CN118552227A - A method, system and storage medium for optimizing charging cost of electric taxis based on alliance game - Google Patents

Abstract

The present invention relates to an electric taxi charging cost optimization method, system and storage medium 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 charging pile rental power capacity and electric taxi mobile energy consumption; 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 charging piles, gives priority to meeting the charging needs of electric taxis, and optimizes the charging cost under the constraints of charging pile rental power.

Description

A method, system and storage medium for optimizing charging cost of electric taxis based on alliance game

Technical Field

The present invention relates to the technical field of electric vehicle charging, and in particular to an electric taxi charging cost optimization method, system and storage medium based on alliance game.

Background Art

The automobile industry is one of the most important industries in the world. As an important means of transportation, the popularity and development of automobiles have brought great convenience to people's lives. It is estimated that the number of cars in the world has exceeded one billion, and this number is still growing rapidly. This rapid growth trend not only reflects the vigorous development of the automobile industry, but also shows the popularity of cars as an important means of transportation. The large-scale popularization of cars has improved people's travel efficiency. However, the surge in the number of cars has also led to a sharp increase in urban air pollution levels. In order to alleviate air pollution and reduce the emission of harmful gases such as carbon monoxide and carbon dioxide, most countries encourage the development and use of electric vehicles.

In response to the challenges of global climate change and urban traffic pollution, public transport electrification is seen as an important solution. Public transport electrification replaces fuel vehicles with electric vehicles, which can significantly reduce vehicle emissions, improve urban air quality, and enhance residents' travel experience. It is one of the key measures to achieve clean and sustainable urban transportation. The promotion and application of electric taxis play an important role in the process of electrification of contemporary urban public transportation. However, with the increase in the number of household electric vehicles, their charging demand is also increasing. Especially during peak hours, 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. 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 cost of charging stations is high, and due to the high mobility and unpredictable charging demand of electric taxis, the construction of a large number of electric taxi charging stations may have problems such as low charging station 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 and other methods. However, there is currently no solution to the charging cost optimization of electric taxi charging pile rental mode from a game perspective.

Summary of the invention

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 cost optimization method, system and storage medium based on alliance game, which saves the construction cost of charging piles, gives priority to meeting the charging needs of electric taxis, and optimizes the charging cost under the constraint of charging pile rental electricity.

According to one aspect of the present invention, the present invention provides an electric taxi charging cost optimization method based on alliance game, the method comprising the following steps:

Determine the total charging cost and the shared cost based on the number of electric taxi charging tasks; construct an optimization model for the charging cost of electric taxis under the constraints of the charging pile rental power capacity and the mobile energy consumption of electric taxis;

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;

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.

Preferably, determining the total charging cost and the shared cost according to the number of electric taxi charging tasks includes:

The total charging cost of charging pile _sj is:

The charging cost that electric taxi v _i should share when charging on charging pile s _j is:

Where f _j () is a monotonically increasing concave function that characterizes the rental scale, T _j = {τ _i {xi _{, j} = 1} represents the set of charging tasks assigned to the charging pile s _j , where _{xi, j} is a binary decision variable for charging task assignment; |T _j | represents the size of T _j ; is the unit electricity price of charging pile _sj , is the base rental price of charging pile _sj , _Qj is the total charge capacity on charging pile _sj ; is the charging demand of electric taxi _vi , _βi is the unit moving energy consumption of electric taxi _vi , and d _i,j is the shortest distance from electric taxi _vi to charging station s _j .

Preferably, the construction of an electric taxi charging cost optimization model under the constraints of charging pile rental power capacity and electric taxi mobility energy consumption includes:

The problem of minimizing the charging cost of electric taxis under the constraints of charging pile rental power capacity and electric taxi mobility energy consumption is formalized as follows:

in, is the leased power capacity of the charging pile _sj , _Ei is the battery capacity of the electric taxi _vi , N = { _s1 , _s2 , ..., _sn } represents the set of leased public charging piles, and T represents the set of charging tasks _τi .

