CN117634713B - Electric taxi charging cost optimization method and system based on charging pile lease - Google Patents

Electric taxi charging cost optimization method and system based on charging pile lease Download PDF

Info

Publication number
CN117634713B
CN117634713B CN202410110203.XA CN202410110203A CN117634713B CN 117634713 B CN117634713 B CN 117634713B CN 202410110203 A CN202410110203 A CN 202410110203A CN 117634713 B CN117634713 B CN 117634713B
Authority
CN
China
Prior art keywords
charging
task
cost
pile
charging pile
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.)
Active
Application number
CN202410110203.XA
Other languages
Chinese (zh)
Other versions
CN117634713A (en
Inventor
徐佳
张毅铭
刘婧怡
王磊
刘林峰
徐力杰
李德强
肖甫
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 CN202410110203.XA priority Critical patent/CN117634713B/en
Publication of CN117634713A publication Critical patent/CN117634713A/en
Application granted granted Critical
Publication of CN117634713B publication Critical patent/CN117634713B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • 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
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0645—Rental transactions; Leasing transactions

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Accounting & Taxation (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Theoretical Computer Science (AREA)
  • Development Economics (AREA)
  • Finance (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Educational Administration (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The invention discloses a charging pile lease-based electric taxi charging cost optimization method and system, which relate to the technical field of electric car charging, and the method comprises the following steps: acquiring information of electric taxis and rentable charging piles, and generating a set; constructing an electric taxi charging pile renting system and establishing a charging cost model based on the task number; according to a charging cost model based on the task number, formalizing the problem of minimizing the charging cost of the electric taxi under the constraint of the renting power capacity of the charging pile; based on the formalized charge cost minimization problem, invoking a charge distribution algorithm based on the task number, and determining a charge distribution strategy of the electric taxi; according to the invention, the charging resources are flexibly configured, the charging benefit is improved, the public charging piles with wide short-term lease distribution are used as temporary special charging piles according to the charging requirement of the electric taxis, the special charging station is not required to be independently built, the high-term construction cost in the early stage is saved, and the charging requirement generated by the random time and position of the electric taxis can be met.

