Charging station real-time electric energy sharing method, system, medium and equipment

Abstract

The invention discloses a charging station real-time electric energy sharing method, a charging station real-time electric energy sharing system, a medium and equipment. The charging station real-time electric energy sharing method comprises the steps of setting a current-round electric energy sharing price of each charging station; the current-round electric energy sharing price of each charging station is sent to the electric vehicle users to participate in electric energy sharing; the final selection probability of the electric vehicle user for each charging station is calculated through an evolutionary game model; the optimal charging and discharging power value of each electric vehicle is reported to the respective selected charging station, and the electric vehicle response power of each charging station is obtained; according to the current-round electric energy sharing price of each charging station and the response power of the electric vehicle, a new-round electric energy sharing price and the current-round shared electric quantity are obtained; and when the difference value between the new round of electric energy sharing price and the current round of electric energy sharing price is smaller than a first convergence threshold value, the current round of shared electric quantity of each charging station is cleared. According to the invention, the actual game decision process of the participant can be accurately described, and the optimal configuration of the electric power resource between the charging station and the electric vehicle can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy sharing, and in particular to a method, system, medium and device for real-time electric energy sharing in a charging station. Background Art

[0002] Electric energy sharing is a technology for optimizing the allocation of electric power resources. By breaking the boundary between the ownership and the right of use of electric energy, the entity with idle electric energy can transfer the right of use of electric energy for the entity in urgent need of electric energy. Its essence is that electric energy flows from the entity with low electricity consumption utility to the user with high electricity consumption utility, so as to realize the optimal allocation of electric power resources. On the one hand, there is no capacity threshold for entities in electric energy sharing, and a large number of small-capacity electricity users can freely participate in electric energy sharing transactions. On the other hand, the trading entities in the electric energy sharing market can achieve point-to-point supply and demand matching, reducing the degree of dependence on the third party while simplifying the trading process and reducing the third-party supervision cost. The V2G (Vehicle-to-Grid) charging pile endows electric vehicles with the ability to feed electricity back to the grid in reverse. At the same time, new energy devices such as distributed photovoltaics and small wind turbines in the electric vehicle charging station also enable the charging station to have a certain amount of adjustable flexible resources. Through electric energy sharing, it is expected to realize the optimal allocation of electric energy between a large number of electric vehicles and the flexible resources in the charging station, and improve the economic benefits of the charging station.

[0003] At present, the decision-making optimization models of each participating entity in the electric energy sharing mode have been widely studied. However, most of the existing research starts from the perspective of classical game theory, that is, it is assumed that the participants in electric energy sharing can obtain complete information in the game process and make optimal decisions completely rationally. Although this assumption greatly simplifies the game analysis process, in the actual transactions of a large number of electric vehicle users, complete rationality and complete information cannot be achieved. Therefore, such an idealized assumption cannot accurately describe the actual game decision-making process of the participants, and further leads to a large deviation between the theoretical analysis results and the actual application results in the idealized electric energy sharing model. Summary of the Invention

[0004] The present invention aims to solve at least some of the technical problems in the related technologies to some extent. For this reason, the first object of the present invention is to provide a method for real-time electric energy sharing in a charging station. This method uses evolutionary game to truly describe the decision-making process of electric vehicle users under incomplete rationality, which helps to reduce the large deviation between the theoretical analysis results and the actual application results, and uses the master-slave game to describe the energy clearing process between the charging station and the electric energy sharing platform, so as to realize the optimal allocation of electric power resources between the charging station and the electric vehicle.

[0005] The second object of the present invention is to provide a system for real-time electric energy sharing in a charging station.

[0006] The third object of the present invention is to provide a computer-readable storage medium.

[0007] The fourth object of the present invention is to provide an electric energy sharing device.

[0008] To achieve the above object, the present invention is realized by the following technical solutions:

[0009] A real-time electric energy sharing method for charging stations, comprising:

[0010] Setting the current round of electric energy sharing prices for each charging station;

[0011] Sending the current round of electric energy sharing prices of each charging station to electric vehicle users to be involved in electric energy sharing through an electric energy sharing platform;

[0012] Based on the current round of electric energy sharing prices of each charging station, calculating the final selection probabilities of electric vehicle users for each charging station through an evolutionary game model;

[0013] Reporting the optimal charge and discharge power values of each electric vehicle to the charging station corresponding to the maximum value of its respective final selection probability, so as to calculate the electric vehicle response power of each charging station;

[0014] Calculating a new round of electric energy sharing prices and the shared electricity cleared in the current round according to the current round of electric energy sharing prices of each charging station and the electric vehicle response power;

[0015] When the difference between the new round of electric energy sharing prices and the current round of electric energy sharing prices of each charging station is less than a first convergence threshold, clearing the shared electricity of each charging station in the current round to achieve real-time electric energy sharing.

