Charging scheduling method and system based on potential game, terminal and medium

Abstract

The invention provides a charging scheduling method based on a potential game, and the method comprises the steps: providing a charging relation network of an electric vehicle, and building a market competition framework of a charging station operator based on the network; based on the market competition framework of the charging station operators, a game model among the power grid, the charging station operators and the electric vehicle users is constructed, the optimal pricing of the charging station operators is obtained, the number and distribution of the charging piles and / or the load of the power grid are dynamically adjusted, and the charging of the electric vehicle users is scheduled. Meanwhile, the invention provides a corresponding system, a terminal and a medium. According to the method, the total revenue of the charging station can be improved, the average waiting time of the user can be reduced, the charging station can be fully utilized while the benefit of a charging station operator is improved, the regional crowding condition of the charging station is improved while the benefit of the charging station is maximized, and the number and distribution of the charging piles of the charging station and the load of a power grid are optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging pricing, and in particular to a charging scheduling method, system, terminal and medium based on potential game and Nash equilibrium theory. Background Art

[0002] According to the "New Energy Vehicle Industry Development Plan (2021-2035)" issued by the Ministry of Industry and Information Technology, by the end of 2023, the number of new energy vehicles in my country has exceeded 7.43 million. With the increasing importance of electric vehicle operators (Electric Vehicle Operator EVO) in the power market, the role of providing charging and discharging services to electric vehicle users has become more prominent. At the same time, the power grid company is responsible for the operation and management of the distribution network and provides value-added services to various users. How to fully consider the complexity of multi-party games and use methods such as random uncertainty and dynamic games to analyze the price linkage of charging stations can effectively achieve power market equilibrium and optimal resource allocation.

[0003] However, the existing game model focuses more on the analysis of the interests of both parties involved in pricing, without specifically analyzing the charging mode, and further research is needed on how to formulate the optimal pricing. Optimal pricing can optimize charging scheduling, balance and stabilize grid load, and is an important basis for achieving power market equilibrium and optimal resource allocation. At present, no description or report of similar technology to the present invention has been found, and similar information at home and abroad has not been collected. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a charging scheduling method, system, terminal and medium based on potential game.

[0005] According to one aspect of the present invention, a charging scheduling method based on potential game is provided, comprising:

[0006] A charging relationship network of electric vehicles is provided, and a market competition framework of charging station operators is established based on the network; wherein the charging relationship network is used to describe the game relationship between electric vehicle groups and charging stations, and the market competition framework is used to describe the competitive relationship between charging station operators;

[0007] Based on the market competition framework of charging station operators, a game model between power grids, charging station operators and electric vehicle users is constructed;

[0008] Based on the game model, optimal pricing for charging station operators is obtained;

[0009] According to the optimal pricing, the number and distribution of charging piles and / or grid load are dynamically adjusted to schedule the charging of electric vehicle users.

[0010] Preferably, the providing of a charging relationship network for an electric vehicle comprises:

[0011] A weighted undirected graph of an electric vehicle charging network is constructed, including: an electric vehicle group A and a plurality of charging stations B connected to the electric vehicle group A, forming nodes of the undirected graph; different charging stations B provide pre-optimized charging strategies to the electric vehicle group A according to their own edge weights, and after receiving the charging strategies, the electric vehicle group feeds back to the charging station B according to its own charging needs, and optimizes and iterates the charging strategies, and obtains the optimal charging strategy after convergence, forming edges of the undirected graph.

[0012] Preferably, the market competition framework for charging station operators is established based on the network, including: operators formulate charging strategies according to time-of-use electricity prices, users select charging strategies and upload charging requirements, so that operators can optimize charging strategies.

[0013] Preferably, the game model between the operator and the electric vehicle user is constructed based on the market competition framework of the charging station operator, including:

[0014] Based on the market competition framework of the charging station operators, the three game entities of the power grid, the charging station operators and the users are obtained; among which, the power grid company and the charging station operators form a master-slave game relationship, and the charging station operators form a power game relationship with the charging operators and the electric vehicle users;

[0015] Construct the operator's optimization problem, including:

[0016] Assume the decision variables of the operator are {p i , π i , R i , C i , ε i , N, q i 、p -i , f}, where p i The pricing strategy selected by operator i, π i is the profit of operator i, R i is the revenue of operator i, C i is the cost of operator i, ε i is the user demand elasticity for each operator, N is the number of charging station operators in the set area, q i is the amount of electricity sold by operator i at a given price, p -i is the pricing strategy of other operators except operator i, and f is a function used to describe the relationship between the pricing strategies of other operators;

[0017] When the pricing strategy selected by operator i is pi When , the objective function of the operator is:

[0018] R i =p i ·q i (1)

[0019] The profit matrix of operator i is expressed as:

[0020]

[0021] Where Payoff represents the operator’s revenue matrix, It represents the benefits brought by the electric vehicle group n choosing to charge under the pricing strategy formulated by operator m;

[0022] The amount of electricity sold by operators satisfies the following relationship:

[0023]

[0024] The profit function of operator i is:

[0025] π i =R i -C i (4)

[0026] The reaction function of operator i is:

[0027]

[0028] in, represents the optimal pricing strategy of operator i;

[0029] The profit function that maximizes the operator is:

[0030] maxπ i =p i ·(N·q i )-C i (6)

[0031] Construct an optimization problem for electric vehicle users, including:

[0032] The total user demand is:

[0033]

[0034] Assuming the pricing coefficient of the charging station operator is λ, the charging objective function of each electric vehicle is:

[0035]

[0036] In the formula, G is the charging price of electric vehicles, λ(t) is the price coefficient, and ci That is the user waiting time cost coefficient, P i (t) represents the strategy p i The power in period t, Δt is the charging period;

[0037] According to the supply and demand relationship, we can get λ(t) and P i The relationship between (t) is:

[0038]