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:

Using marginal utility to measure the strategy of electric taxi users a _i for the corresponding charging alliance The impact is as follows:

Where τ _i is the charging task corresponding to the electric taxi _vi , For charging pile The total charging cost of a _-i is the set of charging allocation strategies for other electric taxis except the electric taxi _vi . Indicates the charging alliance chosen _by the electric taxi vi;

definition is the preference order of electric taxi v _i , for any electric taxi v _i and any two charging alliances Know The order of the charging alliance is:

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

(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 randomly selects a strategy from the feasible strategy space A _i as the current charging allocation strategy a _i and updates the corresponding charging alliance

(5.5) For any charging task τ _i ∈T, reconstruct its feasible strategy space A _i :

(5.6) Select the strategy with the maximum utility in the strategy space _Ai If the utility U _i (a′ _i , a _-i ) gained by joining the new charging alliance is greater than the utility U _i (a _i , a _-i ) gained by joining the old charging alliance, 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.

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

The selfish charging utility U _i (T _j ) of an electric taxi v _i completing charging at a charging pile s _j is:

U _i (T _j )＝-P _i (T _j )

Defining selfish preference relations based on the selfish charging utility of electric taxis

The preference function R _i (T _j ) is calculated based on the selfish charging utility of electric taxis:

Among them, H(i) represents the historical set of electric taxi _vi , recording the charging alliances that have been joined before, _Tj = { _τi | _xi,j = 1} represents the set of charging tasks assigned to charging pile _sj , wherexi _,j is the binary decision variable for charging task assignment; || means "or".

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:

(7.1) Initialize the electric taxi charging alliance division

(7.2) For any charging pile 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 the set of charging alliances that its electric taxi v _i may join History Collection and charging pile index α _i =0;

(7.4) For any charging pile s _j ∈ N, if the remaining battery capacity of the electric taxi _vi is sufficient to reach the charging pile s _j , and after the electric taxi _vi joins the charging alliance T _j , the rented power capacity of the charging pile s _j is sufficient to complete all charging tasks in the alliance, then the set of charging alliances that it may join is updated to A _i =A _i ∪{s _j };

(7.5) The electric taxi v _i randomly selects a charging pile s _j from A _i and updates the corresponding charging alliance T _j = T _j ∪ {τ _i }, the history set H (i) = H (i) ∪ {T _j } and the charging pile index α _i = j;

(7.6) For any electric taxi charging task τ _i ∈ T, the charging pile with the maximum utility is calculated based on the preference function R _i (T _j ) and New Charging Alliance If the preference function value of electric taxi v _i 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.

According to another aspect of the present invention, the present invention also provides an electric taxi charging cost optimization system based on alliance game, the system comprising:

A model building module is used to determine the total charging cost and the shared cost according to the number of electric taxi charging tasks; to build an electric taxi charging cost optimization model under the constraints of charging pile rental power capacity and electric taxi mobility energy consumption;

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;

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.

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.

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.

Beneficial effects: The present invention saves the construction cost of charging piles, gives priority to meeting the charging needs of electric taxis, and optimizes the charging cost under the constraints of charging pile rental electricity.

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

FIG1 is a flow chart of an optimization method for charging costs of electric taxis based on alliance game;

FIG2 is a schematic diagram of an electric taxi charging pile rental system;

FIG3 is a flow chart of a charging allocation method based on alliance game;

FIG4 is a flow chart of a charging allocation method for selfish behavior;

FIG5 is a schematic diagram of an electric taxi charging cost optimization system based on alliance game.

DETAILED DESCRIPTION

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.

Example 1

FIG1 is a flow chart of a method for optimizing the charging cost of an electric taxi based on alliance game. As shown in FIG1 , this embodiment provides a method for optimizing the charging cost of an electric taxi based on alliance game, and the method includes the following steps:

S1: Determine the total charging cost and the shared cost based on the number of electric taxi charging tasks; construct an optimization model for the charging cost of electric taxis under the constraints of the charging pile rental power capacity and the electric taxi mobility energy consumption;

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;

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.

This method saves the construction cost of charging piles, gives priority to meeting the charging needs of electric taxis, and optimizes the charging cost under the constraint of charging pile rental electricity.

Preferably, determining the total charging cost and the shared cost according to the number of electric taxi charging tasks includes:

The total charging cost of charging pile _sj is:

The charging cost that electric taxi v _i should share when charging on charging pile s _j is:

Among them, _fj () is a monotonically increasing concave function that characterizes the rental scale, _Tj = { _τi | _xi,j = 1} represents the set of charging tasks assigned to charging pile _sj , where xi _,j is a binary decision variable for charging task assignment: | _Tj | represents the size of _Tj : is the unit electricity price of charging pile _sj , is the base rental price of charging pile _sj , _Qj is the total charge capacity on charging pile _sj ; is the charging demand of electric taxi _vi , _βi is the unit moving energy consumption of electric taxi _vi , and d _i,j is the shortest distance from electric taxi _vi to charging station s _j .

