CN113054676A - Mobile charging pile scheduling method based on multi-IEV bidding - Google Patents

Mobile charging pile scheduling method based on multi-IEV bidding Download PDF

Info

Publication number
CN113054676A
CN113054676A CN202110325972.8A CN202110325972A CN113054676A CN 113054676 A CN113054676 A CN 113054676A CN 202110325972 A CN202110325972 A CN 202110325972A CN 113054676 A CN113054676 A CN 113054676A
Authority
CN
China
Prior art keywords
charging pile
mobile charging
user
time
bidding
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.)
Granted
Application number
CN202110325972.8A
Other languages
Chinese (zh)
Other versions
CN113054676B (en
Inventor
胡新悦
刘林峰
吴家皋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Posts and Telecommunications
Original Assignee
Nanjing University of Posts and Telecommunications
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Nanjing University of Posts and Telecommunications filed Critical Nanjing University of Posts and Telecommunications
Priority to CN202110325972.8A priority Critical patent/CN113054676B/en
Publication of CN113054676A publication Critical patent/CN113054676A/en
Application granted granted Critical
Publication of CN113054676B publication Critical patent/CN113054676B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/63—Monitoring or controlling charging stations in response to network capacity
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/66—Data transfer between charging stations and vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/67—Controlling two or more charging stations
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/68—Off-site monitoring or control, e.g. remote control
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

A mobile charging pile scheduling method based on multi-IEV bidding is characterized in that a new energy automobile submits an order of a nearest mobile charging pile automobile to a platform, and competition is developed under a mobile charging pile bidding rule; the time for submitting the order of the idle mobile charging pile automobile is cut off, and the number m of competitors can be seen by all new energy automobiles participating in competition; comparing the time urgency distribution according to the collected market information, judging the time urgency grade of the user, and acquiring the proper time urgency grade
Figure DEST_PATH_IMAGE002
Interval, calculating the success/failure of the user to obtain the profit of the mobile charging pile
Figure DEST_PATH_IMAGE004
According to
Figure 605465DEST_PATH_IMAGE002
Interval and profit
Figure 53764DEST_PATH_IMAGE004
Obtaining expected revenue
Figure DEST_PATH_IMAGE006
Best at highest time
Figure 646550DEST_PATH_IMAGE002
And outputting the best bid of the user i. The method is based on Bayesian game, and utilizes the time urgency grade of the user and
Figure 572918DEST_PATH_IMAGE002
calculating success or failure to obtain the income of the mobile electric pile, and traversing all possible income
Figure 918449DEST_PATH_IMAGE002
Calculating the corresponding expected income, and finally obtaining the expected income corresponding to the highest expected income
Figure 22802DEST_PATH_IMAGE002
And calculate a final bid recommendation to the user.