[0039] In the formula, a(t) represents the basic price in each period, and b(t) represents the relationship between the increase rate of the electric vehicle grid access electricity price and the overall charging power of the electric vehicle at this time;

[0040] Introduce the waiting time cost function as f t , where t represents the user waiting time, then the total user demand is corrected to:

[0041]

[0042] Where, t1, t2, t3…t i They respectively represent the waiting time for users to charge at the operator;

[0043] The charging station utilization rate measurement index r is introduced, with a value between 0 and 1. Suppose the number of users that a charging station can serve per unit time is m, and the user waiting time is t i It is expressed as:

[0044]

[0045] Substituting the reaction function of each operator shown in formula (5) back into the profit function shown in formula (6), we get a system equilibrium state, then the total revenue of the operator is R total The objective function is:

[0046]

[0047] The objective function of the total benefit is (p1, p2, p3…p i ) Take the partial derivative and set it equal to 0, and get the first-order condition:

[0048]

[0049] In the formula, π is the profit of the operator; the introduction of the second-order condition requires calculating the Hessian matrix H corresponding to each charging station and judging whether the matrix is ​​positive definite, that is:

[0050]

[0051] Where R is an element in the profit matrix, i.e., the profit obtained by the mth electric car under the nth strategy;

[0052] If H is a positive definite matrix, the solution obtained by the first-order conditions of the three selling prices is the maximum value of the revenue; if H has a non-zero eigenvalue, it means that the current solution is not the optimal solution;

[0053] The following constraints are imposed on the parameters in the optimization problem of the operator and the optimization problem of the electric vehicle user:

[0054] Market demand constraints:

[0055] 0≤P i (t)≤P max (t) (15)

[0056] Where P max (t) is the maximum power of the electric vehicle;

[0057] The charging power of electric vehicles must be within the maximum charging range of the charging pile:

[0058]

[0059] In the formula, D is used to represent the sum of all charging and discharging loads; A, B, and C represent the electric vehicle group, operator, and operator cost respectively:

[0060] The optimal pricing strategy range for operators:

[0061] P min ≤P≤P max (17)

[0062] Where P min is the operator's minimum power; P max is the maximum power of the operator;

[0063] First-order constraints:

[0064]

[0065] In the formula, π is the operator’s profit;

[0066] Second-order constraints:

[0067] H>0 (19)

[0068] Through the above process, the game model between the power grid, charging station operators and electric vehicle users is obtained.

[0069] Preferably, based on the game model, obtaining the optimal pricing of the charging station operator includes:

[0070] Based on the game model, the optimal response function of each charging station operator is determined, which means that when operator i knows the prices of other operators, the price that maximizes profit is selected as the optimal pricing.

[0071] Preferably, the determining of the best response function of each charging station operator may further include the following operations:

[0072] Assume that the profit function of operator i is π i , the total market demand capacity is Q, then the operator's optimal response function is:

[0073]

[0074] Among them, π i (p i ,p -i ) is the maximum revenue of operator i;

[0075] If π i (p i ,p -i ) in p i It is quasi-concave, that is, when the first-order partial derivative of the payoff matrix shown in formula (18) is zero, the payoff has a maximum value. Then the positive definiteness of the second-order partial derivative of the Hessian matrix is ​​used to verify the existence of a unique optimal solution. When H>0, positive definiteness exists, then the optimal response function is unique. When the first-order partial derivative is zero, there is an optimal solution but the Hessian second-order partial derivative matrix does not satisfy the positive definiteness condition of H>0, which proves that there are multiple optimal solutions. At this time, the optimal solution is iteratively solved on the Hessian matrix until the result converges and is verified on the second-order Hessian matrix.

[0076] Preferably, the charging of electric vehicle users is scheduled according to the optimal pricing, including:

[0077] Based on the optimal electricity price selected by the electric vehicle user, the charging station managed by the corresponding operator is selected to realize the charging scheduling of the electric vehicle, so as to meet the optimization of the power grid load.

[0078] According to another aspect of the present invention, a charging scheduling system based on potential game is provided, comprising:

[0079] A relationship building module, which is used to provide a charging relationship network of electric vehicles and establish a market competition framework for charging station operators based on the network; wherein the charging relationship network is used to describe the game relationship between electric vehicle groups and charging stations, and the market competition framework is used to describe the competitive relationship between charging station operators;

[0080] A game model module, which constructs a game model between the power grid, the charging station operator and the electric vehicle user based on the market competition framework of the charging station operator;

[0081] an optimal pricing determination module, which obtains the optimal pricing of the charging station operator based on the game model;

[0082] A charging scheduling module is used to dynamically adjust the number and distribution of charging piles and / or grid load according to the optimal pricing, and schedule the charging of electric vehicle users.

[0083] According to a third aspect of the present invention, there is provided a computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, can be used to execute any one of the methods described above in the present invention, or to run the system described above in the present invention.

[0084] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can be used to execute any of the methods described above in the present invention, or to run the system described above in the present invention.

[0085] Due to the adoption of the above technical solution, the present invention has at least one of the following beneficial effects compared with the prior art:

[0086] The charging scheduling method, system, terminal and medium based on potential game provided by the present invention overcome the defect of complete rationality of participants in classical game theory, and construct an evolutionary game model of EV charging and discharging scheduling based on limited rationality (that is, after receiving the optimal electricity price, the user of the electric vehicle chooses to charge at the nearest real-time electricity price because the distance of the charging pile is too far) and limited information (that is, the operator only determines the charging strategy according to the charging time period and location of the user on that day, and does not directly collect the user's charging satisfaction from the user). The game behavior of EV users in actual decision-making scenarios is characterized by parameter setting to avoid idealization of game conclusions.

[0087] The potential game-based charging scheduling method, system, terminal and medium provided by the present invention construct an EV scheduling evolution model with multiple strategy sets based on the classical evolutionary game model of finite strategies, so as to solve the contradiction between the non-constant EV charging power and the priority strategy of the evolutionary game.

