CN111401627A - Electric vehicle charging scheduling method and device - Google Patents
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Abstract
The embodiment of the invention discloses a method and a device for charging and scheduling an electric automobile, wherein the method comprises the following steps: respectively obtaining a first scheduling scheme aiming at electric automobiles in a preset electric automobile set, and evaluating the first scheduling scheme under the condition of meeting a preset target constraint condition to obtain a first adaptive value; performing mixed variable variation processing on the first scheduling scheme according to a preset operation rule to obtain a variable variation value, and performing cross operation processing according to the first scheduling scheme and the variable variation value to obtain a second scheduling scheme; evaluating the second scheduling scheme under the condition of meeting the target constraint condition to obtain a second adaptive value; and selecting a target optimization scheduling scheme from the first scheduling scheme and the second scheduling scheme for charging scheduling. The method can effectively optimize the travel time and the charging cost, improves the efficiency of the charging scheduling of the electric vehicles in the whole road network, and has stronger global optimization performance.
Description
Technical Field
The embodiment of the invention relates to the field of intelligent transportation, in particular to a method and a device for electric vehicle charging scheduling, and further relates to electronic equipment and a computer-readable storage medium.
Background
In recent years, with the rapid development of scientific technology, the related technology of the electric automobile is gradually perfected and matured. Electric vehicle leasing companies generally grasp resources of a large number of electric vehicles and a plurality of charging stations at the same time, and when the charging state of the electric vehicle after completing the journey needs to be just close to the expectation, so that the charging state is not too low to influence the development of the next journey, and the charging state is not too high to increase the time and charging cost of the current journey, so in terms of decision variables, the electric vehicle needs to optimize not only the selection of the charging stations but also the specific charging amount at each charging station during dispatching. The realization of electric vehicle charging scheduling actually becomes a complex scheduling optimization problem, and solving the problem requires determining the specific charging scheme of each electric vehicle in a traffic network. The charging profile may include information such as the number of charges, the charging station selection, and the amount of charge.
At present, due to the limitation of the number of hardware facilities such as charging stations and the like, the use requirements need to be met in a sharing mode, and the fact that a single electric vehicle changes the charging scheme of the single electric vehicle in the specific implementation process will probably influence the implementation of the charging schemes of other electric vehicles, so that the overall charging scheduling effect of a road network is influenced, therefore, the charging scheduling of the electric vehicles needs to be globally optimized, and the intelligent charging scheduling of the electric vehicles is realized. From the interests of electric vehicle leasing companies, how to quickly and effectively realize electric vehicle charging scheduling gradually becomes a key point of research of technicians in the field.
Disclosure of Invention
Therefore, the embodiment of the invention provides an electric vehicle charging scheduling method, which is used for solving the problem that the actual use requirements of users cannot be met due to low efficiency of a charging vehicle scheduling optimization process in the prior art.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:
in a first aspect, an embodiment of the present invention provides an electric vehicle charging scheduling method, including: respectively obtaining corresponding first scheduling schemes aiming at electric automobiles in a preset electric automobile set, wherein the first scheduling schemes comprise an initial charging station sequence and an initial charging rate sequence corresponding to the electric automobiles; evaluating the first scheduling scheme to obtain a corresponding first adaptive value under the condition that a preset target constraint condition is met; performing mixed variable variation processing on the first scheduling scheme according to a preset operation rule to obtain a corresponding variable variation value; performing cross operation processing according to the first scheduling scheme and the variable variation value to obtain a second scheduling scheme; evaluating the second scheduling scheme to obtain a corresponding second adaptive value under the condition that the target constraint condition is met; and selecting a target optimization scheduling scheme from the first scheduling scheme and the second scheduling scheme based on the first adaptive value and the second adaptive value, and managing and controlling the electric vehicle to perform charging scheduling according to the target optimization scheduling scheme.
Further, the electric vehicle charging scheduling method further includes: presetting target optimization iteration times; and after the target optimization scheduling scheme is selected, judging whether the current optimization iteration number corresponding to the target optimization scheduling scheme is smaller than a preset target optimization iteration number, and if not, managing and controlling the electric vehicle to perform charging scheduling according to the target optimization scheduling scheme.