Description

Electric taxi charging cost optimization method and system based on charging pile lease
Technical Field
The invention relates to the technical field of electric car charging, in particular to an electric taxi charging cost optimization method and system based on charging pile renting.
Background
In order to reduce carbon emission and air pollution, electric vehicles are gradually replaced by fuel vehicles in public transportation, however, the charging problem is always limiting the development of electrification of public transportation. Along with the increase of the number of the household electric vehicles, the charging demand is also increased increasingly, and particularly in the peak period, the electric taxis need to use public charging piles in a competing manner with other household electric vehicles, so that the effective operation time of the taxis is influenced, and the operation income is reduced. However, the cost of building proprietary charging stations is high and there is uncertainty in revenue; more importantly, the electric taxis are different from electric buses, and the charging requirements of the electric taxis are influenced by passenger carrying requirements, so that the electric buses have high space-time randomness. A large number of construction special charging stations may cause low charging station utilization rate, resulting in waste of charging resources.
Although there are many researches on electric vehicle charging schedule, such as optimizing charging delay or charging cost by a centralized method or a distributed game theory method, optimizing charging cost by optimizing charging power based on real-time electricity price, and the like. However, no research work related to the charging pile lease mode of the electric taxis exists at present.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: how to meet the charging requirement of the electric taxis generated at random time and position by means of widely distributed public charging piles.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method for optimizing charging cost of an electric taxi based on charging pile renting, including:
Acquiring information of electric taxis and rentable charging piles, and generating a set;
Based on the generated set, constructing an electric taxi charging pile renting system, and establishing a charging cost model based on the task number;
according to a charging cost model based on the task number, formalizing the problem of minimizing the charging cost of the electric taxi under the constraint of the renting power capacity of the charging pile;
Based on the formalized problem of minimizing the charging cost, a task number-based charging distribution algorithm is called, and an electric taxi charging distribution strategy is determined.
As a preferable scheme of the electric taxi charging cost optimization method based on charging pile lease, the method comprises the following steps:
the generating the set includes:
Is provided with Representing an electric taxi set,/>Representing a set of rentable charging posts.
As a preferable scheme of the electric taxi charging cost optimization method based on charging pile lease, the method comprises the following steps:
the construction of the electric taxi charging pile renting system comprises the following steps:
Electric taxi Submitting charging tasks to charging pile lease platform/>Wherein/>，/>And/>Electric taxis/>, respectivelyIs provided, the charging demand and the battery capacity;
Charging pile Submitting self information to lease platform/>Wherein/>Charging piles/>, respectivelyA reference lease price, a unit power price, a location and a lease power capacity upper limit.
As a preferable scheme of the electric taxi charging cost optimization method based on charging pile lease, the method comprises the following steps:
the establishing the task number-based charging cost model comprises the following steps:
Charging pile The total charging cost of (1) consists of lease cost and power cost, let/>Representation assigned to charging pile/>Wherein/>The charging task allocation binary decision variable;
If the charge task Assigned to charging pile/>Then/>Otherwise/>; Charging pile/>Lease cost of/>Representation, wherein/>Is a monotonically increasing concave function representing the lease size and satisfies，/>; Charging pile/>Is/>Wherein/>Is a charging pile/>Is a total charge amount of (a):
；
wherein, Is the charging task/>Actual charge amount of/(v)Is the energy consumption per unit movement,/>Is an electric taxi/>To the charging pile/>Is the shortest distance of (2);
Definition charging pile The total cost is as follows:
。
As a preferable scheme of the electric taxi charging cost optimization method based on charging pile lease, the method comprises the following steps:
The formalization of the electric taxi charging cost minimization problem under the constraint of the renting power capacity of the charging pile comprises the following steps:
；
constraints that ensure that the total charge of any charging stake cannot exceed its upper rentable power capacity limit are expressed as:
；
The constraint of ensuring that any electric taxi has sufficient power to reach the allocated charging peg is expressed as:
；
Constraints that ensure that each charging task can and can only be assigned to one charging pile are expressed as:
。
As a preferable scheme of the electric taxi charging cost optimization method based on charging pile lease, the method comprises the following steps:
the step of calling a task number-based charging distribution algorithm, and the step of determining the charging distribution strategy of the electric taxi comprises the following steps:
For any arbitrary Initializing a charging task set/>And remaining leased Power Capacity/>；
Initializing an unassigned set of charging tasksLease charging pile set/>And a charging task allocation matrix/>；
For any arbitraryBased on/>Constructing a charging task extension set/>, with minimum average marginal cost；
Calculating a charging pile in which the average marginal cost is minimum:
；
wherein, For any one charging pile/>Charging task set,/>For any one charging pile/>Charging task extension set,/>For any one charging pile/>The upper charge task set is/>Charging cost of/>For any one charging pile/>Upper charging task extension set is/>Charging costs of (2);
Updating Is to be added to the remaining leased power capacity:
；
Updating a set of charging tasks Update lease charging pile set/>Updating unassigned set of charging tasks/>；
Repeatedly constructing a charging task expansion set with minimum average marginal cost, calculating charging piles with minimum average marginal cost, and updatingIs not allocated until/>；
For any arbitrary，/>Let/>; Output charging task allocation matrix/>。
As a preferable scheme of the electric taxi charging cost optimization method based on charging pile lease, the method comprises the following steps:
the constructing the charging task extension set with the minimum average marginal cost comprises the following steps:
setting an initial value: initializing newly increased number of charging tasks Charging pile/>First/>Extended task set/>, after secondary addition of charging taskExtended set index/>, with minimum average marginal charging costCurrently unassigned set of charging tasks/>Currently remaining leased Power Capacity/>；
Calculating a charging task having a minimum actual charge amount:
；
wherein, For unassigned electric taxis/>Charging task of/>For charging tasks/>At the charging pile/>Actual charge on,/>For electric taxis/>Charging demand of/>For electric taxis/>Per unit mobile energy consumption,/>For electric taxis/>To the charging pile/>Is the shortest distance of (2);