[0088] The charging scheduling method, system, terminal and medium based on potential game provided by the present invention take into account the double-layer coupling relationship of bidding in the demand response process, and establish the game model between the power grid (DSO), charging station operator (EVA) and electric vehicle user (EV) respectively; EV charging and discharging scheduling and EVA bidding are taken as a joint solution problem in the power market. The first-order and second-order conditions are used as constraints to limit the selection space of pricing strategies, so as to ensure that the optimal solution complies with market rules and the principle of maximizing the interests of operators, and then the charging stations are scheduled based on the final pricing obtained to improve the congestion in the charging station area.

[0089] The charging scheduling method, system, terminal and medium based on potential game provided by the present invention can improve the total revenue of charging stations compared with peak and valley time-of-use electricity prices by the operator's optimal pricing, and can reduce the average waiting time of users. While improving the interests of charging station operators, the charging stations can also be fully utilized, and while maximizing the interests of charging stations, the congestion of charging station areas can be improved, and the number and distribution of charging piles in charging stations and the load on the power grid can be optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0091] Figure 1 FIG. 1 is a flowchart of a charging scheduling method based on potential game in one embodiment of the present invention.

[0092] Figure 2 The figure is a schematic diagram of the charging relationship network structure of an electric vehicle in a preferred embodiment of the present invention.

[0093] Figure 3 Schematic diagram of the market competition framework structure of charging station operators in a preferred embodiment of the present invention.

[0094] Figure 4 Schematic diagram of the master-slave game structure between a power grid company and a charging station operator in a preferred embodiment of the present invention.

[0095] Figure 5 It is a schematic diagram of the game structure among the power grid company, the charging station operator and the electric vehicle user in a preferred embodiment of the present invention.

[0096] Figure 6 This is a flow chart of the optimal pricing strategy for the operator model in a preferred embodiment of the present invention.

[0097] Figure 7 This is a relationship diagram between the queuing time and charging time of users in some areas in a specific application example of the present invention.

[0098] Figure 8 This is a relationship diagram between the queuing time and charging costs of users in some areas in a specific application example of the present invention.

[0099] Fig. 9 This is a distribution diagram for selecting charging stations based on the proximity principle in a specific application example of the present invention.

[0100] Fig.10 This is a distribution diagram of charging station selection considering price factors in a specific application example of the present invention.

[0101] Fig.11 The figure is a schematic diagram of component modules of a charging scheduling system based on potential game in one embodiment of the present invention. DETAILED DESCRIPTION

[0102] The following is a detailed description of the embodiments of the present invention: This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given. It should be pointed out that for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

[0103] An embodiment of the present invention provides a charging scheduling method based on potential game. The method proposes a new pricing strategy based on game equilibrium theory. By analyzing the profit matrix and competition situation of each operator, after confirming the optimal pricing strategy, the impact of the pricing scheme on revenue and user satisfaction is compared, and charging is scheduled. While maximizing the benefits of the charging station, the congestion in the charging station area is improved.

[0104] Specifically, Figure 1 As shown, the charging scheduling method based on potential game provided in this embodiment may include the following operations:

[0105] S1, providing a charging relationship network of electric vehicles, and establishing a market competition framework for charging station operators based on the network; wherein the charging relationship network is used to describe the game relationship between electric vehicle groups and charging stations, and the market competition framework is used to describe the competitive relationship between charging station operators;

[0106] S2, based on the market competition framework of charging station operators, builds a game model between the power grid, charging station operators and electric vehicle users;

[0107] S3, based on the game model, obtains the optimal pricing for charging station operators;

[0108] S4, based on the optimal pricing, dynamically adjusts the number and distribution of charging piles and / or grid load to schedule the charging of electric vehicle users.

[0109] In some preferred embodiments, the above S1 provides a charging relationship network of electric vehicles, including:

[0110] like Figure 2 As shown in the figure, a weighted undirected graph of the electric vehicle charging network is constructed, and the weighted undirected graph includes an electric vehicle group A and a number of charging stations B connected to the electric vehicle group A, forming the nodes of the undirected graph; among them, different operators provide their own pre-optimized charging strategies to the electric vehicle group according to their own edge weights (generally peak and valley electricity prices), and after receiving the pricing strategy, the user group feeds back to the operator according to its own charging needs (generally refers to the charging period and the matching time-of-use electricity price), and performs optimization iterations. After convergence, the optimal electricity price is obtained, forming the edges of the undirected graph.

[0111] In some preferred implementations, the above S1, based on the network, establishes a market competition framework for charging station operators, including: operators formulate charging strategies based on time-of-use electricity prices, users select charging strategies and upload charging requirements, which are used by operators to optimize charging strategies, thereby bringing benefits to operators and electric vehicle users. Figure 3 shown.

[0112] In some preferred implementations, the above S2, based on the market competition framework of charging station operators, constructs a game model between operators and electric vehicle users, including:

[0113] Based on the market competition framework of charging station operators, the three game entities of the power grid, charging station operators and users are obtained; among them, the power grid company and charging station operators form a master-slave game relationship, and the charging station operators, charging operators and electric vehicle users form a power game relationship;

[0114] Construct the operator's optimization problem, including:

[0115] Assume the decision variables of the operator are {p i , π i , R i , C i , ε i , N, q i 、p -i , f}, where p i The pricing strategy selected by operator i, π i is the profit of operator i, R i is the revenue of operator i, C i is the cost of operator i, ε i is the user demand elasticity for each operator, N is the number of charging station operators in the set area, q i is the amount of electricity sold by operator i at a given price, p -iis the pricing strategy of other operators except operator i, f is a function used to describe the relationship between the pricing strategies of other operators, which includes the prices of other operators;

[0116] When the pricing strategy selected by operator i is p i When , the objective function of the operator is:

[0117] R i =p i ·q i (1)

[0118] The profit matrix of operator i is expressed as:

[0119]

[0120] Where Payoff represents the operator’s revenue matrix, It represents the benefits brought by the electric vehicle group n choosing to charge under the pricing strategy formulated by operator m;

[0121] The amount of electricity sold by operators satisfies the following relationship:

[0122]

[0123] The profit function of operator i is:

[0124] π i =R i -C i (4)

[0125] The reaction function of operator i is:

[0126]

[0127] in, represents the optimal pricing strategy of operator i;

[0128] The profit function that maximizes the operator is:

[0129] maxπ i =p i ·(N·q i )-C i (6)

[0130] Construct an optimization problem for electric vehicle users, including:

[0131] The total user demand is:

[0132]

[0133] Assuming the pricing coefficient of the charging station operator is λ, the charging objective function of each electric vehicle is:

[0134]

[0135] In the formula, G is the charging price of electric vehicles, λ(t) is the price coefficient, and c i That is the user waiting time cost coefficient, P i (t) represents the strategy p i The power in period t, Δt is the charging period;

[0136] According to the supply and demand relationship, we can get λ(t) and P i The relationship between (t) is:

[0137]

[0138] In the formula, a(t) represents the basic price in each period, and b(t) represents the relationship between the increase rate of the electric vehicle grid access electricity price and the overall charging power of the electric vehicle at this time;

[0139] Introduce the waiting time cost function as f t , where t represents the user waiting time, then the total user demand is corrected to:

[0140]

[0141] Where, t1, t2, t3…t i They respectively represent the waiting time for users to charge at the operator;

[0142] The charging station utilization rate measurement index r is introduced, with a value between 0 and 1. Suppose the number of users that a charging station can serve per unit time is m, and the user waiting time is t i It is expressed as:

[0143]

[0144] Substituting the reaction function of each operator (Formula (5)) back into the profit function (Formula (6)), we get a system equilibrium state, then the total revenue of the operator R total The objective function is:

[0145]

[0146] The objective function of the total benefit is (p1, p2, p3…p i ) Take the partial derivative and set it equal to 0, and get the first-order condition:

[0147]

[0148] In the formula, π is the profit of the operator. Introducing the second-order condition requires calculating the Hessian matrix H corresponding to each charging station and determining whether the matrix is ​​positive definite, that is:

[0149]

[0150] Where R is an element in the profit matrix, i.e., the profit obtained by the mth electric car under the nth strategy;

[0151] If H is a positive definite matrix, the solution obtained by the first-order conditions of the three selling prices is the maximum value of the revenue; if H has a non-zero eigenvalue, it means that the current solution is not the optimal solution;

[0152] The following constraints are imposed on the parameters in the optimization problem of the operator and the optimization problem of the electric vehicle user:

[0153] Market demand constraints:

[0154] 0≤P i (t)≤P max (t) (15)

[0155] Where P max (t) is the maximum charging power;

[0156] The charging power of electric vehicles must be within the maximum charging range of the charging pile:

[0157]

[0158] Where D is a sufficiently large constant, generally referring to the sum of all charging and discharging loads; A, B, and C represent the electric vehicle group, operator, and operator cost, respectively;

[0159] The optimal pricing strategy range for operators:

[0160] P min ≤P≤P max (17)

[0161] Where P min is the minimum charging power, P max is the maximum power;

[0162] First-order constraints:

[0163]

[0164] Second-order constraints:

[0165] H>0 (19)

[0166] Through the above process, the game model between the power grid, charging station operators and electric vehicle users is obtained.

[0167] In some preferred implementations, the above S3, based on the game model, obtains the optimal pricing of the charging station operator, and may further include the following operations:

[0168] S31, based on the game model, determine the optimal response function of each charging station operator, which means that when operator i knows the prices of other operators, the price that maximizes the profit is selected as the optimal pricing;

[0169] In some preferred implementations, the above S31, determining the optimal response function of each charging station operator, may further include the following operations:

[0170] Assume that the profit function of operator i is π i , the total market demand capacity is Q, then the operator's optimal response function is:

[0171]

[0172] Among them, π i (p i ,p -i ) is the maximum revenue of operator i;

[0173] If π i (p i ,p -i ) in p i It is quasi-concave, that is, when the first-order partial derivative of the payoff matrix is ​​zero (Formula (18)), the payoff has a maximum value. The positive definiteness of the second-order partial derivative of the Hessian matrix is ​​then used to verify the existence of a unique optimal solution. When H>0, positive definiteness exists, and the optimal response function is unique. When the first-order partial derivative is zero, there is an optimal solution, but the Hessian second-order partial derivative matrix does not satisfy the positive definiteness condition of H>0, which proves that there are multiple optimal solutions. At this time, the optimal solution is iteratively solved on the Hessian matrix until the result converges and is verified on the second-order Hessian matrix.

[0174] In some preferred implementations, the above S4, scheduling charging of electric vehicle users according to the optimal pricing, may further include the following operations:

[0175] According to the optimized electricity price, the number and distribution of charging piles and / or the grid load are dynamically adjusted to schedule the charging of electric vehicle users, thereby optimizing the grid load.

[0176] The technical solutions provided by the above embodiments of the present invention are further described in detail below.

[0177] The charging scheduling method based on potential game provided in the above embodiment of the present invention is implemented through the following parts.

[0178] I. Description of the Operator Competition Framework

[0179] 1. Market competition framework for charging station operators

[0180] In order to better determine the competitive relationship and market structure, it is necessary to analyze the electric vehicle charging network diagram. The electric vehicle charging network diagram can be regarded as a weighted undirected graph, such as Figure 2 As shown. A represents the electric vehicle group, B represents the charging station, the edge weight w represents the operator's pricing, which is generally the peak and valley electricity price, and the edge weight p represents the pricing strategy, which generally refers to the pricing strategy of the operator combined with the day-ahead charging demand q, and the connection and competition between charging stations of different operators.