Further, the obtaining of the corresponding first scheduling schemes for the electric vehicles in the preset electric vehicle set specifically includes: respectively constructing an initial charging station sequence for each electric vehicle in the electric vehicle set, selecting a charging station for each electric vehicle according to the initial charging station sequence, and setting a charging rate for the selected charging station to further form the initial charging rate sequence; obtaining the first scheduling scheme according to the initial charging station sequence and the initial charging rate sequence.
Further, the performing mixed variable variation processing on the first scheduling scheme according to a preset operation rule to obtain a corresponding variable variation value specifically includes: and performing discrete variation operation processing according to the initial charging station sequence corresponding to each electric vehicle in the electric vehicle set, and further performing continuous variation operation processing according to the charging rate set for the selected charging station of each electric vehicle in the electric vehicle set to obtain a corresponding variation value.
Further, the performing, according to the first scheduling scheme and the variable variance variable value, a crossover operation process to obtain a second scheduling scheme specifically includes: and carrying out cross operation processing on the first scheduling scheme and the variable variation value by using a preset binomial cross algorithm to obtain a second scheduling scheme.
Further, the electric vehicle charging scheduling method further includes: if yes, continuing to perform mixed variable variation processing on the first scheduling scheme according to a preset operation rule to obtain a corresponding variable variation value; and carrying out cross operation processing according to the first scheduling scheme and the variable variation value to obtain a new second scheduling scheme.
Further, the electric vehicle charging scheduling method further includes: a set of electric vehicles needing to be subjected to charging scheduling is determined in advance.
In a second aspect, an embodiment of the present invention further provides an electric vehicle charging scheduling apparatus, including: the system comprises a first scheduling scheme obtaining unit, a second scheduling scheme obtaining unit and a control unit, wherein the first scheduling scheme obtaining unit is used for respectively obtaining corresponding first scheduling schemes for electric vehicles in a preset electric vehicle set, and the first scheduling schemes comprise an initial charging station sequence and an initial charging rate sequence corresponding to the electric vehicles; the first evaluation unit is used for evaluating the first scheduling scheme to obtain a corresponding first adaptive value under the condition that a preset target constraint condition is met; a second scheduling scheme obtaining unit, configured to perform mixed variable variation processing on the first scheduling scheme according to a preset operation rule to obtain a corresponding variable variation value; performing cross operation processing according to the first scheduling scheme and the variable variation value to obtain a second scheduling scheme; the second evaluation unit is used for evaluating the second scheduling scheme to obtain a corresponding second adaptive value under the condition that the target constraint condition is met; and the optimal scheduling unit is used for selecting a target optimal scheduling scheme from the first scheduling scheme and the second scheduling scheme based on the first adaptive value and the second adaptive value, and managing and controlling the electric vehicle to perform charging scheduling according to the target optimal scheduling scheme.
Further, the electric vehicle charging scheduling device further includes: the iteration frequency presetting unit is used for presetting the target optimization iteration frequency; and the first judgment processing unit is used for judging whether the current optimization iteration number corresponding to the target optimization scheduling scheme is smaller than a preset target optimization iteration number or not after the target optimization scheduling scheme is selected, and if not, managing and controlling the electric vehicle to perform charging scheduling according to the target optimization scheduling scheme.
Further, the first scheduling scheme obtaining unit is specifically configured to: respectively constructing an initial charging station sequence for each electric vehicle in the electric vehicle set, selecting a charging station for each electric vehicle according to the initial charging station sequence, and setting a charging rate for the selected charging station to further form the initial charging rate sequence; obtaining the first scheduling scheme according to the initial charging station sequence and the initial charging rate sequence.
Further, the second scheduling scheme obtaining unit is specifically configured to: and performing discrete variation operation processing according to the initial charging station sequence corresponding to each electric vehicle in the electric vehicle set, and further performing continuous variation operation processing according to the charging rate set for the selected charging station of each electric vehicle in the electric vehicle set to obtain a corresponding variation value.
Further, the second scheduling scheme obtaining unit is specifically configured to: and carrying out cross operation processing on the first scheduling scheme and the variable variation value by using a preset binomial cross algorithm to obtain a second scheduling scheme.
Further, the electric vehicle charging scheduling device further includes: a second judgment processing unit, configured to continue performing mixed variable variation processing on the first scheduling scheme according to a preset operation rule to obtain a corresponding variable variation value if the first scheduling scheme is the first scheduling scheme; and carrying out cross operation processing according to the first scheduling scheme and the variable variation value to obtain a new second scheduling scheme.
Further, the electric vehicle charging scheduling device further includes: a set of electric vehicles needing to be subjected to charging scheduling is determined in advance.
In a third aspect, an embodiment of the present invention further provides an electronic device, including: a processor and a memory; the memory is used for storing a program of an electric vehicle charging scheduling method, and the electronic device executes any one of the electric vehicle charging scheduling methods after being powered on and running the program of the electric vehicle charging scheduling method through the processor.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium contains one or more program instructions, and the one or more program instructions are used for a server to execute any one of the above electric vehicle charging scheduling methods.
By adopting the electric vehicle charging scheduling method, the travel time and the charging cost can be effectively optimized, the charging state when the electric vehicle reaches the terminal is ensured to be as close to the expected state value as possible, the efficiency of electric vehicle charging scheduling is greatly improved, and the use experience of a user is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
Fig. 1 is a flowchart of an electric vehicle charging scheduling method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an electric vehicle charging scheduling device according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the present invention;
fig. 4 is a complete flowchart of an electric vehicle charging scheduling method according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention are described in the following specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. It is to be understood that the embodiments described below are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments described below without any inventive step, are within the scope of protection of the present invention.
At present, electric vehicle charging scheduling becomes a new complex scheduling optimization problem in the field of intelligent transportation, the technical scheme disclosed by the invention applies a differential evolution algorithm to electric vehicle charging scheduling, searches the optimal combination of all electric vehicle charging scheduling schemes by applying the differential evolution algorithm, comprehensively considers the global optimization targets of multiple aspects such as journey time, charging cost, terminal electric quantity reaching state and the like, adopts a charging station sequence formed by discrete charging station numbers and a continuous charging rate (numerical value after charging quantity is normalized) sequence, and better meets the requirements of practical application. On one hand, the generation and updating operation of the solution is simplified by normalizing the charging rate through the charging amount, and then the validity of the charging scheduling solution in the operation process is ensured by adopting an adaptive value evaluation method meeting the constraint condition; on the other hand, mutation mechanisms supporting mixed variables are designed, and comprise a set-based discrete mutation operator specially processing a charging station sequence and a traditional continuous mutation operator specially processing a charging rate sequence.
The following describes an embodiment of the electric vehicle charging scheduling method based on the present invention in detail. As shown in fig. 1, which is a flowchart of an electric vehicle charging scheduling method according to an embodiment of the present invention, a specific implementation process includes the following steps:
step S101: the method comprises the steps of obtaining corresponding first scheduling schemes aiming at electric automobiles in a preset electric automobile set, wherein the first scheduling schemes comprise an initial charging station sequence and an initial charging rate sequence corresponding to the electric automobiles.
Before step S101 is executed, a set of electric vehicles requiring charging scheduling may be predetermined, and a random first scheduling scheme may be generated for the electric vehicles in the set of electric vehicles based on a preset rule. In the embodiment of the present invention, the obtaining of the corresponding first scheduling schemes for the electric vehicles in the preset electric vehicle set may include: respectively constructing an initial charging station sequence for each electric vehicle in the electric vehicle set, selecting a charging station for each electric vehicle according to the initial charging station sequence, and setting a charging rate for the selected charging station to further form the initial charging rate sequence; obtaining a first scheduling scheme according to the initial charging station sequence and the initial charging rate sequence.
For example, in the calculation implementation process, assume a charging scheduling scheme s of the ith electric vehicle in the electric vehicle set (hereinafter referred to as a)iFrom the charging station sequence ciAnd equal length charging rate sequence eiAnd (4) forming. It should be noted that, because the electric vehicles in a have different journey starting and ending points, the charging station sequence length of each electric vehicle needs to be limited in advance to be J; if the actual length of the charging station sequence of a certain electric vehicle is smaller than J, the charging station sequence can be filled by adding a blank number in the following.
Specifically, initializing an electric vehicle charging schedule solution S ═ SiI ═ 1,2, …, n } (one charging scheduling solution corresponds to one scheduling scheme), which is the ith (i ═ 1,2…, n) electric vehicles generate a random dispatch plan si＝(ci，ei). Firstly, at most J non-repeating charging station numbers are randomly selected to generate a charging station sequence ci＝{c ij1,2, …, J, and c is requiredi0The distance between the charging station and the journey starting point must not exceed the initial mileage of the ith vehicle, and the distance between any two adjacent charging stations in the charging station sequence and the distance between the last charging station and the journey ending point must not exceed the maximum mileage of the ith vehicle; then generate the length and ciSequence of equal charging rates ei＝{e ij1,2, …, J }. Wherein e isijIs at [0, 1]]Random numbers within a range.
Solving S ═ S in constructing charging scheduleiCharging schedule s for ith vehicle in a when i |, 1,2, …, n }iIncluding a charging station sequence c designed for the electric vehiclei＝{c ij1,2, … and an equal length sequence e reflecting the corresponding charge amounti＝{e ij1,2, … }. Wherein the discrete variable cijA number representing a certain charging station in B, and a continuous variable eijThen reflect the ith vehicle at cijActual amount of charge Δ βijMaximum amount of power β that can be conserved due to the ith vehiclei maxPossibly different from other vehicles, and the ith vehicle is at cijIs a minimum amount of charge Δ βij minAlso need to be in accordance with cijAnd ci(j+1)Is determined, and is thus uniform eijThe invention will eijDefined as the charging rate and limited to a value range of [0, 1]]。eijThe calculation formula of (a) is as follows:
step S102: and evaluating the first scheduling scheme to obtain a corresponding first adaptive value under the condition of meeting a preset target constraint condition.
After the obtained scheduling scheme set S is constructed in step S101, in this step, the first scheduling scheme in S may be evaluated and the corresponding first adaptive value f (S) may be obtained under the condition that the preset target constraint condition is satisfied. In the embodiment of the present invention, the target constraint condition includes constraints of two conditions, and each electric vehicle charging scheduling solution S needs to satisfy the constraints of the two conditions in specific implementation, that is: a. the electric quantity of each electric automobile cannot be empty or overcharged at any moment; b. the number of the charged electric automobile books contained in each charging station at any time is not more than that of the charging piles in the charging station.
Specifically, each electric vehicle charging scheduling solution S can be evaluated through the following three steps, and a corresponding adaptive value is obtained: s1: and arranging a charging scheduling scheme of each electric vehicle according to the charging scheduling solution S, and obtaining the time of each electric vehicle reaching each charging station, the charging time and the charging cost under the condition of meeting the constraint condition a, and obtaining the time of the electric vehicle reaching the end point of the journey and the remaining electric quantity. S2: the related vehicle charging scheduling scheme obtained in each charging station arrangement S1 increases the waiting time for some vehicles at busy charging stations under the constraint of satisfying the condition b, and adjusts the arrival time, charging time and charging cost of the vehicles at the subsequent charging stations accordingly, and finally updates the arrival time and the remaining electric quantity at the end of the journey; s3: applying the charging fees at the charging stations, the time at the starting and ending point of the trip, and the arrival time at the ending point of the trip, obtained in S2, to the adaptive value calculation formulas (such as formulas (2) and (6) below) of the preset electric vehicle charging schedule solution, and finally obtaining the first adaptive value f (S).
For further explanation with reference to the example in step S101, in a specific implementation process, the adaptive value f (S) corresponding to the optimized charging scheduling scheme of the present invention is an average value of global optimization objectives of all vehicles in a, which comprehensively considers multiple aspects of journey time, charging cost, and end-of-arrival battery state. Wherein, the journey time is ftime(S) charging fee fexpense(S) and a state of charge of fSoC(S). The journey time of the ith vehicle in A is the total time taken for the vehicle to start from the journey starting point until the journey end point is reached, so that the journey time of the vehicle on the road is includedCharging time at the charging station and possibly additional waiting time. Assume that all vehicles in a depart after time t equals 1 and are expected to be at time
Reach the respective end point, then ftimeThe calculation formula of (S) can be as follows:
wherein: t is ti oriAnd ti desRespectively representing the departure time and arrival time of the ith vehicle,
then the vehicle is tried to be
Reach-before-end and define the following temporal penalty function:
the charging cost of the ith vehicle in A is the sum of the charging costs of the vehicle at all charging stations in the sequence of charging stations constructedijIndicating that the ith vehicle is at the jth charging station c in its sequenceijThe amount of power that is added to the battery,
is a charging price calculation function, and
is a charging cost calculation function, then fexpenseThe calculation formula of (S) can be as follows:
in AThe state of charge of the ith vehicle may be understood as the amount of remaining charge β when the vehicle reaches the endi desExpected remaining power βexpConsidering the maximum amount of power that the ith vehicle can conserve βi maxThe invention uses the surplus power ρ, which may be different from other vehiclesi des＝βi des/βi maxTo meet the expected surplus power ρexpThe comparison is made and the state of charge calculation formula for the ith vehicle is defined as follows:
therefore, in order to make the state of charge of each electric vehicle at the end point not less than the expected state of charge and to keep the reserve rate as small as possible, fSoCThe calculation formula of (S) can then be as follows:
finally, in order to balance multiple optimization objectives, the invention divides the average value of all vehicles in A in terms of travel time, charging cost, charging state and the like by the corresponding maximum value
And
normalization processing is carried out, and then summation is carried out, so that the adaptive value calculation formula of the electric vehicle charging scheduling solution can be expressed as follows:
step S103: performing mixed variable variation processing on the first scheduling scheme according to a preset operation rule to obtain a corresponding variable variation value; and carrying out cross operation processing according to the first scheduling scheme and the variable variation value to obtain a second scheduling scheme.
After the first scheduling scheme is obtained in step S101, in this step, a mixed variable variation process may be performed on the first scheduling scheme to obtain a corresponding variable variation value, and a cross operation process may be performed according to the first scheduling scheme and the variable variation value to obtain a second scheduling scheme.
In this embodiment of the present invention, the performing mixed variable variation processing on the first scheduling scheme according to a preset operation rule to obtain a corresponding variable variation value may specifically include: performing discrete type mutation operation processing by using a preset discrete type mutation operator according to the initial charging station sequence corresponding to each electric vehicle in the electric vehicle set; and further utilizing a preset continuous mutation operator to perform continuous mutation operation processing according to the charging rate set for the selected charging station of each electric vehicle in the electric vehicle set, so as to obtain a corresponding variable variation value. Further, the cross operation processing is performed according to the first scheduling scheme and the variable variance variable value to obtain a second scheduling scheme, specifically, a preset binomial cross algorithm is used to perform the cross operation processing on the first scheduling scheme and the variable variance variable value to obtain the second scheduling scheme. It should be noted that the first scheduling scheme and the second scheduling scheme are a set including at least one specific scheduling scheme; the first scheduling scheme refers to an initial scheduling scheme randomly generated before iterative optimization, and the second scheduling scheme may refer to a plurality of scheduling schemes generated in an iterative optimization process, or may refer to a final scheduling scheme output after the number of iterative optimization times is met, which is not specifically limited herein.
As further described with reference to the example in step S102, in the implementation process, the specific process of the mutation mechanism of the mixed variables may be as follows: solving the kth solution S of electric vehicle charging schedulingkIs represented as a two-dimensional vector (c) containing mixing variablesk，ek) And correspondingly designing a mutation mechanism supporting mixed variables to obtain
The principle is as follows:
wherein: r is1，r2And r3And F is a variation factor.
In specific implementation, the invention respectively processes discrete variables and continuous variables in an individual solution by using the following two mutation operators:
a. a discretized mutation operator that redefines the key mathematical operational relationship of equation (7) using a set-based discretization technique.
① subtraction, since the purpose of the subtraction in equation (7) is to obtain the difference between the two solutions, there is a corresponding need to obtain two charging station sequences
And
the subtraction of the charging station numbers at the same position in the two sequences needs to be redefined as the following formula:
wherein:
indicating an empty charging station number; denotes the number of any charging station that meets the preset target constraint.
②, the addition operation with multiplication, where the multiplication factor is redefined as a probability value for selecting two addends as follows:
wherein F ∈ [0, 1] is a variation factor, and q is a random number in the range of [0,1 ].
Briefly, the discrete mutation operator processes the kth solution S using the following formulakThe jth charging station number c of the charging station sequence of the ith vehiclekij：
b. A continuous mutation operator for processing the kth solution S using the following formulakCharging rate e of the ith vehicle at the jth charging stationkij：
The parameters of the invention are set as follows: group size N is 30, maximum generation gmax100, 0.5 for the variation factor F and 0.1 for the crossover factor C. The final result shows that the algorithm can be simultaneously suitable for a synthetic road network and a real road network, and the average optimization effect is superior to that of the traditional priority algorithm, so that the method is very effective for solving the electric vehicle charging scheduling problem.
Step S104: and evaluating the second scheduling scheme to obtain a corresponding second adaptive value under the condition of meeting the target constraint condition.
Step S105: and selecting a target optimization scheduling scheme from the first scheduling scheme and the second scheduling scheme based on the first adaptive value and the second adaptive value, and managing and controlling the electric vehicle to perform charging scheduling according to the target optimization scheduling scheme.
After the first adaptive value and the second adaptive value are obtained in step S102 and step S104, respectively, in this step, a target optimal scheduling scheme may be selected based on the adaptive values, and the electric vehicle is managed and controlled to perform charging scheduling according to the target optimal scheduling scheme.
In the specific implementation process, the method also comprises the steps of presetting target optimization iteration times; and after the target optimization scheduling scheme is selected, judging whether the current optimization iteration number corresponding to the target optimization scheduling scheme is smaller than a preset target optimization iteration number, and if not, managing and controlling the electric vehicle to perform charging scheduling according to the target optimization scheduling scheme. If yes, continuing to perform mixed variable variation processing on the first scheduling scheme according to a preset operation rule to obtain a corresponding variable variation value; and carrying out cross operation processing according to the first scheduling scheme and the variable variation value to obtain a new second scheduling scheme.
Fig. 4 is a complete flowchart of an electric vehicle charging scheduling method according to an embodiment of the present invention. In order to facilitate understanding of the technical solution disclosed in the present invention, the following describes a specific implementation of the whole algorithm step by step based on the contents of the complete flowchart:
the first step is as follows: for an electric vehicle set (hereinafter referred to as a A) needing to arrange a scheduling scheme, each individual solution S in a population is defined as a set containing each electric vehicle charging scheduling scheme in A. Initializing each individual solution S, firstly constructing a random charging station sequence for each electric vehicle in A, then setting a random value for the charging rate of each electric vehicle in A at each charging station of the constructed charging station sequence, constructing a charging rate sequence with the same length as the charging station sequence, further obtaining a scheduling scheme set S, and evaluating S to obtain a corresponding first adaptive value f (S) under the condition that a preset target constraint condition is met.
The second step is that: setting initial algebra g as 1 for iteratively evolving each individual solution S in the population, and firstly, respectively carrying out different mutation operations on discrete type variables and continuous type variables contained in S so as to obtain SmutantFirstly, performing discrete variation operation based on a set for the charging station sequence of each vehicle in the A, and then performing traditional continuous variation operation for the charging rate of each charging station in the sequence after variation; then using the classical binomial crossover operator pair S and SmutantPerforming a crossover operation to obtain a second scheduling scheme StrialI.e. with a certain probability from S or SmutantSelection inSelecting the charging scheduling scheme of each vehicle in A to generate Strial(ii) a Then evaluating S under the condition of meeting preset target constraint conditionstrialAnd obtaining a corresponding second adaptive value f (S)trial) (ii) a Finally, the first adaptive value f (S) and the second adaptive value f (S) are comparedtrial) To be at S and StrialAnd selecting a better person as an individual solution of the next generation of population, namely optimizing a scheduling scheme aiming at the target of the electric automobile. Increment algebraic g.
The third step: if the algebra g is not less than the preset maximum algebra gmaxThe algorithm is terminated, the charging scheduling scheme of each electric vehicle in the step A is arranged according to the historical optimal individual solution (namely the target optimal scheduling scheme), and otherwise, the step B is switched to the step B to continue to be executed circularly.
By adopting the electric vehicle charging scheduling method, the travel time and the charging cost can be effectively optimized, the charging state when the electric vehicle reaches the terminal is ensured to be as close to the expected state value as possible, the efficiency of electric vehicle charging scheduling in the global road network is greatly improved, and the use experience of users is improved.
Corresponding to the electric vehicle charging scheduling method, the invention also provides an electric vehicle charging scheduling device. Since the embodiment of the device is similar to the above method embodiment, the description is relatively simple, and please refer to the description of the above method embodiment, and the following embodiments of the electric vehicle charging scheduling device are only schematic. Fig. 2 is a schematic view of an electric vehicle charging scheduling device according to an embodiment of the present invention.
The invention relates to an electric automobile charging scheduling device, which comprises the following parts:
a first scheduling scheme obtaining unit 201, configured to obtain, for electric vehicles in a preset electric vehicle set, corresponding first scheduling schemes respectively, where the first scheduling schemes include an initial charging station sequence and an initial charging rate sequence corresponding to the electric vehicles.
The first evaluation unit 202 is configured to evaluate the first scheduling scheme to obtain a corresponding first adaptive value when a preset target constraint condition is met.
A second scheduling scheme obtaining unit 203, configured to perform mixed variable variation processing on the first scheduling scheme according to a preset operation rule to obtain a corresponding variable variation value; and carrying out cross operation processing according to the first scheduling scheme and the variable variation value to obtain a second scheduling scheme.
And the second evaluation unit 204 is configured to evaluate the second scheduling scheme to obtain a corresponding second adaptive value when the target constraint condition is satisfied.
And the optimal scheduling unit 205 is configured to select a target optimal scheduling scheme from the first scheduling scheme and the second scheduling scheme based on the first adaptive value and the second adaptive value, and manage and control the electric vehicle to perform charging scheduling according to the target optimal scheduling scheme.
The electric vehicle charging scheduling device can effectively optimize the travel time and the charging cost, ensures that the charging state when the electric vehicle reaches the terminal is as close as possible to the expected state value, and greatly improves the efficiency of electric vehicle charging scheduling in the global road network, thereby improving the use experience of users.
Corresponding to the electric vehicle charging scheduling method, the invention further provides electronic equipment. Since the embodiment of the electronic device is similar to the above method embodiment, the description is relatively simple, and please refer to the description of the above method embodiment, and the electronic device described below is only schematic. Fig. 3 is a schematic view of an electronic device according to an embodiment of the present invention.
The electronic device specifically includes: a processor 301 and a memory 302; the memory 302 is configured to run one or more program instructions, and is configured to store a program of an electric vehicle charging scheduling method, and after the server is powered on and runs the program of the electric vehicle charging scheduling method through the processor 301, the server executes any one of the electric vehicle charging scheduling methods.
Corresponding to the electric vehicle charging scheduling method, the invention also provides a computer storage medium. Since the embodiment of the computer storage medium is similar to the above method embodiment, the description is simple, and please refer to the description of the above method embodiment, and the computer storage medium described below is only schematic.
The computer storage medium contains one or more program instructions, and the one or more program instructions are used for the server to execute the electric vehicle charging scheduling method. The server may refer to a background server corresponding to the electronic device.
In an embodiment of the invention, the processor or processor module may be an integrated circuit chip having signal processing capabilities. The Processor may be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The processor reads the information in the storage medium and completes the steps of the method in combination with the hardware.
The storage medium may be a memory, for example, which may be volatile memory or nonvolatile memory, or which may include both volatile and nonvolatile memory.
The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory.
By way of example and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous link DRAM (S L DRAM), and Direct Memory bus RAM (DRR RAM).
The storage media described in connection with the embodiments of the invention are intended to comprise, without being limited to, these and any other suitable types of memory.
Those skilled in the art will appreciate that the functionality described in the present invention may be implemented in a combination of hardware and software in one or more of the examples described above. When software is applied, the corresponding functionality may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present invention should be included in the scope of the present invention.
Claims (10)
1. An electric vehicle charging scheduling method is characterized by comprising the following steps:
respectively obtaining corresponding first scheduling schemes aiming at electric automobiles in a preset electric automobile set, wherein the first scheduling schemes comprise an initial charging station sequence and an initial charging rate sequence corresponding to the electric automobiles;
evaluating the first scheduling scheme to obtain a corresponding first adaptive value under the condition that a preset target constraint condition is met;
performing mixed variable variation processing on the first scheduling scheme according to a preset operation rule to obtain a corresponding variable variation value; performing cross operation processing according to the first scheduling scheme and the variable variation value to obtain a second scheduling scheme;
evaluating the second scheduling scheme to obtain a corresponding second adaptive value under the condition that the target constraint condition is met;
and selecting a target optimization scheduling scheme from the first scheduling scheme and the second scheduling scheme based on the first adaptive value and the second adaptive value, and managing and controlling the electric vehicle to perform charging scheduling according to the target optimization scheduling scheme.