Judging whether the charging task can be completed or not and carrying out corresponding operation: if it is AndI.e. the electric taxi with the smallest actual charge can reach the charging pile/>And the remaining lease capacity is able to complete the charging task/>Update the newly added charge task times/>Updating a charging task extension setUpdating the current remaining leased power capacity/>And unassigned set of charging tasks/>Repeating the calculation of the charging task with the minimum actual charging amount, judging whether the charging task can be completed and performing corresponding operation, updating the unassigned charging task set and performing corresponding operation until/>Or/>；
Otherwise, updating the unassigned charging task set and performing corresponding operations: updating unassigned set of charging tasksIf/>Calculating an extended set index of charging tasks with minimum average marginal cost:
；
Output of ；
Otherwise, repeating the calculation of the charging task with the minimum actual charge amount, judging whether the charging task can be completed and performing corresponding operation, updating the unassigned charging task set and performing corresponding operation untilOr/>。
In a second aspect, an embodiment of the present invention provides a system for optimizing a charging cost of an electric taxi based on charging pile renting, including:
The initialization module is used for acquiring information of the electric taxis and the rentable charging piles and generating a set;
the cost model construction module is used for constructing an electric taxi charging pile renting system based on the generated set and establishing a charging cost model based on the task number;
The formalization module is used for formalizing the problem of minimizing the charging cost of the electric taxi under the constraint of the renting power capacity of the charging pile according to the charging cost model based on the task number;
And the charging distribution module is used for calling a task number-based charging distribution algorithm based on the formalized problem of minimizing the charging cost and determining an electric taxi charging distribution strategy.
In a third aspect, embodiments of the present invention provide a computing device comprising:
a memory and a processor;
The memory is configured to store computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to implement a method for optimizing electric taxi charging costs based on charging stake rentals according to any one of the embodiments of the invention.
In a fourth aspect, an embodiment of the present invention provides a computer readable storage medium storing computer executable instructions that when executed by a processor implement the electric taxi charging cost optimization method based on charging stake renting.
The invention has the beneficial effects that: according to the invention, the charging resources are flexibly configured, the charging benefit is improved, the public charging piles with wide short-term lease distribution are used as temporary special charging piles according to the charging requirement of the electric taxis, the special charging station is not required to be independently built, the high-term construction cost in the early stage is saved, and the charging requirement generated by the random time and position of the electric taxis can be met.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a general flow chart of an electric taxi charging cost optimization method based on charging stake renting according to a first embodiment of the invention;
fig. 2 is a schematic diagram of an electric taxi charging pile renting system according to an electric taxi charging cost optimizing method based on charging pile renting according to a first embodiment of the invention;
Fig. 3 is a flow chart of a task number-based charging distribution method of an electric taxi charging cost optimization method based on charging pile renting according to a first embodiment of the invention;
fig. 4 is a flowchart of a charging task extension set with minimum construction average marginal cost for an electric taxi charging cost optimization method based on charging stake renting according to a first embodiment of the invention;
Fig. 5 is a graph comparing total charging costs at different numbers of charging tasks in simulation and comparison examples of an electric taxi charging cost optimization method based on charging stake lease according to a second embodiment of the present invention;
fig. 6 is a graph comparing total charging costs at different rented power capacity intervals in simulation and comparison examples of an electric taxi charging cost optimization method based on charging stake renting according to a second embodiment of the invention;
Fig. 7 is a graph comparing total charging costs at different electricity price intervals in simulation and comparison examples of an electric taxi charging cost optimization method based on charging stake lease according to a second embodiment of the present invention;
fig. 8 is a graph comparing total charging costs at different electricity price intervals in simulation and comparison examples of an electric taxi charging cost optimization method based on charging stake lease according to a second embodiment of the present invention;
Fig. 9 is a graph comparing total charging costs at different reference lease price intervals in simulation and comparison examples of an electric taxi charging cost optimization method based on a charging stake lease according to a second embodiment of the present invention;
fig. 10 is a graph comparing total charging costs at different reference lease price intervals in simulation and comparison examples of an electric taxi charging cost optimization method based on a charging stake lease according to a second embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
Referring to fig. 1-4, in a first embodiment of the present invention, there is provided a method for optimizing charging cost of an electric taxi based on charging pile renting, as shown in fig. 1, including:
S1: acquiring information of electric taxis and rentable charging piles, and generating a set;
Specifically, the generating the set includes:
Is provided with Representing an electric taxi set,/>Representing a set of rentable charging posts.
S2: based on the generated set, constructing an electric taxi charging pile renting system, and establishing a charging cost model based on the task number;
specifically, the construction of the electric taxi charging pile renting system comprises the following steps:
Electric taxi Submitting charging tasks to charging pile lease platform/>Wherein/>，/>And/>Electric taxis/>, respectivelyIs provided, the charging demand and the battery capacity;
Charging pile Submitting self information to lease platform/>Wherein/>Charging piles/>, respectivelyA reference lease price, a unit power price, a location and a lease power capacity upper limit.
Still further, the establishing the task number-based charging cost model includes:
Charging pile The total charging cost of (1) consists of lease cost and power cost, let/>Representation assigned to charging pile/>Wherein/>The charging task allocation binary decision variable;
If the charge task Assigned to charging pile/>Then/>Otherwise/>; Charging pile/>Lease cost of/>Representation, wherein/>Is a monotonically increasing concave function representing the lease size and satisfies，/>; Charging pile/>Is/>Wherein/>Is a charging pile/>Is a total charge amount of (a):
；
wherein, Is the charging task/>Actual charge amount of/(v)Is the energy consumption per unit movement,/>Is an electric taxi/>To the charging pile/>Is the shortest distance of (2);
Definition charging pile The total cost is as follows:
。
S3: according to a charging cost model based on the task number, formalizing the problem of minimizing the charging cost of the electric taxi under the constraint of the renting power capacity of the charging pile;