[0016] Preferably, based on the current round of electric energy sharing prices of each charging station, calculating the final selection probabilities of electric vehicle users for each charging station through an evolutionary game model, including:

[0017] Randomly generating the initial probabilities of electric vehicle users going to different charging stations to participate in electric energy sharing;

[0018] Based on the current round of electric energy sharing prices of each charging station, calculating the optimal charge and discharge power of electric vehicle users going to different charging stations to participate in electric energy sharing, and determining the maximum benefit of electric vehicle users through an evolutionary game model based on the optimal charge and discharge power;

[0019] Based on the initial probabilities of electric vehicle users going to different charging stations to participate in electric energy sharing, the maximum benefit of electric vehicle users, and the replicator dynamic equation of electric vehicle users after discretization, iteratively updating the selection probabilities of electric vehicle users going to different charging stations to participate in electric energy sharing;

[0020] For each charging station, when the difference between the selection probability of the charging station by new-round electric vehicle users and that by this-round electric vehicle users is less than the second convergence threshold, the selection probability of the charging station by this-round electric vehicle users is taken as the final selection probability.

[0021] Preferably, the objective function of the evolutionary game model is a function established with the maximum user benefit of electric vehicles as the goal.

[0022] Preferably, the objective function of the evolutionary game model is constructed based on at least one of the cumulative charging utility of electric vehicles, the cumulative discharging cost, the charging and discharging costs of electric vehicle users participating in power sharing at the charging station, the time cost of electric vehicle users traveling to the charging station to participate in power sharing, the additional energy consumption cost caused by electric vehicle users traveling to the charging station, and the battery degradation cost of electric vehicles.

[0023] Preferably, the constraint conditions of the evolutionary game model include at least one of the electric vehicle user's arrival power constraint condition, the expected journey duration constraint condition for the electric vehicle user to reach the destination from the current location, the electric vehicle charging and discharging constraint condition, the electric vehicle battery energy storage constraint condition, and the electric vehicle user's shared benefit constraint condition.

[0024] Preferably, calculating the new-round power sharing price and the cleared shared power of this round according to the power sharing price of this round of each charging station and the electric vehicle response power includes:

[0025] Establish a master-slave game model between the power sharing platform and the charging stations. The master-slave game model includes an upper-layer optimization problem and a lower-layer optimization problem. The upper-layer optimization problem is the power sharing price clearing problem, and the lower-layer optimization problem is the power clearing decision problem of the power sharing platform for each charging station;

[0026] Transform the master-slave game model into a single-layer generalized Nash game model through the KKT conditions;

[0027] Solve the single-layer generalized Nash game model according to the power sharing price of this round of each charging station and the electric vehicle response power to obtain the new-round power sharing price and the cleared shared power of this round.

[0028] Preferably, transforming the master-slave game model into a single-layer generalized Nash game model through the KKT conditions includes:

[0029] Determine the Lagrangian function of the lower-layer optimization problem and its complementary slackness conditions;

[0030] Use the penalty factor method to linearize the complementary slackness conditions;

[0031] The KKT system site conditions are calculated based on the complementary slackness conditions after linearization and the Lagrangian function of the lower-layer optimization problem;

[0032] Based on the KKT system site conditions, the complementary slackness conditions after linearization, and the Lagrangian function of the lower-layer optimization problem, the KKT system of the lower-layer optimization problem is obtained;

[0033] Taking the KKT system of the lower-layer optimization problem as the constraint condition of the upper-layer optimization problem to transform and obtain a single-layer generalized Nash game model.

[0034] To achieve the above object, the second aspect of the present invention provides a real-time electric energy sharing system for charging stations, including:

[0035] A setting module for setting the current-round electric energy sharing price of each charging station;

[0036] A first sending module for sending the current-round electric energy sharing price of each charging station to electric vehicle users to participate in electric energy sharing through an electric energy sharing platform;

[0037] A first calculation module for calculating the final selection probability of electric vehicle users for each charging station based on the current-round electric energy sharing price of each charging station through an evolutionary game model;

[0038] A second sending module for reporting the optimal charge and discharge power value of each electric vehicle to the charging station corresponding to the maximum value of its respective final selection probability, so as to calculate the electric vehicle response power of each charging station;

[0039] A second calculation module for calculating a new round of electric energy sharing price and the shared electricity cleared in this round according to the current-round electric energy sharing price of each charging station and the electric vehicle response power;

[0040] A processing module for clearing the shared electricity of each charging station in this round when the difference between the new round of electric energy sharing price and the current-round electric energy sharing price of each charging station is less than the first convergence threshold to achieve real-time electric energy sharing.

[0041] Preferably, when the first calculation module calculates the final selection probability of electric vehicle users for each charging station based on the current-round electric energy sharing price of each round through an evolutionary game model, it specifically is used for:

[0042] Randomly generating the initial probability of electric vehicle users going to different charging stations to participate in electric energy sharing;

[0043] Based on the current-round electric energy sharing price of each charging station, calculating the optimal charge and discharge power of electric vehicle users going to different charging stations to participate in electric energy sharing, and determining the maximum benefit of electric vehicle users through an evolutionary game model based on the optimal charge and discharge power;

[0044] Based on the initial probability of electric vehicle users going to different charging stations to participate in electricity sharing, the maximum benefits of electric vehicle users, and the replicator dynamic equation of electric vehicle users after discretization, the selection probability of electric vehicle users going to different charging stations to participate in electricity sharing is iteratively updated;

[0045] For each charging station, when the difference between the selection probability of electric vehicle users for the charging station in the new round and the selection probability of electric vehicle users for the charging station in this round is less than the second convergence threshold, the selection probability of electric vehicle users for the charging station in this round is used as the final selection probability.

