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 PDFInfo
- Publication number
- 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
- Authority
- CN
- China
- Prior art keywords
- index
- weight
- charging
- cost
- calculating
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 83
- 230000008901 benefit Effects 0.000 title claims abstract description 48
- 238000011156 evaluation Methods 0.000 claims abstract description 59
- 230000004069 differentiation Effects 0.000 claims abstract description 26
- 239000013598 vector Substances 0.000 claims abstract description 25
- 238000004587 chromatography analysis Methods 0.000 claims abstract description 8
- 238000005070 sampling Methods 0.000 claims abstract description 8
- 239000011159 matrix material Substances 0.000 claims description 57
- 238000004364 calculation method Methods 0.000 claims description 26
- 238000010276 construction Methods 0.000 claims description 18
- 238000012423 maintenance Methods 0.000 claims description 12
- 238000007726 management method Methods 0.000 claims description 11
- XEEYBQQBJWHFJM-UHFFFAOYSA-N iron Substances [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 9
- 238000010606 normalization Methods 0.000 claims description 8
- 230000009467 reduction Effects 0.000 claims description 7
- 239000010949 copper Substances 0.000 claims description 6
- 230000000694 effects Effects 0.000 claims description 6
- 238000009987 spinning Methods 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 230000007547 defect Effects 0.000 claims description 4
- 241000287196 Asthenes Species 0.000 claims description 3
- 238000012935 Averaging Methods 0.000 claims description 3
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 239000002131 composite material Substances 0.000 claims description 3
- 229910052802 copper Inorganic materials 0.000 claims description 3
- 238000013461 design Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims description 3
- 230000006872 improvement Effects 0.000 claims description 3
- 229910052742 iron Inorganic materials 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 3
- 238000010248 power generation Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 230000001105 regulatory effect Effects 0.000 claims description 3
- 238000013077 scoring method Methods 0.000 claims description 3
- 230000001360 synchronised effect Effects 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 claims 1
- 230000003827 upregulation Effects 0.000 claims 1
- 244000062793 Sorghum vulgare Species 0.000 description 4
- 235000019713 millet Nutrition 0.000 description 4
- 239000006185 dispersion Substances 0.000 description 2
- 238000010586 diagram Methods 0.000 description 1
- 238000011234 economic evaluation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Health & Medical Sciences (AREA)
- Probability & Statistics with Applications (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Primary Health Care (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- General Engineering & Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
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
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
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
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.
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 costAnnual operating costsAnd the network loss cost of the power distribution network systemComposition 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.
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.
Wherein R is the annual economic benefit brought by the improvement of the system network loss level;the value of the system grid loss for the disordered charge,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.
Wherein, CupAdjusting up the reserve capacity price; cdownTo adjust reserve capacity prices downward;in order to adjust up the price of the reserve capacity,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.
Wherein R isr(t) capacity gain for frequency modulation or spinning reserve; er(t) is the reverse discharge electric energy yield,for charging and regulating the associated costs ξr(t) is an environment-friendly patch,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).
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:
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 asThen, the omega' is normalized to obtain the index xjHas a weight coefficient ofThen (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:
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:
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:
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:
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:
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:
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:
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:
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
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
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.
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 costAnnual operating costsAnd the network loss cost of the power distribution network systemComposition 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.
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.
Wherein R is the annual economic benefit brought by the improvement of the system network loss level;the value of the system grid loss for the disordered charge,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.
Wherein, CupAdjusting up the reserve capacity price; cdownTo adjust reserve capacity prices downward;in order to adjust up the price of the reserve capacity,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.
Wherein R isr(t) capacity gain for frequency modulation or spinning reserve; er(t) is the reverse discharge electric energy yield,to chargeElectricity and regulatory related costs, ξr(t) is an environment-friendly patch,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).
