CN117635220A - Electric taxi charging cost optimization method and system - Google Patents
Electric taxi charging cost optimization method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for optimizing charging cost of an electric taxi, and relates to the technical field of electric car charging, comprising the following steps: acquiring a set of charging piles of the electric taxis and the rentable platform, and constructing a renting model of the charging piles of the electric taxis according to service request response relation of the charging piles of the electric taxis and the rentable platform; based on the electric taxi charging pile lease model, constructing a charging cost model according to the charging completion time of an electric taxi charging task; if the lease time of any charging pile in the charging cost model meets the maximum lease time, a charging cost optimization model is built by taking the minimum charging cost of the electric taxi as an objective function; and determining a charging distribution strategy of the electric taxis according to the charging cost optimization model, and optimizing the charging cost of the electric taxis. The invention saves the construction cost of the charging pile, preferentially meets the charging requirement of the electric taxi, improves the charging efficiency of the electric taxi, and reduces the charging cost under the constraint of the maximum lease time of the charging pile.
Description
Technical Field
The invention relates to the technical field of electric car charging, in particular to a method and a system for optimizing charging cost of an electric taxi.
Background
In order to promote the development of new energy automobile industry and reduce carbon emission and air pollution, many traditional fuel taxis in province and city are replaced by electric taxis, however, the problem of charging is always restricted to popularization of the electric taxis. Although public charging infrastructure is increasingly perfect, along with the increase of domestic electric automobile charging demand, more and more electric taxis need to compete with domestic electric automobile for using public charging piles, so that effective operation time of the electric taxis is influenced, and operation income is reduced. The construction of the special charging stations is the most direct method for solving the charging problem of the electric taxis, however, the high construction cost leads to the small number of the special charging stations, and the electric taxis with high space-time randomness is difficult to meet the charging requirement.
The existing charging cost optimization mainly optimizes charging delay or charging cost through a centralized method or a distributed game theory method, optimizes charging cost through optimizing charging power based on real-time electricity price, or the like, or researches charging station site selection based on track analysis, however, the charging cost optimization for a charging pile renting mode of an electric taxi does not exist at present.
Disclosure of Invention
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the invention provides a method and a system for optimizing the charging cost of an electric taxi, which solve the problem that the existing method for optimizing the charging cost cannot optimize the charging cost aiming at a charging pile lease mode of the electric taxi.
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, including:
acquiring a set of charging piles of the electric taxis and the rentable platform, and constructing a renting model of the charging piles of the electric taxis according to service request response relation of the charging piles of the electric taxis and the rentable platform;
based on the electric taxi charging pile lease model, a charging cost model is built according to the charging completion time of an electric taxi charging task;
if the lease time of any charging pile in the charging cost model meets the maximum lease time, a charging cost optimization model is established by taking the minimum charging cost of the electric taxi as an objective function;
and determining a charging distribution strategy of the electric taxis according to the charging cost optimization model, and optimizing the charging cost of the electric taxis.
As a preferable scheme of the electric taxi charging cost optimization method of the invention, wherein: according to the service request response relation between the electric taxi and the rentable platform charging pile, constructing an electric taxi charging pile renting model, comprising:
electric taxiSubmitting a charging task to a charging pile lease platform is expressed as:
；
wherein,is an electric taxi->Is (are) located>Is an electric taxi->Charging demand of->Is the battery capacity;
charging pileSubmitting self information to the lease platform is expressed as:
；
wherein,for charging pile->Reference lease price per lease time, < +.>For charging pile->Price per unit of electricity,/->For charging pile->Is (are) located>For charging pile->Maximum of (2)Lease time.
As a preferable scheme of the electric taxi charging cost optimization method of the invention, wherein: constructing a charging cost model according to the charging completion time of the electric taxi charging task, including:
order theRepresenting charging pile->The charging order is->Charging task of->Charging pile->Middle->The charge completion time of each charge task is expressed as:
；
wherein,for charging pile->The completion time of the last non-taxi charging task being performed, i.e. the earliest charging start time when the electric taxi starts charging, +. >For charging tasks->Time of arrival of->For charging tasks->In charging pileIs>For charging pile->Middle->Charging completion time of the individual charging tasks;
charging pileLease time->The maximum completion time of the charging task allocated to the charging pile is expressed as:
；
wherein,for charging tasks->Assigned binary decision variables +.>For charging tasks->In charging pile->Charge completion time on->For distribution to charging piles->Charging task set of->For charging pile->Middle->Charging completion time of each charging task.
As a preferable scheme of the electric taxi charging cost optimization method of the invention, wherein:
further comprises: the charging pileIs->Wherein->For charging pile->Price per unit of electricity,/->For charging pile->Is a total charge amount of (a);
total charge of charging pileExpressed as:
；
wherein,energy consumption per unit movement->Is an electric taxi->To the charging pile->Is (are) shortest distance->A set of charging tasks;
the charging cost model is expressed as:
；
wherein,for a concave function representing monotonically increasing lease time scale +.>For charging pile->Overall cost.