[0046] Preferably, the objective function of the evolutionary game model adopted by the first calculation module during calculation is a function established with the maximum benefits of electric vehicle users as the goal.

[0047] Preferably, the objective function of the evolutionary game model adopted by the first calculation module during calculation is constructed based on at least one of the cumulative charging utility of electric vehicles, the cumulative discharging cost, the charging and discharging costs of electric vehicle users participating in electricity sharing at the charging station, the time cost of electric vehicle users going to the charging station to participate in electricity sharing, the additional energy consumption cost caused by electric vehicle users going to the charging station, and the battery degradation cost of electric vehicles.

[0048] Preferably, the constraint conditions of the evolutionary game model adopted by the first calculation module during calculation include at least one of the electric vehicle user's arrival power constraint condition, the expected journey duration constraint condition for the electric vehicle user to reach the destination from the current location, the electric vehicle charging and discharging constraint condition, the electric vehicle battery energy storage constraint condition, and the electric vehicle user's shared benefit constraint condition.

[0049] Preferably, when the second calculation module calculates the new round of electricity sharing price and the shared electricity quantity cleared in this round according to the electricity sharing price and the electric vehicle response power of each charging station, it specifically is used for:

[0050] Establish a master-slave game model between the electricity sharing platform and the charging stations. The master-slave game model includes an upper-layer optimization problem and a lower-layer optimization problem. The upper-layer optimization problem is the electricity sharing price clearing problem, and the lower-layer optimization problem is the power clearing decision problem of the electricity sharing platform for each charging station;

[0051] Transform the master-slave game model into a single-layer generalized Nash game model through the KKT conditions;

[0052] Solve the single-layer generalized Nash game model according to the electricity sharing price and the electric vehicle response power of each charging station to obtain the new round of electricity sharing price and the shared electricity quantity cleared in this round.

[0053] Preferably, when transforming the master-slave game model into a single-layer generalized Nash game model through the KKT conditions, the second calculation module is specifically configured to:

[0054] Determine the Lagrangian function of the lower-layer optimization problem and its complementary slackness conditions;

[0055] Use the penalty factor method to linearize the complementary slackness conditions;

[0056] Calculate the KKT system site conditions based on the linearized complementary slackness conditions and the Lagrangian function of the lower-layer optimization problem;

[0057] Obtain the KKT system of the lower-layer optimization problem based on the KKT system site conditions, the linearized complementary slackness conditions, and the Lagrangian function of the lower-layer optimization problem;

[0058] Take the KKT system of the lower-layer optimization problem as the constraint condition of the upper-layer optimization problem to transform and obtain a single-layer generalized Nash game model.

[0059] To achieve the above object, a third aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-described method can be implemented.

[0060] To achieve the above object, a fourth aspect of the present invention provides an electric energy sharing device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-described method can be implemented.

[0061] The present invention has at least the following technical effects:

[0062] The present invention first, based on the evolutionary game model, comprehensively considers the cumulative charging utility and discharging cumulative cost of electric vehicles, charging and discharging fees, time cost, energy consumption cost, and battery degradation cost of electric vehicles, and takes into account the electric vehicle arrival power constraint, expected journey duration constraint, electric vehicle charging and discharging constraint, electric vehicle battery energy storage constraint, and electric vehicle user sharing revenue constraint, to depict the incomplete rational and incomplete information decision-making process of electric vehicles participating in electric energy sharing, and more realistically and accurately establish a response model for electric vehicles participating in electric energy sharing. Then, through the electric energy sharing platform, the shared power between charging stations is cleared, realizing the optimal allocation between the adjustable distributed flexible resources of charging stations and the electric power resources of electric vehicles, which can effectively promote the consumption level of flexible resources of charging stations, improve the economic benefits of charging stations, and increase social benefits.

[0063] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a traffic network-shared network coupling framework diagram of this embodiment.

[0065] Figure 2 The figure is a flow chart of a real-time power sharing method for charging stations according to an embodiment of the present invention.

[0066] Figure 3 It is a structural block diagram of a real-time power sharing system for charging stations according to an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The present embodiment is described in detail below, and examples of the embodiment are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0068] The following describes the real-time power sharing method, system, medium and device of the charging station of this embodiment with reference to the accompanying drawings.

[0069] like Figure 1 As shown, the real-time power sharing method of charging stations based on master-slave-evolution hybrid game provided by an embodiment of the present invention is implemented in a traffic-sharing coupling network with 12 traffic network nodes and 4 charging stations. There are 150 electric vehicle users in the traffic network.