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:
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 asThen, the omega' is normalized to obtain the index xjHas a weight coefficient ofThen (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:
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:
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:
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:
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:
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:
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:
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:
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
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
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
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 costAnnual operating costsAnd the network loss cost of the power distribution network systemComposition 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;
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;
wherein R is system network loss waterThe annual economic benefit brought by improvement is reduced;the value of the system grid loss for the disordered charge,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;
wherein R isr(t) capacity gain for frequency modulation or spinning reserve; er(t) is the reverse discharge electric energy yield,for charging and regulating the associated costs ξr(t) is an environment-friendly patch,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);
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:
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 asThen, the omega' is normalized to obtain the index xjHas a weight coefficient ofThen (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:
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:
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:
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:
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:
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:
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:
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:
wherein Z represents the final score, θiThe basic weight of the i-th index is expressed.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010038348.5A CN111091309A (en) | 2020-01-14 | 2020-01-14 | Method for evaluating economic benefits of electric vehicle charging network operation |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010038348.5A CN111091309A (en) | 2020-01-14 | 2020-01-14 | Method for evaluating economic benefits of electric vehicle charging network operation |
Publications (1)
Publication Number | Publication Date |
---|---|
CN111091309A true CN111091309A (en) | 2020-05-01 |
Family
ID=70399192
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202010038348.5A Pending CN111091309A (en) | 2020-01-14 | 2020-01-14 | Method for evaluating economic benefits of electric vehicle charging network operation |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN111091309A (en) |
Cited By (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111985778A (en) * | 2020-07-20 | 2020-11-24 | 民航成都信息技术有限公司 | Comprehensive evaluation method, device, equipment and medium for parking stall allocation |
CN112101813A (en) * | 2020-09-24 | 2020-12-18 | 贵州电网有限责任公司 | Comprehensive evaluation and sequencing method for testing of distribution automation equipment |
CN112418742A (en) * | 2021-01-20 | 2021-02-26 | 南方电网数字电网研究院有限公司 | Network double-layer control method for electric automobile battery replacement station with information and physical fusion |
CN112791997A (en) * | 2020-12-16 | 2021-05-14 | 北方工业大学 | Method for cascade utilization and screening of retired battery |
CN112926851A (en) * | 2021-02-20 | 2021-06-08 | 国网新源控股有限公司 | Method for calculating benefit of pumped storage power station |
CN113033991A (en) * | 2021-03-23 | 2021-06-25 | 河海大学 | Credit evaluation method and system based on dynamic development trend |
CN113765125A (en) * | 2021-09-23 | 2021-12-07 | 北方工业大学 | Cluster power distribution method for hydrogen energy storage system |
CN114118482A (en) * | 2021-12-03 | 2022-03-01 | 国网上海市电力公司 | Charging station operation quality improving method based on multidimensional charging data |
CN114688981A (en) * | 2022-03-15 | 2022-07-01 | 中南大学 | Method, equipment and medium for identifying thickness of composite steel bar coating and evaluating thickness uniformity |
CN114884133A (en) * | 2022-04-22 | 2022-08-09 | 北京科技大学 | Microgrid economic dispatching optimization method and system considering electric automobile |
-
2020
- 2020-01-14 CN CN202010038348.5A patent/CN111091309A/en active Pending
Cited By (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111985778A (en) * | 2020-07-20 | 2020-11-24 | 民航成都信息技术有限公司 | Comprehensive evaluation method, device, equipment and medium for parking stall allocation |
CN111985778B (en) * | 2020-07-20 | 2024-04-23 | 民航成都信息技术有限公司 | Comprehensive evaluation method, device, equipment and medium for stand allocation |
