A method, system and storage medium for renting electric taxi charging piles based on alliance game

Abstract

The present invention relates to a method, system and storage medium for renting charging piles for electric taxis based on alliance game, and relates to the technical field of electric vehicle charging. The method comprises: constructing an electric taxi charging cost optimization model under the constraints of the maximum rental time of the charging pile and the mobile energy consumption of the electric taxi; constructing an alliance game model, an electric taxi marginal utility function, a preference order and an alliance order according to the charging cost optimization model; determining the first charging allocation strategy of the electric taxi according to the marginal utility function and the alliance preference order to optimize the total charging cost; determining the second charging allocation strategy of the electric taxi according to the selfish utility function and the selfish preference order to achieve Nash equilibrium. The present invention saves the construction cost of the charging pile, and while meeting the charging demand of the electric taxi, determines the charging pile rental and charging allocation strategies under the constraints of the maximum rental time of the charging pile, thereby optimizing the charging cost.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging, and in particular to an electric taxi charging pile leasing method, system and storage medium based on alliance game. Background Art

[0002] An electric car is a car that uses an electric motor as a power source. Compared with traditional fuel vehicles, electric cars drive vehicles through electricity instead of relying on the energy generated by fuel combustion. They have the advantages of environmental protection, high energy efficiency, low cost, and driving comfort. Looking back at the development of electric cars, as an emerging technology introduced after the industrial revolution, electric cars have existed for more than 100 years, and the first electric car was invented by William Morrison around 1890. In the early 20th century, electric cars experienced a brief peak in the US market, occupying 38% of the US auto market. However, due to the development of internal combustion engine technology and the widespread use of oil resources, electric cars began to decline in the mid-20th century. It was not until the beginning of the 21st century that electric cars attracted people's attention again with the intensification of environmental pollution problems, the focus on renewable energy, and the development of electric vehicle technology research.

[0003] In response to the challenges of global climate change and urban traffic pollution, public transport electrification is seen as an important solution. In order to reduce carbon emissions and air pollution, more and more cities have begun to take measures to promote the electrification of public transport, encourage and support the development and popularization of electric taxis, such as Taiyuan, Shenzhen, Chengdu, Beijing, etc. Among them, Shenzhen is the first first-tier city to replace all of its approximately 20,000 taxis with electric vehicles. However, with the growing demand for charging of household electric vehicles, electric taxis need to compete with more and more household electric vehicles for the use of public charging piles, and charging difficulties are becoming increasingly serious. Although the construction of a large number of electric taxi charging stations is the simplest and most direct way to solve this problem. However, the construction of a large number of charging stations has problems such as high cost, low utilization, and unstable income. Existing research on electric taxis mainly solves problems such as electric taxi charging navigation, collaborative charging, and charging station site selection through big data, stochastic optimization, game theory, etc. However, there is currently no solution to the problem of electric taxi charging pile rental from a game perspective. Summary of the invention

[0004] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and to provide an electric taxi charging pile leasing method, system and storage medium based on alliance game, which saves the construction cost of charging piles, and while meeting the charging needs of electric taxis, determines the charging pile leasing and charging allocation strategy under the constraint of the maximum leasing time of the charging piles, thereby optimizing the charging cost.

[0005] According to one aspect of the present invention, the present invention provides an electric taxi charging pile rental method based on alliance game, the method comprising the following steps:

[0006] Determine the total charging cost and the shared cost based on the completion time and rental time of the electric taxi’s charging task; construct an electric taxi charging cost optimization model under the constraints of the maximum rental time of the charging pile and the mobile energy consumption of the electric taxi;

[0007] According to the charging cost optimization model, an alliance game model, an electric taxi marginal utility function, a preference order and an alliance order are constructed; the preference order indicates the charging alliance that the electric taxi tends to join; the alliance order indicates the charging alliance that the electric taxi tends to choose; according to the marginal utility function and the alliance preference order, a first charging allocation strategy for the electric taxi is determined to optimize the total charging cost;

[0008] A selfish utility function and a selfish preference order of an electric taxi are constructed, wherein the selfish preference order is used for the electric taxi to quantify its preference; and a second charging allocation strategy of the electric taxi is determined according to the selfish utility function and the selfish preference order to achieve Nash equilibrium.

[0009] Preferably, the method of determining the total charging cost and the shared cost according to the completion time of the charging task and the rental time of the electric taxi includes:

[0010] Charging stations j The total charging cost is:

[0011]

[0012] Electric taxi v i At the charging station j The charging cost to be shared after completing the charging is:

[0013]

[0014] Among them, c j For charging piles j The total cost on j (·) is a monotonically increasing concave function that characterizes the length of the rental period; For charging piles j The rental period; For charging piles j The base rental price per rental period, For charging piles j The unit electricity price of Q j For charging piles j Total charge on Indicates electric taxi v i At the charging stationj The rental time on For the charging task τ i At the charging station j Charging completion time on For electric taxis i The charging demand, d i，j Is an electric taxi v i To charging stations j The shortest distance, β i Is an electric taxi v i Unit moving energy consumption.

[0015] Preferably, constructing an electric taxi charging cost optimization model under the constraints of the maximum rental time of the charging pile and the mobile energy consumption of the electric taxi includes:

[0016] The problem of minimizing the charging cost of electric taxis under the constraints of the maximum rental time of charging piles and the mobile energy consumption of electric taxis is formalized as follows:

[0017]

[0018]

[0019]

[0020]

[0021]

[0022] in, For charging piles j The maximum rental time, E i For electric taxis i The battery capacity, x i，j For the charging task τ i Assigned binary decision variables.

