ 1,2, …, n), has the following properties:
Figure BDA0002366826530000071
b. calculating a weight coefficient
And according to the judgment matrix A obtained in the last step, the index weight of the index relative to the index of the previous stage is obtained. When calculating the weights, it is assumed that the judgment matrix has consistency, i.e. aij·ajk＝ajk. Calculating the weight coefficient omega of each judgment matrix, multiplying each index by rows and dividing by the power of n, namely obtaining the geometric mean value omega' of each row index as
Figure BDA0002366826530000072
Then, the omega' is normalized to obtain the index xjHas a weight coefficient of
Figure BDA0002366826530000073
Then (omega)1,ω2,…,ωn) The relative weight of each index to the same upper-level index.
c. Performing consistency check
Because personal preferences are different, the judgment matrix given by each expert does not meet the complete consistency, and therefore, the consistency check is needed to be carried out on the judgment matrix. If the test is passed, the feature vector (normalization) is a weight vector; if not, consideration should be given to reconstructing the judgment matrix.
First, the maximum characteristic root lambda of the original matrix is calculatedmaxAnd calculating a consistency test index by using the maximum characteristic root:
Figure BDA0002366826530000074
for the judgment matrix with a large order, the applicability of the consistency check method is reduced, the consistency index CI needs to be corrected, and the average random consistency index RI is introduced and used as a calibration value of the consistency check.
Introducing a consistency ratio index CR, and correcting the consistency criterion of the judgment matrix, wherein the CR is defined as follows:
Figure BDA0002366826530000081
when CR <0.1, the consistency of the decision matrix is considered acceptable; when CR >0.1, the judgment matrix is considered to deviate from consistency, and the judgment matrix is appropriately corrected.
2) Entropy method
The entropy represents the size of the amount of information contained in the index. For an index sample, the higher the degree of ordering, the smaller the sample variance, and the smaller the amount of information it contains; conversely, the higher the degree of disorder, the greater the sample variance, and the greater the amount of information it contains.
In the evaluation index, how much the index contains information amount can be represented by the size of the index weight. The larger the amount of information, the higher the index weight. The advantage of the entropy weight method is that the value and weight of its data are defined entirely from the degree of dispersion of the data itself.
The entropy weight method comprises the following calculation steps:
a. constructing a raw data matrix
And collecting the original data of the indexes and carrying out standardization treatment. And converting each type of index into an extremely large index, and unifying the evaluation standard. The history data of the evaluated object is represented by the following matrix:
Figure BDA0002366826530000082
in the formula, YijThe normalized value of the i index in the j-th year is shown, n is the total index number, and m is the total historical year.
b. Calculating contribution degrees of different historical years
Under the same index, the proportion occupied by different historical years is calculated, and as the contribution degree of the year, the calculation formula is as follows:
Figure BDA0002366826530000091
in the formula, PijAnd the contribution degree of the jth historical year under the ith index attribute is shown.
c. Calculating an entropy value of an indicator
Entropy value eiThe total amount of contribution of all evaluation years to the ith index is expressed by the following formula:
Figure BDA0002366826530000092
e when the contribution degrees of each historical year under a certain index attribute tend to be consistentiTending to 1. Since the contribution degrees tend to be consistent, it is stated that the index attribute does not play a role in the decision making, and particularly when the contribution degrees are completely equal, the target attribute may not be considered, that is, the weight of the index may be considered to be 0.
d. Calculating the difference coefficient of the index
Coefficient of variation giThe degree of inconsistency of the contribution of the ith index in each historical year is shown as follows:
gi＝1-ei(4-125)
obviously, giThe larger the index, the more the index should beIs regarded as important.
e. Determining a weight coefficient
Weight coefficient wiFor the weight coefficient after normalization, the following formula is shown:
Figure BDA0002366826530000093
3) combined empowerment
The combination weighting generally adopts a linear weighting combination method, and the calculation formula is as follows:
θi＝αυi+(1-α)μi(4-127)
in the formula, uiRepresents the subjective weight vector, Σ υi＝1；
μiRepresents the objective weight vector, ∑ μi＝1；
θiRepresents the combining weight vector, ∑ θi＝1；
α represents the importance of the subjective weighting method, 0 ≦ α ≦ 1.
