Community electric vehicle charging station target optimization method and system based on evolutionary game

Abstract

The invention provides a community electric vehicle charging station target optimization method and system based on an evolutionary game, and the method comprises the steps: building an information transmission mechanism between an agent and a user, obtaining an agent operation mode based on the information transmission mechanism, and formulating the electricity price of each time period of the next day; establishing an evolutionary game model between the agent and the user demand according to the electricity price in each time period of the next day and the user charging demand feedback; performing target optimization on the evolutionary game model to obtain an optimal community electric vehicle charging electricity price; and dynamically adjusting the number and distribution of the charging piles, the charging speed and / or the power grid load according to the optimal community electric vehicle charging electricity price. According to the invention, the evolutionary game between the agent and the user is established to obtain the global optimal pricing and power purchasing strategy, so that the number and distribution of the charging piles are optimized, the charging speed is improved, the power grid load is balanced and stabilized, and the benefits of the charging station and the satisfaction degree of the user are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging target optimization and electricity price pricing, and in particular to a community electric vehicle charging station target optimization method and system based on evolutionary game. Background Art

[0002] The load of smart grid includes traditional load and active load. The active load on the distribution network side of my country is mainly composed of electric vehicles. One of its important characteristics is that the power demand changes with the electricity price. With the popularization of electric vehicles, dealers will play an important role in the charging management of electric vehicles in communities. How to formulate the pricing and power purchase strategy of dealers has become an important basis for optimizing the number and distribution of charging piles, improving charging speed, and balancing and stabilizing the load of the power grid.

[0003] However, if agents are used as the main game objects, the participation of electric vehicle users will be weakened, the satisfaction of electric vehicle users will be reduced, and the goal of optimizing the construction of community electric vehicle charging stations cannot be truly achieved. Currently, 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 community electric vehicle charging station target optimization method and system based on evolutionary game.

[0005] According to one aspect of the present invention, a community electric vehicle charging station target optimization method based on evolutionary game is provided, comprising:

[0006] Establishing an information transmission mechanism between agents and users, obtaining the agent operation model based on the information transmission mechanism, and formulating the electricity price for each time period of the next day;

[0007] According to the electricity prices at different time periods of the next day and the user's charging demand feedback, an evolutionary game model between the agent and the user's demand is established;

[0008] Optimizing the target of the evolutionary game model to obtain the optimal community electric vehicle charging electricity price;

[0009] According to the optimal community electric vehicle charging electricity price, the number and distribution of charging piles, charging speed and / or grid load are dynamically adjusted.

[0010] Preferably, the establishment of an information transmission mechanism between agents and users includes:

[0011] The agent is used as an intermediary between the power system and electric vehicle users. By obtaining the power price of the power grid and the user's charging time information, the charging price is set to supply power to electric vehicle users. At the same time, the power purchase demand is output to the power system, and the information transmission mechanism between the agent and the user is obtained.

[0012] Based on the information transmission mechanism, the agent operation mode is obtained, and the electricity price for each time period of the next day is formulated, including:

[0013] The user reports the next day's charging time and power demand information of the electric vehicle to the agent. The agent forms the available charging time information based on the user information, determines the power demand, and completes the equal value aggregation;

[0014] The agent determines the amount of electricity to be purchased in each time period and releases the electricity price information for each time period of the next day to users based on the day-ahead electricity price;

[0015] Users provide feedback to agents based on factors such as charging time and charging price. Agents optimize electricity price information for each time period of the next day based on user feedback and provide it to users again;

[0016] Users automatically control the charging of electric vehicles through smart terminals in real-time operation and pay for charging; at the same time, agents sell and / or buy electricity in the real-time market.

[0017] Preferably, the agent forms available charging time period information according to user information, determines power demand, and completes equal value aggregation, including:

[0018] Using the K-Means clustering algorithm, in the scenario of time periods, the distance between the data point and each cluster center is calculated according to the time difference, and the data point is assigned to the cluster with the closest cluster center. Then the cluster center is updated and the above assignment process is repeated until the cluster center no longer changes significantly or the preset number of iterations is reached.

[0019] The clustering function expression is:

[0020] x i =(x i1 ,x i2 ,x i3 …x in )

[0021] y j =(y j1 ,y j2 ,y j3 …y jn )

[0022]

[0023] Where: x i is the data point, i.e., the time point; yi is the cluster center; n is the dimension of the data; d is the distance from the electric vehicle charging time period to the cluster center.