As a preferable scheme of the electric taxi charging cost optimization method of the invention, wherein: the establishment of the charging cost optimization model comprises the following steps: the method comprises the steps of formalizing the problem of minimizing the charging cost of the electric taxi under the constraint of the maximum lease time of the charging pile, and expressing a charging cost optimization model as follows:
；
Wherein,and (5) collecting rentable charging piles.
As a preferable scheme of the electric taxi charging cost optimization method of the invention, wherein: determining a charging distribution strategy of the electric taxi according to the charging cost optimization model, and optimizing the charging cost of the electric taxi, wherein the method comprises the following steps:
for any arbitraryInitializing a charging task set->；
Initializing an unassigned set of charging tasksLease charging pile set->Charging task allocation matrix->；
When at least one charging task exists in the charging task set, the charging task set is used for anyBased on->Constructing a charging task extension set->The method comprises the steps of carrying out a first treatment on the surface of the The charging pile with the minimum average marginal cost in the charging task extension set is calculated as:
；
wherein,for any one charging pile->Charging task set of->For any one charging pile->Charging task extension set of->For any one charging pile->The upper charging task set is->Charging cost of->For any one charging pile->The upper charging task extension set is +.>Charging costs of (2);
updating a set of charging tasksCharging pile set for renting>And unassigned set of charging tasks +.>Until no charging task exists;
when there is no charging task, for any ，Let->The method comprises the steps of carrying out a first treatment on the surface of the Output charging task allocation matrix->。
As a preferable scheme of the electric taxi charging cost optimization method of the invention, wherein: said for any ofBased on->Constructing a charging task extension set->Comprising:
initializing newly increased number of charging tasksCharging pile->First->Extended task set with secondary charging tasks addedExtended set index with minimum average marginal charging cost +.>The currently unassigned set of charging tasks +.>；
According to the charging completion time of the charging task of the charging pile and the charging pile lease time which are the maximum completion time of the charging task distributed on the charging pile, calculating the lease time of the current charging pile to be expressed as:
；
if the lease time of the current charging pile is not greater than the maximum lease time of the charging pile and at least one charging task exists, calculating the charging task with the minimum charging completion time according to the charging completion time of the charging pile charging task, wherein the charging task is represented as:
；
wherein,for any charging task->In charging pile->Charging completion time on;
if the electric taxi with the minimum charging completion time can reach the charging pile and the current lease time after the new charging task is not more than the maximum lease time, updating the number of times of the new charging task Extended set of charging tasksThe lease time of the current charging pile>And unassigned set of charging tasks +.>Otherwise, directly updating the unassigned set of charging tasks +.>Until the lease time of the current charging pile is greater than the maximum lease time of the charging pile or no charging task exists;
if the lease time of the current charging pile is greater than the maximum lease time of the charging pile or any charging task does not exist, calculating the charging task expansion set index with the minimum average marginal cost to be expressed as:output->。
In a second aspect, the present invention provides an electric taxi charging cost optimization system, comprising:
the electric taxi charging pile lease model building module is used for obtaining an electric taxi and a charging pile set of a rentable platform and building an electric taxi charging pile lease model according to a service request response relationship between the electric taxi and the charging pile of the rentable platform;
the charging cost model building module is used for building a charging cost model according to the charging completion time of the electric taxi charging task based on the electric taxi charging pile lease model; the charging cost optimization model building module is used for building a charging cost optimization model by taking the minimum charging cost of the electric taxi as an objective function if the lease time of any charging pile in the charging cost model meets the maximum lease time;
And the decision optimization module is used for determining a charging distribution strategy of the electric taxi according to the charging cost optimization model and optimizing the charging cost of the electric taxi.
In a third aspect, the present invention provides a computing device comprising:
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, and the computer executable instructions realize the steps of the electric taxi charging cost optimization method when being executed by the processor.
In a fourth aspect, the present invention provides 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.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the electric taxi charging pile renting system is constructed based on the electric taxi charging requirement and the rentable charging piles, and the public charging piles which are widely distributed in short-term renting are used as the special charging piles for the electric taxis, so that the construction cost of a high-rise period can be saved, and the charging requirement of the electric taxis can be preferentially met. The method comprises the steps of establishing a charging cost model based on charging completion time, providing an electric taxi charging distribution algorithm based on the completion time, determining a charging distribution scheme under a charging pile lease system of an electric taxi, improving charging efficiency of the electric taxi, and reducing charging cost of the electric taxi under the constraint of the maximum lease time of the charging pile.