Description

Mobile charging pile scheduling method based on multi-IEV bidding
Technical Field
The invention belongs to the technical field of bidding games, and particularly relates to a mobile charging pile scheduling method based on multi-IEV bidding.
Background
The bidding model of new energy vehicles (IEV) is a non-cooperative incomplete information static game. Competitors strive for the mobile charging pile through bidding behavior without having a alliance relationship, each target is to maximize benefits, and a strategy of selecting the maximum benefits of the competitors is faced when a scheme is selected. Although the action time of the participants has a precedence relationship, the later actor cannot observe the specific action strategy of the earlier actor. And the participants are not completely and clearly aware of the information related to the game, and the charger vehicles participating in bidding need to comprehensively consider the situations of the participants and potential competitors so as to make a strategy for maximizing the benefits of the participants.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects of the prior art, and provide a mobile charging pile scheduling method based on multi-IEV bidding, so that the bidding benefits of users are measured on the basis of Bayesian game, and an optimal bidding scheme is screened out for the users.
The invention provides a mobile charging pile scheduling method based on multi-IEV bidding, which comprises the following steps of,
step S1, submitting an order of the nearest mobile charging pile automobile to the platform by the new energy automobile, wherein the order information comprises the position of the new energy automobile and the required charging amount, and developing competition under the mobile charging pile bidding rule;
step S2, the time for submitting the order of the idle mobile charging pile automobile is cut off, and the number m of competitors can be seen by all new energy automobiles participating in competition;
step S3, comparing the time urgency degree distribution according to the collected market information, judging the time urgency degree grade of the user, and obtaining the proper PTAnd calculating the profit U (m) of the user when the user successfully/unsuccessfully acquires the mobile charging pilei) According to PTSection and profit U (m)i) Obtaining the best P when the expected profit F is the highestTAnd outputting the best bid of the user i.
As a further technical solution of the present invention, in step S1, the new energy vehicle obtains a distance that the nearest mobile charging pile needs to move according to the positioning system, and submits the distance and a required charging amount as order information.
Further, in step S1, the bidding rule of the mobile charging pile is specifically:
new energy automobile bid ViIs composed of
Figure BDA0002994681210000021
Wherein E isiIs the amount of charge required by user i, LiDistance, P, required to move for moving the charging pileTiIs the value that user i is willing to pay in addition for time; delta is the lowest bid the mobile charging pile is willing to receive,
Figure BDA0002994681210000022
the distance and time cost of movement required to move the charging post,
Figure BDA0002994681210000023
wherein g is the cost price per kilometer required by the mobile charging pile to move, s is the unit time cost per kilometer required by the mobile charging pile to move, and v is the average moving speed of the mobile charging pile; the mobile charging pile company receives profit W obtained by the order of the new energy automobile user iiIs Wi＝PTi+EiX m, wherein, the profit brought by each degree of electricity is m, and the comprehensive score I of the new energy automobile user I is given to the mobile charging pile companyiIs Ii＝Wi。
Further, in step S3, when the number of competitors m is 1, PTWhen the number of competitors m is not 1, the market information is collected as 0.
Further, in step S3, according to the central limit theorem PTSubject to a mathematical expectation of mu1Variance is σ1 2Normal distribution of (i.e. P)T～N(μ1，σ1 2) A probability density function of
Figure BDA0002994681210000024
Satisfies the following conditions:
Figure BDA0002994681210000025
