Electric vehicle aggregator bidding and pricing method and system based on multiple game relationships

Abstract

The invention provides an electric vehicle aggregator bidding pricing method and system based on multiple game relationships, and the method comprises the steps: S1, constructing a charging strategy evolutionary game decision model considering the bounded rationality of an electric vehicle user, and enabling a charging strategy to achieve the evolutionary game equilibrium; s2, an electric vehicle aggregator bidding pricing non-cooperative game model based on incomplete information is constructed, and the expected income of a charging station set by each electric vehicle aggregator can be obtained; and S3, according to the charging strategy evolutionary game decision model and the electric vehicle aggregator bidding pricing non-cooperative game model, constructing a master-slave game model between the electric vehicle aggregator and the electric vehicle user, and obtaining an optimal electric vehicle aggregator competitive bidding pricing strategy. According to the method, effective support can be provided for the electric vehicle aggregator to participate in the bidding pricing decision of the energy market, and the optimal bidding pricing strategy can be obtained on the premise of fully considering the faced multiple game relationships.

Description

Technical Field

[0001] The present invention relates to the field of power market bidding, and in particular, to a bidding pricing method and system for electric vehicle aggregators based on multiple game relationships, and in particular, to a bidding pricing method for electric vehicle aggregators to participate in energy markets. Background Art

[0002] Huge fossil fuel consumption and greenhouse gas emissions have led to an increasingly serious energy crisis. The promotion of electric vehicles is seen as an effective way to alleviate these serious problems. However, the large-scale popularization of electric vehicles has greatly increased the charging load of electric vehicles in the distribution network, and there is a risk of overload in the distribution network during peak load periods. In order to coordinate the charging behavior of electric vehicles, the concept of electric vehicle aggregators is introduced, who operate some smart charging facilities in a region. As an intermediary between distribution system operators and electric vehicle users, electric vehicle aggregators conduct arbitrage by purchasing electricity from the day-ahead power market and providing charging services to electric vehicle users, thereby activating electric vehicle users to adjust their charging needs according to the state of the power system.

[0003] The decision-making of EV aggregators involves multiple entities, making the whole process very complicated. On the one hand, since there are multiple EV aggregators providing charging services to a limited number of EV users at the same time, each EV aggregator must compete with other EV aggregators to set competitive charging prices to attract more EV users and thus increase market profits. Therefore, EV aggregators must accurately consider the strategies of their competitors and formulate optimal bidding and pricing strategies. On the other hand, since each EV user has different driving patterns and decision-making preferences, EV aggregators must also consider the interactive behaviors of a large number of EVs.

[0004] In general, EV aggregators face three types of game behaviors in the bidding pricing decision process: (1) Non-cooperative game between EV aggregators. EV aggregators compete for limited charging demand by setting attractive charging prices. Therefore, the profit of EV aggregators depends not only on their own pricing strategy but also on the pricing strategy of their competitors. In addition, in the actual pricing game process, each EV aggregator usually cannot clearly know the specific pricing strategy of its competitors. Therefore, it is necessary to consider the incompleteness of available information in the non-cooperative game process of EV aggregators. (2) Non-cooperative charging decision between EV users. Each EV user makes the best charging decision by weighing the minimization of charging cost and the adjustment of initial driving mode. Since the charging price depends on the supply and demand relationship in each time period, the charging cost of each EV user is affected by the aggregated charging strategy of all EV users. In other words, the charging strategy of EV users is not only related to themselves, but also affected by the charging strategies of other EV users. Therefore, the relationship between EV users is essentially a non-cooperative game. Due to limited cognitive ability and a certain tolerance for suboptimal charging strategies, EV users usually exhibit irrational characteristics. In the non-cooperative charging game process of electric vehicle users, their limited rationality characteristics need to be fully considered (3) the master-slave game between electric vehicle aggregators and electric vehicle users. Usually, electric vehicle users determine their charging strategies based on the published electric vehicle charging prices to save charging costs. Then, electric vehicle aggregators will adjust the charging prices according to specific charging needs to maximize profits.

[0005] A Chinese patent document with publication number CN114662759A discloses a method for optimizing the charging and discharging of electric vehicles on a large scale based on a multi-agent two-layer game, and is specifically implemented according to the following steps: constructing a multi-strategy set evolutionary game model for electric vehicle charging and discharging scheduling based on the logit protocol, and obtaining the optimal charging and discharging power of electric vehicles in each time period through an evolutionary equilibrium solution algorithm; constructing a non-cooperative game model for multiple electric vehicle aggregators to bid for the purchase / sale price of electricity in the power market; using the replicator dynamics in evolutionary game theory to describe the strategy evolution of the distribution network operator in allocating the response power to each aggregator during the demand response period; proposing a method for jointly solving the evolutionary equilibrium solution of the evolutionary game and the Nash equilibrium solution of the non-cooperative game, and obtaining the optimal stable strategy of the three subjects of the two-layer game model; although the invention comprehensively considers the three game relationships, it does not take into account the incompleteness of the information that the electric vehicle aggregator can obtain from its competitors during the pricing game, and still assumes that each electric vehicle aggregator can clearly know the specific pricing strategy of its competitor, resulting in the lack of practicality of the specific game means.

[0006] Due to the complexity of the bidding pricing decisions of electric vehicle aggregators, there has been no research on the competitive bidding pricing method of electric vehicle aggregators that comprehensively considers these three game relationships and fully considers the limited rationality of users and the incomplete information available to aggregators. The research on this issue has certain practical significance. Summary of the invention

[0007] In view of the defects in the prior art, the purpose of the present invention is to provide an electric vehicle aggregator bidding pricing method and system based on multiple game relationships.