[0070] The traffic network model for:

[0071]

[0072] In the formula, is the set of nodes in the transportation network, is the number i in the traffic network T The node, m T is the number of nodes in the transportation network, is the number j in the traffic network T Nodes; is the set of transportation network paths, Number the node i T With node number j T The path between T is the set of transportation network connection relations, Represents node i T j T Is there a path between Parameters, Represents node i T j T There is a path between is the set of initial travel times of the transportation network, is the initial travel time of path ; is the set of road traffic capacities, is the maximum traffic capacity of path ; is the set of distances of the transportation network, is the distance of path ;

[0073] The shared network model is as follows:

[0074]

[0075] In the formula, is the set of nodes of the shared network, is the node numbered i in the shared network D , i D is the node number in the shared network, and m is the number of nodes in the shared network; is the set of branches of the shared network, is the branch between nodes i D j D ; is the node numbered j in the shared network D , ε D is the set of connection relationships of the shared network, indicates the parameter of whether there is a branch between nodes i D j D ; represents that there is a branch between nodes i D j D ; is the set of branch admittances, is the branch admittance of branch ; is the set of maximum transmission powers of branches, is the maximum transmission power of branch ;

[0076] The travel parameters of the electric vehicle user cluster are shown in Table 1.

[0077] Table 1 Parameters of the starting point - destination of the electric vehicle cluster

[0078]

[0079] The parameters of the transportation network and the shared network are shown in Table 2.

[0080] Table 2 Travel time and travel distance of the transportation network path

[0081]

[0082] The impedance parameters of the shared network line are shown in Table 3.

[0083] Table 3 Impedance Parameters of the Shared Network Line

[0084]

[0085] Figure 2 It is a flowchart of the real-time power sharing method for the charging station according to the embodiment of the present invention. As Figure 2 shown, the method includes:

[0086] Step S101: Set the power sharing price of each charging station for this round.

[0087] In this embodiment, the power sharing price of the charging station can be set and the initial quotation round k = 0 is set, and the charging station randomly generates the initial power sharing price where the value range of is [0.3, 0.8].

[0088] Step S102: Send the power sharing price of each charging station for this round to the electric vehicle users to participate in power sharing through the power sharing platform.

[0089] Specifically, the charging station publishes the power sharing price to the electric vehicle users who are ready to participate in power sharing through the power sharing platform.

[0090] Step S103: Based on the power sharing price of each charging station for this round, calculate the final selection probability of the electric vehicle users for each charging station through the evolutionary game model.

[0091] Specifically, after receiving the power sharing price by the electric vehicle users, they conduct evolutionary game through the evolutionary game model to obtain the evolutionary stable strategy, that is, the final selection probability of the electric vehicle users for each charging station.

[0092] In this embodiment, based on the power sharing price of each charging station for this round, calculating the final selection probability of the electric vehicle users for each charging station through the evolutionary game model includes:

[0093] Step S1031: Randomly generate the initial probability of the electric vehicle users going to different charging stations to participate in power sharing

[0094] Step S1032: Based on the power sharing price of each charging station for this round Calculate the optimal charging and discharging power for electric vehicle users to go to different charging stations to participate in power sharing, and determine the maximum benefit of electric vehicle users through an evolutionary game model based on the optimal charging and discharging power

[0095] The biggest benefit for electric car users The calculation formula is:

[0096]

[0097] In the formula, are the cumulative utility of electric vehicle charging and the cumulative cost of discharging, is the charging and discharging cost of electric vehicle user i participating in energy sharing at charging station j at time t, For electric car users 0 The time cost of going to charging station j to participate in energy sharing, The additional energy consumption cost caused by electric vehicle user i going to charging station j, is the battery degradation cost of electric vehicle user i; a t,i,EV , b t,i,EV 、c t,i,EV d t,i,EV are the first to fourth cost fitting coefficients of electric vehicle user i, all of which are positive real numbers, Δt is the duration of electric energy sharing, They are the optimal charging and discharging powers for electric vehicle users to go to different charging stations to participate in energy sharing. is the electricity sharing price at time t, ω i is the time cost loss coefficient, R i,j and κ i They represent the road set from the current location of the electric vehicle user to the destination via the charging station and the road set directly to the destination; T a (t 0 ) is t 0 The time required for an electric car user to pass through road a at a certain moment, is the initial travel time of the road; x a (t 0 ) is t 0 The traffic volume of road a at time h a is the road capacity of road a; is the energy consumption cost coefficient of electric vehicle user i, D a is the driving distance of road a, k i,EV is the linear relationship coefficient between the battery life of electric vehicles and the number of cycles, is the rated capacity of the battery, C B For battery replacement cost, and are the charge and discharge efficiency of the battery respectively.

[0098] It should be noted that the above formula is the objective function of the evolutionary game model, which is established with the maximum user benefit of electric vehicles as the goal.

[0099] The constraints of the evolutionary game of electric vehicle users or the above evolutionary game model are as follows:

[0100]

[0101] In the formula, is the battery charge of electric vehicle user i at time t 0 moment, O i,j is the set of roads for electric vehicle user i to travel from the current location to charging station j, is the minimum charge required to reach the charging station; is the expected travel time for electric vehicle user i to reach the destination from the current location; are the maximum values of the charging power and discharging power of electric vehicle user i respectively, and are the charging and discharging Boolean variables respectively, ensuring that charging and discharging cannot be carried out simultaneously; S t,i is the battery power of the electric vehicle at time t, S t-Δt,i is the battery power of the electric vehicle at time t - Δt, and are the maximum battery charge of the electric vehicle user and the minimum battery charge to ensure the subsequent driving distance of the user respectively. is the optimal value for electric vehicle user i to go to charging station j to participate in the sharing benefit, σ i is the minimum expected benefit. Equation (11) is the power constraint condition for electric vehicle users to reach, Equation (12) is the expected travel time constraint condition for electric vehicle users to reach the destination from the current location, Equations (13)-(15) are the charging and discharging constraint conditions for electric vehicles, (16)-(17) are the battery energy storage constraint conditions for electric vehicles, and Equation (18) is the sharing benefit constraint condition for electric vehicle users.