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. Wherein:
fig. 1 is a schematic flow chart of a method and a system for optimizing charging cost of an electric taxi according to an embodiment of the invention;
fig. 2 is a schematic diagram of an electric taxi charging pile renting system according to an embodiment of the invention;
fig. 3 is a flowchart of a task allocation method of an electric taxi charging cost optimization method and system according to an embodiment of the invention;
fig. 4 is a flowchart of a charging task expansion set of a method and a system for optimizing charging cost of an electric taxi according to an embodiment of the invention;
FIG. 5 is a graph showing the comparison of total charging costs for different charging tasks of an electric taxi charging cost optimization method and system according to an embodiment of the invention;
FIG. 6 is a graph showing the comparison of total charging costs for different maximum lease time intervals of an electric taxi charging cost optimization method and system according to an embodiment of the present invention;
fig. 7 is a graph comparing total charging costs under different electricity price intervals of an electric taxi charging cost optimization method and system according to an embodiment of the invention;
fig. 8 is a graph comparing total charging costs of different electricity price intervals of an electric taxi charging cost optimization method and system according to an embodiment of the invention;
FIG. 9 is a graph comparing total charging costs for different reference lease price intervals of an electric taxi charging cost optimization method and system according to an embodiment of the invention;
fig. 10 is a graph comparing total charging costs of different reference lease price intervals of the electric taxi charging cost optimization method and system according to an embodiment of the 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 to fig. 4, in an embodiment of the present invention, the embodiment provides a method for optimizing charging cost of an electric taxi, including:
s1, acquiring a set of charging piles of an electric taxi and a rentable platform, and constructing a renting model of the charging piles of the electric taxi according to a service request response relationship of the charging piles of the electric taxi and the rentable platform;
the embodiment of the invention is provided withRepresenting a set of electric taxis >Representing a rentable set of charging piles;
further, according to the service request response relationship between the electric taxi and the rentable platform charging pile, a charging pile renting model of the electric taxi is constructed, which comprises:
electric taxiSubmitting a charging task to a charging pile lease platform is expressed as:
；
wherein,is an electric taxi->Is (are) located>Is an electric taxi->Charging demand of->Is the battery capacity;
charging pileSubmitting self information to the lease platform is expressed as:
；
wherein,for charging pile->Reference lease price per lease time, < +.>For charging pile->Price per unit of electricity,/->For charging pile->Is (are) located>For charging pile->Is a maximum lease time of (a).
S2, based on an electric taxi charging pile lease model, constructing a charging cost model according to the charging completion time of an electric taxi charging task;
it should be noted that the total charging cost of the charging pile is composed of lease cost and electric power cost; the lease cost of the charging pile is related to the lease time and the reference lease price.
Implementation of the inventionIn the example, letRepresenting charging pile->Is leased by the charging pile->Is leased at the cost ofWherein->Is a monotonically increasing concave function characterizing the scale of lease time, and +. >，In the embodiment of the invention the function +.>；
Order theRepresenting charging task->Arrival time of (i.e. electric taxi->Arrive at the charging pile->Of (2) movement time of>Is an electric taxi->An average rate of movement of (2);
order theRepresenting charging task->In charging pile->Is set to be equal to or greater than the actual charging time of the battery, wherein>For charging tasks->Is>Is energy consumption per unit movement, < >>Electric taxi->To the charging pile->Is (are) shortest distance->Is a charging pile->Is set to a constant charging power of (a);
order theRepresenting charging task->In charging pile->Charge completion time on->Not only the arrival time of the charging task +.>And actual charging time->In relation to the charging completion time of other charging tasks that have been waiting for charging or are charging in the service queue of the charging pile;
order theRepresenting allocation to charging piles>Charging task set of->Assigning a binary decision variable to the charging task if the charging task is +.>Is assigned to the charging post->Then->Otherwise->。
Further, constructing a charging cost model according to the charging completion time of the electric taxi charging task includes:
order theRepresenting charging pile->The charging order is->Charging task of->Charging pile- >Middle->The charge completion time of each charge task is expressed as:
；
wherein,for charging pile->The completion time of the last non-taxi charging task being performed, i.e. the earliest charging start time when the electric taxi starts charging, +.>For charging tasks->Time of arrival of->For charging tasks->In charging pile->Is>For charging pile->Middle->Charging completion time of the individual charging tasks;
charging pileLease time->The maximum completion time of the charging task allocated to the charging pile is expressed as:
；
wherein,for charging tasks->Assigned binary decision variables +.>For charging tasks->In charging pile->Charge completion time on->For distribution to charging piles->Charging task set of->For charging pile->Middle->Charging completion time of each charging task.
Still further, still include: charging pileIs->Wherein->For charging pile->Price per unit of electricity,/->For charging pile->Is a total charge amount of (a);
total charge of charging pileExpressed as:
；
wherein,energy consumption per unit movement->Is an electric taxi->To the charging pile->Is (are) shortest distance->A set of charging tasks;
the charge cost model is expressed as:
；
wherein, For a concave function representing monotonically increasing lease time scale +.>For charging pile->Overall cost.