[0008] According to the present invention, a bidding pricing method for electric vehicle aggregators based on multiple game relationships includes:

[0009] Step S1: construct a charging strategy evolutionary game decision model that takes into account the limited rationality of electric vehicle users, so that the charging strategy can reach the evolutionary game equilibrium;

[0010] Step S2: constructing a non-cooperative game model for bidding pricing of electric vehicle aggregators based on incomplete information, which can obtain the expected revenue of charging stations set by each electric vehicle aggregator;

[0011] Step S3: Based on the charging strategy evolution game decision model and the electric vehicle aggregator bidding pricing non-cooperative game model, a master-slave game model between electric vehicle aggregators and electric vehicle users is constructed, and the optimal electric vehicle aggregator competitive bidding pricing strategy is obtained through iterative solution.

[0012] Preferably, the step S1 comprises:

[0013] Step S11: setting charging strategy constraints for electric vehicle users and integrating them into a charging strategy set;

[0014] Step S12: Initialize the probability of each charging strategy and calculate the individual fitness function value;

[0015] Step S13: According to the charging strategy set and the individual fitness function value, a charging strategy evolutionary game decision model is constructed through the Logit dynamic function to calculate the evolutionary game equilibrium of the individual charging strategy.

[0016] Preferably, the step S11 includes:

[0017] The electric vehicle user charging strategy constraints include electric vehicle user transfer constraints, electric vehicle charging load constraints and charge state constraints; specifically:

[0018] The electric vehicle user transfer constraint is in, is a Boolean variable;

[0019] Determine whether electric vehicle x is charged from the charging station operated by aggregator i to another charging station operated by aggregator j. If so, then is 1, if not, then is 0;

[0020] The charging load constraint is:

[0021]

[0022] Where: P i,x,t is the charging load of EV x belonging to aggregator i at time t; is the maximum charging load of EV x belonging to aggregator i; and are the time when electric vehicle x belonging to aggregator i arrives at the charging station and the time when it leaves the charging station, respectively;

[0023] P i,x,t The state of charge constraint is also satisfied, which is:

[0024]

[0025] Where: S i,x,t is the state of charge of electric vehicle x belonging to aggregator i at time t; and are the upper and lower limits of the state of charge of electric vehicle x of aggregator i; E i,j E is the additional amount of electricity that an electric vehicle needs to consume when it moves from a charging station operated by aggregator i to a charging station operated by aggregator j; i,x is the battery capacity of electric vehicle x of aggregator i; and are the state of charge of electric vehicle x of aggregator i when it arrives at the charging station and the expected state of charge when it leaves the charging station; Δt is the unit time interval, and η represents the charging efficiency of the electric vehicle;

[0026] According to the charging strategy constraints of electric vehicle users, generate the charging strategy set Φ of electric vehicle x belonging to aggregator i i,x ,for:

[0027]

[0028] Where: s i,x,p is the pth charging strategy, which includes charging load and user transfer conditions; P i,x,1,p is the charging load of EV x of aggregator i at time t under the pth charging strategy.

[0029] Preferably, the step S12 includes:

[0030] Individual fitness function:

[0031] f i,x =-∑ p α i,x,p U i,x,p

[0032] Where: f i,x is the individual fitness function value of the electric car x belonging to aggregator i; α i,x,p is the initial probability of charging strategy p of electric vehicle x belonging to aggregator i, which is a preset value;

[0033] Among them, U i,x,p is the comprehensive charging cost of charging strategy p for EV x belonging to aggregator i, and is;

[0034]

[0035] Where: U i,x,p is the comprehensive charging cost of charging strategy p for EV x belonging to aggregator i; is the charging price of the charging station operated by aggregator j at time t; The additional cost of charging an EV from a charging station operated by aggregator i to a charging station operated by aggregator j.

[0036] Preferably, the step S13 comprises:

[0037] The logit dynamic function is:

[0038]

[0039] Where: i,x,p,q is the probability of transition of electric vehicle x belonging to aggregator i from charging strategy p to charging strategy q in the evolutionary game process; σ is the noise level;

[0040] Calculate the evolutionary game equilibrium of individual charging strategies: Let The charging strategy of electric vehicle x of aggregator i reaches evolutionary game equilibrium.

[0041] Preferably, the step S2 comprises:

[0042] Step S21: Establishing an objective function for maximizing charging benefits;

[0043] Step S22: setting constraints so that the objective function of maximizing charging revenue satisfies the constraints, wherein the constraints include upper and lower limits of charging prices and elasticity constraints of charging demand;

[0044] Step S23: According to the objective function of maximizing the charging revenue after constraints, a non-cooperative game model of bidding pricing for multiple electric vehicle aggregators based on incomplete information is established, and the expected revenue of each electric vehicle aggregator is calculated.

[0045] Preferably, it includes:

[0046] According to the charging strategy evolution game decision model, P is calculated i,t :

[0047]

[0048] The probability of the evolutionary game equilibrium charging strategy of electric vehicle x of aggregator j is expressed as

[0049] The objective function for maximizing the revenue of electric vehicle aggregator i is established as:

[0050]

[0051] Where: are the electricity purchase costs of the charging stations operated by aggregator i at time t;

[0052] in, At the same time:

[0053]

[0054] Where: and are the upper and lower limits of the charging prices of the charging stations operated by aggregator i, forming a pricing range; The charging price is is the initial charging demand of aggregator i at time t, T is the total number of time slots in a day, γ i and a i is the coefficient of the demand elasticity equation.