Step four: calculating the scene probability: generating a scene total set by utilizing a Latin hypercube sampling method, clustering and reducing scenes by utilizing a K-means algorithm, and calculating scene probability;
step five: and calculating an evaluation result: obtaining a corresponding score according to the index value and the evaluation standard; and calculating the adaptability evaluation result of the planning scheme by combining the scene probability and the time differentiation weight.
1) Index calculation
The time and probability factors are comprehensively considered, and the calculation formula of the single index score is as follows:
Figure BDA0002366826530000101
in the formula, ZiDenotes the i-th index score, gtkRepresents the score, p, of the index in the kth scene of year ttkProbability of occurrence of kth scene in t year, wtRepresenting the differentiation weight of the indicator in the t year.
2) Composite score
And multiplying and summing the scores of the single indexes and the basic weights of the indexes to obtain a final evaluation result, wherein the calculation formula is as follows:
Figure BDA0002366826530000102
wherein Z represents the final score, θiThe basic weight of the i-th index is expressed.
Drawings
Fig. 1 is a diagram of a weight calculation model.
Detailed Description
The technical solutions of the present invention will be described clearly and completely by the following embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method for evaluating the economic benefit of the operation of the charging network of the electric automobile is characterized by comprising the following steps of:
①, constructing an index system and setting an evaluation standard;
step ②, determining the basic weight of the index, namely setting the basic weight of the index by utilizing a comprehensive weighting method combining a subjective weighting method-chromatographic analysis method and an objective evaluation method-entropy method;
step ③, determining index differentiation weight, namely endowing each index with time, calculating corresponding time weight vector as differentiation weight;
④, calculating scene probability, namely generating a scene total set by using a Latin hypercube sampling method, performing clustering reduction on the scene by using a K-means algorithm, and calculating the scene probability;
and ⑤, calculating an evaluation result, namely obtaining a corresponding score according to the index value and the evaluation standard, calculating an adaptive evaluation result of the planning scheme by combining scene probability and time differentiation weight, and firstly, constructing an index system and setting the evaluation standard, wherein the index system comprises a charging network builder economic benefit index and an auxiliary service economic benefit index.
(1) Charging network builder economic benefit index
1) Annual construction investment cost of charging station
Figure BDA0002366826530000111
In the formula, eiThe number of the transformers configured in the charging station i; a is the unit price of the transformer; m isiThe number of chargers configured for the charging station i; b is the unit price of the charger; liThe length of a medium-voltage line connected to a power distribution network for a charging station; c. ClThe unit cost of the medium voltage line; omegaiThe capital cost for charging station i; r is0The current rate is the current rate; z is the operating life.
2) Operating and maintenance costs of charging stations
Generally, each cost value is not clear, the annual operation maintenance cost can be considered to be calculated according to the percentage of initial investment, and if the scale factor is η, the annual operation maintenance cost of the charging station i is η
C2i＝(eia+mib+lici+ωi)η (2)
3) Network loss annual cost of charging station
Figure BDA0002366826530000121
In the formula, CFeAnd CCuThe iron and copper losses of the transformer are respectively; cLConverting the line loss in the charging station into the loss value of each charger; cDThe charging loss of a single charger; k is a radical oftThe charging time is the synchronous rate of a plurality of chargers in the charging station; t isvAveraging the effective charging time per day for the charging station; and p is the charging price.
4) Charging service revenue
From the perspective of charging facility operation, charging service revenue I is from facility usage fee I1And charging electric charge I2And subsidies obtained from government I3Earning income from 3 channels
I＝I1+I2+I3(4)
5) Charging station revenue
For power supply enterprises, the fundamental goal of building electric vehicle charging stations is revenue, i.e., economy of operation. The site selection and the location of the electric vehicle charging station are influenced by regional load distribution (charging demand), and the initial construction cost, the annual operation cost and the power distribution system loss caused by the access of a power grid of the charging station can be determined.
Figure BDA0002366826530000131
In the formula, F is the economy of system operation and consists of a revenue function and a cost function; r is an annual economic benefit function as a revenue function of the charging station; cost function from initial construction cost
Figure BDA0002366826530000132
Annual operating costs
Figure BDA0002366826530000133
And the network loss cost of the power distribution network system
Figure BDA0002366826530000134
Composition is carried out; n is the number of charging stations.