Specifically, a collection of public charging piles on an electric taxi and rental platform is obtained, and based on the electric taxi charging task, an electric taxi charging pile rental model is constructed, including the following steps;

Let V = {v ₁ , v ₂ , ..., v _m } represent the set of electric taxis, and N = {s ₁ , s ₂ , ..., s _n } represent the set of rentable public charging piles:

The charging task submitted by an electric taxi v _i ∈ V to the charging pile rental platform is expressed as: in, is the position of the electric taxi v _i , is the charging demand of electric taxi _vi , E _i is the battery capacity of electric taxi _vi ;

The charging pile _sj∈N submits its own information to the rental platform as follows: in, is the base rental price of charging pile _sj , is the unit electricity price of charging pile _sj , is the location of the charging pile _sj , is the leased power capacity of charging pile s _j .

Based on the electric taxi charging pile rental model, a total charging cost model and cost sharing scheme are constructed according to the number of electric taxi charging tasks, including the following steps:

The total charging cost of charging pile si is composed of the charging pile rental cost and the electricity cost. Let T _j = {τ _i | _xi,j = 1} represent the set of charging tasks assigned to charging pile s _j , where x _i,j is the binary decision variable for charging task assignment. If charging task τ _i is assigned to charging pile s _j , then x _i,j = 1, otherwise x _i,j = 0; the rental cost of charging pile s _j is given by Indicates that is a monotonically increasing concave function that characterizes the rental scale and satisfies f _j (0) = 0, 1 ≤ f _j (1) < f _j (2) < ... < f _j (|T _j |) ≤ |T _j |, where |T _j | represents the size of T _j ; let the electricity cost of charging pile s _j be Where _Qj is the total charge on the charging pile _sj :

in is the actual charging amount of the charging task τ _i , β _i is the unit moving energy consumption of the electric taxi _vi , d _{i, j} is the shortest distance from the electric taxi _vi to the charging pile s _j ;

The total charging cost of charging pile _sj is defined as:

The charging cost that electric taxi v _i should share when charging on charging pile s _i is defined as:

in is the electricity cost that the electric taxi should share, _Tj is the charging task set on the charging pile _sj , The cost of renting the charging pile should be shared by the electric taxi.

Preferably, the construction of an electric taxi charging cost optimization model under the constraints of charging pile rental power capacity and electric taxi mobility energy consumption includes:

The problem of minimizing the charging cost of electric taxis under the constraints of charging pile rental power capacity and electric taxi mobility energy consumption is formalized as follows:

in, is the leased power capacity of the charging pile _sj , _Ei is the battery capacity of the electric taxi _vi , N = { _s1 , _s2 , ..., _sn } represents the set of leased public charging piles, and T represents the set of charging tasks _τi .

Specifically, the problem of minimizing the charging cost of electric taxis under the constraints of charging pile rental power capacity and electric taxi mobility energy consumption is constructed and formalized as follows:

Among them, constraint (5) ensures that the total charging capacity of any charging pile cannot exceed the upper limit of its rentable power capacity; constraint (6) ensures that any electric taxi has sufficient power to reach the assigned charging pile; constraint (7) ensures that each charging task can be assigned to and only to one charging pile.

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:

Using marginal utility to measure the strategy of electric taxi users a _i for the corresponding charging alliance The impact is as follows:

Where τ _i is the charging task corresponding to the electric taxi _vi , For charging pile The total charging cost of a _-i is the set of charging allocation strategies for other electric taxis except the electric taxi _vi . Indicates the charging alliance chosen _by the electric taxi vi;

definition is the preference order of electric taxi v _i , for any electric taxi v _i and any two charging alliances and The order of the charging alliance is:

Specifically, the problem of minimizing the charging cost of electric taxis is reconstructed as a coalition formation game G = {V, _Ui , _Ai , Γ}, where V is the participants of the game, i.e., the set of electric taxis, _Ui is the utility function of electric taxi _vi , and _Ai represents the strategy space of electric taxi _vi . For any electric taxi _vi∈V , its charging allocation strategy is defined as _ai , and 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 The strategy set of all electric taxis is defined as a = {a ₁ , a ₂ , ..., a _m }, and the disjoint charging alliance partition is defined as Γ = {T ₁ , T ₂ , ..., T _n };