[0024] Preferably, the agent determines the amount of electricity purchased in each time period, and publishes the electricity price information for each time period of the next day to the user based on the day-ahead electricity price, including:

[0025] The price fluctuations of the power purchase contract and the sales contract are used as the upper and lower limits, and the electricity prices for each time period of the next day are formulated in combination with the power purchase cost and user elasticity demand.

[0026] Preferably, the evolutionary game model between the agent and the user demand is established according to the electricity price of each time period of the next day and the user charging demand feedback, including:

[0027] The objective function of the constructed agent is:

[0028]

[0029] Where: a t is the charging price in period t, i.e. the sales price of electricity of the agent; p it A is the charging power of the i-th electric vehicle in period t; t The contracted electricity purchase amount for the period t in the day-ahead market; The amount of electricity purchased and sold from the real-time market during period t; is the day-ahead contract electricity price; is the purchase and sale price of electricity from the real-time market during period t;

[0030] Among them, item 1 represents the revenue from selling electricity to users; item 2 represents the revenue from selling electricity to the real-time market; item 3 represents the cost of purchasing electricity in the day-ahead market; item 4 represents the cost of purchasing electricity in the real-time market;

[0031] The sales price of the agent meets the following constraints:

[0032]

[0033]

[0034] In the formula, T is the total number of time periods; the superscripts 1 and u represent the lowest and highest electricity prices at time t respectively; a av is the daily average electricity price;

[0035] The agent's contractual purchase of electricity meets the following constraints:

[0036]

[0037] In the formula, F is a sufficiently large positive constant, and F can also be taken as the sum of the maximum charging power of all electrical devices;

[0038] The agent's electricity purchase and sales meet the following constraints:

[0039]

[0040]

[0041] Where: is the discharge and charge amount of the energy storage device during period t; x t is the working state of the energy storage device during period t; when x t =0, the energy storage device is in working state; x t =1, the energy storage device is in a stopped state; Formula (6) stipulates that the charging power of the car and the charging and discharging power of the energy storage device are always equal to the sum of the day-ahead purchase power and the real-time sales and purchase power;

[0042] The constraints of the energy storage device are:

[0043]

[0044] M 1 =M t =M 0 (8)

[0045] Where M t is the capacity of the energy storage device at time t; η + , η - is the charging and discharging efficiency of the energy storage device; M max M is the maximum energy storage of the energy storage device; 1 is the capacity of the final energy storage device; M 0 It is the initial energy storage device;

[0046] Formula (7) and Formula (8) are the state of charge equations of the energy storage device, and the state of charge of the energy storage device in the last period is equal to the initial state of charge, making the state of charge (SOC) of the energy storage device periodic;

[0047] In solving the agent's optimization goal, p it is the charging power of the i-th electric vehicle in period t, corresponding to the optimal charging strategy of the electric vehicle;

[0048] The objective function of constructing the user is:

[0049] R i =argmin∑ t a t p it (9)

[0050] In the formula, R i is the minimum cost for electric vehicle users, pit is the charging power of electric vehicle i in period t; Formula (9) represents the minimization of its own charging cost under the sales electricity price of the agent;

[0051] The total demand for electric vehicle charging is:

[0052]

[0053] In the formula, is the battery capacity of the i-th electric vehicle at the end of charging; is the initial battery power of the i-th electric vehicle; Formula (10) indicates that the charging amount of the electric vehicle should make the battery reach the corresponding charge level;

[0054]

[0055]

[0056] in, is the maximum charging power of the i-th electric vehicle; T a is the charging period of the electric vehicle; Formula (11) represents the restriction on the charging speed; Formula (12) indicates that the charging power is 0 during the unavailable charging period.

[0057] Preferably, the target optimization of the evolutionary game model to obtain the optimal community electric vehicle charging electricity price includes:

[0058] The mixed integer linear programming (MILP) optimization method is adopted. Under the MILP framework, the objective function is a linear function used to measure the goal that the electricity sales operator wants to optimize. Then:

[0059]

[0060] i=1,2,…m

[0061] j=1,2,…n

[0062] In the formula, x ij =1 means that in the game stage j chooses the electricity price strategy i; x ij =0 means no pricing strategy game is conducted.