(2) Auxiliary service economic benefit index
1) Avoiding investment costs of power generation and transmission equipment
The electric automobile is as portable load, thereby through charging the guide realization electric automobile and charging in order to electric automobile, can fill the millet to the electric wire netting peak clipping and bring obvious effect to it is poor to reduce load peak millet, reduces the drawback of the too much input operation of peak value moment unit, avoids equipment to open and stop the cost loss who causes.
Rgen＝CgenΔPpeak(6)
Wherein Cgen is the unit installed capacity cost, △ PpeakReduced system peak charge for the charging network.
2) Reducing capacity expansion cost of power distribution system
Through carrying out orderly management to electric automobile of electric automobile charging network, can reach the effect of avoiding or reducing distribution network dilatation cost. Under the disordered charging state, the phenomena of node voltage reduction, transformer overload, line overload and the like caused by more charging loads of the electric automobile influence the stable operation of the power distribution network, so that the problem of power distribution network capacity expansion caused by overlarge charging network load has to be solved. The problems can be effectively solved through ordered charging management, and the costs of transformer substation capacity expansion, new line construction, new transformer substation construction and the like are reduced. The expansion cost is reduced by constructing an expansion planning model with the minimum investment cost of the power distribution network system as a target.
Figure BDA0002366826530000141
Wherein, ci,aExpanding the unit capacity of the transformer substation i; m isi,aCapacity expanded for substation i; c. Ci,bUnit cost, m, required for new construction of the ith substationi,bCapacity required to be newly built for the ith substation; c. Cij,cThe unit length cost required for newly building the ij section of line; n isij,cThe number of lines, l, to be added to the ij sectionijAn increased line length is required for the ij section.
3) Reducing system network loss cost
The ordered management of the electric automobile can reduce the network loss cost of a power distribution network system and improve the benefit of the power distribution network, and a model adopting a difference comparison method is shown as follows.
Figure BDA0002366826530000142
Wherein R is the annual economic benefit brought by the improvement of the system network loss level;
Figure BDA0002366826530000143
the value of the system grid loss for the disordered charge,
Figure BDA0002366826530000144
the system network loss in the ordered management state is obtained.
4) Reducing ancillary service purchase costs
The implementation of the ordered charging strategy of the electric automobile can effectively reduce the system compliance fluctuation, thereby obviously reducing the requirement of the system on auxiliary service. Here the approximate evaluation is done with reduced spinning reserve procurement costs.
Figure BDA0002366826530000145
Wherein, CupAdjusting up the reserve capacity price; cdownTo adjust reserve capacity prices downward;
Figure BDA0002366826530000146
in order to adjust up the price of the reserve capacity,
Figure BDA0002366826530000147
the reserve capacity price is adjusted downward.
5) Auxiliary service economic benefits
The economic benefits brought by the auxiliary service provided by the electric automobile mainly comprise three aspects, frequency modulation capacity or rotary standby benefits, benefits of electric automobile reverse discharge and environmental protection subsidies. While the costs in the ancient city that provided ancillary services primarily included charging and battery depletion costs.
Figure BDA0002366826530000151
Wherein R isr(t) capacity gain for frequency modulation or spinning reserve; er(t) is the reverse discharge electric energy yield,
Figure BDA0002366826530000152
to chargeElectricity and regulatory related costs, ξr(t) is an environment-friendly patch,
Figure BDA0002366826530000153
at the cost of battery depletion.
Step two: determining the basic weight of the index: setting the basic index weight by a comprehensive weighting method combining a subjective weighting method, a chromatographic analysis method, an objective evaluation method and an entropy value method;
due to different dimensions and orders of magnitude of different indexes, obvious differences exist. Therefore, a method for standardizing the indexes is needed to de-dimension the indexes, and a percentile scoring method is adopted to convert the normalized indexes into the indexes. The max-min normalization function is mainly used. Wherein x isijIs the initialized value of the j index of the ith area. The forward index is normalized by the formula (4-140), the reverse index is normalized by the formula (4-141), and the moderate index is normalized by the formula (4-142).