Using marginal utility to measure the strategy of electric taxi users a _i for the corresponding charging alliance The impact is specifically defined as:

Where τ _i is the charging task corresponding to the electric taxi _vi , For charging pile The total charging cost of the electric taxis is represented by a _-i , which represents the charging allocation strategy set of the electric taxis other than the electric taxi _vi , that is, a _-i = {a ₁ , a ₂ , ..., a _i-1 , a _i+1 , ..., a _n }, represents the charging alliance selected by the electric taxi _vi . It should be noted that the charging task τ _i corresponding to the electric taxi vi _is not in In In combination with formulas (2), (3), and (9), the utility function of electric taxis can be restated as:

This utility function represents the electric taxi v _i joining the charging alliance Given the charging allocation strategy set of other electric taxis, electric taxi v _i always tends to join the charging alliance that minimizes the sum of its own charging cost and the total reduction of the charging costs of alliance members.

definition is the preference order of electric taxi v _i , indicating that electric taxi v _i has a complete, reflexive and transitive binary relationship on all possible charging alliances it can form. Given two charging alliances of electric taxi v _i and Said that electric taxis _are 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 any charging allocation strategy a _i in the strategy space A _i of the electric taxi v _i satisfies and in Ensures that the remaining battery capacity of the electric taxi _VI is sufficient to reach the charging alliance Corresponding charging pile Guaranteed charging station of rentable power capacity Able to complete all charging tasks in the charging alliance T _j ∪{τ _i } after adding charging task τ _i ; is the shortest distance from the electric vehicle v _k to the charging station a _i ; Is the electric car _VI to the charging station The shortest distance.

For any electric taxi v _i and any two of its charging alliances and The order of defining the charging alliance is:

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.

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

(5.1) Initialize the electric taxi charging alliance division

(5.2) For any charging pile _sj∈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 randomly selects a strategy from the feasible strategy space A _i as the current charging allocation strategy a _i and updates the corresponding charging alliance

(5.5) For any charging task τ _i ∈T, reconstruct its feasible strategy space A _i ;

(5.6) Select the strategy with the maximum utility in the strategy space _Ai If the utility U _i (a′ _i , a _-i ) gained by joining the new charging alliance is greater than the utility U _i (a _i , a _-i ) gained by joining the old charging alliance, 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.

Specifically, referring to FIG3 , step (5.3) or (5.5) constructs its feasible strategy space, including the following steps:

(5.3.1) Initialize strategy space

(5.3.2) For any charging pile _sj∈N , if the charging task _τi corresponding to the electric taxi _vi can reach the charging pile _sj and the rentable power capacity of _sj If all charging tasks in the charging alliance T _j ∪{τ _i } can be completed after adding the charging task τ _i , then the charging pile s _j is added to the feasible strategy space A _i of the electric taxi _vi :

(5.3.3) Output the strategy space A _i .

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

The selfish charging utility U _i (T _j ) of an electric taxi v _i completing charging at a charging pile s _j is:

U _i (T _j )＝-P _i (T _j )

Defining selfish preference relations based on the selfish charging utility of electric taxis

The preference function R _i (T _j ) is calculated based on the selfish charging utility of electric taxis:

Among them, H(i) represents the historical set of electric taxi _vi , recording the charging alliances that have been joined before, _Tj = { _τi | _xi,j = 1} represents the set of charging tasks assigned to charging pile _sj , wherexi _,j is the binary decision variable for charging task assignment; || means "or".