S3, if the lease time of any charging pile in the charging cost model meets the maximum lease time, a charging cost optimization model is established by taking the minimum charging cost of the electric taxi as an objective function;
further, establishing the charging cost optimization model includes: the method comprises the steps of formalizing the problem of minimizing the charging cost of the electric taxi under the constraint of the maximum lease time of the charging pile, and expressing a charging cost optimization model as follows:
；
wherein,and (5) collecting rentable charging piles.
It should be noted that, in the embodiment of the present invention, the constraint of the charging cost optimization model is to ensure that the lease time of any charging pile cannot exceed the maximum lease time thereof, that any electric taxi has sufficient electric power to reach an allocated charging pile, and that each charging task can be allocated to only one charging pile.
S4, determining a charging distribution strategy of the electric taxis according to the charging cost optimization model, and optimizing the charging cost of the electric taxis;
further, determining a charging allocation strategy of the electric taxi according to the charging cost optimization model, and optimizing the charging cost of the electric taxi, including:
For any arbitraryInitializing a charging task set->；
Initializing an unassigned set of charging tasksLease charging pile set->Charging task allocation matrix->；
When at least one charging task exists in the charging task set, namelyAt the time of arbitrary->Based on->Constructing a charging task extension set->The method comprises the steps of carrying out a first treatment on the surface of the The charging pile with the minimum average marginal cost in the charging task extension set is calculated as:；
wherein,for any one charging pile->Charging task set of->For any one charging pile->Charging task extension set of->For any one charging pile->The upper charging task set is->Charging cost of->For any one charging pile->The upper charging task extension set is +.>Charging costs of (2);
updating a set of charging tasksCharging pile set for renting>And unassigned set of charging tasks +.>Until there is no charging task +.>；
When (when)At the time of arbitrary->，Let->The method comprises the steps of carrying out a first treatment on the surface of the Output charging task allocation matrix->。
Further, for any ofBased on->Constructing a charging task extension set->Comprising:
initializing newly increased number of charging tasksCharging pile->First->Extended task set with secondary charging tasks addedExtended set index with minimum average marginal charging cost +. >The currently unassigned set of charging tasks +.>；
According to the charging completion time of the charging task of the charging pile and the charging pile lease time which are the maximum completion time of the charging task distributed on the charging pile, calculating the lease time of the current charging pile to be expressed as:
；
if the lease time of the current charging pile is not greater than the maximum lease time of the charging pile and at least one charging task exists, calculating the charging task with the minimum charging completion time according to the charging completion time of the charging pile charging task, wherein the charging task is represented as:
；
wherein,for any charging task->In charging pile->Charging completion time on;
if the electric taxi with the minimum charging completion time can reach the charging pile and the current lease time is not greater than the maximum lease time after the new charging task is addedAnd->Update the newly added charge task times +.>Charging task extension set->The lease time of the current charging pile>And unassigned set of charging tasks +.>Otherwise, directly updating the unassigned set of charging tasks +.>Until the lease time of the current charging pile is longer than the maximum lease time of the charging pile or no charging task exists, namely +.>Or->；
If it isOr->Then the calculation of the charging task extension set index with the smallest average marginal cost is expressed as: / >Output->。
The above is a schematic scheme of the method for optimizing the charging cost of the electric taxi according to the present embodiment. It should be noted that, the technical solution of the electric taxi charging cost optimization system and the technical solution of the electric taxi charging cost optimization method belong to the same concept, and details of the technical solution of the electric taxi charging cost optimization system in this embodiment, which are not described in detail, can be referred to the description of the technical solution of the electric taxi charging cost optimization method.
In this embodiment, an electric taxi charging cost optimization system includes:
the electric taxi charging pile lease model building module is used for obtaining an electric taxi and a charging pile set of a rentable platform and building an electric taxi charging pile lease model according to a service request response relationship between the electric taxi and the charging pile of the rentable platform;
the charging cost model building module is used for building a charging cost model according to the charging completion time of the electric taxi charging task based on the electric taxi charging pile lease model;
the charging cost optimization model building module is used for building a charging cost optimization model by taking the minimum charging cost of the electric taxi as an objective function if the lease time of any charging pile in the charging cost model meets the maximum lease time;
And the decision optimization module is used for determining a charging distribution strategy of the electric taxi according to the charging cost optimization model and optimizing the charging cost of the electric taxi.
The embodiment also provides a computing device, which is suitable for the situation of the electric taxi charging cost optimization method, 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 method for optimizing the charging cost of the electric taxi according to the embodiment.
The present embodiment also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the method for implementing electric taxi charging cost optimization as set forth in the above embodiments.
The storage medium proposed in the present embodiment belongs to the same inventive concept as the method for implementing electric taxi charging cost optimization proposed in the above embodiment, and technical details not described in detail in the present embodiment can be seen in the above embodiment, and the present embodiment has the same beneficial effects as the above embodiment.