wherein the selected time saving cost is Qi＝110％xiI.e. Q to N (110%. mu.)1，(110％σ1)2) Conforming to normal distribution with a corresponding probability density function of f4(q)；
Then, the probability that the user is in the k-level temporal urgency level is:
Figure BDA0002994681210000026
k＝1，2，3，4，5；
fitting is carried out through Matlab software to obtain a probability density function f of the required electric quantity2(e) And the probability density function f of the moving distance required by the mobile charging pile3(l)；
The mobile charging pile evaluates the comprehensive score I of a user to be I ═ PT+E×m；
According to fitted f1(pt)、f2(e) Probability density function f of simplified II(i) Is composed of
Figure BDA0002994681210000031
Further, in step S3, the urgency level is determined according to the urgency level of the charging request, and P of the level in the time zone of the charging request is acquiredTA range; establishing a Bayesian bidding game model, and calculating the profit U (m) when the mobile electric pile is successfully or unsuccessfully obtainedi) Calculating expected income; calculating the best P when the user obtains the expected profit FT. And calculating the bid of the user.
Further, a Bayesian bidding game model is established, and the income U (m) when the mobile electric pile is successfully or unsuccessfully acquired is calculatedi) The specific method for calculating the expected profit is as follows:
for new energy automobile user a, use m1And m2Two types, m, representing its competitors i1Indicating a winner as a competitor, i.e. Ia＜Ii；m2Indicating that the competitor is a culler, i.e. Ia＞Ii；
If the user is not selected, the cost is not counted, if the user successfully obtains the mobile charging pile, the profit is the profit of the electric vehicle minus the extra value paid for time and the cost brought by the distance that the mobile charging pile needs to move, namely,
Figure BDA0002994681210000032
the signal set t contains the portion of value that is willing to pay more for time, the required charge, the moving distance and the distribution f (x) of the competitor's demand for effectiveness: t ═ PT，E，L，f(x)}；
If only 2 users participate in the competition, the competitor passes t in comparison with the competitor iaTo measure the level of itself in a competitor and to measure the signal taThe probability that competitor a is the winner is calculated as
p(m2|ta)＝p(Ia＞Ii|ta) (ii) a Order to
Figure BDA0002994681210000033
Then p (m)2|ta)＝α；
If m ginseng competes with the competition, the probability p (m) of the mobile charging pile can be successfully obtained for the competitor a2|ta)*Is p (m)2|ta)*＝p(m2|ta)m-1＝αm-1Probability p (m) that competitor a fails to acquire mobile charging pile1|ti)*Is p (m)1|ta)*＝1-p(m2|ta)m-1＝1-αm-1(ii) a The expected profit to the competitor F is
F＝U(m1)*p(m1|ta)*+U(m2)*p(m2|ta)*
＝0+(E*δ+Qk-PT-L*g)*αm-1。
Further, the expected yield F is F ═ (E × δ + Q)k-PT-L*g)*αm-1The target function of the model is maxF, and the constraint condition of the model is
Figure BDA0002994681210000041
Get when FmaxOptimum P of timeTFinally, the bid of the user i is calculated as
Figure BDA0002994681210000042
The method has the advantages that the method is based on the Bayesian game and utilizes the time urgency grade and the P of the userTCalculating the success or failure to obtain the income of the mobile electric pile, and traversing all possible PTCalculating the corresponding expected income, and finally obtaining the P corresponding to the highest expected incomeTAnd calculate a final bid recommendation to the user.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
Referring to fig. 1, the present embodiment provides a mobile charging pile scheduling method based on multiple IEV bids, including the following steps:
s1, submitting an order of the nearest mobile charging pile automobile to the platform by the new energy automobile, wherein the order information comprises the position of the new energy automobile and the required charging amount; and (5) developing competition under the bidding rule of the mobile charging pile. Go to step S2;
wherein, the mobile charging stake rules of bidding include:
(1) defining the bid of the new energy automobile, and considering the following 3 parts of factors:
Figure BDA0002994681210000043
wherein E isiIs the amount of electricity required by user i, LiDistance of movement, P, required for moving the charging pileTiIs the portion of value that user i is willing to pay in addition for time; delta is the lowest price that the mobile charging pile is willing to accept; g. and s represents the cost price per km and the cost per unit time required by the movement of the charging pile.
Figure BDA0002994681210000051
Representing the distance and time costs for the user i to move the required movement of the charging post. v represents an average moving speed of the mobile charging pile.
(2) For the mobile charging pile company, if the profit obtained by the order of the new energy automobile user i is received:
Wi＝PTi+Ei×m
where the profit per degree of electricity is represented by m. Defining comprehensive score I of mobile charging pile company on new energy automobile user Ii：
Ii＝Wi
In a certain competition of multi-user participation, the user with the highest composite score is selected.
And S2, stopping the order submitting time of the idle mobile charging pile automobile within a short limited time. The number m of competitors can be seen by all new energy vehicles participating in competition; go to step S4;
s3, the new energy automobile collects market information; go to step S4;
step S3 specifically includes the following steps:
s301, carrying out classification discussion according to the number of competitors: when m is 0, turning to S302; otherwise, go to step S303;
s302, order PT0. Go to step S5;
and S303, collecting market information. Go to step S4;
wherein the market information includes: the distribution state of the time urgency degree, the distribution state of the power demand of the user and the distribution state of the moving distance required by the mobile charging pile; the method specifically comprises the following steps:
(1) degree of time urgency
P for userTBelongs to continuous random variables and is simultaneously influenced by a plurality of factors, wherein the decisive influencing factors do not exist and are independent from each other, and P can be reasonably assumed according to the central limit theoremTSubject to a mathematical expectation of mu1Variance is σ1 2Normal distribution of (i.e. P)T～N(μ1，σ1 2). And the probability density function is:
Figure BDA0002994681210000061
fitting normal distribution through Matlab software according to a large amount of data collected from past car booking charging apps to obtain a corresponding probability density function f1(x) Based on which the urgency of charging the vehicle is based on PTThe 5 levels are divided, corresponding reference coefficients are given, and in the subsequent application process, more accurate parameters can be further fitted by collecting a large amount of data, as shown in table 1,
Figure BDA0002994681210000062
TABLE 1 reference parameters for the degree of urgency at the ordinary time (Unit: Yuan)
Satisfies the following conditions:
Figure BDA0002994681210000063
wherein setting the selected saved time cost to Qi＝110％xiI.e. Q to N (110%. mu.)1，(110％σ1)2) Conforming to normal distribution with a corresponding probability density function of f4(q)；
The probability that a user is in a level k temporal urgency level is:
Figure BDA0002994681210000064
k＝1，2，3，4，5；
and for the time period in one day, the time period is divided into a peak period and a common period, and different corresponding reference coefficients are given. For the peak period, all coefficients are 10% higher than the normal coefficients.
(2) Electric quantity demand and required moving distance of mobile charging pile
According to a large amount of data of the power demand of the past user and the moving distance required by the mobile charging pile collected from past car-booking charging apps, fitting is carried out through Matlab software respectively to obtain a probability density function f of the power demand2(e) And the probability density function f of the moving distance required by the mobile charging pile3(l)。
(3) Mobile charging pile evaluation user comprehensive score I
I＝PT+E×m