[0023] Preferably, the construction of the alliance game model, the electric taxi marginal utility function, the preference order and the alliance order according to the charging cost optimization model includes:

[0024] Using marginal utility to measure the strategy of electric taxi users i For the corresponding charging alliance The impact is as follows:

[0025]

[0026] where τ i For electric taxis i The corresponding charging task, For charging pile The total charging cost, a -i In addition to electric taxis i The charging distribution strategy set of other electric taxis, Indicates electric taxi v i Charging alliance of choice;

[0027] Definition> i For electric taxis i The preference order of any electric taxi v i and any two charging alliances and The order of the charging alliance is:

[0028]

[0029] Preferably, determining the first charging allocation strategy of the electric taxi according to the marginal utility function and the alliance preference order to optimize the total charging cost includes:

[0030] (5.1) Initialize the electric taxi charging alliance division

[0031] (5.2) For any charging pile s j ∈N, initialize the charging task set And update the alliance partition Γ = Γ ∪ T j ;

[0032] (5.3) For any charging task τ i ∈T, construct its feasible strategy space A i ;

[0033] (5.4) Any electric taxi v i ∈V from the feasible strategy space A i Randomly select a strategy as the current charging allocation strategy a i , and update the corresponding charging alliance

[0034] (5.5) For any charging task τ i ∈T, reconstruct its feasible strategy space A i ;

[0035] (5.6) Select strategy space A i The strategy with the greatest utility in If you join the new charging alliance, you will get the utility U i (a′ i , a -i ) is greater than the utility U obtained by the old charging alliance i (a i , a-i ), update the new charging alliance Old Charging Alliance and the current charging allocation strategy a i =a′ i ;

[0036] (5.7) Repeat steps (5.5) and (5.6) until the charging allocation strategy of all electric taxis remains unchanged;

[0037] (5.8) Output the electric taxi charging alliance division Γ.

[0038] Preferably, the constructing of the selfish utility function and selfish preference order of electric taxis comprises:

[0039] Electric taxi v i At the charging station j Selfish charging utility U i (T j ):

[0040]

[0041] Defining selfish preference relations based on the selfish charging utility of electric taxis> i :

[0042]

[0043] Preference function R i (T j ) Calculation of the selfish charging utility of electric taxis:

[0044]

[0045] Where H(i) represents the electric taxi v i A historical set that records the charging alliances that have been joined before, and || means "or".

[0046] Preferably, determining the second charging allocation strategy of the electric taxi according to the selfish utility function and the selfish preference order to achieve Nash equilibrium comprises:

[0047] (7.1) Initialize the electric taxi charging alliance division

[0048] (7.2) For any charging station s j ∈N, initialize the charging task set And update the alliance partition Γ = Γ ∪ T j ;

[0049] (7.3) For any electric taxi charging task τ i∈T, initialize its electric taxi v i Possible charging alliance collection History Collection and charging pile index α i =0;

[0050] (7.4) For any charging station s j ∈N, if the electric taxi v i The remaining battery capacity is enough to reach the charging station j , and in electric taxis v i Join the Charging Alliance j Then at the charging station j If all charging tasks in the alliance can be completed within the maximum rental time, the charging alliance set A that it may join will be updated. i =A i ∪{s j};

[0051] (7.5) Electric taxis i From A i Randomly select a charging station s j , and update the corresponding charging alliance T j =T j ∪{τ i}, history set H(i)=H(i)∪{T j} and charging pile index α i =j;

[0052] (7.6) For any electric taxi charging task τ i ∈T, based on the preference function R i (T j ) Calculate the charging pile with the greatest utility and New Charging Alliance If the electric taxi v i The preference function value of Higher than Join the New Charging Alliance Leaving the old charging alliance Then update the history set and charging pile index α i =j * ;

[0053] (7.7) Repeat step (7.6) until all electric taxis do not change the charging alliance they join;

[0054] (7.8) Output the electric taxi charging alliance partition Γ.

[0055] According to another aspect of the present invention, the present invention also provides an electric taxi charging pile rental system based on alliance game, the system comprising:

[0056] A model building module is used to determine the total charging cost and the shared cost according to the completion time and rental time of the electric taxi's charging task; and to build an electric taxi charging cost optimization model under the constraints of the maximum rental time of the charging pile and the mobile energy consumption of the electric taxi;

[0057] A first determination module is used to construct an alliance game model, an electric taxi marginal utility function, a preference order and an alliance order according to the charging cost optimization model; the preference order indicates the charging alliance that the electric taxi tends to join; the alliance order indicates the charging alliance that the electric taxi tends to choose; and determine a first charging allocation strategy for the electric taxi according to the marginal utility function and the alliance preference order to optimize the total charging cost;

[0058] The second determination module is used to construct a selfish utility function and a selfish preference order for the electric taxi, wherein the selfish preference order is used for the electric taxi to quantify its preference; and determine a second charging allocation strategy for the electric taxi according to the selfish utility function and the selfish preference order to achieve Nash equilibrium.

[0059] According to another aspect of the present invention, the present invention also provides an electronic device, which includes: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, and the computer-executable instructions can implement the above-mentioned method steps when executed by the processor.

[0060] According to another aspect of the present invention, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions implement the above method steps when executed by a processor.