specifically, formalizing the problem of minimizing the electric taxi charging cost under the constraint of the electric capacity leased by the charging pile includes:
；
constraints that ensure that the total charge of any charging stake cannot exceed its upper rentable power capacity limit are expressed as:
；
The constraint of ensuring that any electric taxi has sufficient power to reach the allocated charging peg is expressed as:
；
Constraints that ensure that each charging task can and can only be assigned to one charging pile are expressed as:
。
S4: based on the formalized problem of minimizing the charging cost, a task number-based charging distribution algorithm is called, and an electric taxi charging distribution strategy is determined.
As shown in fig. 3, specifically, the invoking the task number-based charging distribution algorithm, and determining the electric taxi charging distribution policy includes:
For any arbitrary Initializing a charging task set/>And remaining leased Power Capacity/>；
Initializing an unassigned set of charging tasksLease charging pile set/>And a charging task allocation matrix/>；
For any arbitraryBased on/>Constructing a charging task extension set/>, with minimum average marginal cost；
Calculating a charging pile in which the average marginal cost is minimum:
；
wherein, For any one charging pile/>Charging task set,/>For any one charging pile/>Charging task extension set,/>For any one charging pile/>The upper charge task set is/>Charging cost of/>For any one charging pile/>Upper charging task extension set is/>Charging costs of (2);
Updating Is to be added to the remaining leased power capacity:
；
Updating a set of charging tasks Update lease charging pile set/>Updating unassigned set of charging tasks/>；
Repeatedly constructing a charging task expansion set with minimum average marginal cost, calculating charging piles with minimum average marginal cost, and updatingIs not allocated until/>；
For any arbitrary，/>Let/>; Output charging task allocation matrix/>。
As shown in fig. 4, further, the constructing the extended set of charging tasks with the smallest average marginal cost includes:
setting an initial value: initializing newly increased number of charging tasks Charging pile/>First/>Extended task set/>, after secondary addition of charging taskExtended set index/>, with minimum average marginal charging costCurrently unassigned set of charging tasks/>Currently remaining leased Power Capacity/>；
Calculating a charging task having a minimum actual charge amount:
；
wherein, For unassigned electric taxis/>Charging task of/>For charging tasks/>At the charging pile/>Actual charge on,/>For electric taxis/>Charging demand of/>For electric taxis/>Per unit mobile energy consumption,/>For electric taxis/>To the charging pile/>Is the shortest distance of (2);
Judging whether the charging task can be completed or not and carrying out corresponding operation: if it is AndI.e. the electric taxi with the smallest actual charge can reach the charging pile/>And the remaining lease capacity is able to complete the charging task/>Update the newly added charge task times/>Updating a charging task extension setUpdating the current remaining leased power capacity/>And unassigned set of charging tasks/>Repeating the calculation of the charging task with the minimum actual charging amount, judging whether the charging task can be completed and performing corresponding operation, updating the unassigned charging task set and performing corresponding operation until/>Or/>；
Otherwise, updating the unassigned charging task set and performing corresponding operations: updating unassigned set of charging tasksIf/>Calculating an extended set index of charging tasks with minimum average marginal cost:
；
Output of ；
Otherwise, repeating the calculation of the charging task with the minimum actual charge amount, judging whether the charging task can be completed and performing corresponding operation, updating the unassigned charging task set and performing corresponding operation untilOr/>。
It should be noted that the invention provides a problem of minimizing the charging cost of the electric taxi under the constraint of the renting power capacity of the charging pile; aiming at the problem, an electric taxi charging distribution strategy based on the task number is designed, and the approximate ratio isI.e. in the worst case its performance is at most the optimal solution/>Wherein/>Is the number of charging tasks.
Order theFor/>The number of unassigned charging tasks at the beginning of a iteration. Assume that the total number of iterations of the algorithm is/>Therefore we have/>And/>，/>For the number of charging tasks. Let/>For charging pile/>First/>And (5) calculating the obtained charging task extension set. /(I)Initialized to 0,/>Initialized to an empty set. Let/>For/>The final set of charging tasks, therefore, at the completion of all charging task assignments, i.e., at the end of the algorithm, there is/>。
Considered at the firstIn the next iteration, the charging pile/>First/>Sub-computation of the charging task extension set, thus charging stake/>Is set as/>Average marginal cost is/>. Let/>Is the optimal solution of the problem, at most in/>The cost of (2) covers the remaining charging tasks, so there must be one whose average marginal cost is at most/>Is a task set of (1). Because the method selects the charging pile and the task set with the minimum average marginal cost each time, the method comprises the following steps: /(I)；
Because ofInequality (1) can be restated as:
；
Sharing of And (3) iterating, wherein each iteration selects a charging pile with the minimum average marginal cost and a task set thereof, and therefore, the inequality (2) is accumulated to obtain:
；
wherein, Is/>Accumulation/>The total cost of all leased charging piles obtained after the next time. For any/>With inequality/>Thus, there are:
；
Based on inequality (4), there are:
；
the simultaneous inequality (3) and inequality (5) are: the approximation ratio of the present invention is therefore/> 。
The above is an exemplary scheme of the electric taxi charging cost optimization method based on charging pile renting in this embodiment. It should be noted that, the technical scheme of the electric taxi charging cost optimization system based on charging pile lease and the technical scheme of the electric taxi charging cost optimization method based on charging pile lease belong to the same concept, and in this embodiment, details of the technical scheme of the electric taxi charging cost optimization system based on charging pile lease, which are not described in detail, can be referred to the description of the technical scheme of the electric taxi charging cost optimization method based on charging pile lease.
In the system for optimizing the charging cost of the electric taxis based on the charging piles, as shown in fig. 2, the electric taxis submit the charging demand tasks to the charging platform, the charging station provides the charging piles to the charging platform, the charging platform leases part of the charging piles to the charging station and distributes the charging tasks, and the charging platform distributes the electric taxis to the target charging piles for charging; comprising the following steps:
The initialization module is used for acquiring information of the electric taxis and the rentable charging piles and generating a set;
the cost model construction module is used for constructing an electric taxi charging pile renting system based on the generated set and establishing a charging cost model based on the task number;
The formalization module is used for formalizing the problem of minimizing the charging cost of the electric taxi under the constraint of the renting power capacity of the charging pile according to the charging cost model based on the task number;