[0102] In this embodiment, the cost fitting coefficient and expected travel time of the electric vehicle user cluster are shown in Table 4.

[0103] Table 4 Cost fitting coefficient and expected travel time of electric vehicle user cluster

[0104]

[0105]

[0106] In this embodiment, the power sharing duration Δt = 15 min; the linear relationship coefficient k between the battery life and the number of cycles of the electric vehicle user i,EV = 0.000156; the rated battery capacity Battery charge and discharge efficiency Battery replacement cost C B = 10000, the maximum charge and discharge power The time cost loss coefficient and the energy consumption cost coefficient are ω i = 0.05, The minimum expected benefit σ of the electric vehicle user participating in power sharing i = 1.

[0107] Step S1033: Based on the initial probability of the electric vehicle user going to different charging stations to participate in power sharing, the maximum benefit of the electric vehicle user, and the replicator dynamic equation of the electric vehicle user after discretization, the selection probability of the electric vehicle user going to different charging stations to participate in power sharing is iteratively updated.

[0108] The replicator dynamic equation of the electric vehicle user after discretization, that is, the iteration formula is as follows:

[0109]

[0110] In the formula, n is the number of iterations of the evolutionary game, ω is the step size of the evolutionary game iteration, is the probability that the electric vehicle user i selects the charging station j in the nth iteration, is the probability that the electric vehicle user i selects the charging station j in the (n + 1)th iteration, is the average benefit value of the electric vehicle user in the nth iteration, J is the set of charging stations, where ω = 0.5.

[0111] Step S1034: For each charging station, when the difference between the selection probability of the electric vehicle user for the charging station in the new round and the selection probability of the electric vehicle user for the charging station in this round is less than the second convergence threshold, the selection probability of the electric vehicle user for the charging station in this round is used as the final selection probability.

[0112] Specifically, when is obtained, the final selection probability of the electric vehicle user for each charging station is obtained, and the evolutionary game ends, where ε 2 is the second convergence threshold, and ε 2 = 0.0001.

[0113] Step S104: Report the optimal charge and discharge power values of each electric vehicle to the charging station corresponding to the maximum value of their respective final selection probabilities, so as to calculate the electric vehicle response power of each charging station.

[0114] Specifically, after the evolutionary game reaches the evolutionary stable strategy, the electric vehicle users report the optimal charging and discharging power values to the charging stations selected through the evolutionary game. Each charging station calculates the charging and discharging power of all the electric vehicles that have selected this station to obtain the response power of the electric vehicles within this station.

[0115] In this embodiment, the response power of the electric vehicles

[0116] where are respectively the optimal charging and discharging powers of the electric vehicle at the k-th round of iteration.

[0117] Step S105: Calculate the new round of electricity sharing price and the cleared shared electricity quantity of this round according to the electricity sharing price of this round of each charging station and the response power of the electric vehicles.

[0118] In this embodiment, calculating the new round of electricity sharing price and the cleared shared electricity quantity of this round according to the electricity sharing price of this round of each charging station and the response power of the electric vehicles includes: establishing a master-slave game model between the electricity sharing platform and the charging stations. The master-slave game model includes an upper-layer optimization problem and a lower-layer optimization problem. The upper-layer optimization problem is the electricity sharing price clearing problem, and the lower-layer optimization problem is the power clearing decision problem of the electricity sharing platform for each charging station; transforming the master-slave game model into a single-layer generalized Nash game model through the KKT conditions (Karush-Kuhn-Tucker conditions); solving the single-layer generalized Nash game model according to the electricity sharing price of this round of each charging station and the response power of the electric vehicles to obtain the new round of electricity sharing price and the cleared shared electricity quantity of this round.

[0119] Among them, transforming the master-slave game model into a single-layer generalized Nash game model through the KKT conditions includes: determining the Lagrangian function of the lower-layer optimization problem and its complementary slackness conditions; linearly processing the complementary slackness conditions by using the penalty factor method; calculating the KKT system site conditions based on the linearly processed complementary slackness conditions and the Lagrangian function of the lower-layer optimization problem; obtaining the KKT system of the lower-layer optimization problem based on the KKT system site conditions, the linearly processed complementary slackness conditions and the Lagrangian function of the lower-layer optimization problem; using the KKT system of the lower-layer optimization problem as the constraint condition of the upper-layer optimization problem to transform and obtain the single-layer generalized Nash game model.