[0061] Beneficial effects: The present invention saves the construction cost of charging piles, and while meeting the charging needs of electric taxis, determines the charging pile leasing and charging allocation strategy under the constraint of the maximum leasing time of the charging piles, thereby optimizing the charging cost.

[0062] The features and advantages of the present invention will become clear through reference to the following drawings and detailed description of specific embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of the electric taxi charging pile leasing method based on alliance game;

[0064] Figure 2 It is a schematic diagram of the electric taxi charging pile rental system;

[0065] Figure 3 It is a flow chart of the charging allocation method based on alliance game;

[0066] Figure 4 It is a flow chart of the charging allocation method for selfish behavior;

[0067] Figure 5 This is a schematic diagram of the electric taxi charging pile rental system based on alliance game. DETAILED DESCRIPTION

[0068] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] Example 1

[0070] Figure 1 This is a flow chart of the electric taxi charging pile rental method based on alliance game. Figure 1 As shown, this embodiment provides an electric taxi charging pile rental method based on alliance game, and the method includes the following steps:

[0071] S1: Determine the total charging cost and the shared cost based on the charging task completion time and rental time of the electric taxi; construct an electric taxi charging cost optimization model under the constraints of the maximum rental time of the charging pile and the mobile energy consumption of the electric taxi.

[0072] S2: According to the charging cost optimization model, a coalition game model, an electric taxi marginal utility function, a preference order and a coalition order are constructed; the preference order indicates the charging coalition that the electric taxi tends to join; the coalition order indicates the charging coalition that the electric taxi tends to choose; according to the marginal utility function and the coalition preference order, a first charging allocation strategy for the electric taxi is determined to optimize the total charging cost;

[0073] S3: constructing a selfish utility function and a selfish preference order for electric taxis, wherein the selfish preference order is used for the electric taxis to quantify their preferences; and determining a second charging allocation strategy for the electric taxis according to the selfish utility function and the selfish preference order to achieve Nash equilibrium.

[0074] This method determines the charging pile leasing and charging allocation strategy under the constraint of the maximum leasing time of the charging pile, thereby optimizing the charging cost.

[0075] Preferably, the method of determining the total charging cost and the shared cost according to the completion time of the charging task and the rental time of the electric taxi includes:

[0076] Charging stations j The total charging cost is:

[0077]

[0078] Electric taxi v i At the charging station j The charging cost to be shared after completing the charging is:

[0079]

[0080] Among them, c j For charging piles j The total cost on j (·) is a monotonically increasing concave function that characterizes the length of the rental period; For charging piles j The rental period; For charging piles j The base rental price per rental period, For charging piles j The unit electricity price of Q j For charging piles j Total charge on Indicates electric taxi v i At the charging station j The rental time on For the charging task τ i At the charging station j Charging completion time on For electric taxis i The charging demand, d i，j Is an electric taxi v i To charging stations j The shortest distance, β i Is an electric taxi v i Unit moving energy consumption.

[0081] Specifically, let V = {v1, v2, ..., v m} represents the set of electric taxis, N = {s1, s2, ..., s n} indicates a collection of public charging stations that can be rented;

[0082] Electric taxi v i ∈V submits the charging task to the charging pile rental platform as: in, For electric taxis i location, For electric taxis i Charging demand, Ei For electric taxis i Battery capacity;

[0083] Charging stations j ∈N submits its own information to the rental platform as follows: in, For charging piles j The base rental price per rental period, For charging piles j The unit electricity price, For charging piles j location, For charging piles j The maximum lease time.

[0084] definition For the charging task τ i The arrival time of the electric taxi v i Arrival charging station j Moving time:

[0085]

[0086] Among them, d i，j Is an electric taxi v i To charging stations j The shortest distance, r i Is an electric taxi v i The average moving speed of For the charging task τ i At the charging station j Actual charging time:

[0087]

[0088] in, is the charging task τ i The actual charge capacity, β i Is an electric taxi v i Unit moving energy consumption, d i，j Is an electric taxi v i To charging stations j The shortest distance, η j It is a charging station j Constant charging power;

[0089] make Indicates charging piles j The charging task τ with charging order q i , then the charging pile s j The charging completion time of the qth charging task in can be calculated as follows:

[0090]

[0091] in, It is a charging station j The completion time of the non-taxi charging task being executed, that is, the earliest charging start time for the electric taxi charging task to start execution, For charging piles j The charging completion time of the q-1th charging task;

[0092] Charging stations j Rental time The maximum completion time of the charging task assigned to the charging pile is expressed as:

[0093]

[0094] Among them, x i，j For the charging task τ i The binary decision variables assigned, For the charging task τ i At the charging station j The charging completion time, T j To allocate charging piles j A collection of charging tasks, For charging piles j Middle | T j |Charging completion time of a charging task;

[0095] Definition of charging piles j The electricity cost is Where Q j For charging piles j Total charge on:

[0096]

[0097] Where T is the charging task set;

[0098] Definition of charging piles j The total charging cost is:

[0099]

[0100] Among them, h j (·) is a monotonically increasing concave function that characterizes the length of the rental period, c j For charging piles j Total cost on

[0101] make Indicates electric taxi v i At the charging station jThe rental time on the electric taxi v i Rental time The electric taxi is charged at the charging station j The difference between the charging completion time of the previous electric taxi and the charging completion time of the previous electric taxi. Assuming that the electric taxi v i At the charging station j The charging order is q, so combined with the charging completion time formula (3), It can be calculated as follows:

[0102]

[0103] Among them, q is the electric taxi v i The charging sequence is: For charging piles j The electric taxi v with charging order q i The charging task, For charging piles j The electric taxi v with charging order q-1 i′ The charging task is the charging task of the previous electric taxi;

[0104] Definition of electric taxi i At the charging station j The charging cost to be shared after completing the charging is:

[0105]

[0106] in, is the electricity cost that electric taxis should pay, T j The charging pile s j A collection of charging tasks, For charging piles j The rental time, The cost of renting charging piles should be shared by electric taxis.