Figure BDA0002366826530000154
Figure BDA0002366826530000155
Figure BDA0002366826530000156
Step three: determining the index differentiation weight: giving time degrees to each index, calculating corresponding time weight vectors, and taking the time weight vectors as differentiation weights;
the weight calculation is carried out on the constructed index system, the weighting method combining subjectivity and objectivity is adopted, the defect of strong subjectivity caused by the subjective experience of a decision maker on the weight is avoided, and the entropy weight method can carry out objective weighting on the index according to the change degree of the index. The time weight and scene probability technology provided by the above sections is used for carrying out differentiation weight design, so that the problems of different importance degrees of indexes to time sequences and different index numerical values under different scenes are effectively solved. The weight calculation model is shown in fig. 1. 1) AHP analytic hierarchy process
AHP is a decision-making method for layering and mathematics of complex problems by constructing a hierarchical structure model and using less quantitative information after deeply analyzing the intrinsic relation of indexes. The expert compares every two indexes under the same index, determines the importance degree (namely weight) of the indexes in the level relative to the indexes in the upper layer, and then synthesizes the index weights layer by layer to obtain the comprehensive weight of the indexes at the bottommost layer relative to the indexes at the highest layer.
a. Structural judgment matrix
AHP constructs the judging matrix according to the index level separately, as to each index subordinate to the previous level, construct the judging matrix by pairwise comparison method, until the last level. The specific judgment matrix is as follows:
A＝{aij}n×n(4-118)
in the formula, aijThe decision matrix a obtained by this method, which characterizes the relative importance of the indices i and j (i, j ═ 1,2, …, n), has the following properties:
Figure BDA0002366826530000161
b. calculating a weight coefficient
And according to the judgment matrix A obtained in the last step, the index weight of the index relative to the index of the previous stage is obtained. When calculating the weights, it is assumed that the judgment matrix has consistency, i.e. aij·ajk＝ajk. Calculating the weight coefficient omega of each judgment matrix, multiplying each index by rows and dividing by the power of n, namely obtaining the geometric mean value omega' of each row index as
Figure BDA0002366826530000171
Then, the omega' is normalized to obtain the index xjHas a weight coefficient of
Figure BDA0002366826530000172
Then (omega)1,ω2,…,ωn) For the relative weight of each index to the same upper indexAnd (4) heavy.
c. Performing consistency check
Because personal preferences are different, the judgment matrix given by each expert does not meet the complete consistency, and therefore, the consistency check is needed to be carried out on the judgment matrix. If the test is passed, the feature vector (normalization) is a weight vector; if not, consideration should be given to reconstructing the judgment matrix.
First, the maximum characteristic root lambda of the original matrix is calculatedmaxAnd calculating a consistency test index by using the maximum characteristic root:
Figure BDA0002366826530000173
for the judgment matrix with a large order, the applicability of the consistency check method is reduced, the consistency index CI needs to be corrected, and the average random consistency index RI is introduced and used as a calibration value of the consistency check.
Introducing a consistency ratio index CR, and correcting the consistency criterion of the judgment matrix, wherein the CR is defined as follows:
Figure BDA0002366826530000174
when CR <0.1, the consistency of the decision matrix is considered acceptable; when CR >0.1, the judgment matrix is considered to deviate from consistency, and the judgment matrix is appropriately corrected.
2) Entropy method
The entropy represents the size of the amount of information contained in the index. For an index sample, the higher the degree of ordering, the smaller the sample variance, and the smaller the amount of information it contains; conversely, the higher the degree of disorder, the greater the sample variance, and the greater the amount of information it contains.
In the evaluation index, how much the index contains information amount can be represented by the size of the index weight. The larger the amount of information, the higher the index weight. The advantage of the entropy weight method is that the value and weight of its data are defined entirely from the degree of dispersion of the data itself.