[0120] Specifically, the master-slave game model between the electricity sharing platform and the charging stations is:

[0121]

[0122] In the formula, is the charging station utility function, is the response power of the electric vehicle at time t, is the shared power of the charging station at time t, is the adjustable demand power of the charging station, is the adjustable power generation power of the charging station, is the minimum shared electricity price, is the maximum shared electricity price, is the line transmission power value, and ρ is the comprehensive line transmission loss cost, is the line admittance value, is the fixed power generation power of the charging station, is the fixed demand power of the charging station, ξ j,t is the dual variable of the in-station power balance constraint, w j is the set of child nodes of the nodes in the shared network, is the transmission power on the line connecting node j D of the shared network and node g, is node j of the shared network D the dual variable of the node power balance constraint, is the maximum line transmission power, are the positive and negative values of the dual variables of the branch power flow constraint, the adjustable demand power constraint of the charging station, and the adjustable power generation power constraint of the charging station, respectively, is the maximum adjustable demand power of the charging station, is the maximum adjustable power generation power of the charging station.

[0123] It should be noted that in the master-slave game model, the master's optimization problem is the upper layer, which refers to the electricity sharing price clearing problem of the charging station here, and the decision variable is the electricity sharing price. The slave's optimization problem is the lower layer, which refers to the power clearing decision problem of the electricity sharing platform for each charging station here. The objective function is to maximize social welfare, that is, the expression after argmin in Equation (20), and the decision variables are the adjustable charge and discharge power values of each charging station and the transmission power values on each line in the shared network.

[0124] The Lagrangian function of the lower-layer optimization problem is:

[0125]

[0126] The complementary slackness conditions of the Lagrangian function are:

[0127]

[0128] Among them, ⊥ is the complementary symbol, indicating that only one side of the symbol holds.

[0129] Taking the partial derivatives of each variable, the KKT system site conditions are obtained:

[0130]

[0131] Among them, a j , b j , α j , β j are the first to fourth utility coefficients of the charging station, is the dual variable corresponding to the node power balance constraint of the shared network node i D .

[0132] Since the complementary slackness condition is a non-linear constraint, the complementary slackness condition is further linearized by the Big-M (penalty factor method):

[0133]

[0134] Among them, are the first to sixth Boolean variables, taking values from 0 to 1, and M is a relatively large constant.

[0135] Then, based on the KKT system site conditions, the linearized complementary slackness condition, and the Lagrangian function of the lower-level optimization problem, the KKT system of the lower-level optimization problem is obtained as follows:

[0136]

[0137] Among them, C KKT represents the constraint set corresponding to the KKT system of the lower-level optimization problem.

[0138] The transformed single-layer generalized Nash game model is as follows:

[0139]

[0140] Among them,

[0141] The calculation formula for the charging station utility function is:

[0142]

[0143] In the formula, a j , b j , α j , β j Specifically, it is shown in Table 5 as follows.

[0144] Table 5 Charging station utility coefficients

[0145]

[0146] In Table 5, CS1 - CS4 represent four charging stations.

[0147] Step S106: When the difference between the new round of electricity sharing price and the current round of electricity sharing price at each charging station is less than the first convergence threshold, clear the shared electricity quantity of the current round at each charging station to achieve real-time electricity sharing.

[0148] If the quotation reaches the convergence situation Then clear according to the shared electricity quantity of the current round Otherwise, let k = k + 1, and repeat steps S102 - S105 until convergence, where is the electricity sharing price of the (k + 1)-th time, that is, the new round of electricity sharing price, and ε 1 is the first convergence threshold. In this embodiment, ε 1 = 0.001.

[0149] Furthermore, the present invention also provides a real-time electricity sharing system for charging stations, as Figure 3 shown. The real-time electricity sharing system for charging stations includes a setting module, a first sending module, a first calculation module, a second sending module, a second calculation module, and a processing module that are connected in sequence, where the second calculation module is also connected to the first sending module.

[0150] In this embodiment, the setting module is used to set the electricity sharing price of the current round at each charging station; the first sending module is used to send the electricity sharing price of the current round at each charging station to the electric vehicle users to participate in electricity sharing through the electricity sharing platform; the first calculation module is used to calculate the final selection probability of the electric vehicle users for each charging station based on the electricity sharing price of the current round at each charging station through the evolutionary game model; the second sending module is used to report the optimal charge and discharge power values of each electric vehicle to the charging station corresponding to the maximum value of their respective final selection probabilities, so as to calculate the electric vehicle response power of each charging station; the second calculation module is used to calculate the new round of electricity sharing price and the shared electricity quantity cleared in the current round according to the electricity sharing price of the current round at each charging station and the electric vehicle response power; the processing module is used to clear the shared electricity quantity of the current round at each charging station when the difference between the new round of electricity sharing price and the current round of electricity sharing price at each charging station is less than the first convergence threshold to achieve real-time electricity sharing.

[0151] In one embodiment of the present invention, when calculating the final selection probabilities of electric vehicle users for each charging station through an evolutionary game model based on the current round of electricity sharing prices, the first calculation module is specifically configured to: randomly generate the initial probabilities of electric vehicle users going to different charging stations to participate in electricity sharing; calculate the optimal charging and discharging powers of electric vehicle users going to different charging stations to participate in electricity sharing based on the current round of electricity sharing prices of each charging station, and determine the maximum benefits of electric vehicle users through the evolutionary game model based on the optimal charging and discharging powers; iteratively update the selection probabilities of electric vehicle users going to different charging stations to participate in electricity sharing based on the initial probabilities of electric vehicle users going to different charging stations to participate in electricity sharing, the maximum benefits of electric vehicle users, and the replicator dynamic equation of the electric vehicle users after discretization; for each charging station, when the difference between the selection probability of electric vehicle users for the charging station in the new round and the selection probability of electric vehicle users for the charging station in the current round is less than the second convergence threshold, use the selection probability of electric vehicle users for the charging station in the current round as the final selection probability.