[0181] In order to make the competition model purpose of the embodiment of the present invention clearer, the following framework is established: Figure 3 shown.

[0182] Due to the fierce competition among charging station operators in the current market, each operator will consider its own profits and thus formulate the optimal pricing strategy, and also improve user satisfaction.

[0183] 2. Basic setting of the operator competition framework

[0184] Based on the above description of the operator competition framework, the model of the operator market is set. Specifically, it includes:

[0185] 1) The load demand of charging stations is predicted based on historical data, and the historical data is accurate;

[0186] 2) The power grid operates stably, and there are no emergencies such as large-scale power outages;

[0187] 3) Cooperation between different operators is not considered for the time being;

[0188] 4) The operator's charging service fee is already included in the electricity price and does not need to be calculated separately.

[0189] 2. Game Model Establishment

[0190] 1. Game model description

[0191] In the embodiment of the present invention, the power grid company, the charging station operator and the user are the individuals in the three-party game. The power grid company and the charging station operator form a master-slave game relationship; the charging station operator and the charging operator and the electric vehicle user form a power game relationship. The master-slave game structure diagram between the power grid company and the charging station operator is as follows: Figure 4 The game structure diagram of power grid companies, charging station operators and electric vehicle users is shown in Figure 5 shown.

[0192] (1) The optimization problem of the operator is as follows, the decision variables are {p i , πi , R i , C i , ε i , N, q i 、p -i , f}. Assume that there are N charging station operators in a certain area, namely A1, A2, ···A i , the user demand elasticity for each operator is ε i , each operator can choose a different pricing strategy. Assume that the pricing strategy selected by operator i is p i , then the operator’s revenue function can be expressed as:

[0193] R i =p i ·q i (1)

[0194] Among them, q i Indicates the amount of electricity sold by the operator at a given selling price.

[0195] Then the operator's profit matrix can be expressed as:

[0196]

[0197] Among them, R A2B1 It represents the benefits brought by electric vehicle group 1 choosing to charge under this pricing strategy after operator No. 2 formulates the electricity price strategy.

[0198] The amount of electricity sold by operators satisfies the following relationship:

[0199]

[0200] The profit function of operator i is:

[0201] π i =R i -C i (4)

[0202] Among them, C i Denote as the cost of operator i.

[0203] Reaction function of operator i:

[0204]

[0205] Among them, p i represents the optimal pricing strategy of operator i; p -i represents the pricing strategies of other operators except operator i; f is a function including the prices of other operators.

[0206] In order to find the optimal pricing strategy among operators, it is necessary to maximize the operator's profit function:

[0207] maxπ i =p i ·(N·q i )-C i (6)

[0208] (2) The optimization problem of electric vehicle users, the lower-level decision makers in the game, is as follows:

[0209] The total user demand is:

[0210]

[0211] Charging station operators generally use reasonable pricing to encourage electric vehicle users to actively participate. Assume that the pricing coefficient of the charging station operator is λ, and electric vehicle users conduct interactive games within the given λ. In this embodiment of the present invention, a day is divided into 24 time periods of 1 hour each. The charging objective function of each electric vehicle is:

[0212]

[0213] In the formula, λ(t) is the price coefficient, c i That is the user waiting time cost coefficient, P i (t) represents the strategy p i The power during period t.

[0214] According to the supply and demand relationship, assuming that the electricity price increases with the load growth and decreases with the load reduction, we can get λ(t) and P i The relationship between (t) is as follows:

[0215]

[0216] Among them, a(t) represents the basic price in each period, and b(t) represents the relationship between the growth rate of the grid-connected electricity price of electric vehicles and the overall charging power of electric vehicles at this time.

[0217] In order to more specifically reflect the benefits that users enjoy when charging, a function that measures the user's waiting time cost is introduced and added to the user's total demand. Let the waiting time cost function be f t , where t represents the user waiting time. The total user demand is corrected to:

[0218]

[0219] Among them, t1, t2, t3…t iThey represent the waiting time of users charging at the operator. For the charging station operator, a charging station utilization index r is introduced, with a value between 0 and 1, and it is assumed that the number of users that the charging station can serve per unit time is m. The user waiting time can be specifically expressed as:

[0220]

[0221] Then by substituting each operator's reaction function back into the revenue function, we can get an equilibrium state of the system such that each operator cannot increase its revenue by changing its pricing strategy.

[0222] The objective function of total revenue is:

[0223]

[0224] This total benefit function is a nonlinear function, so a smooth differentiability analysis is required. i ) Take the partial derivative and set it equal to 0, and get the first-order condition:

[0225]

[0226] In the formula, R + Represents 1,2,3…n.

[0227] At the same time, in order to ensure the stability of the equilibrium state, it is necessary to analyze the second-order conditions. Next, it is necessary to determine whether the first-order conditions are the global optimal solution. To this end, it is necessary to introduce the second-order conditions when judging whether the selling price solution is the global optimal solution.

[0228] To calculate the Hessian matrix corresponding to each charging station, and determine whether the matrix is ​​positive definite. That is:

[0229]

[0230] If H is a positive definite matrix, the solution obtained by the first-order conditions of the three selling prices is the maximum value of the revenue. If H has a non-zero eigenvalue, it means that the current solution is not the optimal solution.

[0231] The constraints that need to be satisfied to achieve equilibrium are as follows:

[0232] (1) Market demand D constraint:

[0233] 0≤P i (t)≤P max (t) (15)

[0234] (2) The charging power of electric vehicles must be within the maximum charging range of the charging pile:

[0235]

[0236] (3) Optimal pricing strategy range for operators:

[0237]

[0238] (4) The first-order condition constraints are:

[0239]

[0240] (5) The second-order condition constraint is:

[0241] H>0 (19)

[0242] 2. Operator model optimal pricing strategy process

[0243] In the embodiment of the present invention, it is necessary to find a pricing strategy that satisfies the Nash equilibrium. The specific flow chart is as follows: Figure 6 shown.