From the above description of embodiments, it will be clear to a person skilled in the art that the present invention may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, etc., including several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to execute the method of the embodiments of the present invention.
Example 2
Referring to fig. 1 to fig. 4, in an embodiment of the present invention, the method for optimizing charging cost of an electric taxi provided by the present invention aims at minimizing charging cost, and includes the following steps:
in the present embodiment, there is providedRepresenting a collection of electric taxis,representing a collection of charging piles.
Electric taxiSubmitting charging tasks to charging pile lease platform>Expressed as:
；
wherein,is an electric taxi->Is (are) located>Is an electric taxi->Charging demand of->Is the battery capacity. />
The positions of the electric taxis in this embodiment are respectively、、、、、；
The battery capacities are 65, 75, 80, 85 and 70 kilowatt-hours respectively, and the charging requirement of the electric taxi is thatThe method comprises the steps of carrying out a first treatment on the surface of the The mobile energy consumption of the electric taxis is respectively 0.15, 0.16, 0.17, 0.2, 0.25 and 0.18 kilowatt-hour/kilometer, and the average mobile speeds are respectively 39, 42, 45, 48 and 51 kilometer/hour.
Charging pile submits self information to lease platformExpressed as:
；
wherein,for charging pile->Reference lease price per lease time, < +.>For charging pile->Price per unit of electricity,/->For charging pile->Is (are) located>For charging pile->Is a maximum lease time of (a).
In this embodiment, the reference lease prices of unit lease time are respectively 0.3, 0.4, 0.5 and 0.6 yuan/min, the unit power prices are respectively 0.8, 0.9, 0.85 and 1.0 yuan/kwh, the maximum lease time is respectively 60, 70, 80 and 75 minutes, and the positions of the charging piles are respectively 、、、The method comprises the steps of carrying out a first treatment on the surface of the The charging power of the charging pile is 120 kilowatts, 140 kilowatts, 130 kilowatts and 140 kilowatts respectively，The completion time of the non-taxi charging task being executed is 3, 5, 2, 5 minutes respectively.
Function characterizing lease time scale，0.98, 0.95, 0.97, 0.95, respectively.
In the present embodiment, for any one ofInitializing a charging task set->；
Initializing an unassigned set of charging tasksLease charging pile setCharging task allocation matrix->；
For any arbitraryBased on->Constructing a charging task extension set->；
Select electric pile that fillsInitializing the newly added charge task times +.>Charging pile->First->Expansion task set after adding charging task for the second time->Has the following advantages ofExtended set index of minimum average marginal charging cost +.>The currently unassigned set of charging tasks +.>；
According to the charging completion time of the charging task of the charging pile and the charging pile lease time which are the maximum completion time of the charging task distributed on the charging pile, calculating the current charging pile lease time to be expressed as:
；
wherein,for charging tasks->In charging pile->Charge completion time on->For charging pile->Is the current lease time of (a);
if it isAnd->The charging task with the minimum charging completion time calculated according to the charging completion time of the charging task of the charging pile is expressed as follows:
；/>
Wherein,for any charging task->In charging pile->Charging completion time on;
if the electric taxi with the minimum charging completion time can reach the charging pile and the current lease time after the new charging task is not more than the maximum lease time, namelyAnd->Update the newly added charge task times +.>Update charging task extension set->Updating the lease time of the current charging pile>The method comprises the steps of carrying out a first treatment on the surface of the Otherwise update the unassigned set of charging tasks +.>Up to->Or->The method comprises the steps of carrying out a first treatment on the surface of the Thus get +.>。
Calculating the charging task extension set index with the smallest average marginal cost is expressed as:the method comprises the steps of carrying out a first treatment on the surface of the Output->。
Thus (2)Charging pile in the same way>The same process is performed, and the result is；
Calculating a charging pile in which the average marginal cost is minimum:
；
updatingCharging task set->Updating the leased charging pile set>Updating the unassigned set of charging tasks +.>The method comprises the steps of carrying out a first treatment on the surface of the Up to->；
The final result is，，，The corresponding charging cost is: 371.937 yuan;
when (when)At the time of arbitrary->，Let->The method comprises the steps of carrying out a first treatment on the surface of the Output charging task allocation matrix->。
Example 3
Referring to fig. 5 to fig. 10, for better verification and explanation of the technical effects adopted in the method according to the present invention, in this embodiment, three algorithms are designed to perform actual comparison test with the charge distribution algorithm based on the completion time according to the present invention, so as to verify the actual effects of the method according to the present invention, which specifically includes:
(1) Minimum charge travel time algorithm: the algorithm matches each time the minimum travel time between a charging task and an available charging peg, which refers to a charging peg for which the charging task can be completed within its maximum lease time, until all charging tasks are fully assigned.
(2) Earliest charge start time algorithm: the algorithm selects the available charging post with the minimum charging start time and the charging task for matching each time, updates the state of the charging post until all the charging tasks are distributed completely, and the iteration is ended.