And the charging distribution module is used for calling a task number-based charging distribution algorithm based on the formalized problem of minimizing the charging cost and determining an electric taxi charging distribution strategy.
The embodiment also provides a computing device, which is suitable for the situation of the electric taxi charging cost optimization method based on charging pile renting, and comprises the following steps:
a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the electric taxi charging cost optimization method based on charging pile lease, which is provided by the embodiment.
The present embodiment also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the electric taxi charging cost optimization method based on charging stake renting as set forth in the above embodiment.
The storage medium provided in this embodiment belongs to the same inventive concept as the electric taxi charging cost optimization method based on charging stake rental provided in the above embodiment, and technical details not described in detail in this embodiment can be seen in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.
Example 2
Referring to fig. 5-10, for one embodiment of the present invention, an electric taxi charging cost optimization method based on charging pile lease is provided, and in order to verify the beneficial effects of the present invention, scientific demonstration is performed through simulation and comparison experiments.
Simulation experiment: the charge distribution method based on the task number, which aims to minimize the charge cost, includes the following steps:
Is provided with Representing an electric taxi set,/>Representing a collection of charging piles. Electric taxi/>Submitting charging tasks to charging pile lease platform/>Where location of electric taxi/>Respectively/>、、/>、、/>、Battery capacity/>70, 65, 75, 80, 85, 65/>, RespectivelyCharging demand/>. Electric taxi movement energy consumption/>0.15, 0.16, 0.17, 0.2, 0.25, 0.18/>, Respectively. Charging pile/>Submitting self information to lease platform/>Wherein the reference lease price/>8, 6, 7, 5/>, RespectivelyPrice per unit of electric power/>0.8, 0.9, 0.85, 1.0/>, RespectivelyLease of Power Capacity/>120, 130, 110, 100/>, RespectivelyPosition/>Respectively/>、、/>、. Charging pile/>Function/>, characterizing the scale of the number of leasing tasks，/>0.98, 0.95, 0.97, 0.95, Respectively.
(1.1) For any ofInitializing a charging task set/>And remaining leased power capacity；
(1.2) Initializing unassigned set of charging tasksLease charging pile set/>And a charging task allocation matrix/>；
(1.3) For any one ofBased on/>Constructing a charging task extension set/>, with minimum average marginal cost；
(1.3.1) Selecting charging pileInitializing newly-increased charging task times/>Charging pile/>First/>Extended task set/>, after secondary addition of charging taskExtended set index/>, with minimum average marginal charging costCurrently unassigned set of charging tasks/>Currently remaining leased Power Capacity/>；
(1.3.2) Calculating a charging task having a minimum actual charge amount:；
wherein, For unassigned electric taxis/>Charging task of/>For charging tasksAt the charging pile/>Actual charge on,/>For electric taxis/>Charging demand of/>For electric taxis/>Per unit mobile energy consumption,/>For electric taxis/>To the charging pile/>Is the shortest distance of (2);
(1.3.3) if And/>I.e. the electric taxi with the smallest actual charge can reach the charging pile/>And the remaining lease capacity is able to complete the charging task/>Update the newly added charge task times/>Update charging task extension set/>Updating the current remaining leased power capacity/>And unassigned set of charging tasks/>Executing step (1.3.5); otherwise, executing the step (1.3.4);
(1.3.4) updating the unassigned set of charging tasks If/>Executing step (1.3.6); otherwise, executing the step (1.3.5);
(1.3.5) repeating steps (1.3.2), (1.3.3), (1.3.4) until Or/>；
Thus obtaining。/>
(1.3.6) Calculating a charging task extension set index with a minimum average marginal cost: ; output/> 。
Thus (2)For charging pile/>The same procedure as in (1.3.1) to (1.3.6) was carried out to give the result/>；
(1.4) Calculating a charging pile in which the average marginal cost is minimum:
；
wherein, For any one charging pile/>Charging task set,/>For any one charging pile/>Charging task extension set,/>For any one charging pile/>The upper charge task set is/>Is used for the charging cost of the battery,For any one charging pile/>Upper charging task extension set is/>Charging costs of (2);
(1.5) updating Remaining lease Power Capacity/>Update task set/>Update lease charging pile set/>Updating an unassigned set of charging tasks；
(1.6) Repeating steps (1.3), (1.4), (1.5) until;
The final result isThe corresponding charging cost is: 353.86;
(1.7) for any one of ，/>Let/>; Output charging task allocation matrix/>。
Control experiment: in order to verify and explain the charge distribution algorithm based on the task number, three algorithms are designed in the embodiment, and the three algorithms are actually compared with the method of the invention for testing:
(1) Shortest charge distance algorithm: the algorithm selects the closest match between the charging tasks and the available charging posts each time until all the charging tasks are fully assigned. Wherein the available charging pile is a charging pile whose rentable power capacity is able to fulfill the charging task.
(2) Maximum coverage charging pile algorithm: the algorithm selects the charging pile with the largest number of charging tasks to be executed each time and matches the charging tasks. Until all the charging tasks are distributed completely, the iteration is terminated.
(3) Minimum charge cost charging pile algorithm: the algorithm firstly distributes charging tasks for each charging pile according to the ascending order of the actual charging quantity without the charging tasks until the charging tasks cannot be distributed, and then selects the charging pile with the smallest charging cost and matches the corresponding charging tasks. Until all the charging tasks are distributed completely, the iteration is terminated.
Parameter setting: the Shenzhen Luo lake region charging station dataset is used, comprising about 35 fast charge public charging stations, and each electric vehicle charging station data comprises a charging station ID, a charging station position and a charging interface number. The electric taxi track data adopts an electric taxi track data set in Shenzhen Luo lake area. The data set collects 2023, 3 and 3 day and nearly 4000 electric taxi track data, each track data comprising electric taxi ID, GPS location and recording time. The residual electric quantity of the electric taxi is calculated according to the driving distance, and when the residual electric quantity is reduced to 20%, the electric taxi generates a charging requirement. During a charging peak period, the electric taxis submit charging requests to the lease platform to form charging tasks. And the lease platform issues a charging task set submitted by the electric taxis to the public charging pile every 15 minutes and distributes charging tasks. Setting the battery capacity compliance of the electric taxi to 65 to 85 by referring to the basic parameters of the electric taxi which is most widely used in Shenzhen cityIs subject to a mobile energy consumption of 0.15 to 0.25/>Is a uniform distribution of (c). When the residual electric quantity is reduced to 20%, the electric taxi is set to generate a charging requirement, so that the charging requirement of the electric taxi is 80% of the battery capacity. Assuming that each charging station can rent 4 to 6 public charging piles, setting the default value of rentable power capacity of each charging pile to be 120 to 180/>, and. Referring to Shenzhen electric automobile charging electricity price standard, the unit electricity price default value is set to be 0.8 to 1.2/>The reference lease price default value is set to 5 to 15/>. The present embodiment uses the function/>A charging station operator is characterized for a lease-scale-based discount policy. Wherein/>Representing allocation to charging pilesIs not limited to the size of the charging task set. /(I)Is a discount factor. Based on the electric taxi track data and the charging demand analysis, the default value of the number of charging tasks on the leasing platform is set to 150 so as to facilitate experiments. The values of the key parameters will then be changed to explore their effect on the algorithm, with the average value of each measurement exceeding 100 random topologies.