[0244] In the competition game among operators, each operator will choose its own pricing according to its own interests, so it is necessary to determine the optimal response function of each operator. Suppose the profit function of the i-th operator is π i , the total market demand capacity is Q. Then the optimal response function of this operator can be expressed as:

[0245]

[0246] Among them, π i (p i ,p -i ) is the maximum benefit of participant i. If π i (p i ,p -i ) in p i is quasi-concave, that is, when the first-order partial derivative of the revenue function is zero, the revenue has a maximum value. Then the positive definiteness of the second-order partial derivative of the Hessian matrix is ​​used to verify the existence of a unique optimal solution. When H>0 is positive definite, the optimal response function is unique; when the first-order partial derivative is zero, there is an optimal solution but the Hessian second-order partial derivative matrix does not satisfy the positive definiteness condition of H>0, which proves that there are multiple optimal solutions. At this time, the optimal solution obtained should be iteratively solved on formula (14) until the result converges and verified on the second-order Hessian matrix; this optimal response function indicates that when the i-th operator knows the prices chosen by the other two operators, he will choose a price that maximizes his profit.

[0247] The practicability of the technical solution provided by the above embodiment of the present invention is explained below in conjunction with a verification example.

[0248] In this verification example, a community in Shanghai Pudong New Area was selected as the test area for case verification. The community includes three operators and 42 charging stations. The rated power of the charging pile is 120kW and the rated voltage is 200V-750V. The charging power of mainstream electric vehicles is used as the research, and the daily charging demand of electric vehicles is 20kWh. The charging station utilization threshold is 70%, and the construction cost coefficients of the three types of operators are 0.3, 0.4 and 0.5 respectively.

[0249] Most traditional charging station operators use a cost-plus pricing method, without considering user satisfaction and charging station market demand, as shown in Table 1.

[0250] Table 1 Profits under traditional pricing method

[0251]

[0252] This verification example calculates the optimal pricing of three charging station operators based on the potential game and the Nash equilibrium theory model. The calculation results are as follows:

[0253] The optimal pricing strategies are calculated to be 0.9, 0.8, and 1, which is the optimal price combination. This price combination can make the first-order derivative of its revenue function equal to 0, that is, the revenue function can reach its maximum value.

[0254] Table 2 Second-order Hessian matrix

[0255]

[0256]

[0257] The output determinant calculation result 178294141.46437 is greater than 0, which meets the positive definite requirement and the result is optimized. The optimal price strategy grid combination is used in the charging stations of the three operators respectively as the charging price of the operators, and their benefits can be calculated respectively. Table 2 shows the benefits obtained by the three operators at the three optimal pricing.

[0258] The profits obtained by the three operators under the three optimal pricing conditions of Nash equilibrium are shown in Table 3. It can be seen that the optimal pricing strategy can obtain good profits for different operators.

[0259] Table 3 Optimal pricing and revenue under the equilibrium model

[0260]

[0261] The above analysis shows the effectiveness of the pricing strategy provided by the above embodiment of the present invention. At the same time, the impact of factors such as the proportion of different types of electric vehicles, the capacity of energy storage equipment and the lower limit of pricing on the profit of dealers and the charging cost of electric vehicle owners is analyzed.

[0262] This verification example selects part of it as the measurement area for analysis. Assume that a total of 400 electric vehicle users in the area apply for charging. Figure 7 As shown, the average waiting time of users is calculated to be 22.43 minutes. Most users choose a nearby charging station when choosing a charging station. Although the distance is short, more users will choose it at the same time. When choosing a farther charging station, although there will not be too many people choosing it, the distance consumes more electricity. Therefore, the waiting time for users to charge in each time period does not change much.

[0263] By calculating the charging costs of users in different time periods, such as Figure 8 As shown in the figure, the distribution of user charging costs and waiting time can be obtained. A large number of electric vehicle users still choose the principle of charging nearby without considering the price factor. It is assumed that the charging stations with smaller numbers are relatively close to the users, and the charging stations with larger numbers are farther away. Fig. 9 shown.

[0264] When some users consider the price factor, some users will choose to charge at a charging station that is cheaper but farther away. Assuming that 30% of users consider the price factor, the distribution of users' choice of charging stations is as follows: Fig.10 shown.

[0265] Combined with the user selection, considering the impact of price factors on the choice of charging stations for electric vehicle users, the data output by the Baidu map interface is imported into the ArcGis software to obtain the congestion distribution of charging stations, and a part of the congested area is selected for price strategy comparison and verification. The peak and valley time division and calculation results are as follows:

[0266] Table 4 Peak and valley time division

[0267]

[0268] Table 5. Example results of the Pudong test area

[0269]

[0270] By using the pricing strategy proposed in the above embodiment of the present invention as operator pricing to guide users to charge, the average waiting time of electric vehicles is reduced to 3.308 minutes, and the total revenue of the three charging stations is 21,853 yuan. By using the peak-valley time-of-use electricity price method to guide electric vehicles in the area, the maximum waiting time of electric vehicles is reduced to 2.32 minutes, and the total revenue is 20,446 yuan.

[0271] It can be seen from the above verification examples that the charging scheduling method based on potential game provided in the above embodiments of the present invention can not only improve the interests of charging station operators, but also make full use of charging stations, maximize the interests of charging stations, improve the congestion of charging station areas, and optimize the number and distribution of charging piles in charging stations and the grid load.

[0272] Based on the same inventive concept, an embodiment of the present invention provides a charging scheduling system based on potential game.