[0107] Preferably, constructing an electric taxi charging cost optimization model under the constraints of the maximum rental time of the charging pile and the mobile energy consumption of the electric taxi includes:

[0108] The problem of minimizing the charging cost of electric taxis under the constraints of the maximum rental time of charging piles and the mobile energy consumption of electric taxis is formalized as follows:

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] in, For charging piles j The maximum rental time, E i For electric taxis i The battery capacity, x i，j For the charging task τ i Assigned binary decision variables.

[0115] Specifically, the problem of minimizing the charging cost of electric taxis under the constraints of the maximum rental time of charging piles and the mobile energy consumption of electric taxis is formalized as follows:

[0116]

[0117]

[0118]

[0119]

[0120]

[0121] Among them, constraint (10) ensures that the rental time of any charging pile cannot exceed its maximum rental time. Constraint (11) ensures that any electric taxi has sufficient power to reach the assigned charging pile. Constraint (12) ensures that each charging task can be assigned to only one charging pile.

[0122] Preferably, the construction of the alliance game model, the electric taxi marginal utility function, the preference order and the alliance order according to the charging cost optimization model includes:

[0123] Using marginal utility to measure the strategy of electric taxi users i For the corresponding charging alliance The impact is as follows:

[0124]

[0125] where τ i For electric taxis i The corresponding charging task, For charging pile The total charging cost, a -i In addition to electric taxis i The charging distribution strategy set of other electric taxis, Indicates electric taxi v i Charging alliance of choice;

[0126] Definition> i For electric taxis i The preference order of any electric taxi v i and any two charging alliances and The order of the charging alliance is:

[0127]

[0128] Specifically, the problem of minimizing the charging cost of electric taxis is restructured into a coalition formation game G = {V, U i , A i , Γ}, where V is the set of players in the game, i.e., the electric taxis, U i Is an electric taxi v i The utility function of A i Indicates electric taxi v i Strategy space. For any electric taxi v i ∈V, and define its charging allocation strategy as a i , the charging piles allocated by this strategy are defined as The corresponding charging alliance is the charging pile The charging task set on is defined as Define the strategy set of all electric taxis as a = {a1, a2, ..., a m}, the disjoint charging alliance partition is defined as Γ = {T1, T2, ..., T n};

[0129] Using marginal utility to measure the strategy of electric taxi users i For the corresponding charging alliance The impact is specifically defined as:

[0130]

[0131] where τ i For electric taxis i The corresponding charging task, For charging pile The total charging cost, a -i In addition to electric taxis i The charging allocation strategy set of other electric taxis is a -i ={a1, a2, ..., a i-1 , a i+1 , ..., a n}, Indicates electric taxi v iThe selected charging alliance should be noted that at this time, the electric taxi v i The corresponding charging task τ i Not in In In combination with formulas (6), (8), and (14), the utility function of electric taxis can be restated as:

[0132]

[0133] Among them, electric taxis k At the charging station Charging completion time and rental time It needs to be calculated according to formulas (3) and (7);

[0134] This utility function represents the electric taxi v i Join the Charging Alliance After the charging cost changes, given the charging allocation strategy set of other electric taxis, the electric taxi v i They always tend to join a charging alliance that minimizes the sum of their own charging costs and the total reduction of charging costs of alliance members;

[0135] Definition> i For electric taxis i The preference order of electric taxi v i has a complete, reflexive, and transitive binary relation on the set of all possible charging alliances it can form. Given an electric taxi v i Two charging alliances and Indicates electric taxi v i More inclined to join the charging alliance Rather than The choice of an electric taxi to join a charging alliance depends on its preference order for the charging alliance, which will affect the final alliance convergence. In addition, to meet the problem constraints, it is necessary to ensure that the electric taxi v i The strategy space A i Any charging allocation strategy a i satisfy and in Ensure electric taxi v i The remaining battery capacity is enough to reach the charging alliance Corresponding charging pile Ensure that the charging station Maximum rental time Can complete the additional charging task within τi The Charging Alliance All charging tasks in; among them, For electric taxis k At the charging station Charging completion time, For electric taxi electric taxi v i To the charging station The shortest distance;

[0136] For any electric taxi v i and any two charging alliances and The order of defining the charging alliance is:

[0137]

[0138] The alliance order indicates that electric taxis are more inclined to choose the charging alliance with the smallest sum of its own charging cost and the total reduction of the charging costs of alliance members as its own strategic choice.