Specifically, let U _i (T _j ) be the selfish charging utility of electric taxi v _i completing charging on charging pile s _j :

U _i (T _j )＝-P _i (T _j ) (12)

Where T _j is the charging alliance corresponding to the charging pile s _j , P _i (T _j ) is the charging cost that the electric taxi v _i should share when completing charging on the charging pile s _j ;

The 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 the electric taxi:

Where T _j and T _j are the charging alliances that electric taxi _vi may join, and they satisfy τ _i ∈ _{T j} and τ _i ∈ _{T j′} . _{R i} (T _j ) is the preference function of electric taxi _vi with respect to the charging alliance T _j , and the selfish preference relation The electric taxi v _i is allowed to quantify its preferences. For example, for the charging task τ _i of the electric taxi v _i , given two charging alliances T _j and T _j′ , It indicates that electric taxis prefer to join the charging alliance T _j rather than T _j′ ;

(6.3) The preference function R _i (T _j ) is calculated based on the selfish charging utility of electric taxis:

Among them, U _i (T _j ) represents the selfish charging utility of electric taxi _vi in the charging alliance T _j , H(i) represents the history set of electric taxi _vi , which records the charging alliances that have been joined before. Setting the preference function value to negative infinity helps to reduce the complexity of the algorithm; Indicates that the remaining battery capacity of the electric taxi _vi is insufficient to reach the charging pile _sj corresponding to the charging alliance, It means that after the electric taxi _vi joins the charging alliance _Tj , the leased power capacity of the charging pile _sj cannot complete all the charging tasks in the charging alliance. The 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 charging task of the electric taxi _vi is in the charging alliance _Tj . Based on the above selfish preference relationship, the electric taxi will join the charging alliance that maximizes its selfish charging utility, without considering the impact of its decision on other electric taxis, which reflects the selfish behavior of the electric taxi.

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:

(7.1) Initialize the electric taxi charging alliance division

(7.2) For any charging pile 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 the set of charging alliances that its electric taxi v _i may join History Collection and charging pile index α _i =0;

(7.4) For any charging pile s _j ∈ N, if the remaining battery capacity of the electric taxi _vi is sufficient to reach the charging pile s _j , and after the electric taxi _vi joins the charging alliance T _j , the rented power capacity of the charging pile s _j is sufficient to complete all charging tasks in the alliance, then the set of charging alliances that it may join is updated to A _i =A _i ∪{s _j };

(7.5) The electric taxi v _i randomly selects a charging pile s _j from A _i and updates the corresponding charging alliance T _j = T _j ∪ {τ _i }, the history set H (i) = H (i) ∪ {T _j } and the charging pile index α _i = j;

(7.6) For any electric taxi charging task τ _i ∈ T, the charging pile with the maximum utility is calculated based on the preference function R _i (T _j ) and New Charging Alliance If the preference function value of electric taxi v _i 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.

The specific implementation process of this step can be seen in Figure 4.

This embodiment saves the construction cost of charging piles, gives priority to meeting the charging needs of electric taxis, and optimizes the charging cost under the constraint of charging pile rental electricity.

In order to more fully understand the technical solution of the present invention, a specific example is given below.

Assume V = {v ₁ , v ₂ , v ₃ , v ₄ , v ₅ , v ₆ } represents the set of electric taxis, and N = {s ₁ , s ₂ , s ₃ , s ₄ } represents the set of charging piles. Electric taxi v _i submits a charging task to the charging pile rental platform. Where electric taxis are located They are (114.126137°E, 22.53824°N), (114.101875°E, 22.537943°N), (114.1073°E, 22.5517°N), (114.10179°E, 22.559822°N), (114.11312°E, 22.545967°N), (114.12084°E, 22.55866°N), with battery capacities E _i of 70, 65, 75, 80, 85, and 65 kWh, respectively. The charging requirements The mobile energy consumption of electric taxis _βi is 0.15, 0.16, 0.17, 0.2, 0.25, and 0.18 kWh/km respectively. Charging pile _sj submits its own information to the rental platform The base rental price The unit electricity price is 8, 6, 7 and 5 yuan respectively. The rented electricity capacity is 0.8, 0.9, 1.0 and 1.2 yuan/kWh respectively. They are 120, 140, 160, and 180 kWh respectively, and the location They are (114.119251°E, 22.549423°N), (114.181697°E, 22.559031°N), (114.122624°E, 22.580606°N), and (114.116362°E, 22.560308°N) respectively. The charging pile _sj represents the discount function _fj (x)= ^xa of the number of rental tasks, where x is the number of electric taxi charging tasks and a is the discount coefficient, which are 0.98, 0.95, 0.97, and 0.95 respectively.