[0055] Preferably, the step S23 includes:

[0056] Incomplete information characterization rules for setting the revenue pricing strategy of electric vehicle aggregators:

[0057] G i = {g i,1 ,g i,2 ,…,g i,L} is the pricing strategy set of electric vehicle aggregator i, which includes L pricing strategies, and the lth pricing strategy According to the upper and lower limits of the charging price, a pricing range is formed, and it meets and The corresponding probability of each pricing strategy is β i,l;

[0058] Establish a non-cooperative game model of bidding pricing for multiple electric vehicle aggregators based on incomplete information, and calculate the pricing strategy g adopted by electric vehicle aggregator i i,l Expected return:

[0059]

[0060] AG i,l,r When the pricing strategy of electric vehicle aggregator i is g i,l , the pricing strategy of electric vehicle aggregator j is g j,r The maximum revenue of electric vehicle aggregator i when ;

[0061] When the profit is maximized, the non-cooperative game of electric vehicle aggregators based on incomplete information reaches a Nash equilibrium.

[0062] Preferably, step S3 comprises:

[0063] Step S31: According to the non-cooperative game model of bidding pricing for electric vehicle aggregators, the electric vehicle aggregators are required to select a preset pricing range strategy, and the charging prices of all electric vehicle aggregators are initialized to the lower limit of the pricing range, and i=1;

[0064] Step S32: Selecting electric vehicle aggregator i so that the upper limit of the charging price is equal to the lower limit;

[0065] Step S33: Keep the charging price of electric vehicle aggregator j unchanged, and solve the evolutionary game equilibrium of electric vehicle charging according to the charging strategy evolutionary game decision model, and calculate the final charging load of all electric vehicles;

[0066] Step S34: Calculate the revenue of electric vehicle aggregator i through the current charging price according to the non-cooperative game model of bidding pricing of electric vehicle aggregators;

[0067] Step S35: Determine whether the charging price exceeds the upper limit of the preset pricing range. If it does not exceed the upper limit, add the preset amount to the charging price to update the charging price, and return to step S33. If it exceeds the upper limit, proceed to step S36.

[0068] Step S36: Compare the charging benefits corresponding to different charging prices, and select the charging price that maximizes the benefit;

[0069] Step S37: Set the total number of electric vehicle aggregators to I, let i=i+1, compare I with i, if i>I, proceed to step S38, if i<I, return to step S32, until all electric vehicle aggregators reach the charging price that maximizes their profits;

[0070] Step S38: The charging price reaches a Nash equilibrium, and the charging strategy corresponding to the charging price is the optimal competitive bidding pricing strategy of electric vehicle aggregators.

[0071] According to the present invention, a bidding pricing system for electric vehicle aggregators based on multiple game relationships is provided, comprising:

[0072] Module M1: used to construct a charging strategy evolutionary game decision model that takes into account the limited rationality of electric vehicle users, which can enable the charging strategy to reach evolutionary game equilibrium;

[0073] Module M2: used to construct a non-cooperative game model for bidding pricing of electric vehicle aggregators based on incomplete information, which can obtain the expected revenue of charging stations set by each electric vehicle aggregator;

[0074] Module M3: used to construct a master-slave game model between electric vehicle aggregators and electric vehicle users, and obtain the optimal competitive bidding pricing strategy for electric vehicle aggregators through iterative solution.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] 1. The present invention comprehensively considers the game competition faced by electric vehicle users in the charging decision-making process and their own limited rational nature, and can more accurately model the charging decision-making behavior of electric vehicle users, and assist electric vehicle aggregators in making optimal bidding pricing decisions;

[0077] 2. The present invention can provide a competitive bidding pricing strategy for electric vehicle aggregators that comprehensively considers multiple game relationships. In the decision-making process, the limited rationality of electric vehicle users and the incompleteness of information available to electric vehicle aggregators are fully considered. It can provide effective support for the bidding pricing decision-making of electric vehicle aggregators participating in the energy market. It has engineering practical value and can obtain the optimal bidding pricing strategy under the premise of fully considering the multiple game relationships faced.

[0078] 3. The present invention constructs a charging strategy evolutionary game decision model that takes into account the limited rationality of electric vehicle users, which can simulate the selection behavior of electric vehicle users when facing different charging strategies, and guide these behaviors to gradually tend to a stable evolutionary game equilibrium state, which helps to improve the rationality of electric vehicle charging strategies and reduce conflicts and uncertainties in the charging process;

[0079] 4. The present invention constructs a master-slave game model between electric vehicle aggregators and electric vehicle users, comprehensively considers the needs of electric vehicle users and the benefits of electric vehicle aggregators, and finds the optimal competitive bidding pricing strategy for electric vehicle aggregators through iterative solution. It not only meets the charging needs of electric vehicle users, but also ensures that electric vehicle aggregators can obtain reasonable benefits, thus achieving a win-win situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] 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:

[0081] Figure 1 It is a flow chart of the bidding pricing method for electric vehicle aggregators based on multiple game relationships in the present invention;

[0082] Figure 2 Schematic diagram of the process of the method of embodiment 1 of the present invention;

[0083] Figure 3 Schematic diagram of the process of step S3 in embodiment 1 of the present invention;

[0084] Figure 4 This is a topological diagram of the power distribution network in Example 1 of the present invention. DETAILED DESCRIPTION

[0085] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0086] In view of the defects in the prior art, the purpose of the present invention is to provide a bidding pricing method and system for electric vehicle aggregators based on a comprehensive multiple game relationships, which can provide effective support for the bidding pricing decisions of electric vehicle aggregators participating in the energy market, has engineering practical value, and can obtain the optimal bidding pricing strategy under the premise of fully considering the multiple game relationships faced.