[0139] Preferably, determining the first charging allocation strategy of the electric taxi according to the marginal utility function and the alliance preference order to optimize the total charging cost comprises:

[0140] (5.1) Initialize the electric taxi charging alliance division

[0141] (5.2) For any charging pile s j ∈N, initialize the charging task set And update the alliance partition Γ = Γ ∪ T j ;

[0142] (5.3) For any charging task τ i ∈T, construct its feasible strategy space A i ;

[0143] (5.4) Any electric taxi v i ∈V from the feasible strategy space A i Randomly select a strategy as the current charging allocation strategy a i , and update the corresponding charging alliance

[0144] (5.5) For any charging task τ i ∈T, reconstruct its feasible strategy space A i ;

[0145] (5.6) Select strategy space A i The strategy with the greatest utility in If you join the new charging alliance, you will get the utility Ui (a′ i , a -i ) is greater than the utility U obtained by the old charging alliance i (a i , a -i ), update the new charging alliance Old Charging Alliance and the current charging allocation strategy a i =a′ i ;

[0146] (5.7) Repeat steps (5.5) and (5.6) until the charging allocation strategy of all electric taxis remains unchanged;

[0147] (5.8) Output the electric taxi charging alliance division Γ.

[0148] Specifically, see Figure 3 , step (5.3) or (5.5) constructs its feasible strategy space, including the following steps:

[0149] (5.3.1) Initialize strategy space

[0150] (5.3.2) For any charging pile s j ∈N, if the electric taxi v i The corresponding charging task τ i Able to reach charging stations j And at the charging station j Maximum rental time Can complete the additional charging task within τ i The charging alliance T j ∪{τ i}, then the charging pile s j Add to Electric Taxi i The feasible strategy space A i ;

[0151] (5.3.3) Output strategy space A i .

[0152] Preferably, the constructing of the selfish utility function and selfish preference order of electric taxis comprises:

[0153] Electric taxi v i At the charging station j Selfish charging utility U i (T j ):

[0154]

[0155] Defining selfish preference relations based on the selfish charging utility of electric taxis> i :

[0156]

[0157] Preference function R i (T j ) Calculation of the selfish charging utility of electric taxis:

[0158]

[0159] Where H(i) represents the electric taxi v i A historical set that records the charging alliances that have been joined before, and || means "or".

[0160] Specifically, let U i (T j ) is an electric taxi v i At the charging station j Selfish charging utility for completing charging:

[0161]

[0162] Where T j For charging piles j The corresponding charging alliance, P i (T j ) is an electric taxi v i At the charging station j The charging cost to be shared after the charging is completed;

[0163] Electric taxi v i ∈V needs to establish preferences among the charging alliances it may join and sort the charging alliances according to its preferences. The selfish preference relationship is defined based on the selfish charging utility of electric taxis. i :

[0164]

[0165] Where T j and T j′ For electric taxis i Possible charging alliances that meet τ i ∈T j and τ i ∈T j′ . R i (T j ) is an electric taxi v i About Charging Alliance j Preference function, selfish preference relation> i Allow electric taxis iQuantify their preferences. For example, for an electric taxi v i The charging task τ i , given two charging alliances T j and T j′ , T j > i T j′ Indicates that electric taxis are more inclined to join the charging alliance T j Instead of T j′ ;

[0166] Preference function R i (T j ) is based on the selfish charging utility calculation of electric taxis:

[0167]

[0168] Among them U i (T j ) indicates Charging Alliance T j Electric taxi v i The selfish charging utility of H(i) represents the electric taxi v i The historical set records the charging alliances that have been joined before. Setting the preference function value to negative infinity helps reduce the complexity of the algorithm; Indicates electric taxi v i The remaining battery capacity is not enough to reach the charging pile s corresponding to the charging alliance j , Indicates that in electric taxi v i Join the Charging Alliance j Then at the charging station j All charging tasks in the charging alliance cannot be completed within the maximum rental time, and its preference function value is set to negative infinity to ensure that the problem constraints are met. It should be noted that at this time, the electric taxi v i The charging task is in the charging alliance T j Based on the above selfish preference relationship, electric taxis will join the charging alliance that maximizes their selfish charging utility without considering the impact of their decision on other electric taxis, reflecting the selfish behavior of electric taxis.

[0169] Preferably, determining the second charging allocation strategy of the electric taxi according to the selfish utility function and the selfish preference order to achieve Nash equilibrium comprises:

[0170] (7.1) Initialize the electric taxi charging alliance division

[0171] (7.2) For any charging station s j ∈N, initialize the charging task set And update the alliance partition Γ = Γ ∪ T j ;

[0172] (7.3) For any electric taxi charging task τ i ∈T, initialize its electric taxi v i Possible charging alliance collection History Collection and charging pile index α i =0;

[0173] (7.4) For any charging station s j ∈N, if the electric taxi v i The remaining battery capacity is enough to reach the charging station j , and in electric taxis v i Join the Charging Alliance j Then at the charging station j If all charging tasks in the alliance can be completed within the maximum rental time, the charging alliance set A that it may join will be updated. i =A i ∪{s j};

[0174] (7.5) Electric taxis i From A i Randomly select a charging station s j , and update the corresponding charging alliance T j =T j ∪{τ i}, history set H(i)=H(i)∪{T j} and charging pile index α i =j;

[0175] (7.6) For any electric taxi charging task τ i ∈T, based on the preference function R i (T j ) Calculate the charging pile with the greatest utility and New Charging Alliance If the electric taxi v i The preference function value of Higher than Join the New Charging Alliance Leaving the old charging alliance Then update the history set and charging pile index α i =j * ;

[0176] (7.7) Repeat step (7.6) until all electric taxis do not change the charging alliance they join;

[0177] (7.8) Output the electric taxi charging alliance partition Γ.