2. The electric vehicle charging scheduling method of claim 1, further comprising:
presetting target optimization iteration times;
and after the target optimization scheduling scheme is selected, judging whether the current optimization iteration number corresponding to the target optimization scheduling scheme is smaller than a preset target optimization iteration number, and if not, managing and controlling the electric vehicle to perform charging scheduling according to the target optimization scheduling scheme.
3. The electric vehicle charging scheduling method according to claim 1, wherein the obtaining of the corresponding first scheduling schemes for the electric vehicles in the preset electric vehicle set specifically includes:
respectively constructing an initial charging station sequence for each electric vehicle in the electric vehicle set, selecting a charging station for each electric vehicle according to the initial charging station sequence, and setting a charging rate for the selected charging station to further form the initial charging rate sequence; obtaining the first scheduling scheme according to the initial charging station sequence and the initial charging rate sequence.
4. The electric vehicle charging scheduling method according to claim 1, wherein performing mixed variable variation processing on the first scheduling scheme according to a preset operation rule to obtain a corresponding variable variation value specifically comprises:
and performing discrete variation operation processing according to the initial charging station sequence corresponding to each electric vehicle in the electric vehicle set, and further performing continuous variation operation processing according to the charging rate set for the selected charging station of each electric vehicle in the electric vehicle set to obtain a corresponding variation value.
5. The electric vehicle charging scheduling method according to claim 1, wherein the performing the cross operation processing according to the first scheduling scheme and the variable variance value to obtain a second scheduling scheme specifically comprises:
and carrying out cross operation processing on the first scheduling scheme and the variable variation value by using a preset binomial cross algorithm to obtain a second scheduling scheme.
6. The electric vehicle charging scheduling method of claim 2, further comprising:
if yes, continuing to perform mixed variable variation processing on the first scheduling scheme according to a preset operation rule to obtain a corresponding variable variation value; and carrying out cross operation processing according to the first scheduling scheme and the variable variation value to obtain a new second scheduling scheme.
7. The electric vehicle charging scheduling method of claim 1, further comprising: a set of electric vehicles needing to be subjected to charging scheduling is determined in advance.
8. The utility model provides an electric automobile scheduling device that charges which characterized in that includes:
the system comprises a first scheduling scheme obtaining unit, a second scheduling scheme obtaining unit and a control unit, wherein the first scheduling scheme obtaining unit is used for respectively obtaining corresponding first scheduling schemes for electric vehicles in a preset electric vehicle set, and the first scheduling schemes comprise an initial charging station sequence and an initial charging rate sequence corresponding to the electric vehicles;
the first evaluation unit is used for evaluating the first scheduling scheme to obtain a corresponding first adaptive value under the condition that a preset target constraint condition is met;
a second scheduling scheme obtaining unit, configured to perform mixed variable variation processing on the first scheduling scheme according to a preset operation rule to obtain a corresponding variable variation value; performing cross operation processing according to the first scheduling scheme and the variable variation value to obtain a second scheduling scheme;
the second evaluation unit is used for evaluating the second scheduling scheme to obtain a corresponding second adaptive value under the condition that the target constraint condition is met;
and the optimal scheduling unit is used for selecting a target optimal scheduling scheme from the first scheduling scheme and the second scheduling scheme based on the first adaptive value and the second adaptive value, and managing and controlling the electric vehicle to perform charging scheduling according to the target optimal scheduling scheme.
9. An electronic device, comprising:
a processor; and
a memory for storing a program of an electric vehicle charging scheduling method, wherein the electronic device executes the electric vehicle charging scheduling method according to any one of claims 1 to 7 after being powered on and running the program of the electric vehicle charging scheduling method through the processor.
10. A computer-readable storage medium, wherein the computer-readable storage medium contains one or more program instructions for executing, by a server, the electric vehicle charging scheduling method according to any one of claims 1 to 7.
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