[0087] According to the present invention, a bidding pricing method for electric vehicle aggregators based on multiple game relationships includes:

[0088] Step S1: construct a charging strategy evolutionary game decision model that takes into account the limited rationality of electric vehicle users, so that the charging strategy can reach the evolutionary game equilibrium;

[0089] Step S2: constructing a non-cooperative game model for bidding pricing of electric vehicle aggregators based on incomplete information, which can obtain the expected revenue of charging stations set by each electric vehicle aggregator;

[0090] Step S3: Based on the charging strategy evolution game decision model and the electric vehicle aggregator bidding pricing non-cooperative game model, a master-slave game model between electric vehicle aggregators and electric vehicle users is constructed, and the optimal electric vehicle aggregator competitive bidding pricing strategy is obtained through iterative solution.

[0091] Specifically, the step S1 includes:

[0092] Step S11: setting charging strategy constraints for electric vehicle users and integrating them into a charging strategy set;

[0093] Step S12: Initialize the probability of each charging strategy and calculate the individual fitness function value;

[0094] Step S13: According to the charging strategy set and the individual fitness function value, a charging strategy evolutionary game decision model is constructed through the Logit dynamic function to calculate the evolutionary game equilibrium of the individual charging strategy.

[0095] Specifically, the step S11 includes:

[0096] The electric vehicle user charging strategy constraints include electric vehicle user transfer constraints, electric vehicle charging load constraints and charge state constraints; specifically:

[0097] The electric vehicle user transfer constraint is in, is a Boolean variable;

[0098] Determine whether electric vehicle x is charged from the charging station operated by aggregator i to another charging station operated by aggregator j. If so, then is 1, if not, then is 0;

[0099] The charging load constraint is:

[0100]

[0101] Where: P i,x,t is the charging load of EV x belonging to aggregator i at time t; is the maximum charging load of EV x belonging to aggregator i; and are the time when electric vehicle x belonging to aggregator i arrives at the charging station and the time when it leaves the charging station, respectively;

[0102] P i,x,tThe state of charge constraint is also satisfied, which is:

[0103]

[0104] Where: S i,x,t is the state of charge of electric vehicle x belonging to aggregator i at time t; and are the upper and lower limits of the state of charge of electric vehicle x of aggregator i; E i,j E is the additional amount of electricity that an electric vehicle needs to consume when it moves from a charging station operated by aggregator i to a charging station operated by aggregator j; i,x is the battery capacity of electric vehicle x of aggregator i; and are the state of charge of electric vehicle x of aggregator i when it arrives at the charging station and the expected state of charge when it leaves the charging station; Δt is the unit time interval, and η represents the charging efficiency of the electric vehicle;

[0105] According to the charging strategy constraints of electric vehicle users, generate the charging strategy set Φ of electric vehicle x belonging to aggregator i i,x ,for:

[0106]

[0107] Where: s i,x,p is the pth charging strategy, which includes charging load and user transfer conditions; P i,x,1,p is the charging load of EV x of aggregator i at time t under the pth charging strategy.

[0108] Specifically, the step S12 includes:

[0109] Individual fitness function:

[0110] f i,x =-∑ p α i,x,p U i,x,p

[0111] Where: f i,x is the individual fitness function value of the electric car x belonging to aggregator i; α i,x,p is the initial probability of charging strategy p of electric vehicle x belonging to aggregator i, which is a preset value;

[0112] Among them, U i,x,p is the comprehensive charging cost of charging strategy p for EV x belonging to aggregator i, and is;

[0113]

[0114] Where: Ui,x,p is the comprehensive charging cost of charging strategy p for EV x belonging to aggregator i; is the charging price of the charging station operated by aggregator j at time t; The additional cost of charging an EV from a charging station operated by aggregator i to a charging station operated by aggregator j.

[0115] Specifically, step S13 includes:

[0116] The logit dynamic function is:

[0117]

[0118] Where: i,x,p,q is the probability of transition of electric vehicle x belonging to aggregator i from charging strategy p to charging strategy q in the evolutionary game process; σ is the noise level;

[0119] Calculate the evolutionary game equilibrium of individual charging strategies: Let The charging strategy of electric vehicle x of aggregator i reaches evolutionary game equilibrium.

[0120] Specifically, step S2 includes:

[0121] Step S21: Establishing an objective function for maximizing charging benefits;

[0122] Step S22: setting constraints so that the objective function of maximizing charging revenue satisfies the constraints, wherein the constraints include upper and lower limits of charging prices and elasticity constraints of charging demand;

[0123] Step S23: According to the objective function of maximizing the charging revenue after constraints, a non-cooperative game model of bidding pricing for multiple electric vehicle aggregators based on incomplete information is established, and the expected revenue of each electric vehicle aggregator is calculated.

[0124] Specifically, they include:

[0125] According to the charging strategy evolution game decision model, P is calculated i,t :

[0126]

[0127] The probability of the evolutionary game equilibrium charging strategy of electric vehicle x of aggregator j is expressed as

[0128] The objective function for maximizing the revenue of electric vehicle aggregator i is established as:

[0129]

[0130] Where: are the electricity purchase costs of the charging stations operated by aggregator i at time t;

[0131] in, At the same time:

[0132]

[0133] Where: and are the upper and lower limits of the charging prices of the charging stations operated by aggregator i, forming a pricing range; The charging price is is the initial charging demand of aggregator i at time t, T is the total number of time slots in a day, γ i and a i is the coefficient of the demand elasticity equation.