The entropy weight method comprises the following calculation steps:
a. constructing a raw data matrix
And collecting the original data of the indexes and carrying out standardization treatment. And converting each type of index into an extremely large index, and unifying the evaluation standard. The history data of the evaluated object is represented by the following matrix:
Figure BDA0002366826530000181
in the formula, YijThe normalized value of the i index in the j-th year is shown, n is the total index number, and m is the total historical year.
b. Calculating contribution degrees of different historical years
Under the same index, the proportion occupied by different historical years is calculated, and as the contribution degree of the year, the calculation formula is as follows:
Figure BDA0002366826530000182
in the formula, PijAnd the contribution degree of the jth historical year under the ith index attribute is shown.
c. Calculating an entropy value of an indicator
Entropy value eiThe total amount of contribution of all evaluation years to the ith index is expressed by the following formula:
Figure BDA0002366826530000183
e when the contribution degrees of each historical year under a certain index attribute tend to be consistentiTending to 1. Since the contribution degrees tend to be consistent, it is stated that the index attribute does not play a role in the decision making, and particularly when the contribution degrees are completely equal, the target attribute may not be considered, that is, the weight of the index may be considered to be 0.
d. Calculating the difference coefficient of the index
Coefficient of variation giThe degree of inconsistency of the contribution of the ith index in each historical year is shown as follows:
gi＝1-ei(4-125)
obviously, giThe larger the index, the more important the index should be.
e. Determining a weight coefficient
Weight coefficient wiFor the weight coefficient after normalization, the following formula is shown:
Figure BDA0002366826530000191
3) combined empowerment
The combination weighting generally adopts a linear weighting combination method, and the calculation formula is as follows:
θi＝αυi+(1-α)μi(4-127)
in the formula, uiRepresents the subjective weight vector, Σ υi＝1；
μiRepresents the objective weight vector, ∑ μi＝1；
θiRepresents the combining weight vector, ∑ θi＝1；
α represents the importance of the subjective weighting method, 0 ≦ α ≦ 1.
Step four: calculating the scene probability: generating a scene total set by utilizing a Latin hypercube sampling method, clustering and reducing scenes by utilizing a K-means algorithm, and calculating scene probability;
step five: and calculating an evaluation result: obtaining a corresponding score according to the index value and the evaluation standard; and calculating the adaptability evaluation result of the planning scheme by combining the scene probability and the time differentiation weight.
1) Index calculation
The time and probability factors are comprehensively considered, and the calculation formula of the single index score is as follows:
Figure BDA0002366826530000201
in the formula, ZiDenotes the i-th index score, gtkRepresents the score, p, of the index in the kth scene of year ttkProbability of occurrence of kth scene in t year, wtRepresenting the differentiation weight of the indicator in the t year.
2) Composite score
And multiplying and summing the scores of the single indexes and the basic weights of the indexes to obtain a final evaluation result, wherein the calculation formula is as follows:
Figure BDA0002366826530000202
wherein Z represents the final score, θiThe basic weight of the i-th index is expressed.
The above examples are merely illustrative of embodiments of the present invention, which are described in more detail and detail, and should not be construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.

Claims (1)

1. The method for evaluating the economic benefit of the operation of the charging network of the electric automobile is characterized by comprising the following steps of:
①, constructing an index system and setting an evaluation standard;
step ②, determining the basic weight of the index, namely setting the basic weight of the index by utilizing a comprehensive weighting method combining a subjective weighting method-chromatographic analysis method and an objective evaluation method-entropy method;
step ③, determining index differentiation weight, namely endowing each index with time, calculating corresponding time weight vector as differentiation weight;
④, calculating scene probability, namely generating a scene total set by using a Latin hypercube sampling method, performing clustering reduction on the scene by using a K-means algorithm, and calculating the scene probability;
step ⑤, calculating an evaluation result, namely obtaining a corresponding score according to the index value and the evaluation standard, and calculating an adaptive evaluation result of the planning scheme by combining scene probability and time differentiation weight;
wherein:
in step ①, constructing an index system and setting an evaluation standard, wherein the index system comprises an economic benefit index of a charging network builder and an economic benefit index of an auxiliary service;
(1) charging network builder economic benefit index
1) Annual construction investment cost of charging station
Figure FDA0002366826520000011