[0178] Specifically, the implementation process of the above steps can be found in Figure 4 .

[0179] This embodiment saves the construction cost of charging piles, and while meeting the charging needs of electric taxis, determines the charging pile leasing and charging allocation strategy under the constraint of the maximum leasing time of the charging piles, thereby optimizing the charging cost.

[0180] In order to more fully describe the technical solution of the present invention, a specific example is given below.

[0181] Assume that V = {v1, v2, v3, v4, v5, v6} represents the set of electric taxis, and the set of charging piles is N = {s1, s2, s3, s4}.

[0182] Electric taxi v i Submit charging tasks to the charging pile rental platform The locations of the electric taxis are (114.14338°E, 22.604673°N), (114.122925°E, 22.540268°N), (114.1108°E, 22.53801°N), (114.1398°E, 22.578032°N), (114.14538°E, 22.59166°N), (114.19243°E, 22.556576°N); the battery capacities are 65, 75, 75, 80, 85, and 70 kWh, respectively. The charging requirement of the electric taxis is 0.8*E i The energy consumption of electric taxis is 0.15, 0.16, 0.17, 0.2, 0.25, and 0.18 kWh / km, respectively, and the average moving speed is 39, 39, 42, 45, 48, and 51 km / h, respectively. j Submit your information to the rental platform The base rental prices per unit rental time are 0.3, 0.4, 0.5, and 0.6 yuan / minute, the unit electricity prices are 0.8, 0.9, 0.85, and 1.0 yuan / kWh, the maximum rental time is 60, 70, 80, and 75 minutes, and the locations of the charging piles are (114.109572°E, 22.556972°N), (114.160456°E, 22.558205°N), (114.127742°E, 22.569581′N), and (114.117972′E, 22.534666°N); the charging power of the charging piles is 120, 140, 130, and 140 kilowatts, and the completion time of the non-taxi charging tasks being executed is 3, 5, 2, and 5 minutes. The discount function h that characterizes the rental time scale j (t) = t a , where t is the charging pile rental time, and a represents the discount coefficient, which are 0.98, 0.95, 0.97, and 0.95 respectively.

[0183] In this embodiment, the electric taxi charging allocation strategy is determined according to the marginal utility function and the alliance preference order described above to optimize the total charging cost, including:

[0184] (5.1) Initialize the electric taxi charging alliance division

[0185] (5.2) For any charging pile s j ∈N, initialize the charging task set And update the alliance partition Γ = Γ ∪ T j ;

[0186] (5.3) For any charging task τ i ∈T, construct its feasible strategy space A i ;

[0187] (5.3.1) Select charging task τ5 and initialize its strategy space

[0188] (5.3.2) For any charging pile s j ∈N, if the charging task τ5 corresponding to the electric taxi v5 can reach the charging pile s j And charging piles j Maximum rental time Can complete the additional charging task within τ i The charging alliance T j ∪{τ i}, then the charging pile s j Added to the feasible strategy space A5 of electric taxi v5;

[0189] (5.3.3) Output strategy space A5;

[0190] (5.4) Electric taxi v5 randomly selects a strategy from the feasible strategy space A5 as the current charging allocation strategy a5=s1, and updates the corresponding charging alliance T1=T1∪{τ5};

[0191] Executing (5.3.1) to (5.4) for the remaining charging tasks, we can obtain T1 = {τ5}, T2 = {τ2}, T3 = {τ1, τ6}, T4 = {τ4, τ3}.

[0192] (5.5) For any charging task τ i ∈T, reconstruct its feasible strategy space A i ;

[0193] (5.6) Select the charging task τ4 and choose the strategy with the maximum utility in its strategy space A4 Because the utility U4(a′4, a -4 ) is greater than the utility U4(a4, a -4 ), update the new charging alliance Old Charging Alliance and the current charging allocation strategy a4=a′4=s2;

[0194] For the remaining charging tasks, executing (5.6) we can obtain T1 = {τ5}, T2 = {τ2, τ4}, T3 = {τ1, τ6}, T4 = {τ3}.

[0195] (5.7) Repeat steps (5.5) and (5.6) until the charging allocation strategy of all electric taxis remains unchanged, and finally obtain T1 = {τ5}, T2 = {τ2, τ4}, T3 = {τ1, τ6}, T4 = {τ3};

[0196] (5.8) Output the electric taxi charging alliance division Γ = {T1, T2, T3, T4}, corresponding to the total charging cost: 393.32.

[0197] Furthermore, in this embodiment, the charging allocation strategy of the electric taxi is determined according to the selfish utility function and the selfish preference order to achieve Nash equilibrium, including:

[0198] (7.1) Initialize the electric taxi charging alliance division

[0199] (7.2) For any charging station s j ∈N, initialize the charging task set And update the alliance partition Γ = Γ ∪ T j ;

[0200] (7.3) For any electric taxi charging task τ i ∈T, initialize its electric taxi v i Possible charging alliance collection History Collection and charging pile index α i =0;

[0201] (7.4) Select the electric taxi charging task τ5, for any charging pile s j ∈N, if the remaining battery capacity of the electric taxi v5 is sufficient to reach the charging station s j , and joined the charging alliance T in the electric taxi v5 j Then at the charging station j If the maximum rental time is sufficient to complete all charging tasks in the alliance, then the charging alliance set that it may join is updated A5 = A5∪{s j};

[0202] (7.5) Electric taxi v5 randomly selects a charging pile s3 from A5 and updates the corresponding charging alliance T3 = T3∪{τ5}, history set H(5) = H(5)∪{T3} and charging pile index α5 = 3;

[0203] For the remaining charging tasks, perform the above steps (7.4) to (7.5) to obtain T2={τ1, τ4}, T3={τ5, τ6}, T4={τ2, τ3}.