[0063] Preferably, the dynamically adjusting the number and distribution of charging piles, charging speed and / or grid load according to the optimal community electric vehicle charging electricity price includes:

[0064] Based on the optimal community electric vehicle charging electricity price obtained, the vehicle charging behavior is scheduled and arranged, and the number and distribution of charging piles, charging speed and / or grid load are dynamically adjusted.

[0065] According to another aspect of the present invention, a community electric vehicle charging station target optimization system based on evolutionary game is provided, comprising:

[0066] The next day electricity price setting module is used to establish an information transmission mechanism between agents and users, obtain the agent operation mode based on the information transmission mechanism, and set the electricity price for each time period of the next day;

[0067] An evolutionary game model module, which is used to establish an evolutionary game model between agents and user demands based on the electricity prices at different time periods of the next day and user charging demand feedback;

[0068] A charging electricity price optimization module, which is used to optimize the target of the evolutionary game model to obtain the optimal community electric vehicle charging electricity price;

[0069] A charging station target optimization module is used to dynamically adjust the number and distribution of charging piles, charging speed and / or grid load according to the optimal community electric vehicle charging electricity price.

[0070] 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.

[0071] 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.

[0072] 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:

[0073] The community electric vehicle charging station target optimization method and system based on evolutionary game provided by the present invention overcomes the defect of complete rationality of participants in classical game theory through the optimal pricing strategy based on evolutionary game, and constructs an evolutionary game model of EV charging and discharging scheduling on the basis of 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 charging pile is too far away) and limited information (that is, the agent 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), characterizes the game behavior of EV users in actual decision-making scenarios, avoids the idealization of game conclusions, and obtains more realistic and effective charging station construction target optimization results.

[0074] The community electric vehicle charging station target optimization method and system based on evolutionary game provided by the present invention construct an EV scheduling evolution model with multiple strategy sets on the basis of the classical evolutionary game model with finite strategies, so as to solve the contradiction between the non-constant EV charging power and the priority strategy of evolutionary game.

[0075] The community electric vehicle charging station target optimization method and system based on evolutionary game provided by the present invention increases the proportion of electric vehicle users participating through evolutionary game, avoids the situation where charging companies are dominant, and increases the frequency of electric vehicle users participating in vehicle-grid interaction. While optimizing the number and distribution of charging piles in charging stations, charging speed and grid load, it also increases the revenue of charging companies and improves the satisfaction of electric vehicle users. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] 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:

[0077] Figure 1 The present invention is a flowchart of a community electric vehicle charging station target optimization method based on evolutionary game in one embodiment of the present invention.

[0078] Figure 2 Schematic diagram of the information transmission mechanism between agents and users in a preferred embodiment of the present invention.

[0079] Figure 3 This is a diagram of the evolutionary game structure between agents and users in a preferred embodiment of the present invention.

[0080] Figure 4 The figure is a schematic diagram of the real-time electricity price and its upper and lower limits in a specific application example of the present invention.

[0081] Figure 5 This is a comparison chart of agent profits after the first optimization in a specific application example of the present invention.

[0082] Figure 6 It is a schematic diagram of the optimization result after the second game in a specific application example of the present invention.

[0083] Figure 7 It is a schematic diagram of the final optimization result in a specific application example of the present invention.

[0084] Figure 8 The figure is a comparison chart of user benefits in a specific application example of the present invention.

[0085] Fig. 9 It is a schematic diagram of the component modules of the community electric vehicle charging station target optimization based on evolutionary game in one embodiment of the present invention. DETAILED DESCRIPTION

[0086] 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.

[0087] An embodiment of the present invention provides a community electric vehicle charging station target optimization method based on evolutionary game. The method models the pursuit of profit maximization by agents and car owners as evolutionary games, and uses the MILP (mixed integer linear programming) method to convert the nonlinear constraints therein into linear constraints, so as to obtain the global optimal pricing and power purchasing strategy, thereby optimizing the number and distribution of charging piles, improving the charging speed, balancing and stabilizing the power grid load, and improving the benefits of charging stations and user satisfaction.