In the formula, eiThe number of the transformers configured in the charging station i; a is the unit price of the transformer; m isiThe number of chargers configured for the charging station i; b is the unit price of the charger; liThe length of a medium-voltage line connected to a power distribution network for a charging station; c. ClThe unit cost of the medium voltage line; omegaiThe capital cost for charging station i; r is0The current rate is the current rate; z is the operating life;
2) operating and maintenance costs of charging stations
The operation and maintenance cost of the charging station mainly comprises equipment maintenance cost, equipment depreciation cost, personnel wages and the like of the charging station, generally, all the cost values are not clear, the annual operation and maintenance cost can be calculated according to the percentage of initial investment by considering the annual operation and maintenance cost, and if the scaling factor is η, the annual operation and maintenance cost of the charging station i is η
C2i＝(eia+mib+lici+ωi)η (2)
3) Network loss annual cost of charging station
Figure FDA0002366826520000021
In the formula, CFeAnd CCuThe iron and copper losses of the transformer are respectively; cLConverting the line loss in the charging station into the loss value of each charger; cDThe charging loss of a single charger; k is a radical oftThe charging time is the synchronous rate of a plurality of chargers in the charging station; t isvAveraging the effective charging time per day for the charging station; p is the charging price;
4) charging service revenue
From the perspective of charging facility operation, charging service revenue I is from facility usage fee I1And charging electric charge I2And subsidies obtained from government I3Earning income from 3 channels
I＝I1+I2+I3(4)
5) Charging station revenue
For power supply enterprises, the fundamental goal of building electric vehicle charging stations is revenue, i.e., economy of operation; the site selection and the location of the electric vehicle charging station are influenced by regional load distribution (charging demand), and the initial construction cost, the annual operation cost and the power distribution system loss caused by the access of a power grid of the charging station can be determined
Figure FDA0002366826520000022
In the formula, F is the economy of system operation and consists of a revenue function and a cost function; r is an annual economic benefit function as a revenue function of the charging station; cost function from initial construction cost
Figure FDA0002366826520000031
Annual operating costs
Figure FDA0002366826520000032
And the network loss cost of the power distribution network system
Figure FDA0002366826520000033
Composition is carried out; n is the number of charging stations;
(2) auxiliary service economic benefit index
1) Avoiding investment costs of power generation and transmission equipment
The electric automobile is used as a movable load, the electric automobile is charged in order by charging and guiding the electric automobile, and the electric automobile can perform obvious effects on peak clipping and valley filling of a power grid, so that the load peak-valley difference is reduced, the defect that a unit is excessively put into operation at the peak time is overcome, and the cost loss caused by starting and stopping of equipment is avoided;
Rgen＝CgenΔPpeak(6)
wherein Cgen is the unit installed capacity cost, △ PpeakReduced system peak charge for the charging network;
2) reducing capacity expansion cost of power distribution system
The electric automobiles of the electric automobile charging network are managed in order, so that the effect of avoiding or reducing the capacity expansion cost of the power distribution network can be achieved; in the disordered charging state, the phenomena of node voltage reduction, transformer overload, line overload and the like caused by more charging loads of the electric automobile influence the stable operation of the power distribution network, so that the problem of power distribution network capacity expansion caused by overweight charging network load has to be faced; the problems can be effectively solved through ordered charging management, and the costs of transformer substation capacity expansion, circuit new construction, transformer substation new construction and the like are reduced; the expansion cost is reduced by constructing an expansion planning model with the minimum investment cost of the power distribution network system as a target;
Figure FDA0002366826520000034
wherein, ci,aExpanding the unit capacity of the transformer substation i; m isi,aCapacity expanded for substation i; c. Ci,bUnit cost, m, required for new construction of the ith substationi,bCapacity required to be newly built for the ith substation; c. Cij,cThe unit length cost required for newly building the ij section of line; n isij,cThe number of lines, l, to be added to the ij sectionijThe line length required to be increased for the ij section;
3) reducing system network loss cost
The ordered management of the electric automobile can reduce the network loss cost of a power distribution network system and improve the benefit of the power distribution network, and a model adopting a difference comparison method is shown as follows;
Figure FDA0002366826520000041
wherein R is system network loss waterThe annual economic benefit brought by improvement is reduced;