[0204] (7.6) For any electric taxi charging task τ i ∈T, based on the preference function R i (T j ) Calculate the charging pile with the greatest utility and New Charging Alliance If the electric taxi v i The preference function value of Higher than Join the New Charging Alliance Leaving the old charging alliance Then update the history set and charging pile index α i =j * ;

[0205] Select the electric taxi charging task τ5 and calculate its charging pile with the maximum utility and New Charging Alliance Because the preference function value of electric taxi v5 Higher than Join the New Charging Alliance Leaving the old charging alliance Then update the history set and charging pile index

[0206] For the remaining charging tasks, executing (7.6) we can obtain T1 = {τ5}, T2 = {τ1, τ4}, T3 = {τ2, τ6}, T4 = {τ3}.

[0207] (7.7) Repeat step (7.6) until all electric taxis do not change the charging alliance they join, and finally obtain T1 = {τ5}, T2 = {τ1, τ4}, T3 = {τ2, τ6}, T4 = {τ3};

[0208] (7.8) Output the electric taxi charging alliance division Γ = {T1, T2, T3, T4}, corresponding to a total charging cost of 392.84.

[0209] Example 2

[0210] Figure 5 This is a schematic diagram of the electric taxi charging pile rental system based on alliance game. Figure 5 As shown, this embodiment provides an electric taxi charging pile rental system based on alliance game, and the system includes:

[0211] Model building module 501, used to determine the total charging cost and the shared cost according to the charging task completion time and the rental time of the electric taxi; build an electric taxi charging cost optimization model under the constraints of the maximum rental time of the charging pile and the mobile energy consumption of the electric taxi;

[0212] The first determination module 502 is used to construct an alliance game model, an electric taxi marginal utility function, a preference order and an alliance order according to the charging cost optimization model; the preference order indicates the charging alliance that the electric taxi tends to join; the alliance order indicates the charging alliance that the electric taxi tends to choose; and determine the first charging allocation strategy of the electric taxi according to the marginal utility function and the alliance preference order to optimize the total charging cost;

[0213] The second determination module 503 is used to construct a selfish utility function and a selfish preference order for the electric taxi, wherein the selfish preference order is used for the electric taxi to quantify its preference; and determine a second charging allocation strategy for the electric taxi according to the selfish utility function and the selfish preference order to achieve Nash equilibrium.

[0214] The specific implementation process of the functions realized by each module in this embodiment 2 is the same as the implementation process of each step in embodiment 1, and will not be repeated here.

[0215] Example 3

[0216] This embodiment provides an electronic device, which includes: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the method steps in Embodiment 1 are implemented.

[0217] The specific implementation process of this embodiment can refer to the implementation process of the method steps in Example 1, which will not be repeated here.

[0218] Example 4

[0219] This embodiment provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the method steps in Embodiment 1 are implemented.

[0220] The specific implementation process of this embodiment can refer to the implementation process of the method steps in Example 1, which will not be repeated here.

[0221] It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0222] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0223] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0224] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for renting electric taxi charging piles based on alliance game, characterized in that: The method comprises the following steps: Determine the total charging cost and the shared cost based on the completion time and rental time of the electric taxi’s charging task; construct an electric taxi charging cost optimization model under the constraints of the maximum rental time of the charging pile and the mobile energy consumption of the electric taxi; According to the charging cost optimization model, an alliance game model, an electric taxi marginal utility function, a preference order and an alliance order are constructed; the preference order indicates the charging alliance that the electric taxi tends to join; the alliance order indicates the charging alliance that the electric taxi tends to choose; according to the marginal utility function and the alliance preference order, a first charging allocation strategy for the electric taxi is determined to optimize the total charging cost; Constructing a selfish utility function and a selfish preference order of an electric taxi, wherein the selfish preference order is used for the electric taxi to quantify its preference; determining a second charging allocation strategy of the electric taxi according to the selfish utility function and the selfish preference order to achieve Nash equilibrium; The construction of the alliance game model, the electric taxi marginal utility function, the preference order and the alliance order according to the charging cost optimization model includes: Using marginal utility to measure the strategy of electric taxi users i For the corresponding charging alliance The impact is as follows: where τ i For electric taxis i The corresponding charging task, For charging pile The total charging cost, a -i In addition to electric taxis i The charging distribution strategy set of other electric taxis, Indicates electric taxi v i Charging alliance of choice; Definition> i For electric taxis i The preference order of any electric taxi v i and any two charging alliances and The order of the charging alliance is:

2. The method according to claim 1, characterized in that Determining the total charging cost and the shared cost according to the completion time of the charging task and the rental time of the electric taxi includes: Charging stations j The total charging cost is: Electric taxi v i At the charging station j The charging cost to be shared after completing the charging is: Among them, c j For charging piles j The total cost on j (·) is a monotonically increasing concave function that characterizes the length of the rental period; For charging piles j The rental period; For charging piles j The base rental price per rental time. For charging piles j The unit electricity price of Q j For charging piles j Total charge on Indicates electric taxi v i At the charging station j The rental time on For the charging task τ i At the charging station j Charging completion time on For electric taxis i The charging demand, d i,j Is an electric taxi v i To charging stations j The shortest distance, β i Is an electric taxi v i Unit moving energy consumption.