(3) Minimum charge cost charging pile algorithm: firstly, distributing charging tasks to each charging pile according to the ascending sequence of the charging completion time of the unassigned tasks when the algorithm iterates each time until the tasks cannot be distributed; and then selecting a charging pile with the minimum charging cost and matching corresponding charging tasks until all the charging tasks are distributed completely, and ending the iteration.
In this embodiment, a local charging station dataset is used, which contains 35 fast charge public charging stations, each fast charge public charging station dataset containing a charging station ID, a charging station location and a charging interface number. 4000 electric taxi track data sets were used for 3.3.3.3.d. in a certain urban area, each track data set containing an electric taxi ID, a GPS position and a recording time. And calculating the residual electric quantity of the electric taxi according to the driving distance, and when the residual electric quantity is reduced to 20%, generating a charging requirement for the electric taxi. 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.
Based on basic parameters of an electric taxi in a certain urban area, the battery capacity of the electric taxi is set to be uniformly distributed from 65 kilowatt hours to 85 kilowatt hours, the mobile energy consumption is uniformly distributed from 0.15 kilowatt hours/kilometer to 0.25 kilowatt hours/kilometer, and the default parameters of the average mobile speed are 40 kilometers/hour to 60 kilometers/hour. 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 public charging piles to 6 public charging piles, the maximum lease time default value of each charging pile is set to be 60 minutes to 90 minutes, the completion time default value of the non-taxi charging task being executed is 0 minutes to 15 minutes, and the charging power parameters are 120 kilowatts to 150 kilowatts. Referring to a charging electricity price standard of an electric automobile in a certain urban area, a unit electricity price default value is set to 0.8 yuan/kilowatt-hour to 1.2 yuan/kilowatt-hour, and a reference lease price default value based on lease time is set to 0.2 yuan/minute to 0.6 yuan/minute.
In the present embodiment, functions are usedCharacterizing charging station operators based on lease time scale rebate policiesAnd is omitted. Based on electric taxi track data and charging demand analysis, setting a default value of the number of charging tasks on a leasing platform to 150 for a test experiment, and observing algorithm performance change by changing the values of key parameters, wherein the average value of each measured value exceeds 100 random topologies.
As shown in fig. 5, when the number of electric taxis is increased from 50 to 150, the total charging cost of the charge distribution algorithm based on the completion time is reduced by 4.75%, 13.06% and 14.15% respectively compared with the minimum charging cost charging stake algorithm, the minimum charge travel time algorithm and the earliest charge start time algorithm. As can be seen from fig. 5, since the minimum charging cost charging pile algorithm does not consider the heterogeneity of the maximum lease time of the charging piles, the charging pile with the minimum charging cost and the task set thereof have the advantages of small number of charging tasks capable of being allocated and low total charging amount because the charging pile itself has the shorter maximum lease time. Minimum charge travel time algorithms tend to assign charging tasks to the nearest travel time charging peg, optimizing the total charging cost by reducing electric taxi movement energy consumption and charging peg lease time; the earliest charge start time algorithm focuses on the charge completion time of the electric taxi, and optimizes the total charge cost by reducing the charge completion time of the electric taxi; the earliest charge starting time algorithm and the minimum charge travel time algorithm both ignore the difference of the electricity prices among the charging piles and the reference lease price, and cannot ensure reasonable utilization of electric power resources and economic benefits of users.
In this embodiment, the effect of the maximum lease time parameter of the charging stake on the total charging cost is further tested, and the maximum lease time of the charging stake is set to be increased from [60,90] to [150,180]. As shown in fig. 6, the charge distribution algorithm and the minimum charge cost charging pile algorithm based on the completion time increase with the maximum lease time, more charge tasks can be matched to the charging piles with lower electricity prices and lease prices, and the total charge cost thereof significantly decreases with the increase of the maximum lease time. The minimum charging travel time algorithm optimizes the cost by optimizing the travel time of the electric taxi, and under the condition that the position of the charging pile is unchanged, more charging tasks cannot be matched to the charging pile with lower electricity price and lease price when the maximum lease time is increased, so that the total charging cost of the electric taxi has a slight descending trend, but no obvious change exists. When the maximum lease time is continuously increased, the total charging cost of the earliest charging start time algorithm is kept unchanged, and because the algorithm tends to pay attention to the charging completion time of the electric taxi, if any charging pile leased in a smaller maximum lease time range lacks the constraint of lease time to make the charging pile unusable, the charging task allocation strategy is not changed when the maximum lease time of the charging pile is increased. As can be seen from fig. 6, the total charge cost of the charge distribution algorithm based on the completion time is reduced by 4.84%, 11.93% and 15.26% compared to the total charge cost of the minimum charge cost charge stake algorithm, the minimum charge travel time algorithm and the earliest charge start time algorithm, respectively.