[0152] In one embodiment of the present invention, the objective function of the evolutionary game model adopted by the first calculation module during calculation is a function established with the maximum benefits of electric vehicle users as the objective.

[0153] In one embodiment of the present invention, the objective function of the evolutionary game model adopted by the first calculation module during calculation is constructed based on at least one of the cumulative charging utility of electric vehicles, the cumulative discharging cost, the charging and discharging costs of electric vehicle users participating in electricity sharing at the charging station, the time cost of electric vehicle users going to the charging station to participate in electricity sharing, the additional energy consumption cost caused by electric vehicle users going to the charging station, and the battery degradation cost of electric vehicles.

[0154] In one embodiment of the present invention, the constraint conditions of the evolutionary game model adopted by the first calculation module during calculation include at least one of the electric vehicle user's arrival power constraint condition, the expected journey duration constraint condition for the electric vehicle user to reach the destination from the current location, the electric vehicle charging and discharging constraint condition, the electric vehicle battery energy storage constraint condition, and the electric vehicle user's shared benefit constraint condition.

[0155] In an embodiment of the present invention, when calculating the new round of electricity sharing price and the shared electricity volume cleared in this round based on the electricity sharing price and the electric vehicle response power of each charging station, the second calculation module is specifically configured to: establish a master-slave game model between the electricity sharing platform and the charging stations, where the master-slave game model includes an upper-layer optimization problem and a lower-layer optimization problem. The upper-layer optimization problem is the electricity sharing price clearing problem, and the lower-layer optimization problem is the power clearing decision problem of the electricity sharing platform for each charging station; transform the master-slave game model into a single-layer generalized Nash game model through the KKT conditions; solve the single-layer generalized Nash game model according to the electricity sharing price and the electric vehicle response power of each charging station to obtain the new round of electricity sharing price and the shared electricity volume cleared in this round.

[0156] In an embodiment of the present invention, when transforming the master-slave game model into a single-layer generalized Nash game model through the KKT conditions, the second calculation module is specifically configured to: determine the Lagrangian function of the lower-layer optimization problem and its complementary slackness conditions; perform linearization processing on the complementary slackness conditions by using the penalty factor method; calculate the KKT system site conditions based on the linearized complementary slackness conditions and the Lagrangian function of the lower-layer optimization problem; obtain the KKT system of the lower-layer optimization problem based on the KKT system site conditions, the linearized complementary slackness conditions, and the Lagrangian function of the lower-layer optimization problem; use the KKT system of the lower-layer optimization problem as the constraint condition of the upper-layer optimization problem to transform and obtain a single-layer generalized Nash game model.

[0157] It should be noted that for the specific implementation manner of the real-time electricity sharing system of the electric vehicle charging station in this embodiment, reference can be made to the specific implementation manner of the above-mentioned real-time electricity sharing method for the electric vehicle charging station. To avoid redundancy, it will not be elaborated here.

[0158] Furthermore, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned real-time electricity sharing method for the electric vehicle charging station can be implemented.

[0159] Furthermore, the present invention also provides an electricity sharing device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned real-time electricity sharing method for the electric vehicle charging station can be implemented.

[0160] In summary, the present invention first based on the evolutionary game model, comprehensively considered the cumulative charging utility and cumulative discharging cost of electric vehicles, charging and discharging costs, time costs, energy consumption costs and battery degradation costs of electric vehicles, and considered the power arrival constraint of electric vehicles, the expected journey duration constraint, the charging and discharging constraint of electric vehicles, the battery energy storage constraint of electric vehicles, and the shared revenue constraint of electric vehicle users, characterized the incomplete rationality and incomplete information decision-making process of electric vehicles participating in electric energy sharing, and more truly and accurately established the response model of electric vehicles participating in electric energy sharing. Then, through the electric energy sharing platform, the shared electric energy between charging stations was cleared, realizing the optimal allocation between the adjustable distributed flexible resources of charging stations and the electric power resources of electric vehicles, effectively promoting the consumption level of flexible resources of charging stations, improving the economic benefits of charging stations, and increasing social benefits.

[0161] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0162] Although the content of the present invention has been introduced in detail through the above preferred embodiments, it should be recognized that the above description should not be considered as a limitation of the present invention. After those skilled in the art have read the above content, various modifications and substitutions to the present invention will be obvious. Therefore, the protection scope of the present invention should be defined by the appended claims.