Comparing the charge distribution algorithm based on the task number with the other three algorithms, as the number of electric taxis charging tasks increases from 50 to 150, as shown in fig. 5, the total charge cost of the charge distribution algorithm based on the task number is reduced by 10.86%, 11.61% and 5.68% compared with the shortest charge distance algorithm, the maximum coverage charge pile algorithm and the minimum charge cost charge pile algorithm, respectively. This is because the minimum charge cost charging stake algorithm selects a task set with the smallest charge cost after each generation of a charge task set, compared to the present algorithm, without taking into account the heterogeneity of the charging stake rentable capacity, the task set with the smallest charge cost is due to the low renting capacity of the charging stake, and ignoring some of the charge tasks in the task set may have other lower charge costs. The shortest charging distance algorithm tends to select the nearest charging post for matching, and the total charging cost can be optimized by reducing the mobile energy consumption, but the difference of electricity prices and lease prices among charging posts is not considered. The maximum coverage charging pile algorithm will rent the charging pile with the most number of coverage tasks, and although the algorithm can rent the least charging pile and make the most use of the discount strategy based on the lease scale, the renting of the least charging pile does not mean that the total charging cost can be reduced when the unit price and the reference lease price difference are taken into consideration.
Charging pile leasing power capacity is an important parameter for balancing public and private charging piles. As the charging pile leasing power capacity increases from interval [90, 120] to interval [210,240], as shown in fig. 6, the total charging cost of the mission-based charging distribution algorithm and the minimum charging cost charging pile algorithm significantly decreases with increasing charging pile leasable capacity, while the total charging cost of the minimum charging distance algorithm and the maximum coverage charging pile algorithm does not significantly change with increasing charging pile leasable capacity. This is because as the charging pile leases power capacity increases, a charging pile with a lower price of electricity and lease can allocate more charging tasks, which causes the total charging cost of the task number-based charging allocation algorithm and the minimum charging cost charging pile algorithm to show a downward trend. The shortest charging distance algorithm tends to select the nearest charging pile for matching, the maximum coverage charging pile algorithm tends to rent the charging pile with the largest number of coverage tasks, and the allocation strategy is irrelevant to the capacity of the charging pile. The total charging cost of the task number-based charging distribution algorithm is reduced by 8.52%, 8.86% and 4.05% compared with the shortest charging distance algorithm, the maximum coverage charging pile algorithm and the minimum charging cost charging pile algorithm.
Considering the time-sharing electricity price mechanism adopted by the charging market, the unit electricity price of the charging pile is divided into intervalsIncreased to/>To test the total charging costs at different electricity price intervals. As shown in fig. 7, the total charging cost increases as the price of electricity per charging pile increases. Under different electricity price intervals, the total charging cost of the charging distribution algorithm based on the task number is reduced by 10.42%, 11.96% and 4.20% respectively compared with the shortest charging distance algorithm, the maximum coverage charging pile algorithm and the minimum charging cost charging pile algorithm. In addition, the embodiment also tests the influence of the unit price isomerism among the charging piles on the algorithm by increasing the unit price interval length. With the unit electricity price interval from/>Gradually expand to interval/>The interval length is increased from 0.2 to 0.8, and as shown in fig. 8, the overall charging cost increasing trend of the task number-based charging distribution algorithm is slower than that of the shortest charging distance algorithm and the maximum coverage charging pile algorithm. When the electricity price interval is/>When the method is used, the total charging cost of the charging distribution algorithm based on the task number is reduced by 6.19 percent and 8.14 percent respectively compared with the shortest charging distance algorithm and the maximum coverage charging pile algorithm. And when the electricity price interval is/>When the method is used, the total charging cost of the charging distribution algorithm based on the task number is reduced by 14.56% and 16.15% respectively compared with the shortest charging distance algorithm and the maximum coverage charging pile algorithm. This is because the shortest charging distance algorithm tends to select the nearest charging pile for matching, and the maximum coverage charging pile algorithm selects the charging pile with the largest leasing coverage tasks, ignoring the isomerism of unit electricity prices among the charging piles.
And finally, testing the total charging cost under the isomerism of the reference lease prices among different reference lease price intervals and charging piles. As shown in fig. 9, the total charging cost of the task number-based charging distribution algorithm is reduced by 8.83%, 9.43% and 3.46% compared with the shortest charging distance algorithm, the maximum coverage charging pile algorithm and the minimum charging cost charging pile algorithm, respectively, in different reference lease price intervals. When the charging pile foundation quasi-rent borrows the price from the sectionWhen the interval length is increased from 5 to 20, the total charging cost increasing trend of the task number-based charging distribution algorithm is also slower than that of the shortest charging distance algorithm and the maximum coverage charging pile algorithm as shown in fig. 10, changing to interval [5,25 ]. When the reference lease price interval is [5,10], the total charging cost of the task number-based charging distribution algorithm is reduced by 6.19% and 8.14% respectively compared with the shortest charging distance algorithm and the maximum coverage charging pile algorithm. And when the reference lease price interval is [5,25], the total charging cost of the charging distribution algorithm based on the task number is reduced by 10.53% and 11.29% respectively compared with the shortest charging distance algorithm and the maximum coverage charging pile algorithm.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.