From the above analysis, P is knownTAnd E, a probability density function and two factors are mutually independent and jointly linearly form a mobile charging pile, and the mobile charging pile is synthesized to obtain I. F is fitted according to the reality1(pt)、f2(e) The probability density function of I, finally reduced in connection with the mathematical method, is a function f with respect to II(i)。
Figure BDA0002994681210000071
S4, obtaining proper PTInterval, calculating the optimum PT(ii) a Go to step S5;
step S4 specifically includes the following steps:
s401, determining an urgent grade according to the urgent degree of charging, and acquiring P of the grade in a time period needing chargingTAnd (3) a range. Go to step S402;
s402, establishing a Bayesian bidding game model, and calculating the income U (m) when the mobile electric pile is successfully or unsuccessfully acquiredi) And calculating the expected profit. Go to step S403;
the specific contents of step S402 include the following:
(1) for a new energy automobile user a, use m1And m2To indicate two types of competitors i, m1Indicating a winner as a competitor, i.e. Ia＜Ii；m2Indicating that the competitor is a culler, i.e. Ia＞Ii。
(2) Defining the user benefits: if not, the profit is less than zero and equal to the time cost it is wasted. The time wasted by unsuccessful ordering is negligible for the user. If the mobile charging pile is successfully acquired, the profit is the profit of the electric automobile minus the extra value paid for time and the cost brought by the distance that the mobile charging pile needs to move.
Figure BDA0002994681210000072
(3) The observable signal set t contains the portion of value that is willing to pay more for time, the required charge, the distance traveled, and the distribution of competitor's time-effectiveness requirements f (x):
t＝{PT，E，L，f(x)}
(4) for the case where only 2 users are in competition, there are two possible scenarios of solution value as compared to competitor I, Ia＞IiOr Ia＜Ii(since the probability of both being equal and slight will not be discussed). Competitor passing taTo measure the level of itself in a competitor and to measure the signal taThe probability that competitor a is the winner is calculated as follows:
p(m2|ta)＝p(Ia＞Ii|ta)
order to
Figure BDA0002994681210000081
Thus:
p(m2|ta)＝α
if m ginseng compete with the current time, the time urgency, the required electric quantity and the moving distance of each competitor are mutually independent, and therefore the probability of winning or losing between every two competitors is also mutually independent. Then for competitor a, probability p (m) of being able to successfully acquire mobile charging pile2|ta)*Comprises the following steps:
p(m2|ta)*＝p(m2|ta)m-1＝αm-1
similarly, probability p (m) that competitor a fails to acquire the mobile charging pile1|ti)*Comprises the following steps:
p(m1|ta)*＝1-p(m2|ta)m-1＝1-αm-1
(5) the expected revenue F for a certain charging car competitor is:
F＝U(m1)*p(m1|ta)*+U(m2)*p(m2|ta)*
＝0+(E*δ+Qk-PT-L*g)*αm-1
s403, calculating the best P when the user obtains the expected profit F with the highest valueT. And calculating the bid of the user.
Go to step S5;
the specific contents of step S403 include the following:
(1) the expected revenue F for a certain charging car competitor is:
F＝(E*δ+Qk-PT-L*g)*αm-1
objective function of the model:
maxF
constraint conditions of the model:
Figure BDA0002994681210000091
(2) f can be obtained by Matlab programmingmaxOptimum P of timeTAnd finally, calculating the bid of the user i:
Figure BDA0002994681210000092
and S5, recommending the best bid to the user.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can understand that the modifications or substitutions within the technical scope of the present invention are included in the scope of the present invention, and therefore, the scope of the present invention should be subject to the protection scope of the claims.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are intended to further illustrate the principles of the invention, and that various changes and modifications may be made without departing from the spirit and scope of the invention, which is intended to be protected by the appended claims. The scope of the invention is defined by the claims and their equivalents.