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:

(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 ;

(5.3.1) Select the charging task τ ₃ and initialize its strategy space

(5.3.2) For any charging pile _sj∈N , if the charging task _τ3 corresponding to the electric taxi _v3 can reach the charging pile _sj and the rentable power capacity of _sj is If all charging tasks in the charging alliance T _j ∪{τ ₃ } after adding charging task τ ₃ can be completed, then the charging pile s _j is added to the feasible strategy space A ₃ of the electric taxi v ₃ ;

(5.3.3) Output strategy space A ₃ ;

(5.4) Electric taxi v ₃ randomly selects a strategy from the feasible strategy space A ₃ as the current charging allocation strategy a ₃ = s ₁ , and updates the corresponding charging alliance T ₁ = T ₁ ∪ {τ ₃ };

By executing (5.3.1) to (5.4) for the remaining charging tasks, we can obtain T ₁ ={τ ₃ }, T ₂ ={τ ₂ ,τ ₁ }, T ₃ ={τ ₅ }, T ₄ ={τ ₄ ,τ ₆ }.

(5.5) For any charging task τ _i ∈T, reconstruct its feasible strategy space A _i :

(5.6) Select the charging task τ ₄ and the strategy with the maximum utility in its strategy space A ₄ Because the utility U ₄ (a′ ₄ , a ₋₄ ) obtained by joining the new charging alliance is greater than the utility U ₄ (a ₄ , a ₋₄ ) obtained by the old charging alliance, the new charging alliance is updated. Old Charging Alliance and the current charging allocation strategy a _i =a′ _i =s ₃ ;

For the remaining charging tasks, executing (5.6), we can obtain T ₁ ={τ ₃ ,τ ₂ }, T ₂ ={τ ₅ ,τ ₁ }, T ₃ ={τ ₄ ,τ ₆ },

(5.7) Repeat steps (5.5) and (5.6) until the charging allocation strategy of all electric taxis remains unchanged, and finally obtain T ₁ = {τ ₃ , τ ₂ }, T ₂ = {τ ₅ , τ ₁ }, T ₃ = {τ ₄ , τ ₆ },

(5.8) Output electric taxi charging alliance division Γ = {T ₁ , T ₂ , T ₃ , T ₄ }, corresponding to the total charging cost: 362.25.

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:

(7.1) Initialize the electric taxi charging alliance division

(7.2) For any charging pile 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 the set of charging alliances that its electric taxi v _i may join History Collection and charging pile index α _i =0;

(7.4) Select the electric taxi charging task τ ₄ . For any charging pile s _j ∈ N , if the remaining battery capacity of the electric taxi v ₄ is sufficient to reach the charging pile s _j , and after the electric taxi v ₄ joins the charging alliance T _j , the rented power capacity of the charging pile s _j is sufficient to complete all charging tasks in the alliance, then update the set of charging alliances that it may join A ₄ =A ₄ ∪{s _j };

(7.5) Electric taxi v ₄ randomly selects a charging pile s ₃ from A ₄ and updates the corresponding charging alliance T ₃ =T ₃ ∪{τ ₄ }, the history set H(4) =H(4)∪{T ₃ } and the charging pile index α ₄ =3;

By executing the above steps (7.4) to (7.5) for the remaining charging tasks, we can obtain T ₁ ={τ ₂ }, T ₂ ={τ ₃ ,τ ₁ }, T ₃ ={τ ₄ ,τ ₅ }, T ₄ ={τ ₆ }.

(7.6) For any electric taxi charging task τ _i ∈ T, the charging pile with the maximum utility is calculated based on the preference function R _i (T _j ) and New Charging Alliance If the preference function value of electric taxi v _i Higher than Join the New Charging Alliance Leaving the old charging alliance Then update the history set and charging pile index α _i = j ^* ;

Select the electric taxi charging task τ ₃ and calculate the charging pile with the maximum utility and New Charging Alliance Because the preference function value of electric taxi v ₃ Higher than Join the New Charging Alliance Leaving the old charging alliance Then update the history set and charging pile index α ₃ =j ^* ;

For the remaining charging tasks, executing (7.6), we can obtain T ₁ ={τ ₃ ,τ ₂ }, t ₂ ={τ ₄ ,τ ₁ }, T ₃ ={τ ₅ ,τ ₆ },

(7.7) Repeat step (7.6) until all electric taxis do not change the charging alliance they join, and finally obtain T ₁ = {τ ₃ , τ ₂ }, T ₂ = {τ ₄ , τ ₁ }, T ₃ = {τ ₅ , τ ₆ },

(7.8) Output the electric taxi charging alliance partition Γ = {T ₁ , T ₂ , T ₃ , T ₄ }, corresponding to a total charging cost of 362.88.