Claims (5)

1. The electric taxi charging cost optimization method based on charging pile renting is characterized by comprising the following steps of:
Acquiring information of electric taxis and rentable charging piles, and generating a set;
the generating the set includes:
Let v= { V 1,v2,...,vm } denote an electric taxi set, n= { s 1,s2,...,sn } denote a rentable charging pile set;
Based on the generated set, constructing an electric taxi charging pile renting system, and establishing a charging cost model based on the task number;
the construction of the electric taxi charging pile renting system comprises the following steps:
Electric taxi v i submits charging task to charging pile lease platform Wherein/>And E i are the position, charging demand and battery capacity of electric taxi v i, respectively;
Charging pile s j submits self information to lease platform Wherein/>The reference lease price, the unit power price, the position and the lease power capacity upper limit of the charging pile s j are respectively;
the establishing the task number-based charging cost model comprises the following steps:
The total charging cost of charging stake s j consists of lease cost and power cost, let T j＝{τi|xi,j =1 } represent the set of charging tasks assigned to charging stake s j, where x i,j is the charging task assignment binary decision variable;
If a charging mission τ i is assigned to a charging peg s j, x i,j =1, otherwise x i,j =0; lease cost of charging pile s j is determined by Representation, wherein f j (·) is a monotonically increasing concave function that characterizes lease size and satisfies f j(0)＝0,1≤fj(1)＜fj(2)＜…＜fj(|Tj|)≤|Tj |; the power cost of the charging pile s j is/>Where Q j is the total charge amount of the charging peg s j:
wherein, Is the actual charge of the charging mission τ i, β i is the unit mobile energy consumption, d i,j is the shortest distance from the electric taxi v i to the charging pile s j;
The total cost on the charging post s j is defined as:
；
according to a charging cost model based on the task number, formalizing the problem of minimizing the charging cost of the electric taxi under the constraint of the renting power capacity of the charging pile;
The formalization of the electric taxi charging cost minimization problem under the constraint of the renting power capacity of the charging pile comprises the following steps:
constraints that ensure that the total charge of any charging stake cannot exceed its upper rentable power capacity limit are expressed as:
The constraint of ensuring that any electric taxi has sufficient power to reach the allocated charging peg is expressed as:
Constraints that ensure that each charging task can and can only be assigned to one charging pile are expressed as:
Based on the formalized charge cost minimization problem, invoking a charge distribution algorithm based on the task number, and determining a charge distribution strategy of the electric taxi;
the step of calling a task number-based charging distribution algorithm, and the step of determining the charging distribution strategy of the electric taxi comprises the following steps:
For any s j E N, initialize the charging task set And remaining leased Power Capacity/>
Initializing an unallocated charging task set T re =t, leasing a charging pile setAnd a charging task allocation matrix X;
For any s j epsilon N, constructing a charging task expansion set with minimum average marginal cost based on T j
Calculating a charging pile in which the average marginal cost is minimum:
Wherein T j′ is the charging task set of any one charging pile s j′, The charging task expansion set for any one charging pile s j′, c j′(Tj′) is the charging cost of the charging task set T j′ on any one charging pile s j′,/>The charging task on any one charging pile s j′ is expanded to be/>Charging costs of (2);
Updating s j remaining lease power capacity:
Updating a set of charging tasks Updating the leased charging pile set W=W { s j }, and updating the unassigned charging task set T re＝Tre\Tj;
Repeating the construction of the extended set of charging tasks with the minimum average marginal cost, calculating the charging pile with the minimum average marginal cost, updating s j the remaining leased power capacity, the set of charging tasks, the set of leased charging piles, the set of unassigned charging tasks until
Let x i,j =1 for any s j∈N,τi∈Tj; and outputting a charging task allocation matrix X.
2. The electric taxi charging cost optimization method based on charging stake renting of claim 1, wherein constructing the charging task extension set with the smallest average marginal cost includes:
Setting an initial value: initializing newly added charging task times k=0, expanding task set T j(k)＝Tj of charging pile s j after the kth charging task is added, expanding set index k min =0 with minimum average marginal charging cost, charging task set T' re＝Tre which is not allocated currently, and remaining leasing power capacity currently
Calculating a charging task having a minimum actual charge amount:
Wherein τ i′ is the charging task of unassigned electric taxi v i′, For the actual charge of the charging mission τ i′ on the charging pile s j,/>For the charging requirement of the electric taxi v i′, beta i′ is the unit mobile energy consumption of the electric taxi v i′, and d i′,j is the shortest distance from the electric taxi v i′ to the charging pile s j;
Judging whether the charging task can be completed or not and carrying out corresponding operation: if it is And/> That is, the electric taxi with the smallest actual charge amount can reach the charging pile s j and the residual lease capacity can complete the charging task τ i, the newly added charging task times k=k+1 are updated, the charging task expansion set T j(k)＝Tj(k-1)∪τi is updated, and the current residual lease power capacity/> And an unassigned set of charging tasks T' re＝T′re\{τi, repeatedly calculating a charging task having a minimum actual charge, determining whether the charging task can be completed and performing a corresponding operation, updating the unassigned set of charging tasks and performing a corresponding operation until/>Or/>
Otherwise, updating the unassigned charging task set and performing corresponding operations: updating the unassigned set of charging tasks T' re＝T′re\{τi ifCalculating an extended set index of charging tasks with the smallest average marginal cost:
Output T j(kmin);
otherwise, repeating the calculation of the charging task with the minimum actual charge amount, judging whether the charging task can be completed and performing corresponding operation, updating the unassigned charging task set and performing corresponding operation until Or/>
3. A system employing the electric taxi charging cost optimization method based on charging stake lease as claimed in any one of claims 1 to 2, characterized by comprising:
The initialization module is used for acquiring information of the electric taxis and the rentable charging piles and generating a set;
the generating the set includes:
Let v= { V 1,v2,...,vm } denote an electric taxi set, n= { s 1,s2,...,sn } denote a rentable charging pile set;
the cost model construction module is used for constructing an electric taxi charging pile renting system based on the generated set and establishing a charging cost model based on the task number;
the construction of the electric taxi charging pile renting system comprises the following steps:
Electric taxi v i submits charging task to charging pile lease platform Wherein/>And E i are the position, charging demand and battery capacity of electric taxi v i, respectively;
Charging pile s j submits self information to lease platform Wherein/>The reference lease price, the unit power price, the position and the lease power capacity upper limit of the charging pile s j are respectively;
the establishing the task number-based charging cost model comprises the following steps:
The total charging cost of charging stake s j consists of lease cost and power cost, let T j＝{τi|xi,j =1 } represent the set of charging tasks assigned to charging stake s j, where x i,j is the charging task assignment binary decision variable;
If a charging mission τ i is assigned to a charging peg s j, x i,j =1, otherwise x i,j =0; lease cost of charging pile s j is determined by Representation, wherein f j (·) is a monotonically increasing concave function that characterizes lease size and satisfies f j(0)＝0,1≤fj(1)＜fj(2)＜…＜fj(|Tj|)≤|Tj |; the power cost of the charging pile s j is/>Where Q j is the total charge amount of the charging peg s j:
wherein, Is the actual charge of the charging mission τ i, β i is the unit mobile energy consumption, d i,j is the shortest distance from the electric taxi v i to the charging pile s j;
The total cost on the charging post s j is defined as:
The formalization module is used for formalizing the problem of minimizing the charging cost of the electric taxi under the constraint of the renting power capacity of the charging pile according to the charging cost model based on the task number;
The formalization of the electric taxi charging cost minimization problem under the constraint of the renting power capacity of the charging pile comprises the following steps:
constraints that ensure that the total charge of any charging stake cannot exceed its upper rentable power capacity limit are expressed as:
The constraint of ensuring that any electric taxi has sufficient power to reach the allocated charging peg is expressed as:
Constraints that ensure that each charging task can and can only be assigned to one charging pile are expressed as:
The charging distribution module is used for calling a charging distribution algorithm based on the task number based on the formalized problem of minimizing the charging cost and determining a charging distribution strategy of the electric taxi;
the step of calling a task number-based charging distribution algorithm, and the step of determining the charging distribution strategy of the electric taxi comprises the following steps:
For any s j E N, initialize the charging task set And remaining leased Power Capacity/>
Initializing an unallocated charging task set T re =t, leasing a charging pile setAnd a charging task allocation matrix X;
For any s j epsilon N, constructing a charging task expansion set with minimum average marginal cost based on T j
Calculating a charging pile in which the average marginal cost is minimum:
Wherein T j′ is the charging task set of any one charging pile s j′, The charging task expansion set for any one charging pile s j′, c j′(Tj′) is the charging cost of the charging task set T j′ on any one charging pile s j′,/>The charging task on any one charging pile s j′ is expanded to be/>Charging costs of (2);
Updating s j remaining lease power capacity:
Updating a set of charging tasks Updating the leased charging pile set W=W { s j }, and updating the unassigned charging task set T re＝Tre\Tj;
Repeating the construction of the extended set of charging tasks with the minimum average marginal cost, calculating the charging pile with the minimum average marginal cost, updating s j the remaining leased power capacity, the set of charging tasks, the set of leased charging piles, the set of unassigned charging tasks until
Let x i,j =1 for any s j∈N,τi∈Tj; and outputting a charging task allocation matrix X.
4. A computing device, comprising:
a memory and a processor;
The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, where the computer executable instructions when executed by the processor implement the steps of the electric taxi charging cost optimization method based on charging stake lease as set forth in any one of claims 1 to 2.
5. A computer readable storage medium storing computer executable instructions which when executed by a processor implement the steps of the electric taxi charging cost optimization method based on charging stake lease of any one of claims 1 to 2.
CN202410110203.XA 2024-01-26 2024-01-26 Electric taxi charging cost optimization method and system based on charging pile lease Active CN117634713B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202410110203.XA CN117634713B (en) 2024-01-26 2024-01-26 Electric taxi charging cost optimization method and system based on charging pile lease