Claims (8)

1. A mobile charging pile scheduling method based on multi-IEV bidding is characterized by comprising the following steps,
step S1, submitting an order of the nearest mobile charging pile automobile to the platform by the new energy automobile, wherein the order information comprises the position of the new energy automobile and the required charging amount, and developing competition under the mobile charging pile bidding rule;
step S2, the time for submitting the order of the idle mobile charging pile automobile is cut off, and the number m of competitors can be seen by all new energy automobiles participating in competition;
step S3, comparing the time urgency degree distribution according to the collected market information, judging the time urgency degree grade of the user, and obtaining the proper PTAnd calculating the profit U (m) of the user when the user successfully/unsuccessfully acquires the mobile charging pilei) According to PTSection and profit U (m)i) Obtaining the best P when the expected profit F is the highestTAnd outputting the best bid of the user i.
2. The method for scheduling the mobile charging pile based on multi-IEV bidding according to claim 1, wherein in step S1, the new energy vehicle obtains a distance that the nearest mobile charging pile needs to move according to a positioning system, and submits the distance and the required charging amount as order information.
3. The method for scheduling the mobile charging pile based on multi-IEV bidding according to claim 1, wherein in step S1, the mobile charging pile bidding rule is specifically as follows:
new energy automobile bid ViIs composed of
Figure FDA0002994681200000011
Wherein E isiIs the amount of charge required by user i, LiDistance, P, required to move for moving the charging pileTiIs the value that user i is willing to pay in addition for time; delta is the lowest unit electricity price that the mobile charging pile is willing to accept,
Figure FDA0002994681200000012
the distance and time cost of movement required to move the charging post,
Figure FDA0002994681200000013
wherein g is the cost price per kilometer required by the mobile charging pile to move, s is the unit time cost per kilometer required by the mobile charging pile to move, and v is the average moving speed of the mobile charging pile; the mobile charging pile company receives profit W obtained by the order of the new energy automobile user iiIs Wi＝PTi+EiX m, wherein, the profit brought by each degree of electricity is m, and the comprehensive score I of the new energy automobile user I is given to the mobile charging pile companyiIs Ii＝Wi。
4. The method for dispatching mobile charging piles based on multi-IEV bidding according to claim 1, wherein in step S3, when the number of competitors m is 1, P is PTWhen the number of competitors m is not 1, the market information is collected as 0.
5. The method for dispatching mobile charging piles based on multi-IEV bidding according to claim 1, wherein in step S3, the central limit theorem P is appliedTSubject to a mathematical expectation of mu1Variance is σ1 2Normal distribution of (i.e. P)T～N(μ1，σ1 2) A probability density function of
Figure FDA0002994681200000021
Based on this urgency to charge the car, the value (x) the user is willing to pay in addition0,x1]、(x1,x2]、(x2,x3]、(x3,x4]、(x4,x5]∪(x5, + ∞) into 5 classes;
satisfies the following conditions:
Figure FDA0002994681200000022
wherein the selected time saving cost is Qi＝110％xiI.e. Q to N (110%. mu.)1，(110％σ1)2) Conforming to normal distribution with a corresponding probability density function of f4(q)；
Then, the probability that the user is in the k-level temporal urgency level is:
Figure FDA0002994681200000023
k＝1，2，3，4，5；
fitting past market data through Matlab software to obtain a probability density function f of required electric quantity2(e) And the probability density function f of the moving distance required by the mobile charging pile3(l)；
The mobile charging pile evaluates the comprehensive score I of a user to be I ═ PT+E×m；
According to fitted f1(pt)、f2(e) Probability density function f of simplified II(i) Is composed of
Figure FDA0002994681200000024
6. The method of claim 1, wherein the mobile charging pile scheduling method based on multi-IEV bidding is implemented,in step S3, the urgency level is determined according to the urgency level of the charging request, and P of the level in the time zone of the charging request is acquiredTA range; establishing a Bayesian bidding game model, and calculating the profit U (m) when the mobile electric pile is successfully or unsuccessfully obtainedi) Calculating expected income; calculating the best P when the user obtains the expected profit FT. And calculating the bid of the user.
7. The method of claim 6, wherein the establishing of the Bayesian bidding game model calculates the yield U (m) of successful or failed acquisition of the mobile electric pilei) The specific method for calculating the expected profit is as follows:
for new energy automobile user a, use m1And m2Two types, m, representing its competitors i1Indicating a winner as a competitor, i.e. Ia＜Ii；m2Indicating that the competitor is a culler, i.e. Ia＞Ii；
If the user is not selected, the cost is not counted, if the user successfully obtains the mobile charging pile, the profit is the profit of the electric vehicle minus the extra value paid for time and the cost brought by the distance that the mobile charging pile needs to move, namely,
Figure FDA0002994681200000031
the signal set t contains the portion of value that is willing to pay more for time, the required charge, the moving distance and the distribution f (x) of the competitor's demand for effectiveness: t ═ PT，E，L，F(x)}；
If only 2 users participate in the competition, the competitor passes t in comparison with the competitor iaTo measure the level of itself in a competitor and to measure the signal taThe probability that competitor a is the winner is calculated as p (m)2|ta)＝p(Ia＞Ii|ta) (ii) a Order to
Figure FDA0002994681200000032
Then p (m)2|ta)＝α；
If m ginseng competes with the competition, the probability p (m) of the mobile charging pile can be successfully obtained for the competitor a2|ta)*Is p (m)2|ta)*＝p(m2|ta)m-1＝αm-1Probability p (m) that competitor a fails to acquire mobile charging pile1|ti)*Is p (m)1|ta)*＝1-p(m2|ta)m-1＝1-αm-1(ii) a The expected profit to the competitor F is
F＝U(m1)*p(m1|ta)*+U(m2)*p(m2|ta)*
＝0+(E*δ+Qk-PT-L*g)*αm-1。
8. The method of claim 6, wherein the expected revenue F is F ═ E × δ + Qk-PT-L*g)*αm-1The target function of the model is maxF, and the constraint condition of the model is
Figure FDA0002994681200000041
Get when FmaxOptimum P of timeTFinally, the bid of the user i is calculated as
Figure FDA0002994681200000042
CN202110325972.8A 2021-03-26 2021-03-26 Mobile charging pile scheduling method based on multi-IEV bidding Active CN113054676B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110325972.8A CN113054676B (en) 2021-03-26 2021-03-26 Mobile charging pile scheduling method based on multi-IEV bidding