Figure FDA0002366826520000042
the value of the system grid loss for the disordered charge,
Figure FDA0002366826520000043
the system network loss is under the ordered management state;
4) reducing ancillary service purchase costs
The implementation of the ordered charging strategy of the electric automobile can effectively reduce the system compliance fluctuation, thereby obviously reducing the requirement of the system on auxiliary service; here, approximate evaluation is adopted to reduce the cost of spinning reserve purchase;
Cres＝CupΔPt up+CdownΔPt down(9)
wherein, CupAdjusting up the reserve capacity price; cdownTo adjust reserve capacity prices downward; delta Pt upFor up-regulation of reserve capacity price, Δ Pt downLowering reserve capacity price;
5) auxiliary service economic benefits
The economic benefits brought by the auxiliary service provided by the electric automobile mainly comprise three aspects, namely frequency modulation capacity or rotation standby benefits, benefits of reverse discharge of the electric automobile and environmental protection subsidies; while costs in the ancient city that provided ancillary services primarily included charging and battery depletion costs;
Figure FDA0002366826520000044
wherein R isr(t) capacity gain for frequency modulation or spinning reserve; er(t) is the reverse discharge electric energy yield,
Figure FDA0002366826520000045
for charging and regulating the associated costs ξr(t) is an environment-friendly patch,
Figure FDA0002366826520000046
cost for battery depletion;
determining the basic index weight in step ②, setting the basic index weight by means of the comprehensive weighting method combining subjective weighting method-chromatographic analysis method and objective evaluation method-entropy method;
because the dimensions of different indexes are different and the magnitude level is also obviously different; therefore, the index needs to be standardized, the de-dimensionalized index is converted into the normalized index by a percentage scoring method; a max-min standardized function is mainly adopted; wherein x isijIs the initialized value of the jth index of the ith area; the forward index is standardized by the formula (4-140), the reverse index is standardized by the formula (4-141), and the moderate index is standardized by the formula (4-142);
Figure FDA0002366826520000051
Figure FDA0002366826520000052
Figure FDA0002366826520000053
in step ③, determining index differentiation weight, namely, giving time degree to each index, calculating corresponding time weight vector as differentiation weight;
the weight calculation is carried out on the constructed index system, and a weighting method combining subjectivity and objectivity is adopted, so that the defect of strong subjectivity caused by the subjective experience of a decision maker on the weight is avoided, and the entropy weight method can carry out objective weighting on the index according to the change degree of the index; differential weight design is carried out based on the time weight and scene probability technology provided by the above sections, so that the problems of different importance degrees of indexes on time sequences and different index values under different scenes are effectively solved; the weight calculation model is shown in FIG. 1; 1) AHP analytic hierarchy process
AHP is a decision-making method which carries out deep analysis on the intrinsic relation of indexes, constructs a hierarchical structure model and utilizes less quantitative information to make the complex problem be in hierarchical mathematics; the expert compares every two indexes under the same index, determines the importance degree (namely weight) of the indexes in the level relative to the indexes of the upper layer, and then synthesizes the index weights layer by layer to obtain the comprehensive weight of the indexes of the bottommost layer relative to the indexes of the highest layer;
a. structural judgment matrix
AHP constructs the matrix of judgement separately according to the index level, as to belonging to each index of the previous stage, construct the matrix of judgement with two-by-two comparison method, until the final stage; the specific judgment matrix is as follows:
A＝{aij}n×n(4-118)
in the formula, aijThe decision matrix a obtained by this method, which characterizes the relative importance of the indices i and j (i, j ═ 1,2, …, n), has the following properties:
Figure FDA0002366826520000061
b. calculating a weight coefficient
According to the judgment matrix A obtained in the previous step, the index weight of the index relative to the index of the previous stage is calculated; when calculating the weights, it is assumed that the judgment matrix has consistency, i.e. aij·ajk＝ajk(ii) a Calculating the weight coefficient omega of each judgment matrix, multiplying each index by rows and dividing by the power of n, namely obtaining the geometric mean value omega' of each row index as
Figure FDA0002366826520000062
Then, the omega' is normalized to obtain the index xjHas a weight coefficient of
Figure FDA0002366826520000063
Then (omega)1,ω2,…,ωn) Relative weight of each index to the same upper index;
c. performing consistency check
Because personal preferences are different, the judgment matrix given by each expert does not meet the requirement of complete consistency, and therefore consistency check needs to be carried out on the judgment matrix; if the test is passed, the feature vector (normalization) is a weight vector; if not, considering to reconstruct a judgment matrix;