[0134] Specifically, step S23 includes:

[0135] Incomplete information characterization rules for setting the revenue pricing strategy of electric vehicle aggregators:

[0136] G i = {g i,1 ,g i,2 ,…,g i,L} is the pricing strategy set of electric vehicle aggregator i, which includes L pricing strategies, and the lth pricing strategy According to the upper and lower limits of the charging price, a pricing range is formed, and it meets and The corresponding probability of each pricing strategy is β i,l ;

[0137] Establish a non-cooperative game model of bidding pricing for multiple electric vehicle aggregators based on incomplete information, and calculate the pricing strategy g adopted by electric vehicle aggregator i i,l Expected return:

[0138]

[0139] AG i,l,r When the pricing strategy of electric vehicle aggregator i is g i,l , the pricing strategy of electric vehicle aggregator j is g j,r The maximum revenue of electric vehicle aggregator i when ;

[0140] When the profit is maximized, the non-cooperative game of electric vehicle aggregators based on incomplete information reaches the Nash equilibrium.

[0141] Specifically, step S3 includes:

[0142] Step S31: According to the non-cooperative game model of bidding pricing for electric vehicle aggregators, the electric vehicle aggregators are required to select a preset pricing range strategy, and the charging prices of all electric vehicle aggregators are initialized to the lower limit of the pricing range, and i=1;

[0143] Step S32: Selecting electric vehicle aggregator i so that the upper limit of the charging price is equal to the lower limit;

[0144] Step S33: Keep the charging price of electric vehicle aggregator j unchanged, and solve the evolutionary game equilibrium of electric vehicle charging according to the charging strategy evolutionary game decision model, and calculate the final charging load of all electric vehicles;

[0145] Step S34: Calculate the revenue of electric vehicle aggregator i through the current charging price according to the non-cooperative game model of bidding pricing of electric vehicle aggregators;

[0146] Step S35: Determine whether the charging price exceeds the upper limit of the preset pricing range. If it does not exceed the upper limit, add the preset amount to the charging price to update the charging price, and return to step S33. If it exceeds the upper limit, proceed to step S36.

[0147] Step S36: Compare the charging benefits corresponding to different charging prices, and select the charging price that maximizes the benefits;

[0148] Step S37: Set the total number of electric vehicle aggregators to I, let i=i+1, compare I with i, if i>I, proceed to step S38, if i<I, return to step S32, until all electric vehicle aggregators reach the charging price that maximizes their profits;

[0149] Step S38: The charging price reaches a Nash equilibrium, and the charging strategy corresponding to the charging price is the optimal competitive bidding pricing strategy of electric vehicle aggregators.

[0150] Example 1

[0151] like Figure 2 As shown, the present invention provides a bidding pricing method and system for electric vehicle aggregators based on multiple game relationships, comprising the following steps:

[0152] Step S1: Construct a charging strategy evolutionary game decision model that considers the limited rationality of electric vehicle users to obtain the charging load of the charging stations operated by electric vehicle aggregators. Specifically:

[0153] Step S11: generating a charging strategy set according to the charging strategy constraints of each electric vehicle user;

[0154] Step S12: Initialize the probability of each charging strategy and calculate the individual fitness function value;

[0155] Step S13: dynamically calculating the evolutionary game equilibrium of the individual charging strategy according to Logit.

[0156] Step S2: Construct a non-cooperative game model for bidding pricing of electric vehicle aggregators based on incomplete information to obtain the charging prices of charging stations set by each electric vehicle aggregator, specifically:

[0157] Step S21: Establishing an objective function for maximizing charging benefits;

[0158] Step S22: Set constraints, including upper and lower limits of charging prices and charging demand elasticity constraints

[0159] Step S23: Use different charging strategies and corresponding probabilities to represent the incomplete information held by each electric vehicle aggregator, and calculate the expected benefits of each electric vehicle aggregator according to the Nash equilibrium principle.

[0160] Step S3: Construct a master-slave game model between electric vehicle aggregators and electric vehicle users, and obtain the optimal competitive bidding pricing strategy of electric vehicle aggregators by iteratively solving the electric vehicle user charging strategy evolution game decision model and the electric vehicle aggregator bidding pricing non-cooperative game model.

[0161] Each electric vehicle user's charging strategy should meet the following constraints:

[0162] 1) Electric vehicle user transfer constraints

[0163]

[0164] Where: It is a Boolean variable, indicating whether electric vehicle x is charged from the charging station operated by aggregator i to the charging station operated by aggregator j. If yes, it is set to 1, otherwise it is set to 0.

[0165] 2) Charging power constraints

[0166]

[0167] Where: P i,x,t is the charging load of EV x belonging to aggregator i at time t; is the maximum charging power of EV x belonging to aggregator i; and are the time when electric vehicle x belonging to aggregator i arrives at the charging station and the time when it leaves the charging station, respectively.

[0168] 3) State of charge constraints

[0169]

[0170] Where: S i,x,t is the state of charge of electric vehicle x belonging to aggregator i at time t; and are the upper and lower limits of the state of charge of electric vehicle x of aggregator i; E i,j E is the additional amount of electricity that an electric vehicle needs to consume when it moves from a charging station operated by aggregator i to a charging station operated by aggregator j; i,x is the battery capacity of electric vehicle x of aggregator i; and are the state of charge of electric vehicle x of aggregator i when it arrives at the charging station and the expected state of charge when it leaves the charging station. Δt is the unit time interval, usually Δt = 1h, and η represents the charging efficiency of the electric vehicle.