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202410110203.XA CN117634713B (en) 2024-01-26 2024-01-26 Electric taxi charging cost optimization method and system based on charging pile lease

Publications (2)

Publication Number Publication Date
CN117634713A CN117634713A (en) 2024-03-01
CN117634713B true CN117634713B (en) 2024-05-24

Family

ID=90023817

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202410110203.XA Active CN117634713B (en) 2024-01-26 2024-01-26 Electric taxi charging cost optimization method and system based on charging pile lease

Country Status (1)

Country Link
CN (1) CN117634713B (en)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113054676A (en) * 2021-03-26 2021-06-29 南京邮电大学 Mobile charging pile scheduling method based on multi-IEV bidding
CN113222248A (en) * 2021-05-12 2021-08-06 广东工业大学 Charging pile selection method for automatically driving taxi
JP2023133750A (en) * 2022-03-14 2023-09-27 ダイハツ工業株式会社 Transportation planning system
CN117252393A (en) * 2023-10-31 2023-12-19 国网四川省电力公司经济技术研究院 Charging pile planning method considering coordinated operation of multiple distributed resources
CN117350424A (en) * 2023-09-22 2024-01-05 国网黑龙江省电力有限公司 Economic dispatching and electric vehicle charging strategy combined optimization method in energy internet
CN117372055A (en) * 2022-07-05 2024-01-09 通用汽车环球科技运作有限责任公司 Cost of ownership optimization for fleet having electric vehicles
CN117391564A (en) * 2023-10-19 2024-01-12 北京氢远质投新能源汽车有限公司 New energy logistics vehicle energy supplementing and scheduling data model and scheduling optimization method

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113054676A (en) * 2021-03-26 2021-06-29 南京邮电大学 Mobile charging pile scheduling method based on multi-IEV bidding
CN113222248A (en) * 2021-05-12 2021-08-06 广东工业大学 Charging pile selection method for automatically driving taxi
JP2023133750A (en) * 2022-03-14 2023-09-27 ダイハツ工業株式会社 Transportation planning system
CN117372055A (en) * 2022-07-05 2024-01-09 通用汽车环球科技运作有限责任公司 Cost of ownership optimization for fleet having electric vehicles
CN117350424A (en) * 2023-09-22 2024-01-05 国网黑龙江省电力有限公司 Economic dispatching and electric vehicle charging strategy combined optimization method in energy internet
CN117391564A (en) * 2023-10-19 2024-01-12 北京氢远质投新能源汽车有限公司 New energy logistics vehicle energy supplementing and scheduling data model and scheduling optimization method
CN117252393A (en) * 2023-10-31 2023-12-19 国网四川省电力公司经济技术研究院 Charging pile planning method considering coordinated operation of multiple distributed resources

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Charging-Expense Minimization Through Assignment Rescheduling of Movable Charging Stations in Electric Vehicle Networks;Linfeng Liu 等;《IEEE Transactions on Intelligent Transportation Systems》;20220308;第23卷(第10期);第17212-17223页 *
共享模式下的充电桩引导与充电价格研究;李东东 等;《电网技术》;20170925;第41卷(第12期);第3971-3979页 *

Also Published As

Publication number Publication date
CN117634713A (en) 2024-03-01

Similar Documents

Publication Publication Date Title
CN103413209B (en) Many client many warehouses logistics distribution routing resources
Davidov et al. Optimization model for charging infrastructure planning with electric power system reliability check
US20180012164A1 (en) Method of planning for deployment of facilities and apparatus associated therewith
Shi et al. Memory-based ant colony system approach for multi-source data associated dynamic electric vehicle dispatch optimization
CN112487622B (en) Method and device for locating and sizing electric vehicle charging pile and terminal equipment
CN110866636A (en) Microgrid planning method comprehensively considering electric vehicle charging station and distributed energy
CN114330898A (en) Electric vehicle charging station site selection planning method and device and terminal equipment
CN117035185A (en) Electric vehicle charging station layout optimization method and system based on dynamic charging demand
CN114580251A (en) Method and device for analyzing charging load of electric vehicle in power distribution area
Yu et al. Quantifying the real-time energy flexibility of commuter plug-in electric vehicles in an office building considering photovoltaic and load uncertainty
Jiang et al. On large action space in EV charging scheduling optimization
CN115713130A (en) Vehicle scheduling method based on hyper-parameter network weight distribution deep reinforcement learning
CN115587645A (en) Electric vehicle charging management method and system considering charging behavior randomness
CN112581313B (en) Photovoltaic charging station resource distribution and adjustment method and system
CN117634713B (en) Electric taxi charging cost optimization method and system based on charging pile lease
CN113112097A (en) Electric vehicle load prediction and charging facility layout optimization method
CN111401627A (en) Electric vehicle charging scheduling method and device
Wang et al. Order dispatching optimization in ride-sourcing market by considering cross service modes
CN114707239B (en) Electric energy resource allocation planning method and device and electronic equipment
CN117635220B (en) Electric taxi charging cost optimization method and system
CN115593264A (en) Charging optimization control method and device based on edge calculation and computer equipment
CN113705891B (en) Urban commercial complex building parking demand prediction method based on MRA-BAS-BP algorithm
CN113052402A (en) Time-period joint pricing method and system for electric vehicle photovoltaic charging station network
CN114331633A (en) Pairing method and device for shared parking, computer program product and electronic device
Zhu et al. A Budget-aware Incentive Mechanism for Vehicle-to-Grid via Reinforcement Learning

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