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110325972.8A CN113054676B (en) 2021-03-26 2021-03-26 Mobile charging pile scheduling method based on multi-IEV bidding

Publications (2)

Publication Number Publication Date
CN113054676A true CN113054676A (en) 2021-06-29
CN113054676B CN113054676B (en) 2022-10-14

Family

ID=76515581

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110325972.8A Active CN113054676B (en) 2021-03-26 2021-03-26 Mobile charging pile scheduling method based on multi-IEV bidding

Country Status (1)

Country Link
CN (1) CN113054676B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117162845A (en) * 2023-11-01 2023-12-05 南通国轩新能源科技有限公司 Movable energy storage charging method and device
CN117634713A (en) * 2024-01-26 2024-03-01 南京邮电大学 Electric taxi charging cost optimization method and system based on charging pile lease

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106934542A (en) * 2017-03-09 2017-07-07 国网江苏省电力公司电力科学研究院 A kind of electric automobile demand response regulation and control method based on Stark Burger game theory
CN111445161A (en) * 2020-04-09 2020-07-24 国网浙江省电力有限公司经济技术研究院 Electric vehicle charging pile benefit risk assessment method based on risk value

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106934542A (en) * 2017-03-09 2017-07-07 国网江苏省电力公司电力科学研究院 A kind of electric automobile demand response regulation and control method based on Stark Burger game theory
CN111445161A (en) * 2020-04-09 2020-07-24 国网浙江省电力有限公司经济技术研究院 Electric vehicle charging pile benefit risk assessment method based on risk value

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117162845A (en) * 2023-11-01 2023-12-05 南通国轩新能源科技有限公司 Movable energy storage charging method and device
CN117162845B (en) * 2023-11-01 2023-12-29 南通国轩新能源科技有限公司 Movable energy storage charging method and device
CN117634713A (en) * 2024-01-26 2024-03-01 南京邮电大学 Electric taxi charging cost optimization method and system based on charging pile lease
CN117634713B (en) * 2024-01-26 2024-05-24 南京邮电大学 Electric taxi charging cost optimization method and system based on charging pile lease

Also Published As

Publication number Publication date
CN113054676B (en) 2022-10-14

Similar Documents

Publication Publication Date Title
CN113054676B (en) Mobile charging pile scheduling method based on multi-IEV bidding
JP5948674B2 (en) Car soliciting device, in-vehicle terminal device, car soliciting method and program thereof
CN111160588B (en) Method and system for reserving charging service
US7970533B2 (en) System and method for matching participants in a rideshare program
US20130054281A1 (en) Methods and systems for rideshare
CN112950251B (en) Reputation-based vehicle crowd sensing node reverse combination auction excitation optimization method
US20090018902A1 (en) Commuter credits system and method
CN112765656B (en) Electric car sharing charging credible system and method based on block chain
Jin et al. Optimal aggregation design for massive V2G participation in energy market
CN112498164A (en) Processing method and device of charging strategy
Scorrano et al. Mandating the use of the electric taxis: The case of Florence
CN111861661A (en) Electric vehicle charging transaction method and device
CN112507506A (en) Multi-objective optimization method for sharing automobile pricing planning model based on genetic algorithm
CN113505907B (en) Incentive mechanism method suitable for decision selection
Babic et al. Modelling electric vehicle owners’ willingness to pay for a charging service
CN116014773A (en) Scheduling method for two-stage electric vehicle cooperative networking auxiliary service based on deep reinforcement learning
He et al. Factors affecting the adoption intention of electric vehicles: The roles of objective, perceived and prospective accessibility
US20240086981A1 (en) Taxi vehicle management method and taxi vehicle management system
KR20190053096A (en) Car sharing fee setting device, method and system
Rodier et al. The Central Valley Initiative
CN112235805B (en) Electric power wireless heterogeneous network service connection competition method and connection method
CN112801750B (en) Order allocation method and device
US11769211B2 (en) Vehicle insurance system, vehicle dealer terminal, and vehicle sales method
US20220300838A1 (en) Behavior prediction device, behavior prediction method, and behavior prediction program
CN110648411B (en) Road congestion charging system and method based on mileage distribution

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant