CN112507506A - Multi-objective optimization method for sharing automobile pricing planning model based on genetic algorithm - Google Patents

Multi-objective optimization method for sharing automobile pricing planning model based on genetic algorithm Download PDF

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CN112507506A
CN112507506A CN202010987510.8A CN202010987510A CN112507506A CN 112507506 A CN112507506 A CN 112507506A CN 202010987510 A CN202010987510 A CN 202010987510A CN 112507506 A CN112507506 A CN 112507506A
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王澍
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Abstract

The multi-objective optimization method of the shared automobile pricing planning model based on the genetic algorithm comprises the following steps: firstly, acquiring historical data of shared automobile flows of each path of a road network, and the driving speed and the driving time of the path; carrying out day-ahead prediction and map mapping on the flow of the shared electric vehicle of each time period of each path by a big data method, and then establishing a multi-target model; constructing constraint conditions, solving the multi-target model through a genetic algorithm, finding a non-dominant solution set, and finding an optimal scheme in all non-dominant solutions by using a compromise solution method; the invention saves the operation cost of the sharing system, increases the system income, simultaneously improves the satisfaction degree of the user for the use of the sharing service and reduces the negative influence of the charging behavior of the sharing electric automobile on the stability of the power grid.

Description

Multi-objective optimization method for sharing automobile pricing planning model based on genetic algorithm
Technical Field
The invention relates to the technical field of power grids, in particular to a multi-objective optimization method of a shared automobile pricing planning model based on a genetic algorithm, which can be applied to shared electric automobile charging planning and path pricing decision considering the price elasticity of time and paths.
Background
The electric automobile sharing system is a novel city sharing economy in recent years. The electric automobile is used as an environment-friendly and low-carbon traffic mode, the geographical service coverage range is greatly improved by combining with a sharing system, and a flexible travel plan is provided for a user. So far, research on sharing an electric vehicle pricing scheme at home and abroad is still preliminary, and a complete and systematic pricing decision model and optimization method are not formed yet. First, most of the existing research does not consider the reverse decision of customers on different shared price responses. In fact, the change of the sharing price not only affects the number of users, but also changes the path selection and the use time of the users. Therefore, the optimal management and profit improvement of operators can be promoted only by establishing a time-space distribution model of the customer path preference and the travel request based on the price. Meanwhile, when the charging plan and the mileage price of the shared electric vehicle are decided, the influence of the electric vehicle on the operation of the external power grid is considered in the decision process of the shared pricing scheme. In addition, the quality of service of the vehicle sharing system is generally required to be measured and required in the vehicle sharing price decision process. Unlike traditional car rental services, when the battery capacity of the shared electric vehicle cannot meet the mileage requirement of the user, the time required to charge the shared electric vehicle is often much longer than the refueling time of the traditional vehicle. Current research measures the quality of service of an electric vehicle sharing system by the percentage of rejection of user demand, assuming that a user order will be cancelled if all electric vehicles fail to provide service that meets the customer's mileage demand. However, in practical situations, users often allow and want to wait a small amount of time until the electric vehicle is recharged to complete reuse of the shared electric vehicle, which is more practical than simply rejecting the user's demand.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a multi-objective optimization method of a shared automobile pricing planning model based on a genetic algorithm, simultaneously considers the influence of a shared charging behavior on the stability of a power grid and maximizes the shared service quality experience of a user, and considers the reduction of the waiting charging time of the user to improve the service quality of an electric automobile shared system under the condition of meeting related constraint conditions.
In order to achieve the purpose, the invention adopts the following technical scheme:
the multi-objective optimization method of the shared automobile pricing planning model based on the genetic algorithm comprises the following steps:
firstly, acquiring historical data of shared automobile flows of each path of a road network, and the driving speed and the driving time of the path; carrying out day-ahead prediction and map mapping on the flow of the shared electric vehicle of each time period of each path by a big data method, and then establishing a multi-target model;
and secondly, constructing constraint conditions, solving the multi-target model through a genetic algorithm, finding a non-dominant solution set, and finding an optimal scheme in all non-dominant solutions by using a compromise solution method.
Step one, establishing a multi-target pricing decision model, which is three models: the method comprises the following steps of maximizing a profit and income target model of a sharing system, maximizing a user sharing service satisfaction target model and minimizing a power distribution network system voltage fluctuation influence target model, and specifically comprises the following steps:
the profit of the maximized sharing system is equal to the difference between the user travel income RCR and the cost RCS of other electric vehicle sharing systems, and a profit target model WPR of the maximized sharing system is calculated and expressed by the following formula:
Figure BDA0002689740730000031
in the formula: k, j represents a starting point and an end point of the route, A1 is a set of the number of shared electric vehicles on the route kj in time T, K 'represents a set of shared charging stations, T' is a set of time,
Figure BDA0002689740730000032
represents the travel time of the route kj; rCSThe system consists of site maintenance cost, electric automobile depreciation cost, electric automobile charging cost, electric automobile maintenance cost and moving cost;
wherein, the user income RCRThe sharing of system costs with other electric vehicles is calculated as follows:
Figure BDA0002689740730000033
Figure BDA0002689740730000034
in the formula:
Figure BDA0002689740730000035
in order to make a decision on the variables for the price,
Figure BDA0002689740730000036
representing the path kj of the car after the price change from t to t
Figure BDA0002689740730000037
The flow of the shared electric vehicle in the time interval, Cv, Cre, Cmv and Cmp respectively represent the depreciation cost, the vehicle relocation cost, the vehicle maintenance cost and the parking lot maintenance cost of the shared electric vehicle per minute; zk represents the total number of parking spaces of the charging station k, PELt represents the price of electricity for purchasing electricity from the grid at time t, PCH represents the charging power of the shared electric vehicle, ηchIndicating the charging efficiency of the shared electric vehicle charging,
Figure BDA0002689740730000038
representing the number of shared electric vehicles that migrated from station k to j at time t,
Figure BDA0002689740730000039
the number of vehicles at a station k at the moment t for the shared electric vehicle;
maximizing user shared service satisfaction objective WSATThe model, represented by the following formula:
Figure BDA00026897407300000310
Figure BDA00026897407300000311
in the formula: the factors of the shared customer satisfaction comprise the dissatisfaction cost R of the customer caused by the change of the shared price of the electric automobilePL(ii) a Dissatisfaction cost of loss of service demand RDL(ii) a Unsatisfactory alternative cost R when a customer uses a taxi as a similar alternative means of transportationAT(ii) a And time cost R for customer waiting for available shared electric vehiclesWC；
The method for analyzing the driving behaviors of the shared automobile and calculating the waiting time of the user comprises the following steps:
Figure BDA0002689740730000041
in the formula:
Figure BDA0002689740730000042
the waiting queue length of the electric vehicle is shared for the path kj at time t,
Figure BDA0002689740730000043
the waiting queue length of the electric vehicle is shared for the path kj at time t-1,
Figure BDA0002689740730000044
for the starting number of the shared electric vehicles with the battery capacity not lower than e at the time t of the path kj, the starting flow of the shared electric vehicles is calculated as follows:
Figure BDA0002689740730000045
wherein:
Figure BDA0002689740730000046
Figure BDA0002689740730000047
in the formula:
Figure BDA0002689740730000048
the number of vehicles which reach the point k at the time t and have the electric quantity not lower than e at the battery station of the shared electric vehicle is counted;
Figure BDA0002689740730000049
f is the total charge amount in the unit time period.
Target model W for minimizing voltage fluctuation influence of power distribution grid systemGRIDExpressed by the following formula:
min WGRID＝||J-1[ΔP,ΔQ]||2
in the formula: DeltaP and DeltaQ are vectors of active and reactive power changes caused by charging of the shared electric vehicle, J-1Is an inverse jacobian matrix of the power system.
The constraint conditions constructed in the second step comprise a shared electric vehicle flow integer constraint, a shared charging station capacity constraint and a shared electric vehicle battery capacity constraint;
the flow integral constraint of the shared electric vehicle is as follows:
Figure BDA0002689740730000051
Figure BDA0002689740730000052
in the formula: eDemandRepresenting shared user paths and time price elastic coefficient, x0In order to fix the original price of the path,
Figure BDA0002689740730000053
the traffic flow for each path is predicted for the original.
The capacity constraint of the shared charging station is as follows:
Figure BDA0002689740730000054
in the formula: zkRepresenting the total number of parking spaces of the charging station k,
Figure BDA0002689740730000055
the number of vehicles at a station k at the moment t for the shared electric vehicle;
the shared electric vehicle battery capacity constraint is as follows:
Figure BDA0002689740730000056
in the formula: b isLTAnd BUTRepresenting the upper and lower limits of the charge capacity of the shared electric automobile,
Figure BDA0002689740730000057
for sharing the battery capacity percentage of the electric vehicle v at time t, BvRepresents the total battery capacity;
the solving model in the second step is specifically as follows:
step S031, inputting simulation data into the multi-target model and the constraint, wherein the data comprises traffic network, distribution network and predicted shared electric vehicle requirements;
step S032, encoding: coding price decision variables and traffic flow variables in the multi-target model into each individual variable in the genetic algorithm in sequence;
step S033, initializing a population: randomly generating an initial population consisting of N individuals in the value range of the multi-target model variable, and then evenly distributing the individuals into M sub-populations;
step S034, random migration: in each generation, each individual decides whether to leave the current sub-population and join another sub-population according to random migration;
step S035, positioning and generalized searching: after making a sub-population migration decision, each individual in a certain sub-population performs positioning search, and for optimization in each target model, only the fitness value of the individual on all targets is improved, and the change of values is acceptable;
step S036 selection and termination: at the end of each generation, updating the sub-populations; when the maximum generation time is reached, the algorithm terminates; outputting a non-dominant solution;
step S037, decision making: and after the non-dominant solution is obtained, searching an optimal multi-target shared pricing decision result in all the non-dominant solutions by using a compromise solution method.
Compared with the prior art, the method establishes the multi-objective optimization model by reasonably planning and making pricing decisions of different paths of the shared electric automobile at different time intervals, and the results obtained by the calculation show that on the basis of the calculation of the genetic algorithm: the model saves operation cost for the sharing system, increases system income, improves the satisfaction degree of users for the use of the sharing service, and reduces the negative influence of the charging behavior of the sharing electric automobile on the stability of the power grid.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a plot of shared system profit-gain versus shared quality of service pareto optima.
FIG. 3 is a plot of shared system profit margin versus voltage offset pareto optima.
FIG. 4 shows 18:00 and 22: PAL value of each path at 00.
FIG. 5 shows 18:00 and 22: and 00, the user requirement of each path under the non-pricing decision.
FIG. 6 shows 18:00 and 22: user demand of each path under pricing decision at 00.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
Referring to fig. 1, the multi-objective optimization method of the shared automobile pricing planning model based on the genetic algorithm comprises the following steps:
firstly, acquiring historical data of automobile traffic flow shared by each path of a road Network, path driving speed and driving duration by a big data algorithm (DCRNN); carrying out day-ahead prediction and map mapping on the flow of the shared electric vehicle of each time period of each path by a big data method, then establishing a multi-target model, and substituting the day-ahead predicted path flow and a price elastic coefficient equation into the multi-target model;
and secondly, constructing constraint conditions, solving the multi-target model through a genetic algorithm, finding a non-dominant solution set, and finding an optimal scheme in all non-dominant solutions by using a compromise solution method.
Step one, establishing a multi-target pricing decision model, which is three models: the method comprises the following steps of maximizing a profit and income target model of a sharing system, maximizing a user sharing service satisfaction target model and minimizing a power distribution network system voltage fluctuation influence target model, wherein the method specifically comprises the following steps:
maximizing system profit equals to user travel income RCRSharing System cost R with other electric vehiclesCSThe difference, the maximum share system profit-gain objective model WPR is calculated as:
Figure BDA0002689740730000071
in the formula: k, j represents a starting point and an end point of the route, A1 is a set of the number of shared electric vehicles on the route kj in time T, K 'represents a set of shared charging stations, T' is a set of time,
Figure BDA0002689740730000072
represents the travel time of the route kj, RCSThe system consists of site maintenance cost, electric automobile depreciation cost, electric automobile charging cost, electric automobile maintenance cost and moving cost;
wherein, the user income RCRThe sharing of system costs with other electric vehicles is calculated as follows:
Figure BDA0002689740730000081
Figure BDA0002689740730000082
in the formula:
Figure BDA0002689740730000083
in order to make a decision on the variables for the price,
Figure BDA0002689740730000084
representing the path kj of the car after the price change from t to t
Figure BDA0002689740730000085
Shared electric vehicle flow over time, Cv、Cre、Cmv、CmpRespectively representing per minute depreciation cost, relocation vehicle cost, vehicle maintenance cost and parking lot maintenance cost of the shared electric vehicle; zkIndicating the total number of parking spaces, PEL, of the charging station ktRepresenting the price of electricity, P, for purchasing power from the grid at time tCHRepresenting the charging power, η, of a shared electric vehiclechIndicating the charging efficiency of the shared electric vehicle charging,
Figure BDA0002689740730000086
representing the number of shared electric vehicles that migrated from station k to j at time t,
Figure BDA0002689740730000087
the number of vehicles at a station k at the moment t for the shared electric vehicle;
a maximize user shared service satisfaction objective (WSAT) model, represented by the following equation:
Figure BDA0002689740730000088
Figure BDA0002689740730000089
in the formula: shared customer satisfaction factor packageCustomer dissatisfaction cost R caused by electric automobile sharing price changePL(ii) a Dissatisfaction cost of loss of service demand RDL(ii) a Unsatisfactory alternative cost R when a customer uses a taxi as a similar alternative means of transportationAT(ii) a And time cost R for customer waiting for available shared electric vehiclesWC；
The method for analyzing the driving behaviors of the shared automobile and calculating the waiting time of the user comprises the following steps:
Figure BDA00026897407300000810
in the formula:
Figure BDA0002689740730000091
the waiting queue length of the electric vehicle is shared for the path kj at time t,
Figure BDA0002689740730000092
the waiting queue length of the electric vehicle is shared for the path kj at time t-1,
Figure BDA0002689740730000093
for the starting number of the shared electric vehicles with the battery capacity not lower than e at the time t of the path kj, the starting flow of the shared electric vehicles is calculated as follows:
Figure BDA0002689740730000094
wherein:
Figure BDA0002689740730000095
Figure BDA0002689740730000096
in the formula:
Figure BDA0002689740730000097
the number of vehicles which reach the point k at the time t and have the electric quantity not lower than e for the battery station of the shared electric vehicle,
Figure BDA0002689740730000098
f is the total charge amount in the unit time period.
Minimizing power distribution grid system voltage fluctuation impact objective model (W)GRID) Expressed by the following formula:
min WGRID＝||J-1[ΔP,ΔQ]||2
in the formula: Δ P and Δ Q are vectors of active and reactive power changes caused by charging of the shared electric vehicle. J. the design is a square-1Is an inverse jacobian matrix of the power system.
The construction constraint conditions of the second step comprise a shared electric vehicle flow integer constraint, a shared charging station capacity constraint and a shared electric vehicle battery capacity constraint;
the flow integral constraint of the shared electric vehicle is as follows:
Figure BDA0002689740730000099
Figure BDA0002689740730000101
in the formula: eDemandRepresenting shared user paths and time price elastic coefficient, x0In order to fix the original price of the path,
Figure BDA0002689740730000102
the traffic flow for each path is predicted for the original.
The shared charging station capacity constraint
Figure BDA0002689740730000103
In the formula: zkRepresenting the total number of parking spaces of the charging station k,
Figure BDA0002689740730000104
the number of vehicles at a station k at the moment t for the shared electric vehicle;
the shared electric vehicle battery capacity constraint
Figure BDA0002689740730000105
In the formula: b isLTAnd BUTRepresenting the upper and lower limits of the charge capacity of the shared electric automobile,
Figure BDA0002689740730000106
for sharing the battery capacity percentage of the electric vehicle v at time t, BvRepresents the total battery capacity;
the solving model in the second step is specifically as follows:
step S031, inputting simulation data into the multi-target model and the constraint, wherein the data comprises traffic network, distribution network and predicted shared electric vehicle requirements;
step S032, encoding: coding price decision variables and traffic flow variables in the multi-target model into each individual variable in the genetic algorithm in sequence;
step S033, initializing a population: randomly generating an initial population consisting of N individuals in the value range of the multi-target model variable, and then evenly distributing the individuals into M sub-populations;
step S034, random migration: in each generation, each individual decides whether to leave the current sub-population and join another sub-population according to random migration;
step S035, positioning and generalized searching: after making a sub-population migration decision, each individual in a certain sub-population performs positioning search, and for optimization in each target model, only the fitness value of the individual on all targets is improved, and the change of values is acceptable;
step S036 selection and termination: at the end of each generation, updating the sub-populations; when the maximum generation time is reached, the algorithm terminates; outputting a non-dominant solution;
step S037, decision making: and after the non-dominant solution is obtained, searching an optimal multi-target shared pricing decision result in all the non-dominant solutions by using a compromise solution method.
Example simulation:
step S041 example data
The present invention simulates traffic flow for shared electric vehicles using a drip company vehicle sharing demand data set collected in shenyang from 2016, 12, 5, and 2017, 2, 4. The data set includes a path time matrix for the shared service, with each data item containing the time and location (latitude and longitude) of the vehicle request for the user's respective path. The data set is first preprocessed to manually map the data in the data packets in order to specific geographic locations and time intervals. In the embodiment, the invention uses an ieee14 power distribution network node reference system. The shared system pricing operating time simulated in this example was set to 17 to 22pm with 1 hour per time step. All shared charging sites are located on different PQ buses. The initial energy of the shared electric vehicle was randomly set within [ 50%, 80% ] of the battery capacity and the battery parameters were set according to the specification of the daily wind. The charging power of the electric vehicle is set to 20 kW.
Step S042. calculating the example result
The invention applies a genetic algorithm to solve the multi-target pricing model proposed by the patent, and the projection of the obtained pareto optimal value on the target 1 and the target 2 is shown in figure 2. It is observed that user satisfaction decreases as the overall profit of the electric vehicle sharing system increases. This is because the operators of the shared system increase the system profit by lowering the shared price, thereby increasing the SEV demand. Therefore, the time for the customer to wait for the available electric vehicle service increases, thereby reducing customer satisfaction. Although the increase in demand may result in an increase in the cost of shared electric vehicle migration and charging, the sharing system may still realize profitability through the travel fee revenue paid by the customer. Fig. 3 shows the projection of the pareto optimum on target 1 and target 3. The results indicate that profitability is a somewhat conflicting goal with maintaining bus voltage. This is mainly because the operator lowers the shared use price in order to increase the total profit of the system, resulting in increased electric vehicle demand and greater stress on the operation of the grid.
In order to facilitate the observation of the result, the invention uses PAL value to analyze the price change, and the expression is
Figure RE-GDA0002915298930000121
Under the selected decision, the shared pricing decision model brings 3264 euro profit to the shared system for the entire run, and there are 2179 trips. Without the pricing decision model, there were 2254 trips and a 2760 euro profit. Comparing these two cases, the pricing decision model brings a significant profit growth (18.26%) and a demand loss of 3.32%. Fig. 4 shows PAL values applied to the optimized share price for 30 of the total 143 path pairs. We chose two typical times for analysis: peak hours (18 points) and off-peak hours (22 points). Fig. 5 and 6 are the user demand in the 30-pair path without/with pricing decision model, respectively. As can be seen from FIGS. 5-6, at the optimized share price, PAL is approximately inversely proportional to user demand. This reflects the fact that customers prefer to drive using an electric vehicle sharing inexpensive route.
During peak hours (point 18), fig. 4 shows that the PAL for most paths is higher than the static price (i.e. the price when PAL is 1), and therefore the demand under the pricing decision model is reduced. This shows that the pricing decision model discourages customers from using shared electric vehicles during peak hours to avoid long waits, thereby increasing customer satisfaction. At the same time, the reduction in demand will also reduce bus voltage variations, thereby mitigating the negative impact of SEV charging on the grid during peak hours. Accordingly, FIG. 4 shows that the PAL values on most paths are below the static price for off-peak hours (22pm) and the demand of the user increases significantly compared to the case without the pricing decision model. This means that power system planning encourages users to shift the use of shared cars from peak hours to off-peak hours, thereby balancing customer satisfaction with grid bus voltage variations by changing the shared prices. The selected solution can reduce the customer waiting cost in all paths over the entire cycle, with a customer waiting cost of 613 euros compared to the case without the pricing decision, while the customer waiting cost is 741 euros for the example without the pricing decision model. The pricing scheme selected at the same time also achieves smaller voltage variations (3.614% versus 4.802%). The calculation results prove that the pricing decision model provided by the invention optimizes a shared price incentive mechanism, the electric vehicle sharing system can obtain more profits by changing the service time of a client, the variation of the bus voltage can be reduced, and the user satisfaction and the power grid operation can be improved.

Claims (4)

1. The multi-objective optimization method of the shared automobile pricing planning model based on the genetic algorithm is characterized by comprising the following steps of:
firstly, acquiring historical data of shared automobile flows of each path of a road network, and the driving speed and the driving time of the path; carrying out day-ahead prediction and map mapping on the flow of the shared electric vehicle of each time period of each path by a big data method, and then establishing a multi-target model;
and secondly, constructing constraint conditions, solving the multi-target model through a genetic algorithm, finding a non-dominant solution set, and finding an optimal scheme in all non-dominant solutions by using a compromise solution method.
2. The multi-objective optimization method for a shared automotive pricing planning model based on genetic algorithms as claimed in claim 1, characterized in that,
step one, establishing a multi-target pricing decision model, which is three models: the method comprises the following steps of maximizing a profit and income target model of a sharing system, maximizing a user sharing service satisfaction target model and minimizing a power distribution network system voltage fluctuation influence target model, and specifically comprises the following steps:
the profit of the maximized sharing system is equal to the difference between the user travel income RCR and the cost RCS of other electric vehicle sharing systems, and a profit target model WPR of the maximized sharing system is calculated and expressed by the following formula:
Figure FDA0002689740720000011
in the formula: k, j represents a starting point and an end point of the route, A1 is a set of the number of shared electric vehicles on the route kj in time T, K 'represents a set of shared charging stations, T' is a set of time,
Figure FDA0002689740720000012
represents the travel time of the route kj; rCSThe system consists of site maintenance cost, electric automobile depreciation cost, electric automobile charging cost, electric automobile maintenance cost and moving cost;
wherein, the user income RCRThe sharing of system costs with other electric vehicles is calculated as follows:
Figure FDA0002689740720000021
Figure FDA0002689740720000022
in the formula:
Figure FDA0002689740720000023
in order to make a decision on the variables for the price,
Figure FDA0002689740720000024
representing the path kj of the car after the price change from t to t
Figure FDA0002689740720000025
The flow of the shared electric vehicle in the time interval, Cv, Cre, Cmv and Cmp respectively represent the depreciation cost, the vehicle relocation cost, the vehicle maintenance cost and the parking lot maintenance cost of the shared electric vehicle per minute; zk represents the total number of parking spaces of charging station k, PELt represents the time t fromThe price of electricity for the electric power purchased by the grid, PCH represents the charge power of the shared electric vehicle, ηchIndicating the charging efficiency of the shared electric vehicle charging,
Figure FDA0002689740720000026
representing the number of shared electric vehicles that migrated from station k to j at time t,
Figure FDA0002689740720000027
the number of vehicles at a station k at the moment t for the shared electric vehicle;
maximizing user shared service satisfaction objective WSATThe model, represented by the following formula:
Figure FDA0002689740720000028
Figure FDA0002689740720000029
in the formula: the factors of the shared customer satisfaction comprise the dissatisfaction cost R of the customer caused by the change of the shared price of the electric automobilePL(ii) a Dissatisfaction cost of loss of service demand RDL(ii) a Unsatisfactory alternative cost R when a customer uses a taxi as a similar alternative means of transportationAT(ii) a And time cost R for customer waiting for available shared electric vehiclesWC；
The method for analyzing the driving behaviors of the shared automobile and calculating the waiting time of the user comprises the following steps:
Figure FDA00026897407200000210
in the formula:
Figure FDA00026897407200000211
sharing the waiting queue length of the electric vehicle for the path kj at time tThe degree of the magnetic field is measured,
Figure FDA00026897407200000212
the waiting queue length of the electric vehicle is shared for the path kj at time t-1,
Figure FDA0002689740720000031
for the starting number of the shared electric vehicles with the battery capacity not lower than e at the time t of the path kj, the starting flow of the shared electric vehicles is calculated as follows:
Figure FDA0002689740720000032
wherein:
Figure FDA0002689740720000033
Figure FDA0002689740720000034
in the formula:
Figure FDA0002689740720000035
the number of vehicles which reach the point k at the time t and have the electric quantity not lower than e at the battery station of the shared electric vehicle is counted;
Figure FDA0002689740720000036
f is the total charge amount in the unit time period.
Target model W for minimizing voltage fluctuation influence of power distribution grid systemGRIDExpressed by the following formula:
min WGRID＝||J-1[ΔP,ΔQ]||2
in the formula: DeltaP and DeltaQ are vectors of active and reactive power changes caused by charging of the shared electric vehicle, J-1Is an electric power systemAn inverse jacobian matrix.
3. The multi-objective optimization method for the shared automobile pricing planning model based on the genetic algorithm as claimed in claim 1, wherein the constraint conditions constructed in the second step include a shared electric vehicle flow integer constraint, a shared charging station capacity constraint and a shared electric vehicle battery capacity constraint;
the flow integral constraint of the shared electric vehicle is as follows:
Figure FDA0002689740720000037
Figure FDA0002689740720000041
in the formula: eDemandRepresenting shared user paths and time price elastic coefficient, x0In order to fix the original price of the path,
Figure FDA0002689740720000042
the traffic flow for each path is predicted for the original.
The capacity constraint of the shared charging station is as follows:
Figure FDA0002689740720000043
in the formula: zkRepresenting the total number of parking spaces of the charging station k,
Figure FDA0002689740720000044
the number of vehicles at a station k at the moment t for the shared electric vehicle;
the shared electric vehicle battery capacity constraint is as follows:
Figure FDA0002689740720000045
in the formula: b isLTAnd BUTRepresenting the upper and lower limits of the charge capacity of the shared electric automobile,
Figure FDA0002689740720000046
for sharing the battery capacity percentage of the electric vehicle v at time t, BvRepresenting the total battery capacity.
4. The multi-objective optimization method for the shared automobile pricing planning model based on the genetic algorithm as claimed in claim 1, wherein the solution model of the second step is specifically:
step S031, inputting simulation data into the multi-target model and the constraint, wherein the data comprises traffic network, distribution network and predicted shared electric vehicle requirements;
step S032, encoding: coding price decision variables and traffic flow variables in the multi-target model into each individual variable in the genetic algorithm in sequence;
step S033, initializing a population: randomly generating an initial population consisting of N individuals in the value range of the multi-target model variable, and then evenly distributing the individuals into M sub-populations;
step S034, random migration: in each generation, each individual decides whether to leave the current sub-population and join another sub-population according to random migration;
step S035, positioning and generalized searching: after making a sub-population migration decision, each individual in a certain sub-population performs positioning search, and for optimization in each target model, only the fitness value of the individual on all targets is improved, and the change of values is acceptable;
step S036 selection and termination: at the end of each generation, updating the sub-populations; when the maximum generation time is reached, the algorithm terminates; outputting a non-dominant solution;
step S037, decision making: and after the non-dominant solution is obtained, searching an optimal multi-target shared pricing decision result in all the non-dominant solutions by using a compromise solution method.
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