[0088] Specifically, Figure 1 As shown, the community electric vehicle charging station target optimization method based on evolutionary game provided in this embodiment may include the following operations:

[0089] S1, establish an information transmission mechanism between agents and users, obtain the agent operation model based on the information transmission mechanism, and formulate the electricity price for each time period of the next day;

[0090] S2, based on the electricity price at each time period of the next day and the user’s charging demand feedback, establish an evolutionary game model between the agent and user demand;

[0091] S3, optimize the target of the evolutionary game model to obtain the optimal community electric vehicle charging electricity price;

[0092] S4, dynamically adjusts the number and distribution of charging piles, charging speed and / or grid load based on the optimal community electric vehicle charging electricity price.

[0093] In some preferred implementations, the above S1, establishing an information transmission mechanism between agents and users, may further include the following operations:

[0094] S11, the agent is used as an intermediary between the power system and the electric vehicle users. By obtaining the power grid electricity price and the user's charging time information, the charging electricity price is formulated to supply electricity to the electric vehicle users. At the same time, the power purchase demand is output to the power system, and the information transmission mechanism between the agent and the user is obtained.

[0095] In some preferred implementations, the above S1, based on the information transmission mechanism, obtains the agent operation mode and formulates the electricity price for each time period of the next day, and can further include the following operations:

[0096] S12, the user reports the charging time period and power demand information of the electric vehicle for the next day to the agent, and the agent forms the available charging time period information based on the user information, determines the power demand, and completes equal value aggregation;

[0097] S13, the agent determines the amount of electricity to be purchased in each time period, and publishes the electricity price information for each time period of the next day to the user based on the day-ahead electricity price;

[0098] S14, the user provides feedback to the agent based on factors such as charging time and charging price, and the agent optimizes the electricity price information for each time period of the next day based on the user feedback and provides it to the user again;

[0099] S15, the user automatically controls the charging of the electric vehicle through the smart terminal in real-time operation and pays the charging fee; at the same time, the agent sells and / or purchases electric energy in the real-time market.

[0100] In some preferred implementations, the above S12, where the agent forms available charging time period information based on user information, determines power demand, and completes equal value aggregation, may further include the following operations:

[0101] The K-Means clustering algorithm is used to divide the data set into K clusters, so that the similarity of data points within a cluster is as high as possible, while the similarity between clusters is as low as possible. For time period clustering, each data point can be a specific time period (for example, represented by a start time and an end time).

[0102] The K-Means clustering algorithm assigns data points to the cluster with the closest cluster center by calculating the distance between the data point and each cluster center (such as Euclidean distance, etc.). In the scenario of time periods, the calculation of distance can be defined based on the time difference, such as a combination of the start time difference and the end time difference of two time periods. Then update the cluster center and repeat the above assignment process until the cluster center no longer changes significantly or reaches the preset number of iterations. For example, in this article, the charging time of electric vehicles is clustered to K = 3, which are [22:00-6:00], [8:00-20:00] and [20:00-8:00, 13:00-15:00].

[0103] The clustering function expression is:

[0104] x i =(x i1 ,x i2 ,x i3 …x in )

[0105] y j =(y j1 ,y j2 ,y j3 …yjn )

[0106]

[0107] Where: x i is the data point, i.e., the time point; y i is the cluster center; n is the dimension of the data, and the time clustering in this paper is 2; d is the distance from the electric vehicle charging time period to the cluster center.

[0108] In some preferred implementations, the above S13, where the agent determines the amount of electricity to be purchased in each time period, and publishes the electricity price information for each time period of the next day to the user based on the day-ahead electricity price, may further include the following operations:

[0109] The electricity price for each time period of the next day takes into account the impact of power purchase costs, operating costs, user elastic demand, government price control policies, power purchase contracts and sales contracts, and price fluctuation risks. In this embodiment, operating costs and government price policies are not considered. Power purchase contracts and sales contracts and price fluctuation risks are upper and lower bounds. For example, the power purchase price contract cannot exceed the day-ahead power price, the power sales contract price cannot exceed the next day's real-time power price, and the price cannot exceed the upper and lower bounds when the real-time power price fluctuates.

[0110] Furthermore, the first electricity price setting mainly took into account the operator's electricity purchase cost and the user's elastic demand.