[0171] According to the above charging strategy constraints, the charging strategy set of electric vehicle x belonging to aggregator i can be generated as follows:

[0172]

[0173] Where: Φ i,x is the set of charging strategies for electric vehicle x belonging to aggregator i, which contains P elements. The pth specific charging strategy s i,x,p Including the charging power at each moment, and whether charging is carried out from the charging station operated by aggregator i to the charging station operated by aggregator j. Then the comprehensive charging cost of the pth charging strategy is:

[0174]

[0175] Where: U i,x,p is the comprehensive charging cost of charging strategy p for EV x belonging to aggregator i; is the charging price of the charging station operated by aggregator j at time t; Δt is the unit time interval; The additional cost of charging an EV from a charging station operated by aggregator i to a charging station operated by aggregator j.

[0176] Initialize the probability of each charging strategy and calculate the individual fitness function value as follows:

[0177] f i,x =-∑ p α i,x,p U i,x,p (12)

[0178] Where: f i,x is the individual fitness function value of the electric car x belonging to aggregator i; α i,x,p is the initial probability of the charging strategy p of EV x belonging to aggregator i.

[0179] Step S13: Calculate the evolutionary game equilibrium of the individual charging strategy according to the Logit dynamics. The Logit dynamic equation is as follows:

[0180]

[0181] Where: i,x,p,q is the probability of transition from charging strategy p to charging strategy q of electric vehicle x belonging to aggregator i in the evolutionary game; σ is the noise level, which is used to distinguish the differences between different charging strategies.

[0182] when When , the probability of each charging strategy no longer changes, and the charging strategy of electric vehicle x belonging to aggregator i reaches the evolutionary game equilibrium, which can be expressed as Similarly, the probability of the evolutionary game equilibrium charging strategy of electric vehicle x of aggregator j is expressed as The charging load of the charging station operated by aggregator i is

[0183] The objective function of EV aggregators to maximize charging revenue during bidding pricing is:

[0184]

[0185] Where: and are the charging price and electricity purchase cost of the charging station operated by aggregator i at time t; P i,t is the charging load of the charging station operated by aggregator i at time t.

[0186] The following constraints are met:

[0187]

[0188] Where: and are the upper and lower limits of the charging price of the charging station operated by aggregator i; is the initial charging demand of aggregator i, which is a function of the charging price, γ i and a i is the coefficient of the demand elasticity equation. The charging price is The initial charging demand of aggregator i can be represented by the number of electric vehicles that need to be charged. T is the total number of time periods in a day. Usually, T = 24.

[0189] Assume that the upper and lower constraints of each EV aggregator’s pricing strategy can be divided into several smaller pricing ranges. Therefore, the pricing strategy of each EV aggregator can be represented by different scenarios. For example, suppose G i = {g i,1 ,g i,2 ,…,g i,L} is the pricing strategy set of electric vehicle aggregator i. And meet and The corresponding probability of each pricing strategy is β i,l Within the pricing range for each strategy, the EV aggregator will choose the charging price that maximizes its profit.

[0190] Set AG i,l,r When electric vehicle aggregator i has a pricing range of g i,l , the pricing range of EV aggregator j is g j,r When AG i,l,r is the conditional profit, so EV aggregator i adopts pricing strategy g i,l The expected profit is defined as follows:

[0191] EAG i,l =∑ r AG i,l,r β j,r (34)

[0192] Assuming that the total number of electric vehicle aggregators is I, when all electric vehicle aggregators are unable to increase their profits by changing their pricing strategies, it indicates that the non-cooperative game of electric vehicle aggregators based on incomplete information has reached a Nash equilibrium.

[0193] Finally, the master-slave game is used to characterize the relationship between electric vehicle aggregators and electric vehicle users. The optimal competitive bidding pricing strategy of electric vehicle aggregators is obtained by iteratively solving the evolutionary game decision model of electric vehicle users' charging strategies and the non-cooperative game model of electric vehicle aggregators' bidding pricing.

[0194] The bidding pricing method and system solution process of electric vehicle aggregators based on multiple game relationships are as follows: Figure 3 shown.

[0195] The step S3 comprises: step S31: according to the non-cooperative game model of bidding pricing of electric vehicle aggregators, the electric vehicle aggregators are required to select a preset pricing range strategy, and the charging prices of all electric vehicle aggregators are initialized to the lower limit of the pricing range, and i=1;

[0196] Step S32: Selecting electric vehicle aggregator i so that the upper limit of the charging price is equal to the lower limit;

[0197] Step S33: Keep the charging price of electric vehicle aggregator j unchanged, and solve the evolutionary game equilibrium of electric vehicle charging according to the charging strategy evolutionary game decision model, and calculate the final charging load of all electric vehicles;

[0198] Step S34: Calculate the revenue of electric vehicle aggregator i through the current charging price according to the non-cooperative game model of bidding pricing of electric vehicle aggregators;

[0199] Step S35: Determine whether the charging price exceeds the upper limit of the preset pricing range. If it does not exceed the upper limit, add the preset amount to the charging price to update the charging price, and return to step S33. If it exceeds the upper limit, proceed to step S36.

[0200] Step S36: Compare the charging benefits corresponding to different charging prices, and select the charging price that maximizes the benefits;

[0201] Step S37: Set the total number of electric vehicle aggregators to I, let i=i+1, compare I with i, if i>I, proceed to step S38, if i<I, return to step S32, until all electric vehicle aggregators reach the charging price that maximizes their profits;

[0202] Step S38: Repeat steps S32-S37 until the charging prices of all electric vehicle aggregators no longer change, and then proceed to step S39;

[0203] Step S39: The charging price reaches Nash equilibrium, and the charging strategy corresponding to the charging price is the optimal electric vehicle aggregator competitive bidding pricing strategy.