Claims

1. A real-time electric energy sharing method for a charging station, characterized in that: include: Set the current round of electricity sharing price for each charging station; The current round of power sharing prices of each charging station are sent to electric vehicle users who are to participate in power sharing through the power sharing platform; Based on the current round of electricity sharing prices of each charging station, the final selection probability of electric vehicle users for each charging station is calculated through the evolutionary game model; The optimal charging and discharging power value of each electric vehicle is reported to the charging station corresponding to the maximum final selection probability, so as to calculate the electric vehicle response power of each charging station; The new round of electricity sharing price and the shared electricity volume cleared in this round are calculated based on the current round electricity sharing price of each charging station and the response power of electric vehicles; When the difference between the new round of electricity sharing price of each charging station and the current round of electricity sharing price is less than the first convergence threshold, the current round of shared electricity of each charging station is cleared to achieve real-time electricity sharing.

2. The real-time power sharing method of a charging station as claimed in claim 1, characterized in that: Based on the current round of electricity sharing prices of each charging station, the final selection probability of electric vehicle users for each charging station is calculated through the evolutionary game model, including: Randomly generate the initial probability of electric vehicle users going to different charging stations to participate in power sharing; Based on the current round of electricity sharing prices of each charging station, the optimal charging and discharging power of electric vehicle users going to different charging stations to participate in electricity sharing is calculated, and the maximum benefit of electric vehicle users is determined through an evolutionary game model based on the optimal charging and discharging power; Based on the initial probability of electric vehicle users going to different charging stations to participate in power sharing, the maximum benefit of electric vehicle users and the replicator dynamic equation of electric vehicle users after discretization, the selection probability of electric vehicle users going to different charging stations to participate in power sharing is iteratively updated; For each charging station, when the difference between the probability of electric vehicle users selecting the charging station in the new round and the probability of electric vehicle users selecting the charging station in the current round is less than the second convergence threshold, the probability of electric vehicle users selecting the charging station in the current round is taken as the final selection probability.

3. The real-time power sharing method of a charging station as claimed in claim 1, characterized in that: The objective function of the evolutionary game model is a function established with the goal of maximizing user benefits of electric vehicles.

4. The real-time power sharing method of a charging station as claimed in claim 3, characterized in that: The objective function of the evolutionary game model is constructed based on at least one of the cumulative utility of electric vehicle charging, the cumulative cost of discharging, the charging and discharging costs of electric vehicle users participating in energy sharing at charging stations, the time cost of electric vehicle users going to charging stations to participate in energy sharing, the additional energy consumption cost caused by electric vehicle users going to charging stations, and the battery degradation cost of electric vehicles.

5. The real-time power sharing method of a charging station as claimed in claim 4, characterized in that: The constraints of the evolutionary game model include at least one of an electric vehicle user's arrival power constraint, an electric vehicle user's expected journey time constraint from a current location to a destination, an electric vehicle charging and discharging constraint, an electric vehicle battery energy storage constraint, and an electric vehicle user's shared revenue constraint.

6. The real-time power sharing method of a charging station as claimed in claim 1, characterized in that: The new round of power sharing price and the shared power cleared in this round are calculated based on the current round power sharing price of each charging station and the response power of electric vehicles, including: Establish a master-slave game model between the power sharing platform and the charging station, the master-slave game model includes an upper optimization problem and a lower optimization problem, the upper optimization problem is the power sharing price clearing problem, and the lower optimization problem is the power clearing decision problem of the power sharing platform for each charging station; The master-slave game model is transformed into a single-layer generalized Nash game model through the KKT condition; The single-layer generalized Nash game model is solved according to the current round of power sharing price of each charging station and the response power of the electric vehicle to obtain the new round of power sharing price and the shared power cleared in this round.

7. The real-time power sharing method of a charging station as claimed in claim 6, characterized in that: The master-slave game model is transformed into a single-layer generalized Nash game model through the KKT condition, including: Determine the Lagrangian function of the underlying optimization problem and its complementary relaxation conditions; The penalty factor method is used to linearize the complementary relaxation conditions; The KKT system site conditions are calculated based on the complementary relaxation conditions after linearization and the Lagrangian function of the lower optimization problem; Based on the site conditions of the KKT system, the complementary relaxation conditions after linearization and the Lagrangian function of the lower optimization problem, the KKT system of the lower optimization problem is obtained; The KKT system of the lower-level optimization problem is used as a constraint condition for the upper-level optimization problem to transform into a single-layer generalized Nash game model.

8. A real-time power sharing system for charging stations, characterized in that: include: A setting module, used to set the current round of electricity sharing price of each charging station; The first sending module is used to send the current round of power sharing prices of each charging station to the electric vehicle users who are to participate in power sharing through the power sharing platform; The first calculation module is used to calculate the final selection probability of electric vehicle users for each charging station through an evolutionary game model based on the current round of power sharing prices of each charging station; The second sending module is used to report the optimal charging and discharging power value of each electric vehicle to the charging station corresponding to the maximum value of the final selection probability, so as to calculate the electric vehicle response power of each charging station; The second calculation module is used to calculate the new round of power sharing price and the shared power cleared in this round according to the current round of power sharing price of each charging station and the response power of the electric vehicle; The processing module is used to clear the current round of shared electricity of each charging station when the difference between the new round of electricity sharing price of each charging station and the current round of electricity sharing price is less than a first convergence threshold, so as to realize real-time electricity sharing.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electric energy sharing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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