[0273] Specifically, Fig.11 As shown, the charging scheduling system based on potential game provided in this embodiment may include the following modules:

[0274] A relationship building module, which is used to provide an electric vehicle charging relationship network and establish a market competition framework for charging station operators based on the network; wherein the charging relationship network is used to describe the game relationship between electric vehicle groups and charging stations, and the market competition framework is used to describe the competitive relationship between charging station operators;

[0275] The game model module, which builds a game model between the power grid, charging station operators and electric vehicle users based on the market competition framework of charging station operators;

[0276] The optimal pricing determination module, which is based on a game model to obtain the optimal pricing for charging station operators;

[0277] The charging scheduling module is used to dynamically adjust the number and distribution of charging piles and / or grid load according to the optimal pricing, and schedule the charging of electric vehicle users.

[0278] It should be noted that the steps in the method provided by the present invention can be implemented by using corresponding modules, devices, units, etc. in the system. Those skilled in the art can refer to the technical solution of the method to realize the composition of the system, that is, the embodiments in the method can be understood as preferred examples for constructing the system, which will not be elaborated here.

[0279] An embodiment of the present invention further provides a computer terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor can be used to execute any method of the above-mentioned embodiments of the present invention, or to run any system of the above-mentioned embodiments of the present invention.

[0280] Optionally, the memory is used to store programs; the memory may include volatile memory (English: volatile memory), such as random-access memory (English: random-access memory, abbreviated: RAM), such as static random-access memory (English: static random-access memory, abbreviated: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic Random Access Memory, abbreviated: DDR SDRAM), etc.; the memory may also include non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory). The memory is used to store computer programs (such as applications, functional modules, etc. that implement the above method), computer instructions, etc., and the above computer programs, computer instructions, etc. can be partitioned and stored in one or more memories. And the above computer programs, computer instructions, data, etc. can be called by the processor.

[0281] The above-mentioned computer programs, computer instructions, etc. may be stored in one or more memories in partitions, and the above-mentioned computer programs, computer instructions, data, etc. may be called by a processor.

[0282] The processor is used to execute the computer program stored in the memory to implement the various steps of the method or various modules of the system involved in the above embodiments. For details, please refer to the relevant descriptions in the above method and system embodiments.

[0283] The processor and the memory may be independent structures or integrated structures. When the processor and the memory are independent structures, the memory and the processor may be coupled and connected via a bus.

[0284] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it can be used to execute any method of the above embodiments of the present invention, or to run any system of the above embodiments of the present invention.

[0285] Among them, computer-readable media include computer storage media and communication media, wherein the communication media include any media that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general or special-purpose computer. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a user device. Of course, the processor and the storage medium can also be present in a communication device as discrete components.

[0286] The charging scheduling method, system, terminal and medium based on potential game provided by the above-mentioned embodiments of the present invention overcome the defect of complete rationality of participants in classical game theory, and construct an evolutionary game model of EV charging and discharging scheduling based on limited rationality and limited information, characterize the game behavior of EV users in actual decision-making scenarios, and avoid idealization of game conclusions; construct an EV scheduling evolutionary model of multiple strategy sets based on the classical evolutionary game model of limited strategies to solve the contradiction between the non-constant EV charging power and the priority strategy of evolutionary game; the proposed game model takes into account the double-layer coupling relationship of bidding in the demand response process, and establishes game models between the power grid (DSO), charging station operator (EVA) and electric vehicle user (EV); EV charging and discharging scheduling and EVA bidding are taken as a joint problem to be solved in the power market. By using first-order and second-order conditions as constraints, the choice space of pricing strategies is limited, thereby ensuring that the optimal solution complies with market rules and the principle of maximizing the interests of operators, and then scheduling charging stations based on the final pricing obtained to improve the congestion in the charging station area; compared with the peak and valley time-of-use electricity price, the operator's optimal pricing can increase the total revenue of the charging station and reduce the average waiting time of users. While improving the interests of charging station operators, the charging stations can also be fully utilized, and while maximizing the interests of the charging stations, the congestion in the charging station area can be improved, and the number and distribution of charging piles in the charging station and the grid load can be optimized.

[0287] All matters not covered in the above embodiments of the present invention are well known in the art.

[0288] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art may make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A charging scheduling method based on potential game, characterized in that: include: A charging relationship network of electric vehicles is provided, and a market competition framework of charging station operators is established based on the network; wherein the charging relationship network is used to describe the game relationship between electric vehicle groups and charging stations, and the market competition framework is used to describe the competitive relationship between charging station operators; Based on the market competition framework of charging station operators, a game model between power grids, charging station operators and electric vehicle users is constructed; Based on the game model, optimal pricing for charging station operators is obtained; According to the optimal pricing, the number and distribution of charging piles and / or grid load are dynamically adjusted to schedule the charging of electric vehicle users.

2. The charging scheduling method based on potential game according to claim 1 is characterized in that: The provision of a charging relationship network for an electric vehicle includes: A weighted undirected graph of an electric vehicle charging network is constructed, including: an electric vehicle group A and a plurality of charging stations B connected to the electric vehicle group A, forming nodes of the undirected graph; different charging stations B provide pre-optimized charging strategies to the electric vehicle group A according to their own edge weights, and after receiving the charging strategies, the electric vehicle group feeds back to the charging station B according to its own charging needs, and optimizes and iterates the charging strategies, and obtains the optimal charging strategy after convergence, forming edges of the undirected graph.

3. The charging scheduling method based on potential game according to claim 1 is characterized in that: The market competition framework for charging station operators is established based on the network, including: operators formulate charging strategies according to time-of-use electricity prices, users select charging strategies and upload charging requirements, so that operators can optimize charging strategies.