[0204] The travel mode parameters of electric vehicles are shown in Table 1:

[0205] Table 1

[0206]

[0207]

[0208] The topology of the distribution network and the location of the charging station are as follows: Figure 4 shown.

[0209] In order to verify the effectiveness of the bidding pricing method and system for electric vehicle aggregators based on multiple game relationships proposed in this patent, a comparison is made with and without considering the limited rationality of electric vehicle users. The results are shown in Tables 2 and 3. Compared with not considering the limited rationality of electric vehicle users, considering the limited rationality of electric vehicle users can significantly improve the profits of electric vehicle aggregators.

[0210] Table 2

[0211] Pricing Strategy <![CDATA[Profit of EVA-A / 10 3 $]]> <![CDATA[Profit of EVA-B / 10 3 $]]> Strategy 1 3.523 4.030 Strategy 2 3.635 4.358 Strategy 3 3.629 4.633

[0212] Table 3

[0213]

[0214] The present invention also provides an electric vehicle aggregator bidding and pricing system based on multiple game relationships. The electric vehicle aggregator bidding and pricing system based on multiple game relationships can be implemented by executing the process steps of the electric vehicle aggregator bidding and pricing method based on multiple game relationships, that is, those skilled in the art can understand the electric vehicle aggregator bidding and pricing method based on multiple game relationships as a preferred implementation of the electric vehicle aggregator bidding and pricing system based on multiple game relationships.

[0215] According to the present invention, a bidding and pricing system for electric vehicle aggregators based on multiple game relationships includes: module M1: used to construct a charging strategy evolutionary game decision model that takes into account the limited rationality of electric vehicle users, which can enable the charging strategy to reach evolutionary game equilibrium; module M2: used to construct a non-cooperative game model for bidding and pricing for electric vehicles aggregators based on incomplete information, which can obtain the expected revenue of the charging stations set by each electric vehicle aggregator; module M3: used to construct a master-slave game model between electric vehicle aggregators and electric vehicle users, and obtain the optimal competitive bidding and pricing strategy for electric vehicle aggregators through iterative solution.

[0216] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0217] 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 can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A bidding pricing method for electric vehicle aggregators based on multiple game relationships, characterized in that: include: Step S1: construct a charging strategy evolutionary game decision model that takes into account the limited rationality of electric vehicle users, so that the charging strategy can reach the evolutionary game equilibrium; Step S2: constructing a non-cooperative game model for bidding pricing of electric vehicle aggregators based on incomplete information, which can obtain the expected revenue of charging stations set by each electric vehicle aggregator; Step S3: Based on the charging strategy evolution game decision model and the electric vehicle aggregator bidding pricing non-cooperative game model, a master-slave game model between electric vehicle aggregators and electric vehicle users is constructed, and the optimal electric vehicle aggregator competitive bidding pricing strategy is obtained through iterative solution.

2. The bidding pricing method for electric vehicle aggregators based on multiple game relationships according to claim 1 is characterized in that: The step S1 comprises: Step S11: setting charging strategy constraints for electric vehicle users and integrating them into a charging strategy set; Step S12: Initialize the probability of each charging strategy and calculate the individual fitness function value; Step S13: According to the charging strategy set and the individual fitness function value, a charging strategy evolutionary game decision model is constructed through the Logit dynamic function to calculate the evolutionary game equilibrium of the individual charging strategy.

3. The bidding pricing method for electric vehicle aggregators based on multiple game relationships according to claim 2 is characterized in that: The step S11 comprises: The electric vehicle user charging strategy constraints include electric vehicle user transfer constraints, electric vehicle charging load constraints and charge state constraints; specifically: The electric vehicle user transfer constraint is in, is a Boolean variable; Determine whether electric vehicle x is charged from the charging station operated by aggregator i to another charging station operated by aggregator j. If so, then is 1, if not, then is 0; The charging load constraint is: Where: P i,x,t is the charging load of EV x belonging to aggregator i at time t; is the maximum charging load of EV x belonging to aggregator i; and are the time when electric vehicle x belonging to aggregator i arrives at the charging station and the time when it leaves the charging station, respectively; P i,x,t The state of charge constraint is also satisfied, which is: Where: S i,x,t is the state of charge of electric vehicle x belonging to aggregator i at time t; and are the upper and lower limits of the state of charge of electric vehicle x of aggregator i; E i,j E is the additional amount of electricity that an electric vehicle needs to consume when it moves from a charging station operated by aggregator i to a charging station operated by aggregator j; i,x is the battery capacity of electric vehicle x of aggregator i; and are the state of charge of electric vehicle x of aggregator i when it arrives at the charging station and the expected state of charge when it leaves the charging station; Δt is the unit time interval, and η represents the charging efficiency of the electric vehicle; According to the charging strategy constraints of electric vehicle users, generate the charging strategy set φ of electric vehicle x belonging to aggregator i i,x ,for: Where: s i,x,p is the pth charging strategy, which includes charging load and user transfer conditions; P i,x,1,p is the charging load of EV x of aggregator i at time t under the pth charging strategy.