Example 2

FIG5 is a schematic diagram of an electric taxi charging cost optimization system based on alliance game. As shown in FIG5 , this embodiment provides an electric taxi charging cost optimization system based on alliance game, and the system includes:

Model building module 501, used to determine the total charging cost and the shared cost according to the number of charging tasks for electric taxis; construct an optimization model for charging cost of electric taxis under the constraints of charging pile rental power capacity and electric taxi mobility energy consumption;

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;

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.

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.

Example 3

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.

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.

Example 4

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.

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.

It should be understood that each process and/or box in the flowchart and/or block diagram, as well as the combination of the processes and/or boxes 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 a device for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

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 (10)

1. The electric taxi charging cost optimization method based on alliance gaming is characterized by comprising the following steps of:

determining the total charging cost and the allocation cost according to the number of the electric taxi charging tasks; constructing an electric taxi charging cost optimization model under the constraint of electric capacity leasing and electric taxi movement energy consumption leasing of a charging pile;

constructing a alliance game model, an electric taxi marginal utility function, a preference sequence and an alliance sequence according to the charging cost optimization model; the preference sequence represents a charging consortium to which the electric taxis are prone to join; the coalition order represents a charging coalition that the electric taxis tend to select; determining a first charging allocation strategy of the electric taxi according to the marginal utility function and the alliance preference sequence so as to optimize the total charging cost;

Constructing a selfish utility function and a selfish preference sequence of the electric taxi, wherein the selfish preference sequence is used for quantifying the preference of the electric taxi; and determining a second charging distribution strategy of the electric taxi according to the selfish utility function and the selfish preference sequence so as to realize Nash equilibrium.

2. The method of claim 1, wherein the determining the total and apportioned costs from the number of electric taxi charging tasks comprises:

the total charging cost of the charging pile s _j is:

The charging allocation cost for the electric taxi v _i to finish charging on the charging pile s _j is as follows:

Wherein f _j () is a monotonically increasing concave function characterizing lease size, T _j＝{τ_i|x_ij =1 } represents a set of charging tasks assigned to charging post s _j, where x _i,j is a charging task assignment binary decision variable; t _j represents the size of T _j; For the price per unit power of the charging pile s _j, A reference lease price for charging pile s _j, Q _j is the total charge amount on charging pile s _j; For the charging demand of electric taxi v _i, β _i is the unit mobile energy consumption of electric taxi v _i, and d _i,j is the shortest distance from electric taxi v _i to charging peg s _j.

3. The method of claim 2, wherein constructing the electric taxi charging cost optimization model under the electric capacity constraint and the electric taxi movement energy consumption constraint comprises:

The method comprises the steps of constructing a charging pile to rent the electric power capacity constraint and the electric taxi movement energy consumption constraint to minimize the charging cost of the electric taxi, and formalizing:

wherein, For the leased power capacity of the charging pile s _j, E _i is the battery capacity of the electric taxi v _i, n= { s ₁,s₂,...,s_n } represents a set of rentable public charging piles, and T represents a set of charging tasks τ _i.

4. The method of claim 3, wherein constructing a coalition gaming model, an electric taxi marginal utility function, a preference order, and a coalition order from the charging cost optimization model comprises:

Policy a _i for measuring electric taxi users by marginal utility corresponds to charging alliance The influence of (a) is specifically:

wherein tau _i is the charging task corresponding to electric taxi v _i, For charging pilesA _-i represents a set of charging allocation policies for other electric taxis than electric taxi v _i,A charging consortium selected by the electric taxi v _i;

Definition of the definition For the preference sequence of the electric taxis v _i, for any electric taxis v _i and any two charging alliances thereofAndThe charging alliance sequence is as follows:

5. the method of claim 4, wherein the determining a first charge allocation policy for the electric taxis according to the marginal utility function and the coalition preference order to optimize a total charge cost comprises:

(5.1) initializing electric taxi charging alliance partitioning

(5.2) Initializing a charging task set for any charging pile s _j ε NAnd updates the coalition partition Γ=Γ -U-T _j;

(5.3) for any charging task τ _i ε T, construct its feasible policy space A _i;