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. The total charging cost of the charging distribution algorithm based on the completion time is reduced by 4.18%, 11.31% and 12.39% respectively compared with the minimum charging cost charging pile algorithm, the minimum charging travel time algorithm and the earliest charging start time algorithm in different electricity price intervals. In addition, the influence of the unit price isomerism among the charging piles on the algorithm is tested by increasing the unit price interval length. With the unit price interval from->Gradually expand to section +.>The interval length increases from 0.2 to 0.8, and as shown in fig. 8, the overall charge cost increase trend of the charge distribution algorithm based on the completion time is slower than that calculated by the minimum charge travel time algorithm and the earliest charge start timeA method of manufacturing the same. When the electricity price interval is +.>When the total charge cost of the charge distribution algorithm based on the completion time is reduced by 6.28% and 8.15% respectively compared to the minimum charge travel time algorithm and the earliest charge start time algorithm. And when the electricity price interval is +.>When the total charge cost of the charge distribution algorithm based on the completion time is reduced by 16.15% and 17.49% compared to the minimum charge travel time algorithm and the earliest charge start time algorithm, respectively. This is because the minimum charge travel time algorithm and the earliest charge start time algorithm ignore the heterogeneity of unit electricity prices among the charging piles, resulting in high charging costs.
In this embodiment, the total charging cost under the standard lease price heterogeneity between charging piles is further tested in different standard lease price intervals. As shown in fig. 9, the total charge cost increases with the increase of the reference lease price, and the total charge cost of the charge distribution algorithm based on the completion time is reduced by 3.72%, 9.06% and 11.08% compared to the minimum charge cost charge stake algorithm, the minimum charge travel time algorithm and the earliest charge start time algorithm, respectively. When the reference lease price interval length increases, that is, the inter-charging-pile reference lease price heterogeneity increases, as shown in fig. 10, the overall charge cost increasing trend of the charge allocation algorithm based on the completion time is retarded to the minimum charge travel time algorithm and the earliest charge start time algorithm. When the reference lease price interval isWhen the total charge cost of the charge distribution algorithm based on the completion time is reduced by 9.38% and 10.39% respectively compared to the minimum charge travel time algorithm and the earliest charge start time algorithm. When the interval is +.>When the method is used, the total charging cost of the charging distribution algorithm based on the completion time is reduced by 11.99 percent and the earliest charging start time algorithm is reduced by the minimum charging travel time algorithm and the earliest charging start time algorithm respectively13.66%. This is because the minimum charge travel time algorithm and the earliest charge start time algorithm ignore the heterogeneity of the inter-fill-pile reference lease prices.
According to the method, public charging piles with wide short-term lease distribution are used as temporary special charging piles according to the charging requirements of the electric taxis. High construction costs can be saved by means of widely distributed public charging piles; the charging requirement of the electric taxis generated at random time and position is met; under the condition of charging congestion, the public charging pile is temporarily used as a special charging pile for the electric taxis due to the payment of a certain lease fee, and the charging service is preferentially provided for the electric taxis; the high construction cost of the charging pile can be saved, the charging requirement of the electric taxi can be met preferentially, the charging efficiency of the electric taxi is improved, and the charging cost of the electric taxi is reduced under the constraint of the maximum lease time of the charging pile.
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 (10)
1. The method for optimizing the charging cost of the electric taxi is characterized by comprising the following steps of:
acquiring a set of charging piles of the electric taxis and the rentable platform, and constructing a renting model of the charging piles of the electric taxis according to service request response relation of the charging piles of the electric taxis and the rentable platform;
based on the electric taxi charging pile lease model, a charging cost model is built according to the charging completion time of an electric taxi charging task;
if the lease time of any charging pile in the charging cost model meets the maximum lease time, a charging cost optimization model is established by taking the minimum charging cost of the electric taxi as an objective function;
and determining a charging distribution strategy of the electric taxis according to the charging cost optimization model, and optimizing the charging cost of the electric taxis.
2. The method for optimizing charging cost of an electric taxi according to claim 1, wherein constructing a charging pile lease model of the electric taxi according to a service request response relation between the electric taxi and a charging pile of a leasable platform comprises:
electric taxiSubmitting a charging task to a charging pile lease platform is expressed as:
；
wherein,is an electric taxi->Is (are) located >Is an electric taxi->Charging demand of->Is the battery capacity;
charging pileSubmitting self information to the lease platform is expressed as:
；
wherein,for charging pile->Reference lease price per lease time, < +.>For charging pile->Is a price per unit of electric power of (a),for charging pile->Is (are) located>For charging pile->Is a maximum lease time of (a).