4. The bidding pricing method for electric vehicle aggregators based on multiple game relationships according to claim 2 is characterized in that: The step S12 comprises: Individual fitness function: f i,x =-∑ p a i,x,p U i,x,p Where: f i,x is the individual fitness function value of the electric car x belonging to aggregator i; α i,x,p is the initial probability of charging strategy p of electric vehicle x belonging to aggregator i, which is a preset value; Among them, U i,x,p is the comprehensive charging cost of charging strategy p for EV x belonging to aggregator i, and is; Where: U i,x,p is the comprehensive charging cost of charging strategy p for EV x belonging to aggregator i; is the charging price of the charging station operated by aggregator j at time t; The additional cost of charging an EV from a charging station operated by aggregator i to a charging station operated by aggregator j.

5. The bidding pricing method for electric vehicle aggregators based on multiple game relationships according to claim 2 is characterized in that: The step S13 comprises: The logit dynamic function is: Where: i,x,p,q is the probability of transition of electric vehicle x belonging to aggregator i from charging strategy p to charging strategy q in the evolutionary game process; σ is the noise level; Calculate the evolutionary game equilibrium of individual charging strategies: Let The charging strategy of electric vehicle x of aggregator i reaches evolutionary game equilibrium.

6. The bidding pricing method for electric vehicle aggregators based on multiple game relationships according to claim 1 is characterized in that: The step S2 comprises: Step S21: Establishing an objective function for maximizing charging benefits; Step S22: setting constraints so that the objective function of maximizing charging revenue satisfies the constraints, wherein the constraints include upper and lower limits of charging prices and elasticity constraints of charging demand; Step S23: According to the objective function of maximizing the charging revenue after constraints, a non-cooperative game model of bidding pricing for multiple electric vehicle aggregators based on incomplete information is established, and the expected revenue of each electric vehicle aggregator is calculated.

7. The bidding pricing method for electric vehicle aggregators based on multiple game relationships according to claim 6 is characterized in that: include: According to the charging strategy evolution game decision model, P is calculated i,t : The probability of the evolutionary game equilibrium charging strategy of electric vehicle x of aggregator j is expressed as The objective function for maximizing the revenue of electric vehicle aggregator i is established as: Where: are the electricity purchase costs of the charging stations operated by aggregator i at time t; in, At the same time: Where: and are the upper and lower limits of the charging prices of the charging stations operated by aggregator i, forming a pricing range; The charging price is is the initial charging demand of aggregator i at time t, T is the total number of time slots in a day, γ i and a i is the coefficient of the demand elasticity equation.

8. The bidding pricing method for electric vehicle aggregators based on multiple game relationships according to claim 6 is characterized in that: The step S23 comprises: Incomplete information characterization rules for setting the revenue pricing strategy of electric vehicle aggregators: G i = {g i,1 ,g i,2 ,…,g i,L } is the pricing strategy set of electric vehicle aggregator i, which includes L pricing strategies, and the lth pricing strategy According to the upper and lower limits of the charging price, a pricing range is formed, and it meets and The corresponding probability of each pricing strategy is β i,l ; Establish a non-cooperative game model of bidding pricing for multiple electric vehicle aggregators based on incomplete information, and calculate the pricing strategy g adopted by electric vehicle aggregator i i,l Expected return: AG i,l,r When the pricing strategy of electric vehicle aggregator i is g i,l , the pricing strategy of electric vehicle aggregator j is g j,r The maximum revenue of electric vehicle aggregator i when ; When the profit is maximized, the non-cooperative game of electric vehicle aggregators based on incomplete information reaches the Nash equilibrium.

9. The bidding pricing method for electric vehicle aggregators based on multiple game relationships according to claim 1 is characterized in that: The step S3 comprises: Step S31: According to the non-cooperative game model of bidding pricing for electric vehicle aggregators, the electric vehicle aggregators are required to select a preset pricing range strategy, and the charging prices of all electric vehicle aggregators are initialized to the lower limit of the pricing range, and i=1; Step S32: Selecting electric vehicle aggregator i so that the upper limit of the charging price is equal to the lower limit; Step S33: Keep the charging price of electric vehicle aggregator j unchanged, and solve the evolutionary game equilibrium of electric vehicle charging according to the charging strategy evolutionary game decision model, and calculate the final charging load of all electric vehicles; Step S34: Calculate the revenue of electric vehicle aggregator i through the current charging price according to the non-cooperative game model of bidding pricing of electric vehicle aggregators; Step S35: Determine whether the charging price exceeds the upper limit of the preset pricing range. If it does not exceed the upper limit, add the preset amount to the charging price to update the charging price, and return to step S33. If it exceeds the upper limit, proceed to step S36. Step S36: Compare the charging benefits corresponding to different charging prices, and select the charging price that maximizes the benefits; Step S37: Set the total number of electric vehicle aggregators to I, let i=i+1, compare I with i, if i>I, proceed to step S38, if i<I, return to step S32, until all electric vehicle aggregators reach the charging price that maximizes their profits; Step S38: The charging price reaches a Nash equilibrium, and the charging strategy corresponding to the charging price is the optimal competitive bidding pricing strategy of electric vehicle aggregators.

10. An electric vehicle aggregator bidding pricing system based on multiple game relationships, characterized in that: include: Module M1: used to construct a charging strategy evolutionary game decision model that takes into account the limited rationality of electric vehicle users, which can enable the charging strategy to reach evolutionary game equilibrium; Module M2: used to construct a non-cooperative game model for bidding pricing of electric vehicle aggregators based on incomplete information, which can obtain the expected revenue of charging stations set by each electric vehicle aggregator; Module M3: used to construct a master-slave game model between electric vehicle aggregators and electric vehicle users, and obtain the optimal competitive bidding pricing strategy for electric vehicle aggregators through iterative solution.

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