first, the maximum characteristic root lambda of the original matrix is calculatedmaxAnd calculating a consistency test index by using the maximum characteristic root:
Figure FDA0002366826520000071
for a judgment matrix with a large order, the applicability of the consistency check method is reduced, a consistency index CI needs to be corrected, and an average random consistency index RI is introduced and used as a calibration value of consistency check;
introducing a consistency ratio index CR, and correcting the consistency criterion of the judgment matrix, wherein the CR is defined as follows:
Figure FDA0002366826520000072
when CR <0.1, the consistency of the decision matrix is considered acceptable; when CR is greater than 0.1, considering that the judgment matrix deviates from consistency, and properly correcting the judgment matrix;
2) entropy method
The entropy represents the size of the information amount contained in the index; for an index sample, the higher the degree of ordering, the smaller the sample variance, and the smaller the amount of information it contains; conversely, the higher the degree of disorder, the larger the sample variance, and the larger the amount of information it contains;
in the evaluation index, how much the index contains information amount can be represented by the size of the index weight; the larger the information quantity is, the higher the index weight is; the entropy weight method has the advantages that the value and the weight of the data are completely defined by the discrete degree of the data;
the entropy weight method comprises the following calculation steps:
a. constructing a raw data matrix
Collecting original index data and carrying out standardization treatment; converting each type of index into an extremely large index, and unifying the evaluation standard; the history data of the evaluated object is represented by the following matrix:
Figure FDA0002366826520000081
in the formula, YijThe normalized value of the i index in the j year is shown, n is the total index number, and m is the total historical year;
b. calculating contribution degrees of different historical years
Under the same index, the proportion occupied by different historical years is calculated, and as the contribution degree of the year, the calculation formula is as follows:
Figure FDA0002366826520000082
in the formula, PijRepresenting the contribution degree of the jth historical year under the ith index attribute;
c. calculating an entropy value of an indicator
Entropy value eiThe total amount of contribution of all evaluation years to the ith index is expressed by the following formula:
Figure FDA0002366826520000083
e when the contribution degrees of each historical year under a certain index attribute tend to be consistentiTo 1; because the contribution degrees tend to be consistent, the index attribute does not play a role in decision making, and particularly when the contribution degrees are completely equal, the target attribute can not be considered, namely the weight of the index is considered to be 0;
d. calculating the difference coefficient of the index
Coefficient of variation giThe degree of inconsistency of the contribution of the ith index in each historical year is shown as follows:
gi＝1-ei(4-125)
obviously, giThe larger the index is, the more important the index is;
e. determining a weight coefficient
Weight coefficient wiFor the weight coefficient after normalization, the following formula is shown:
Figure FDA0002366826520000091
3) combined empowerment
The combination weighting generally adopts a linear weighting combination method, and the calculation formula is as follows:
θi＝αυi+(1-α)μi(4-127)
in the formula, uiRepresents the subjective weight vector, Σ υi＝1；
μiRepresents the objective weight vector, ∑ μi＝1；
θiRepresents the combining weight vector, ∑ θi＝1；
α represents the importance degree of the subjective weighting method, 0 is less than or equal to α is less than or equal to 1;
in step ④, calculating scene probability, namely generating a scene total set by using a Latin hypercube sampling method, performing clustering reduction on scenes by using a K-means algorithm, and calculating the scene probability;
step ⑤, calculating an evaluation result, namely obtaining a corresponding score according to the index value and the evaluation standard, and calculating an adaptability evaluation result of the planning scheme by combining scene probability and time differentiation weight;
1) index calculation
The time and probability factors are comprehensively considered, and the calculation formula of the single index score is as follows:
Figure FDA0002366826520000092
in the formula, ZiDenotes the i-th index score, gtkRepresents the score, p, of the index in the kth scene of year ttkProbability of occurrence of kth scene in t year, wtRepresenting the differentiation weight of the index in the t year;
2) composite score
And multiplying and summing the scores of the single indexes and the basic weights of the indexes to obtain a final evaluation result, wherein the calculation formula is as follows:
Figure FDA0002366826520000101
wherein Z represents the final score, θiThe basic weight of the i-th index is expressed.
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