CN110222364B - Method and system for locating and sizing electric automobile emergency rescue station on highway - Google Patents
Method and system for locating and sizing electric automobile emergency rescue station on highway Download PDFInfo
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Abstract
The invention discloses a method and a system for locating and sizing an emergency rescue station of an electric automobile on a highway, which aim at minimizing social annual cost, give consideration to the benefits of a station building party and an electric automobile user on the premise of ensuring the requirement of rescue timeliness, decide the number and the position of the rescue stations and the number of rescue vehicles in the stations by comprehensively considering the influence factors such as the fault rate of a quick charge station, the vehicle flow, the electricity price, the station building cost and the like, and solve a built model by adopting an adaptive genetic algorithm. The article takes the Shandong expressway fast charging service network as an example, and the validity of the method is verified.
Description
Technical Field
The invention relates to a site selection and volume fixing method and system for an electric automobile emergency rescue station on a highway.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In response to government planning, the national grid company has built 1200 highway fast charging stations in more than 20 provinces and the direct municipalities. Although the infrastructure of charging the expressway is becoming perfect, the popularization and the use of the electric automobile are still limited due to the problems that the electric automobile cannot reach the quick charging station due to the fault of the quick charging station or the insufficient electric power of the electric automobile. Therefore, the research on the electric automobile emergency rescue method in the expressway environment has very important practical significance for promoting the further development of the electric automobile industry in China.
The inventor finds that the current research on electric automobile emergency rescue is relatively less, and the existing research mainly focuses on the equipment for emergency rescue. In the prior art, an electric automobile emergency rescue service application program based on an android system is designed and developed, and effective response of rescue demands is realized through data interaction among the electric automobile, a rescue server and a rescue vehicle. The prior art analyzes the economic practicability of the whole life cycle of three emergency rescue vehicles, and the conclusion that the mode of combining diesel and a storage battery has higher rescue economy is obtained. Although the above studies have achieved some results, there is no systematic study for emergency assistance services. If emergency rescue of large-scale electric vehicles is to be realized, an electric vehicle emergency rescue station network needs to be built in a road network, and an emergency charging vehicle is adopted to carry out emergency charging on the electric vehicles which cannot be charged or is pulled to a nearest charging station, so that the cruising ability and the running reliability of the electric vehicles are guaranteed. Therefore, the location and the volume of the electric automobile emergency rescue station become the key for determining the timeliness and the economy of the emergency rescue.
At present, the research on locating and sizing of an electric automobile emergency rescue station is still in an initial stage, and operation economy and rescue timeliness need to be considered in decision making, so that various elements such as a station building position, rescue station vehicle configuration, a rescue path and the like need to be reasonably planned according to actual rescue requirements. The prior art provides an emergency rescue station site selection model with minimized construction cost, a rescue station site selection scheme with the minimum investment cost is obtained, and the model lacks consideration on user rescue waiting time cost and rescue cost.
Disclosure of Invention
In order to solve the problems, the invention provides a site selection and volume fixing method and a site selection and volume fixing system for an electric automobile emergency rescue station on a highway, aiming at minimizing annual social cost, taking the latest arrival time of rescue as constraint, comprehensively considering influence factors such as fault rate of a quick charge station, traffic flow, electricity price, station building cost and the like under the condition of considering the interests of a station building party and users of electric automobiles, building a site selection model, and determining the number of rescue automobiles in the station according to the rescue demand number in the service range of the rescue station.
In some embodiments, the following technical scheme is adopted:
a method for locating and sizing an electric automobile emergency rescue station on a highway comprises the following steps:
acquiring fault probability data of a quick charging station, quantity data of electric vehicles needing rescue during the fault of the quick charging station and electricity price data of the rescue station;
aiming at minimizing the comprehensive cost converted to annual electric vehicle rescue, establishing a site selection and volume determination model of the electric vehicle emergency rescue station on the highway; wherein, the comprehensive cost includes: annual user waiting cost, annual station building cost, equipment cost and annual rescue cost;
determining an optimization constraint condition of a location and volume selection model of an electric automobile emergency rescue station on a highway, comprising the following steps of: the method comprises the following steps of (1) minimum rescue station quantity constraint, maximum rescue time constraint, diesel vehicle quantity constraint and storage battery vehicle quantity constraint;
solving a site selection constant volume model of the electric automobile emergency rescue station on the highway by adopting a self-adaptive genetic algorithm to obtain an optimal site selection scheme;
the optimal addressing scheme comprises the following steps: the positions and the number of the rescue stations, the number of various types of rescue vehicles in the stations, and the rescue range of each rescue station.
Further, the highway electric automobile emergency rescue station site selection and volume fixing model specifically comprises the following steps:
minf＝ω1C1+ω2C2+ω3C3
wherein, C1Waiting for the cost for the annual user; c2The annual station building cost and equipment cost; c3The annual rescue cost; omega1、ω2、 ω3Are respectively corresponding weight coefficients, and1+ω2+ω3＝1。
further, a self-adaptive genetic algorithm is adopted to solve the locating and sizing model of the expressway electric vehicle emergency rescue station, and the method specifically comprises the following steps:
acquiring fault probability data of a quick charging station, quantity data of electric vehicles needing rescue during the fault of the quick charging station and electricity price data of the rescue station;
setting population scale and genetic algebra, and generating an initial population, namely an initial site selection scheme;
determining the service range of the rescue station and the number of rescue vehicles, and calculating a group fitness value, namely target cost;
selecting an addressing scheme with lower target cost in the population as a next generation genetic parent gene;
adjusting the cross mutation probability according to the individual fitness, reserving an addressing scheme with low target cost, and generating a new individual;
and if the genetic algebra meets the requirement, finding out the optimal individual in the final generation population as a final addressing scheme.
Further, a linear adaptive genetic algorithm is adopted to adjust the cross probability pcAnd the probability of variation pmThe value of (c):
wherein f is an individual fitness value; f' is the greater fitness value of the two parties selected as cross-swaps; f. ofmaxThe maximum fitness value of the population;
is the population mean fitness value; k is a radical of1～k4Is less than or equal to 1.0 and is a constant.
In other embodiments, the following technical scheme is adopted:
the highway electric vehicle emergency rescue station site selection and volume fixing system comprises a server, wherein the server comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and the processor executes the program to realize the highway electric vehicle emergency rescue station site selection and volume fixing method.
In other embodiments, the following technical scheme is adopted:
a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method for locating and sizing a highway electric vehicle emergency rescue station described above.
Compared with the prior art, the invention has the beneficial effects that:
through the test analysis of the highway quick-charging network in Shandong province, the method is proved to be capable of giving consideration to the waiting cost of the user and the construction and operation cost of the investor under the condition of ensuring the timeliness of rescue, so that better economy is obtained. The model can solve the emergency situations that the highway quick charging station has faults and the electric automobile cannot be charged in time, and powerful guarantee is provided for the quick development of the highway electric automobile quick charging network.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate exemplary embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a fishbone diagram illustrating the charging influencing factors of an electric vehicle according to one embodiment;
FIG. 2 is a schematic diagram of a service area and traffic flow of a medium and high speed highway according to an embodiment;
FIG. 3 is a diagram illustrating a relationship between the number of emergency events and the number of rescue stations according to an embodiment;
fig. 4 is a flow chart of a location and volume selecting algorithm for an electric vehicle emergency rescue station on a medium and high speed highway in the embodiment.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
1 description of the problems
1.1 rescue station demand analysis
At present, a quick charging network taking a highway as a backbone network frame is rapidly formed, and the problem of long-distance traveling of an electric automobile is expected to be solved. However, if the electric vehicle cannot be charged in time on the expressway due to a failure of the quick charging station or insufficient electric quantity, traffic jam will be caused, and rear-end accidents will be caused. There are many reasons for the electric vehicle being unable to be charged, and they can be roughly classified into a quick charging station fault, an electric vehicle power shortage, etc., and their finely divided fishbone diagram is shown in fig. 1.
Among the above factors, the probability of occurrence of the fault of the quick charging station and the fault of the electric vehicle is relatively small, and the probability of occurrence of the insufficient power of the electric vehicle is often large. However, once the quick charging station fails, it is difficult to ensure normal charging of the electric vehicle, which may cause a large impact. Therefore, in the present embodiment, the level of the event is divided according to the event occurrence probability and the size of the influence range, as shown in table 1. The present embodiment is mainly studied for the first two cases in which the event level in the table is classified as high (for the third case, it can be handled by the highway administration department in combination with other non-electric vehicles).
TABLE 1 charging emergency situation table for electric vehicles on highway
In addition, compared with urban traffic, the driving environment of the highway is special, and the following three points are provided for explaining the rescue requirement of the highway for establishing a model:
(1) the expressway service areas are two-way homonymous service areas, most of the two-way service areas are distributed at symmetrical positions on two sides of a road, as shown in figure 2, the two-way service areas are communicated with each other, and if the quick charging stations on any side are in fault, the rescue vehicle can reach the fault quick charging station from the other side through the communicated road, so that the quick charging stations on the two sides can be fitted into a rescue demand point.
(2) Because the number of vehicles entering and leaving the station is the same, when the traffic flow of the quick charging station is counted, only the traffic flow data in the entering direction (or the exiting direction) is counted, and the traffic flow data in the entering direction is selected for analysis in the embodiment. In fig. 2, for the Y service area, vehicles belong to the inbound traffic volume, and vehicles belong to the X service area.
(3) In order to simplify analysis and calculation, aiming at the electric vehicle power shortage event, a quick charging station to which the electric vehicle arrives is taken as a demand point of equivalent rescue. For example, if an emergency rescue event occurs at a location, the rescue distance is equivalent to the distance from the nearest rescue station to the X service area (a direction).
1.2 site selection principle
Aiming at the faults of the quick charge station and the electric power shortage events of the electric automobile, the site selection of the emergency rescue station should meet the following requirements:
(1) in order to ensure timely and effective rescue, the rescue station must be selected so that the rescue vehicle can reach the place where the event occurs within the specified time;
(2) in order to reduce the construction cost and increase the convenience, the original service area in the road network is generally used as an alternative point of the rescue station;
(3) when the number and the positions of the rescue stations are determined, the economy of station building needs to be considered, and the station building cost, the equipment cost, the operation cost, the user waiting cost and the like are mainly included.
In a word, the site selection of the rescue station should comprehensively consider the factors, and the station building position which meets the rescue timeliness requirement and gives consideration to the operation economy is selected through integral optimization.
1.3 constant volume principle
1.3.1 Classification configuration mode of rescue vehicle
Emergency vehicles that can perform rescue tasks are mainly of the following two types: a diesel generating set mobile charging vehicle (hereinafter referred to as a diesel vehicle) and a storage battery set mobile charging vehicle (hereinafter referred to as a storage battery vehicle). Because the two types of rescue vehicles have different characteristics, the applicable emergency requirements of the events are different, and the characteristic parameters of the two types of emergency rescue vehicles provided by the same manufacturer are given in table 2.
TABLE 2 two types of rescue vehicle parameter comparison
As can be seen from the above table:
(1) the diesel vehicle can continuously work for 8 hours under the condition of full fuel tank, and can supplement energy at any time by depending on a gas station in a service area, thereby providing uninterrupted charging service for a large number of electric vehicles. The storage battery car cannot continuously work for a long time due to the limitation of the capacity of the storage battery car, and the average capacity of quickly charging the electric car in each rescue task is 8kWh, so that the storage battery car can provide emergency charging for 9 cars at most, and the storage battery car can only return to a rescue station or a nearby quick charging station to charge the storage battery after the electric quantity of the storage battery is exhausted.
(2) The diesel vehicle has relatively large weight and volume, so the flexibility is poor and the vehicle speed is slow. On the contrary, the volume and the weight of the accumulator vehicle are smaller, so that the running is more flexible and the running cost per unit distance is lower.
The combination of the table 1 and the table 2 shows that the fault probability of the quick charging station is low, but the number of the electric vehicles needing emergency charging in each fault is large, so that the diesel emergency vehicle which is large in capacity, multiple in charging interfaces and convenient to supplement electricity is more suitable for the emergency situation. For the situation that the electric automobile with high occurrence probability is insufficient in power, the number of vehicles needing rescue in a single rescue task is small, and the rescue environment is relatively complex, so that a storage battery vehicle with high speed, light weight and small size is selected as an emergency rescue vehicle. Therefore, when emergency rescue vehicles are configured for the rescue station, the configuration conditions of the two charging vehicles are determined according to the rescue requirements of the two emergency situations.
1.3.2 configuration principle of two types of rescue vehicles
In order to meet the rescue requirements of each quick charge station and the electric automobile in a rescue range and simultaneously keep economic efficiency, the minimum configuration quantity of two types of rescue vehicles in the rescue station needs to be limited, so that the quantity of idle rescue vehicles in the station at any time is not less than the quantity of emergency rescue events possibly occurring at the time. The constraint may be set as follows.
For a given rescue vehicle of a certain type, the emergency rescue period (a rescue period from the time when the vehicle receives a rescue notice to the time when the rescue is completed and the next rescue task can be executed) is set to be constant and T. Therefore, according to the probability distribution of the occurrence of the rescue events, the number F of the emergency events under a certain confidence level alpha in the time period T can be obtainedT. And yet to be at this confidence level,ensuring that the number of idle rescue vehicles in the station at any moment is not less than the number of emergency rescue events possibly occurring at the moment, and meeting the requirement that N is more than or equal to F for the number of vehicles N to be configuredT. This constraint can be understood in practice as: after an emergency rescue vehicle is occupied after receiving a rescue task, the same type of emergency rescue vehicle still needs to be enough in the station to meet the emergency requirement of the region under jurisdiction before the emergency rescue vehicle can participate in rescue again.
For example, assume that a certain rescue vehicle has a maximum number of emergency events 4, i.e., F, within the jurisdiction during its rescue cycle TTAs can be seen from the above analysis, a minimum of 4 rescue vehicles need to be installed in the rescue station. Fig. 3 shows the situation where exactly 4 rescue events occur during each rescue cycle. Ordinate N in FIG. 3tRepresenting the idle number of the rescue vehicles in the station at the time t; t is t1,t2,t3… denotes the moment when rescue occurs.
As can be seen from FIG. 3, the rescue vehicles respectively return to and recover to the idle state after T time, and T is determined as the maximum number of emergency events is 4 in any T time period5(fifth rescue occurrence moment) can only be at t1After the moment + T, the first rescue vehicle returns at the moment and can perform the operation on T5The emergency rescue events are responded in time, and the rest can be known, so that the rescue requirements can be met by configuring 4 rescue vehicles in the station under the condition that the maximum number of the emergency events is 4 in any T time period.
The emergency resource allocation method is respectively implemented on a diesel rescue vehicle and a storage battery rescue vehicle, so that the allocation quantity of two types of emergency vehicles of each rescue station aiming at the faults of the quick charge stations in the district and the electric power shortage events of the electric vehicles under a certain confidence level can be obtained.
2 optimization model
2.1 objective function
The constructed model aims to reduce the comprehensive cost of electric vehicle rescue every year to the minimum on the premise of ensuring the timeliness of rescue. The comprehensive cost mainly comprises: the waiting cost of the annual users, the annual station building cost, the equipment cost and the annual rescue cost are three parts. For the investors of the construction of the rescue stations, the more the construction number of the rescue stations is, the higher the construction cost is, but the waiting time cost of the users can be reduced, and vice versa. Meanwhile, the electricity price of the station building position is also required to be considered during station building and volume fixing so as to save the rescue cost of the emergency rescue station. Thus, the objective function employed in the present embodiment can be expressed as follows:
minf＝ω1C1+ω2C2+ω3C3 (1)
in the formula, C1Waiting for the cost for the annual user; c2The annual station building cost and equipment cost; c3The annual rescue cost; omega1、ω2、 ω3Are respectively corresponding weight coefficients, and1+ω2+ω3＝1。
user waiting cost of 2.1.1 years
The annual user waiting cost includes two parts: the waiting time cost of the vehicles to be charged in the station under the condition of the fault of the quick charging station and the rescue waiting time cost caused by the electric power shortage event of the electric vehicle are set as eta, the eta is a time cost conversion coefficient and represents the economic value lost by the hourly waiting time of the electric vehicle user, and the annual user waiting cost mainly depends on the rescue waiting time under two emergency conditions.
Under the condition of the fault of the quick charging station j, before the rescue vehicle arrives, the expected value of the annual charging waiting time of the electric vehicle to be charged in the quick charging station is Tj wsThe calculation method is as follows. As the vehicles arrive at the quick charging station independently, the characteristics of stability and no aftereffect are met, so that the quantity of the electric vehicles which arrive at the quick charging station and wait for charging can be assumed to obey the parameters as
The poisson process of (a), wherein,
the expected value of the number of the electric vehicles which arrive at the quick charging station in unit time (1 hour) and wait for charging is obtained. Setting the running time between the rescue station i and the quick charging station j as TijInstant if 0 moment fast charging stationObstacle, then TijThe rescue vehicle arrives at the time (0, T)ij]The number of electric vehicles arriving internally is denoted by N (T)ij) The formula of the expected value of the Poisson process
Meanwhile, if (0, T) is knownij]There are N electric vehicles arriving in the time period, namely N (T)ij) N, and the arrival time of the kth electric vehicle is WkAnd k is 1,2, …, n, then (0, T)ij]The sum of the rescue waiting time of the electric automobile arriving at the quick charging station in time is
Obviously, W (T)ij) Is composed of random variables n and WkAnd k is a random variable determined by 1,2, … and n. Thus, from the conditional probability expectation formula E (X) ═ E (X | Y)), the calculation formula of the total expected value of the in-station vehicle rescue waiting time is:
meanwhile, for the Poisson process that the electric vehicle to be charged arrives at the quick charging station, the [0, T ] is knownij]On the premise that the inner n to-be-charged automobiles arrive, the arrival time of each automobile can be regarded as mutually independent random variables and all the variables obey 0, Tij]Uniformly distributed. Remember Y1，Y2,…,YnFor n independent obeys [0, Tij]Random variables distributed uniformly, so:
substituting equation (4) into equation (3) can obtain:
the expected value of the annual fault frequency of the quick charging station j is set as
The waiting time of the annual users of the quick charging station j caused by the fault of the quick charging station
Comprises the following steps:
for the rescue waiting time under the electric vehicle power shortage event, based on the description in section 1.1, the rescue waiting time caused by the electric vehicle power shortage event with the quick charging station j as the equivalent rescue demand point can be known
The calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
corresponding to the annual expected value T of the quantity of electric vehicles needing to be rescued when the electric power is insufficient for the quick charging station jijThe time required from the nearest rescue station i to the quick charge station j is indicated.
To sum up, the annual user waiting cost C1Can be expressed as follows:
in the formula, N is the number of the quick charging stations in the road network. SiAnd gammaijAll are {0,1} variables, the specific meaning of which is: if a rescue station is built at the position i of the quick charging station, S i1, otherwise S i0; if the quick charging station j is closest to the rescue station at the position i, gamma is determinedij1, j belongs to the rescue range of the rescue station at i, otherwise, γijAnd (5) dividing the rescue task of the rescue station by adopting a principle of being close to 0.
Cost for building station and equipment cost in 2.1.2 years
The annual station construction cost and equipment cost of a rescue station can be expressed as:
in the formula (I), the compound is shown in the specification,
the number of diesel vehicles configured in the rescue station i is represented; cdIs a unit price of the diesel vehicle;
the number of the accumulator vehicles configured in the rescue station i; ccThe unit price of the storage battery car is;
and 5, building station cost for the rescue station in i years.
2.1.3 years rescue cost
The rescue cost mainly comes from diesel oil and electric quantity consumed in the rescue task, and the cost is related to the number of electric automobiles needing rescue in a road network, the electricity price at a rescue station and the diesel oil price. Definition of Nj TSThe expected value of the number of the electric vehicles which charge the quick charging station j during the fault period; TS represents the fault rescue period of the quick charging station, and the number of hours of the fault repair time of the quick charging station is rounded up
In the formula (I), the compound is shown in the specification,
each small of the quick charging station jThe expected value of the time station-entering traffic flow, alpha represents the proportion of the electric vehicles charged in the station-entering traffic flow, and the quantity of the electric vehicles needing rescue in the fault period of the quick charging station in one year can be represented as
The expected value of the number of electric automobiles needing rescue due to insufficient electric power in the quick charging station j incoming vehicles in one year is
To sum up, the annual rescue cost C3The expression is as follows:
in the formula, PwThe expected value of the normal charging electric quantity of the electric automobile; j. the design is a squaredrAs diesel fuel price (dollar/liter); xi is the conversion efficiency of the diesel generator set; pmThe expected value of the charging electric quantity for emergency rescue under the emergency condition of insufficient electric power of the electric automobile;
the price of electricity for the rescue station at i. In the formula (11), the first half part in brackets represents the rescue cost of the diesel vehicle with the fault of the quick charging station, and the second half part represents the rescue cost of the battery rescue vehicle with insufficient electric quantity.
2.2 optimization constraints
In the optimizing process of site selection and volume fixing of the emergency rescue station, the following constraint conditions are met.
1) Minimum number of rescue stations constraint
The constraint indicates that there are at least 1 rescue station in the road network.
2) Maximum time constraint for rescue
In the formula, TmaxThe maximum rescue time. The constraint shows that the time from the rescue station in the road network to the quick charging station in the rescue range is not more than TmaxThereby ensuring the timeliness of emergency rescue.
3) Quantity constraint of diesel vehicle
In the formula, Fi TSRepresenting the number of emergency events occurring within the quick charge station fault rescue period TS at a given confidence level (0.8 is taken herein). According to the principle of the number configuration of the rescue vehicles in 1.3.2, the restriction can ensure the rescue requirements of each quick charging station in the rescue range.
Fi TSThe calculation method of (2) is as follows: the hourly fault probability of the quick charging station is the same and is PjAnd in the rescue period TS, the fault probability P of the quick charging station jj TSCan be expressed as:
Pj TS＝1-(1-Pj)TS (15)
further, the number F of faults of the quick charging station in the service range of the rescue station under the confidence coefficient of 0.8 in the rescue period can be obtained by the following formulai TS：
In the formula, xiAnd the number of the quick charging stations with faults in the service range of the rescue station in the rescue period is represented.
4) Number constraint of accumulator cars
In the formula, Fi TERepresenting the number of emergency events occurring within the TE time at 0.8 confidence; TE is set as the rescue period of the electric vehicle under-power event, the charging time in the rescue process is ignored, and TE is set to be 2TmaxThe rescue cycle is the maximum time for the accumulator vehicle to come and go to the rescue demand point.
Fi TEThe calculation process is as follows: definition of Ni EhFor the number of electric automobile power shortage events occurring in the rescue range of the rescue station i per hour, the calculation formula is as follows:
in the formula, beta represents the proportion of the electric automobile with the power shortage event to the flow of the incoming automobile, and alpha + beta is less than or equal to 1. Therefore, in the (0, TE) time, the compliance parameter of the incoming vehicle of the quick charging station j is
The number of electric vehicle power shortage events F of the rescue station under 0.8 confidence coefficienti TECan be obtained from the following formula
2.3 model solution
The Genetic Algorithm is a global search method with high robustness and good randomness, and in order to avoid premature convergence and too slow convergence, the embodiment adopts an Adaptive Genetic Algorithm (AGA) to solve the highway electric vehicle emergency rescue station location and volume model constructed in the text. The adaptive genetic algorithm is an improvement on the basic genetic algorithm by making the cross probability pcAnd the probability of variation pmChanges with the fitness value, thereby preserving good overall situation of the basic genetic algorithmThe convergence precision and the convergence speed of the genetic algorithm are further improved while the characteristics and the randomness are realized, and the specific principle and the realization method are as follows:
the crossover refers to the exchange of partial genes of two individuals to form two new individuals, the larger the crossover probability is, the more easily the population generates the new individuals, but the retention rate of good individuals in the population is also reduced. The mutation operation is to change partial genes in an individual, if the mutation probability is high, the algorithm genetics are weakened and tend to random search, and if the mutation probability is too low, the algorithm global search capability is reduced, and the premature phenomenon occurs. Thus, in the early stages of evolution, larger p's were selectedc、pmA value such that the population's versatility is preserved; and at the later stage, the probability of intersection and mutation is reduced, so that more detailed search is carried out, and the optimal solution is prevented from being damaged. The method adopts a linear adaptive genetic algorithm, namely, p is adjusted as the fitness of an individual is changed between population average fitness and maximum fitnessc、pmThe value:
wherein f is an individual fitness value; f' is the greater fitness value of the two parties selected as cross-swaps; f. ofmaxThe maximum fitness value of the population;
is the population mean fitness value; k is a radical of1～k4Is less than or equal to 1.0 and is a constant. The algorithm flow chart is shown in fig. 4, and includes:
acquiring fault probability data of a quick charging station, quantity data of electric vehicles needing rescue during the fault of the quick charging station and electricity price data of the rescue station;
setting population scale and genetic algebra, and generating an initial population, namely an initial site selection scheme;
determining the service range of the rescue station and the number of rescue vehicles, and calculating a group fitness value, namely target cost;
selecting an addressing scheme with lower target cost in the population as a next generation genetic parent gene;
adjusting the cross mutation probability according to the individual fitness, reserving an addressing scheme with low target cost, and generating a new individual;
if the genetic algebra meets the requirement, finding out the optimal individual in the last generation population as a final addressing scheme; otherwise, the group fitness value is determined again, and the iterative process is continued.
The model solution can be calculated by programming Matlab-R2014a, and the algorithm parameters are set as follows: number of particles 2000, genetic algebra 200, adaptive cross probability coefficient: k is a radical of1＝0.9，k20.9, coefficient of variation probability k3＝0.01，k4＝0.01。
And finally, obtaining an optimal site selection scheme by solving the model, wherein the scheme comprises the position and the number of the optimal rescue stations, the number of various types of rescue vehicles in the stations and the rescue range of each rescue station.
3 example analysis
3.1 Shandong fast charging network
The method of the embodiment is tested and analyzed by taking the highway quick charging service network in Shandong province as an example. 59 service areas for bidirectional construction of the rapid charging station in the network, 1 service area for unidirectional construction of the rapid charging station, wherein the two Laiwu service areas are close to each other, and are fitted into 1 service area for simplifying calculation. Therefore, 59 demand points in total are in the highway quick-charging network in Shandong province. The individual service area demand points are shown in table 3.
TABLE 3 construction situation table for highway quick charging station in Shandong province
3.2 parameter values
Of three costs in the objective function of the embodimentThe weights are set to ω respectively1＝0.1，ω2＝0.5，ω＝0.4。
The basic parameters of each quick charging station are shown in table 4, the shortest path matrix of the road network is obtained by using a Floyd algorithm according to the adjacent matrix between the quick charging stations in the road network, and the shortest time matrix between the quick charging stations, namely T is obtained by assuming that the speed per hour of the rescue vehicle is 80km/h59×59。
In addition, the hourly fault probability of each quick charging station is between 0.005 and 0.05 percent; the expected value of the vehicle flow per hour of the quick charging station j is 10-30; the station building cost (per year) at the quick charging station j ranges from 30 to 60 thousands. Meanwhile, for no loss of generality, the price of electricity is set to be three: 0.5 yuan/kWh, 1 yuan/kWh, 1.5 yuan/kWh.
Table 4 quick charging station data table
Setting alpha to be 0.15; β ═ 0.01; the time cost coefficient eta is 30 yuan/h; assuming that the life of the rescue vehicle is 20 years, the annual cost of the diesel generator set charging vehicle can be reduced to 1 ten thousand, and the annual cost of the storage battery set charging vehicle can be reduced to 0.8 ten thousand; the capacity of the electric automobile is 25kWh which is the mode of the battery capacity of the electric automobile in the market; the expected value of the normal charging electric quantity of the electric automobile is 80 percent of the battery capacity of the electric automobile, namely Pw20 kWh; the electric automobile obtains 40 percent of the battery capacity of the electric automobile, namely P, of the electric automobile by using the emergency rescue charging electric quantity required when the electric automobile fails to reach the quick charging station due to insufficient electric powerm10 kWh; the conversion efficiency xi of the diesel generating set is 0.33; the price of diesel oil is 6.4 yuan/liter; the maximum arrival time of rescue is set as Tmax＝2h。
3.3 decision results
The calculation results obtained by solving the model by the adaptive genetic algorithm according to the road network data, the algorithm parameters and the set parameter values in the model are shown in table 5.
TABLE 5 results of site selection for rescue station
As can be seen from the calculation results in table 5, to meet the emergency charging requirement of the expressway, at least 7 rescue stations should be established in the highway network of shandong province, and the station establishment positions are numbered 6 (overpass service area), 17 (move away service area), 24 (huge service area), 44 (near-yi service area), 48 (Qingzhou 2 service area), 56 (jiao zhou service area), and 59 (breast mountain service area). Further, to clarify the rescue range of each rescue station, table 7 shows the distribution condition of the fast charging stations in the rescue range of each rescue station.
TABLE 6 service Range of rescue station
In addition, the configuration conditions of the number of the fast charging stations and the number of the rescue vehicles in the stations within the rescue range obtained by solving the model are shown in table 7.
TABLE 7 number of quick charge stations and number of rescue vehicles in station within service range of rescue station
And dividing the fast-charging network of the Shandong expressway according to the site selection result. The calculation result shows that the rescue arrival time of each quick charging station is TmaxThe range of 2h, and the mean value of the rescue arrival time of the fast filling station is only 0.973h, thereby illustrating the effectiveness of the siting capacity model proposed in the embodiment.
3.4 influence of weights on decision results
In order to analyze the influence of the selection of the weight coefficients in the objective function on the addressing result, when the results are shown in table 8 when the weight coefficients are respectively set, the costs of each part and the addressing scheme obtained after the calculation of the solution model are shown in table 9.
TABLE 8 three groups of different weight values
TABLE 9 cost and site selection scheme (Unit: million) under different weights
Comparing the two groups of weight values 1 and 2 and the corresponding calculation results, it can be seen that when the rescue cost weight is unchanged, as the user waiting cost weight increases and the station building cost weight decreases, the user waiting cost will decrease, and the station building cost will increase. Meanwhile, the number of the rescue stations is increased to 8 immediately, and the average time of rescue arrival of the corresponding quick charge station is reduced to 0.901 h. Therefore, if the station building cost is sacrificed, the station building number is increased, so that the waiting cost of the user can be reduced, the rescue arrival time can be reduced, and the requirement on the timeliness of rescue can be met.
Comparing the weight values of the two groups of 1 and 3 and the corresponding calculation results, when the time cost weight is unchanged, the station building cost weight is reduced and the rescue cost weight is increased, the station building cost is increased, the rescue cost is reduced, the station building position 44 with higher electricity price is eliminated in the site selection scheme, the average electricity price of the rescue station is reduced from 0.929 yuan/kwh to 0.812 yuan/kwh, namely after the rescue cost weight is increased, the site selection scheme is more biased to the station building position with lower electricity price, and therefore the rescue cost is reduced.
Compared with two groups of data of 2 and 3, under the weight value of 2 groups, the waiting cost of the user is relatively low, the rescue cost is high, the average electricity price of each rescue station is 1 yuan/kwh, when the rescue cost weight is increased and the waiting cost weight of the user is reduced, the average time of the rescue arrival is increased to 0.935h, the rescue cost is reduced to 73 ten thousand yuan, and the average electricity price of the rescue stations is reduced by 0.188 yuan/kwh in the obtained site selection scheme.
Through the comparative analysis, for the investment builders of the rescue stations, if the number of the rescue stations in the road network is large, the station building cost is increased, but the rescue service level is improved, the waiting rescue time of the user is reduced, and the waiting cost is reduced; on the contrary, if the number of rescue stations is reduced, the station building cost is reduced, and the event cost spent by a user for waiting for rescue is increased after an emergency event occurs; in addition, as can be seen from the comparative analysis of the results, if the station building scheme is used for paying attention to the rescue cost, the station building is prone to be located at a place with a lower electricity price, but the station building cost and the waiting cost of a user are increased. The model established by the embodiment takes the minimum sum of the user waiting cost, the station establishing cost and the rescue cost as a target, and gives consideration to the benefits of both the rescue station investor and the user. However, the optimization of a single target is also chosen by adjusting the weight according to the actual situation and the emphasis of the relevant party.
The embodiment firstly introduces the current development situation of the current expressway electric vehicle quick-charging network, analyzes and explains the necessity of emergency rescue, and provides a site selection and volume determination model of an emergency rescue station on the basis. The constructed model is solved by adopting a self-adaptive genetic algorithm. Through the test analysis of the highway quick-charging network in Shandong province, the method has the advantages that the waiting cost of the user and the construction and operation cost of the investor can be considered under the condition that the timeliness of rescue is guaranteed, and therefore better economy is achieved. The model can solve the emergency situations that the highway quick charging station has faults and the electric automobile cannot be charged in time, and provides powerful guarantee for the quick development of the highway electric automobile quick charging network.
Example two
The highway electric vehicle emergency rescue station locating and sizing system disclosed in one or more embodiments comprises a server, wherein the server comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and the processor executes the program to realize the highway electric vehicle emergency rescue station locating and sizing method in the first embodiment.
EXAMPLE III
In one or more embodiments, a computer-readable storage medium is disclosed, on which a computer program is stored, wherein the program, when executed by a processor, performs a method for locating and sizing a highway electric vehicle emergency rescue station in the first embodiment.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts based on the technical solutions of the present invention.
Claims (7)
1. A method for locating and sizing an electric automobile emergency rescue station on a highway is characterized by comprising the following steps:
acquiring fault probability data of a quick charging station, quantity data of electric vehicles needing rescue during the fault of the quick charging station and electricity price data of the rescue station;
aiming at minimizing the comprehensive cost converted to annual electric vehicle rescue, establishing a site selection and volume determination model of the electric vehicle emergency rescue station on the highway; wherein, the comprehensive cost includes: annual user waiting cost, annual station building cost, equipment cost and annual rescue cost;
determining an optimization constraint condition of a location and volume selection model of an electric automobile emergency rescue station on a highway, comprising the following steps of: the method comprises the following steps of carrying out rescue station minimum number constraint, rescue maximum time constraint, diesel vehicle number constraint and storage battery vehicle number constraint;
solving the site selection constant volume model of the electric automobile emergency rescue station on the highway by adopting a self-adaptive genetic algorithm to obtain an optimal site selection scheme;
the annual user waiting costs include: waiting time cost of vehicles to be charged in the station under the condition of a fault of the quick charging station and rescue waiting time cost caused by an electric vehicle power shortage event;
the annual user waiting cost C1The method specifically comprises the following steps:
wherein N is the number of quick charging stations in the road network, Fj syExpected number of annual faults, N, for a fast charge station jj uyCorresponding to the annual expected value T of the quantity of electric vehicles needing to be rescued when the electric power is insufficient for the quick charging station jijThe time required from the nearest rescue station i to the quick charging station j is represented, eta is a time cost conversion coefficient and represents the economic value lost by the waiting time of an electric vehicle user per hour; siAnd gammaijAll are {0,1} variables, the specific meaning of which is: if a rescue station is built at the position i of the quick charging station, Si1, otherwise Si0; if the quick charging station j is closest to the rescue station at the position i, gamma is determinedij1, j belongs to the rescue range of the rescue station at i, otherwise, γijDividing rescue tasks of the rescue station by adopting a principle of being close to 0;
the annual rescue cost comprises the rescue cost of a diesel vehicle with a fault of the quick charging station and the rescue cost of a storage battery rescue vehicle with insufficient electric quantity; annual rescue cost C3The method specifically comprises the following steps:
wherein, PwThe expected value of the normal charging electric quantity of the electric automobile; j. the design is a squaredrThe price of diesel oil; xi is the conversion efficiency of the diesel generating set; pmThe expected value of the charging electric quantity for emergency rescue under the emergency condition of insufficient electric power of the electric automobile;
the price of electricity of the rescue station at the position i; fj syExpected number of annual faults, N, for a fast charge station jj TSThe expected value of the number of the electric vehicles which charge the quick charging station j during the fault period; siAnd gammaijAll are {0,1} variables, the specific meaning of which is: if a rescue station is built at the position i of the quick charging station, Si1, otherwise Si0; if the quick charging station j is closest to the rescue station at the position i, gamma is determinedij1, j belongs to the rescue range of the rescue station at i, otherwise, γijAnd (5) dividing the rescue task of the rescue station by adopting a principle of being close to 0.
2. The method for locating and sizing the emergency rescue station of the electric automobile on the highway according to claim 1, wherein the model for locating and sizing the emergency rescue station of the electric automobile on the highway specifically comprises the following steps:
minf＝ω1C1+ω2C2+ω3C3
wherein, C1Waiting for the cost for the annual user; c2The annual station building cost and equipment cost; c3The annual rescue cost; omega1、ω2、ω3Are respectively corresponding weight coefficients, and1+ω2+ω3＝1。
3. the method for locating and sizing the expressway electric vehicle emergency rescue station as claimed in claim 1, wherein the annual station building cost and the equipment cost C2The method specifically comprises the following steps:
in the formula (I), the compound is shown in the specification,
the number of diesel vehicles configured in the rescue station i is represented; cdIs a unit price of the diesel vehicle;
the number of the accumulator vehicles configured in the rescue station i; ccThe unit price of the storage battery car is;
cost for building rescue station in year i(ii) a N is the number of quick charging stations in the road network, SiIs a variable {0,1} and has specific meanings as follows: if a rescue station is built at the position i of the quick charging station, Si1, otherwise Si＝0。
4. The method for locating and sizing the emergency rescue station of the electric automobile on the highway according to claim 1, wherein a self-adaptive genetic algorithm is adopted to solve a locating and sizing model of the emergency rescue station of the electric automobile on the highway, and specifically comprises the following steps:
acquiring fault probability data of a quick charging station, quantity data of electric vehicles needing rescue during the fault of the quick charging station and electricity price data of the rescue station;
setting population scale and genetic algebra, and generating an initial population, namely an initial site selection scheme;
determining the service range of the rescue station and the number of rescue vehicles, and calculating a group fitness value, namely target cost;
selecting an addressing scheme with lower target cost in the population as a next generation genetic parent gene;
adjusting the cross mutation probability according to the individual fitness, reserving an addressing scheme with low target cost, and generating a new individual;
and if the genetic algebra meets the requirement, finding out the optimal individual in the final generation population as a final addressing scheme.
5. The expressway electric vehicle emergency rescue station site selection and volume fixing method according to claim 4, wherein a linear adaptive genetic algorithm is adopted to adjust the cross probability pcAnd the probability of variation pmThe value of (c):
wherein f isA volume fitness value; f' is the greater fitness value of the two parties selected as cross-swaps; f. ofmaxThe maximum fitness value of the population is obtained;
is the population mean fitness value; k is a radical of1～k4Is less than or equal to 1.0 and is a constant.
6. The highway electric vehicle emergency rescue station site selection and volume fixing system is characterized by comprising a server, wherein the server comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and the processor executes the program to realize the highway electric vehicle emergency rescue station site selection and volume fixing method as claimed in any one of claims 1-5.
7. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, performs a method for locating a highway electric vehicle emergency rescue station according to any one of claims 1 to 5.
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