(5.4) random electric taxis V _i E V randomly selecting a policy from the feasible policy space A _i as the current charging allocation policy a _i, and updating the corresponding charging alliance

(5.5) Reconstructing its feasible strategy space A _i for any charging mission τ _i ε T;

(5.6) selecting the policy with the greatest utility in policy space A _i If the utility U _i(a′_i,α_-i) obtained by joining the new charging coalition is greater than the utility U _i(a_i,a_-i obtained by the old charging coalition), updating the new charging coalitionOld charge allianceAnd a current charge allocation policy a _i＝a′_i;

(5.7) repeating the steps (5.5) and (5.6) until the charging allocation strategy of all the electric taxis is unchanged;

and (5.8) outputting the electric taxi charging alliance division Γ.

6. The method of claim 5, wherein the constructing the electric taxi selfish utility function and selfish preference order comprises:

Selfish charging utility U _i(T_j for electric taxi v _i to complete charging on charging peg s _j):

U_i(T_j)＝-P_i(T_j)

self-privating preference relation based on electric taxi self-privating charging utility definition

Preference function R _i(T_j) then based on the selfish charging utility calculation for electric taxis:

Wherein H (i) represents a historical set of electric taxis v _i, a charging consortium that has been previously joined is recorded, T _j＝{τ_i|x_i,i =1 } represents a set of charging tasks assigned to charging posts s _j, where x _i,j is a charging task assignment binary decision variable; the expression "or".

7. The method of claim 6, wherein the determining a second charge allocation policy for the electric taxi based on the selfie utility function and the selfie preference order to achieve nash equalization comprises:

(7.1) initializing electric taxi charging alliance partitioning

(7.2) Initializing a charging task set for any charging pile s _j ε NAnd updates the coalition partition Γ=Γ -U-T _j;

(7.3) initializing a charging alliance set to which electric taxis v _i may be added for any electric taxi charging task τ _i εT History setAnd a charging pile index α _i =0;

(7.4) for any charging stake s _j e N, if the remaining battery capacity of electric taxi v _i is sufficient to reach charging stake s _j and the leased power capacity of charging stake s _j is sufficient to complete all charging tasks within the alliance after electric taxi v _i joins charging alliance T _j, updating charging alliance set a _i＝A_i∪{s_j to which it may join;

(7.5) electric taxi v _i randomly selects one charging pile s _j from a _i, and updates the corresponding charging coalition T _j＝T_j∪{τ_i, history set H (i) =h (i)/(u { T _j } and charging pile index α _i =j;

(7.6) for any electric taxi charging mission τ _i ε T, calculating the charging pile with maximum utility based on preference function R _i(T_j) New charge alliance If the preference function value of v _i of electric taxiHigher thanThen join the new charging consortiumLeave old charge allianceThen update the history setAnd a charging pile index α _i＝j^*;

(7.7) repeating the step (7.6) until all electric taxis do not change the charging consortium to which they are added;

and (7.8) outputting the electric taxi charging alliance division Γ.

8. An electric taxi charging cost optimization system based on alliance gaming, the system comprising:

The model building module is used for determining the total charging cost and the allocation cost according to the number of the electric taxi charging tasks; constructing an electric taxi charging cost optimization model under the constraint of electric capacity leasing and electric taxi movement energy consumption leasing of a charging pile;

The first determining module is used for constructing a alliance game model, an electric taxi marginal utility function, a preference sequence and an alliance sequence according to the charging cost optimizing model; the preference sequence represents a charging consortium to which the electric taxis are prone to join; the coalition order represents a charging coalition that the electric taxis tend to select; determining a first charging allocation strategy of the electric taxi according to the marginal utility function and the alliance preference sequence so as to optimize the total charging cost;

The second determining module is used for constructing a selfish utility function and a selfish preference sequence of the electric taxi, wherein the selfish preference sequence is used for quantifying the preference of the electric taxi; and determining a second charging distribution strategy of the electric taxi according to the selfish utility function and the selfish preference sequence so as to realize Nash equilibrium.

9. An electronic device, the electronic device comprising:

A memory and a processor;

The memory is configured to store computer-executable instructions, the processor being configured to execute the computer-executable instructions, which when executed by the processor implement the method of any one of claims 1-7.

10. A computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, the computer executable instructions, when executed by a processor, implement the method of any of claims 1-7.