4. The charging scheduling method based on potential game according to claim 1 is characterized in that: The market competition framework based on the charging station operator is used to construct a game model between the operator and the electric vehicle user, including: Based on the market competition framework of the charging station operators, the three game entities of the power grid, the charging station operators and the users are obtained; among which, the power grid company and the charging station operators form a master-slave game relationship, and the charging station operators form a power game relationship with the charging operators and the electric vehicle users; Construct the operator's optimization problem, including: Assume the decision variables of the operator are {p i , π i , R i , C i , ε i , N, q i 、p -i , f}, where p i is the pricing strategy chosen by operator i, π i is the profit of operator i, R i is the revenue of operator i; C i is the cost of operator i, ε i is the user demand elasticity for each operator, N is the number of charging station operators in the set area, q i is the amount of electricity sold by operator i at a given price, p -i is the pricing strategy of other operators except operator i, and f is a function used to describe the relationship between the pricing strategies of other operators; When the pricing strategy selected by operator i is p i When , the objective function of the operator is: R i =p i ·q i (1) The profit matrix of operator i is expressed as: Where Payoff represents the operator’s revenue matrix, It represents the benefits brought by the electric vehicle group n choosing to charge under the pricing strategy formulated by operator m; The amount of electricity sold by operators satisfies the following relationship: The profit function of operator i is: p i =R i -C i (4) The reaction function of operator i is: in, represents the optimal pricing strategy of operator i; The profit function that maximizes the operator is: maxπ i =p i ·(N·q i )-C i (6) Construct an optimization problem for electric vehicle users, including: The total user demand is: Assuming the pricing coefficient of the charging station operator is λ, the charging objective function of each electric vehicle is: In the formula, G is the charging price of electric vehicles, λ(t) is the price coefficient, and c i That is the user waiting time cost coefficient, P i (t) represents the strategy p i The power in period t, Δt is the charging period; According to the supply and demand relationship, we can get λ(t) and P i The relationship between (t) is: In the formula, a(t) represents the basic price in each period, and b(t) represents the relationship between the increase rate of the electric vehicle grid access electricity price and the overall charging power of the electric vehicle at this time; Introduce the waiting time cost function as f t , where t represents the user waiting time, then the total user demand is corrected to: Where, t1, t2, t3…t i They respectively represent the waiting time for users to charge at the operator; Introducing the charging station utilization index r, assuming that the number of users that a charging station can serve per unit time is m, and the user waiting time is t i It is expressed as: Substituting the reaction function of each operator shown in formula (5) back into the profit function shown in formula (6), we get a system equilibrium state, then the total revenue of the operator is R total The objective function is: The objective function of the total benefit is (p1, p2, p3…p i ) Take the partial derivative and set it equal to 0, and get the first-order condition: In the formula, π is the operator’s profit; The introduction of the second-order condition requires calculating the Hessian matrix H corresponding to each charging station and determining whether the matrix is ​​positive definite, that is: Where R is an element in the profit matrix, i.e., the profit obtained by the mth electric car under the nth strategy; If H is a positive definite matrix, the solution obtained by the first-order conditions of the three selling prices is the maximum value of the revenue; if H has a non-zero eigenvalue, it means that the current solution is not the optimal solution; The following constraints are imposed on the parameters in the optimization problem of the operator and the optimization problem of the electric vehicle user: Market demand constraints: 0≤P i (t)≤P max (t) (15) Where, P max (t) is the maximum power of the electric vehicle; The charging power of electric vehicles must be within the maximum charging range of the charging pile: In the formula, D is used to represent the sum of all charging and discharging loads; A, B, and C represent the electric vehicle group, operator, and operator cost respectively; The optimal pricing strategy range for operators: P min ≤P≤P max (17) Where P min is the operator's minimum power; P max is the maximum power of the operator; First-order constraints: Second-order constraints: H>0 (19) Through the above process, the game model between the power grid, charging station operators and electric vehicle users is obtained.

5. The charging scheduling method based on potential game according to claim 1 is characterized in that: Based on the game model, the optimal pricing for charging station operators is obtained, including: Based on the game model, the optimal response function of each charging station operator is determined, which means that when operator i knows the prices of other operators, the price that maximizes profit is selected as the optimal pricing.

6. The charging scheduling method based on potential game according to claim 5 is characterized in that: The determining of the optimal response function of each charging station operator may further include the following operations: Assume that the profit function of operator i is π i , the total market demand capacity is Q, then the operator's optimal response function is: Among them, π i (p i ,p -i ) is the maximum revenue of operator i; If π i (p i ,p -i ) in p i It is quasi-concave, that is, when the first-order partial derivative of the payoff matrix shown in formula (18) is zero, the payoff has a maximum value. Then the positive definiteness of the second-order partial derivative of the Hessian matrix is ​​used to verify the existence of a unique optimal solution. When H>0, positive definiteness exists, then the optimal response function is unique. When the first-order partial derivative is zero, there is an optimal solution but the Hessian second-order partial derivative matrix does not satisfy the positive definiteness condition of H>0, which proves that there are multiple optimal solutions. At this time, the optimal solution is iteratively solved on the Hessian matrix until the result converges and is verified on the second-order Hessian matrix.

7. The charging scheduling method based on potential game according to claim 1 is characterized in that: The step of scheduling charging of electric vehicle users according to the optimal pricing includes: Based on the optimal electricity price selected by the electric vehicle user, the charging station managed by the corresponding operator is selected to realize the charging scheduling of the electric vehicle, so as to meet the optimization of the power grid load.

8. A charging scheduling system based on potential game, characterized in that: include: A relationship building module, which is used to provide a charging relationship network of electric vehicles and establish a market competition framework for charging station operators based on the network; wherein the charging relationship network is used to describe the game relationship between electric vehicle groups and charging stations, and the market competition framework is used to describe the competitive relationship between charging station operators; A game model module, which constructs a game model between the power grid, the charging station operator and the electric vehicle user based on the market competition framework of the charging station operator; an optimal pricing determination module, which obtains the optimal pricing of the charging station operator based on the game model; A charging scheduling module is used to dynamically adjust the number and distribution of charging piles and / or grid load according to the optimal pricing, and schedule the charging of electric vehicle users.

9. A computer terminal 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, it can be used to perform the method described in any one of claims 1 to 7, or to run the system described in claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can be used to perform the method described in any one of claims 1 to 7, or to run the system described in claim 8.

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