3. The electric taxi charging cost optimization method of claim 1 or 2, wherein constructing a charging cost model according to a charging completion time of the electric taxi charging task comprises:
order theRepresenting charging pile->The charging order is->Charging task of->Charging pile->Middle->The charge completion time of each charge task is expressed as:
；
wherein,for charging pile->The completion time of the last non-taxi charging task being performed, i.e. the earliest charging start time when the electric taxi starts charging, +.>For charging tasks->Time of arrival of->For charging tasks->In charging pile->Is>For charging pile->Middle->Charging completion time of the individual charging tasks;
charging pileLease time->The maximum completion time of the charging task allocated to the charging pile is expressed as:
；
wherein, For charging tasks->Assigned binary decision variables +.>For charging tasks->In charging pile->Charge completion time on->For distribution to charging piles->Charging task set of->For charging pile->Middle->Charging completion time of each charging task.
4. The electric taxi charging cost optimization method of claim 3, further comprising
The method comprises the following steps: the charging pileIs->Wherein->For charging pile->Price per unit of electricity,/->For charging pile->Is a total charge amount of (a);
total charge of charging pileExpressed as:
；
wherein,energy consumption per unit movement，Is an electric taxi->To the charging pile->Is (are) shortest distance->A set of charging tasks;
the charging cost model is expressed as:
；
wherein,for a concave function representing monotonically increasing lease time scale +.>For charging pile->Overall cost.
5. The electric taxi charging cost optimization method of claim 4, wherein establishing the charging cost optimization model comprises: the method comprises the steps of formalizing the problem of minimizing the charging cost of the electric taxi under the constraint of the maximum lease time of the charging pile, and expressing a charging cost optimization model as follows:
；
wherein,and (5) collecting rentable charging piles.
6. The method for optimizing the charging cost of the electric taxi according to claim 5, wherein determining the charging allocation policy of the electric taxi according to the charging cost optimization model, and optimizing the charging cost of the electric taxi, comprises:
for any arbitraryInitializing a charging task set->；
Initializing an unassigned set of charging tasksLease charging pile set->Charging task allocation matrix->；
When at least one charging task exists in the charging task set, the charging task set is used for anyBased on->Constructing a charging task extension set->The method comprises the steps of carrying out a first treatment on the surface of the The charging pile with the minimum average marginal cost in the charging task extension set is calculated as:；
wherein,for any one charging pile->Charging task set of->For any one charging pile->Charging task extension set of->For any one charging pile->The upper charging task set is->Charging cost of->For any one charging pile->The upper charging task extension set is +.>Charging costs of (2);
updating a set of charging tasksCharging pile set for renting>And unassigned set of charging tasks +.>Until no charging task exists;
when there is no charging task, for any，Let->The method comprises the steps of carrying out a first treatment on the surface of the Output charging task allocation matrix- >。
7. The electric taxi charging cost optimization method of claim 6, wherein the method is for any ofBased on->Constructing a charging task extension set->Comprising:
initializing newly increased number of charging tasksCharging pile->First->Extended task set with secondary charging tasks addedExtended set index with minimum average marginal charging cost +.>The currently unassigned set of charging tasks +.>；
According to the charging completion time of the charging task of the charging pile and the charging pile lease time which are the maximum completion time of the charging task distributed on the charging pile, calculating the lease time of the current charging pile to be expressed as:
；
if the lease time of the current charging pile is not greater than the maximum lease time of the charging pile and at least one charging task exists, calculating the charging task with the minimum charging completion time according to the charging completion time of the charging pile charging task, wherein the charging task is represented as:
；
wherein,for any charging task->In charging pile->Charging completion time on;
if the electric taxi with the minimum charging completion time can reach the charging pile and the current lease time after the new charging task is not more than the maximum lease time, updating the number of times of the new charging taskExtended set of charging tasks The lease time of the current charging pile>And unassigned set of charging tasks +.>Otherwise, directly updating the unassigned set of charging tasks +.>Until the lease time of the current charging pile is greater than the maximum lease time of the charging pile or no charging task exists;
if the lease time of the current charging pile is greater than the maximum lease time of the charging pile or any charging task does not exist, calculating the charging task expansion set index with the minimum average marginal cost to be expressed as:output->。
8. An electric taxi charging cost optimization system, comprising:
the electric taxi charging pile lease model building module is used for obtaining an electric taxi and a charging pile set of a rentable platform and building an electric taxi charging pile lease model according to a service request response relationship between the electric taxi and the charging pile of the rentable platform;
the charging cost model building module is used for building a charging cost model according to the charging completion time of the electric taxi charging task based on the electric taxi charging pile lease model; the charging cost optimization model building module is used for building a charging cost optimization model by taking the minimum charging cost of the electric taxi as an objective function if the lease time of any charging pile in the charging cost model meets the maximum lease time;
And the decision optimization module is used for determining a charging distribution strategy of the electric taxi according to the charging cost optimization model and optimizing the charging cost of the electric taxi.
9. An electronic device, comprising:
a memory and a processor;
the memory is configured to store computer-executable instructions that, when executed by the processor, implement the steps of the electric taxi charging cost optimization method of any one of claims 1 to 7.
10. 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 of any one of claims 1 to 7.
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