3. The method according to claim 2, characterized in that The construction of an electric taxi charging cost optimization model under the constraints of the maximum rental time of the charging pile and the mobile energy consumption of the electric taxi includes: The problem of minimizing the charging cost of electric taxis under the constraints of the maximum rental time of charging piles and the mobile energy consumption of electric taxis is formalized as follows: in, For charging piles j The maximum rental time, E i For electric taxis i Battery capacity, x i,j For the charging task τ i Assigned binary decision variables.

4. The method according to claim 1, characterized in that: Determining the first charging allocation strategy of the electric taxi according to the marginal utility function and the alliance preference order to optimize the total charging cost includes: (5.1) Initialize the electric taxi charging alliance division (5.2) For any charging pile s j ∈N, initialize the charging task set And update the alliance partition Γ = Γ ∪ T j ; (5.3) For any charging task τ i ∈T, construct its feasible strategy space A i ; (5.4) Any electric taxi v i ∈V from the feasible strategy space A i Randomly select a strategy as the current charging allocation strategy a i , and update the corresponding charging alliance (5.5) For any charging task τ i ∈T, reconstruct its feasible strategy space A i ; (5.6) Select strategy space A i The strategy with the greatest utility in If you join the new charging alliance, you will get the utility U i (a′ i ,a -i ) is greater than the utility U obtained by the old charging alliance i (a i ,a -i ), update the new charging alliance Old Charging Alliance and the current charging allocation strategy a i =a′ i ; (5.7) Repeat steps (5.5) and (5.6) until the charging allocation strategy of all electric taxis remains unchanged; (5.8) Output the electric taxi charging alliance division Γ.

5. The method according to claim 4, characterized in that The construction of the selfish utility function and selfish preference order of the electric taxi includes: Electric taxi v i At the charging station j The selfish charging utility U i (T j ): Defining selfish preference relations based on the selfish charging utility of electric taxis> i : Preference function R i (T j ) Calculation of the selfish charging utility of electric taxis: Where H(i) represents the electric taxi v i The historical set records the charging alliances that have been joined before, and || means "or".

6. The method according to claim 5, characterized in that Determining the second charging allocation strategy of the electric taxi according to the selfish utility function and the selfish preference order to achieve Nash equilibrium includes: (7.1) Initialize the electric taxi charging alliance division (7.2) For any charging station s j ∈N, initialize the charging task set And update the alliance partition Γ = Γ ∪ T j ; (7.3) For any electric taxi charging task τ i ∈T, initialize its electric taxi v i Possible charging alliance collection History Collection and charging pile index α i =0; (7.4) For any charging station s j ∈N, if the electric taxi v i The remaining battery capacity is enough to reach the charging station j , and in electric taxis v i Join the Charging Alliance j Then at the charging station j If all charging tasks in the alliance can be completed within the maximum rental time, the charging alliance set A that it may join will be updated. i =A i ∪{s j }; (7.5) Electric taxis i From A i Randomly select a charging station s j , and update the corresponding charging alliance T j =T j U{τ i }, history set H(i)=H(i)∪{T j } and charging pile index α i =j; (7.6) For any electric taxi charging task τ i ∈T, based on the preference function R i (T j ) Calculate the charging pile with the greatest utility and New Charging Alliance If the electric taxi v i The preference function value of Higher than Join the New Charging Alliance Leaving the old charging alliance Then update the history set and charging pile index α i =j * ; (7.7) Repeat step (7.6) until all electric taxis do not change the charging alliance they join; (7.8) Output the electric taxi charging alliance partition Γ.

7. An electric taxi charging pile rental system based on alliance game, characterized in that: The system comprises: A model building module is used to determine the total charging cost and the shared cost according to the completion time and rental time of the electric taxi's charging task; and to build an electric taxi charging cost optimization model under the constraints of the maximum rental time of the charging pile and the mobile energy consumption of the electric taxi; A first determination module is used to construct an alliance game model, an electric taxi marginal utility function, a preference order and an alliance order according to the charging cost optimization model; the preference order indicates the charging alliance that the electric taxi tends to join; the alliance order indicates the charging alliance that the electric taxi tends to choose; and determine a first charging allocation strategy for the electric taxi according to the marginal utility function and the alliance preference order to optimize the total charging cost; A second determination module is used to construct a selfish utility function and a selfish preference order of the electric taxi, wherein the selfish preference order is used for the electric taxi to quantify its preference; and to determine a second charging allocation strategy of the electric taxi according to the selfish utility function and the selfish preference order to achieve Nash equilibrium; According to the charging cost optimization model, the alliance game model, the electric taxi marginal utility function, the preference order and the alliance order are constructed including: Using marginal utility to measure the strategy of electric taxi users i For the corresponding charging alliance The impact is as follows: where τ i For electric taxis i The corresponding charging task, For charging pile The total charging cost, a -i In addition to electric taxis i The charging distribution strategy set of other electric taxis, Indicates electric taxi v i Charging alliance of choice; Definition> i For electric taxis i The preference order of any electric taxi v i and any two charging alliances and The order of the charging alliance is:

8. An electronic device, characterized in that: The electronic device comprises: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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