CN112101813A (en) * | 2020-09-24 | 2020-12-18 | 贵州电网有限责任公司 | Comprehensive evaluation and sequencing method for testing of distribution automation equipment |
CN112791997A (en) * | 2020-12-16 | 2021-05-14 | 北方工业大学 | Method for cascade utilization and screening of retired battery |
CN112418742B (en) * | 2021-01-20 | 2021-08-31 | 南方电网数字电网研究院有限公司 | Network double-layer control method for electric automobile battery replacement station with information and physical fusion |
CN112418742A (en) * | 2021-01-20 | 2021-02-26 | 南方电网数字电网研究院有限公司 | Network double-layer control method for electric automobile battery replacement station with information and physical fusion |
CN112926851A (en) * | 2021-02-20 | 2021-06-08 | 国网新源控股有限公司 | Method for calculating benefit of pumped storage power station |
CN113033991A (en) * | 2021-03-23 | 2021-06-25 | 河海大学 | Credit evaluation method and system based on dynamic development trend |
CN113765125A (en) * | 2021-09-23 | 2021-12-07 | 北方工业大学 | Cluster power distribution method for hydrogen energy storage system |
CN114118482A (en) * | 2021-12-03 | 2022-03-01 | 国网上海市电力公司 | Charging station operation quality improving method based on multidimensional charging data |
CN114688981A (en) * | 2022-03-15 | 2022-07-01 | 中南大学 | Method, equipment and medium for identifying thickness of composite steel bar coating and evaluating thickness uniformity |
CN114688981B (en) * | 2022-03-15 | 2023-01-31 | 中南大学 | Method, equipment and medium for identifying thickness of composite steel bar coating and evaluating thickness uniformity |
CN114884133A (en) * | 2022-04-22 | 2022-08-09 | 北京科技大学 | Microgrid economic dispatching optimization method and system considering electric automobile |
CN114884133B (en) * | 2022-04-22 | 2023-10-10 | 北京科技大学 | Micro-grid economic dispatching optimization method and system considering electric automobile |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN111091309A (en) | Method for evaluating economic benefits of electric vehicle charging network operation | |
CN107239874B (en) | Rail transit-oriented electric energy quality and energy-saving analysis system | |
CN112907129B (en) | Energy storage comprehensive benefit evaluation index system | |
CN112465286A (en) | Method for evaluating service capability of electric vehicle charging network | |
CN113313613B (en) | Power distribution network modularized movable battery energy storage MMBES optimal configuration method | |
CN110689240A (en) | Fuzzy comprehensive evaluation method for economic operation of power distribution network | |
CN112086980B (en) | Public distribution transformer constant volume type selection method and system considering charging pile access | |
CN108985602B (en) | Power grid classification project input and output evaluation method and system considering risks | |
CN111709655A (en) | Energy storage type charging station running state evaluation method based on AHP-entropy weight method | |
CN112001598A (en) | Energy storage configuration evaluation and operation optimization method for different users based on energy storage type selection | |
CN112766730A (en) | Distribution network planning scheme adaptability evaluation method for distributed resource access | |
CN111460378A (en) | Power distribution network accurate investment project optimization method considering risk measure | |
Cui et al. | Multi-dimensional evaluation of power market based on multiple attribute decision making | |
CN112993985A (en) | Micro-grid multi-target planning method considering uncertainty | |
CN112734274A (en) | Mining and comprehensive evaluation method for low-carbon power grid operation leading influence factors | |
CN117035158A (en) | System and method for evaluating energy storage optimization configuration of user side based on multiple profit modes | |
CN116245386A (en) | Distribution network investment benefit portrait method considering multidimensional driving factors | |
CN112861376B (en) | Evaluation method and device based on unit scheduling model | |
CN115983664A (en) | Comprehensive evaluation method for trading effect of energy storage participation in electric power market based on fuzzy analysis | |
CN111553525A (en) | Power grid investment strategy optimization method considering power transmission and distribution price supervision | |
CN110929968A (en) | Comprehensive regulation and control method and system for multi-energy combined supply of smart city | |
CN114913032A (en) | Transformer substation operation energy consumption assessment method | |
CN114547821A (en) | Schedulable flexible resource identification method based on grey correlation theory and storage medium | |
CN113469436A (en) | Urban power grid side energy storage planning method | |
CN113255957A (en) | Quantitative optimization analysis method and system for uncertain factors of comprehensive service station |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20200501 |
|
WD01 | Invention patent application deemed withdrawn after publication |