[0111] In some preferred implementations, the above S2, based on the electricity price of each time period of the next day and the user's charging demand feedback, establishes an evolutionary game model between the agent and the user's demand, and may further include the following operations:

[0112] The objective function of the constructed agent is:

[0113]

[0114] Where: a t is the charging price in period t, i.e. the sales price of electricity of the agent; p it A is the charging power of the i-th electric vehicle in period t; t The contracted electricity purchase amount for the period t in the day-ahead market; The amount of electricity purchased and sold from the real-time market during period t; is the day-ahead contract electricity price; is the purchase and sale price of electricity from the real-time market during period t;

[0115] Among them, item 1 represents the revenue from selling electricity to users; item 2 represents the revenue from selling electricity to the real-time market; item 3 represents the cost of purchasing electricity in the day-ahead market; item 4 represents the cost of purchasing electricity in the real-time market;

[0116] The sales price of the agent meets the following constraints:

[0117]

[0118]

[0119] Where T is the total number of time periods; superscript 1 and u represent the lowest and highest charging prices in time period t; a av is the daily average electricity price;

[0120] The agent's contractual purchase of electricity meets the following constraints:

[0121]

[0122] In the formula, F is a sufficiently large positive constant, and F can also be taken as the sum of the maximum charging power of all electrical devices;

[0123] The agent's electricity purchase and sales meet the following constraints:

[0124]

[0125]

[0126] Where: is the discharge and charge amount of the energy storage device during period t; x t is the working state of the energy storage device during period t; when x t =0, the energy storage device is in working state; x t =1, the energy storage device is in a stopped state; Formula (6) stipulates that the charging power of the car and the charging and discharging power of the energy storage device are always equal to the sum of the day-ahead purchase power and the real-time sales and purchase power;

[0127] The constraints of the energy storage device are:

[0128]

[0129] M 1 =M t =M 0 (8)

[0130] Where M t is the capacity of the energy storage device at time t; η + , η - is the charging and discharging efficiency of the energy storage device; M max is the maximum capacity of the energy storage device; M 1 is the capacity of the energy storage device at the last moment; M 0 is the capacity of the energy storage device at the initial moment;

[0131] Formula (7) and Formula (8) are the state of charge equations of the energy storage device, and the state of charge of the energy storage device in the last period is equal to the initial state of charge, making the state of charge (SOC) of the energy storage device periodic;

[0132] In solving the agent's optimization goal, p it is the charging power of the i-th electric vehicle in period t, corresponding to the optimal charging strategy of the electric vehicle;

[0133] The objective function of constructing the user is:

[0134] R i =argmin∑ t a t p it (9)

[0135] In the formula, R i is the minimum cost (i.e., the least expenditure) for electric vehicle users, p it is the charging power of electric vehicle i in period t; Formula (9) represents the minimization of its own charging cost under the sales electricity price of the agent;

[0136] The total demand for electric vehicle charging is:

[0137]

[0138] In the formula, is the battery capacity of the i-th electric vehicle at the end of charging; is the initial battery power of the i-th electric vehicle; Formula (10) indicates that the charging amount of the electric vehicle should make the battery reach the corresponding charge level;

[0139]

[0140]

[0141] in, is the maximum charging power of the i-th electric vehicle; T a is the charging period of the electric vehicle; Formula (11) represents the restriction on the charging speed; Formula (12) indicates that the charging power is 0 during the unavailable charging period.

[0142] In some preferred implementations, the above S3, optimizing the target of the evolutionary game model to obtain the optimal community electric vehicle charging electricity price, may further include the following operations:

[0143] The optimization method uses the mixed integer linear programming (MILP) optimization method. Under the MILP framework, the objective function is a linear function used to measure the goal that the electricity sales operator wants to optimize:

[0144]

[0145] i=1,2,…m

[0146] j=1,2,…n

[0147] Where x ij =1 means that in the game stage j chooses the electricity price strategy i; x ij =0 means no pricing strategy game is conducted.

[0148] In some preferred embodiments, the above S4, dynamically adjusting the number and distribution of charging piles, charging speed and / or grid load according to the optimal community electric vehicle charging electricity price, may further include the following operations:

[0149] According to the game result of charging strategy, that is, the optimal community electric vehicle charging price is obtained, the charging behavior of the vehicle is dispatched and arranged to dynamically adjust the number and distribution of charging piles, charging speed and / or grid load.

[0150] The technical solutions provided by the above embodiments of the present invention are further described in detail below.

[0151] The community electric vehicle charging station target optimization method based on evolutionary game provided in the above embodiment of the present invention is implemented through the following parts.

[0152] 1. Description of agent operation model and pricing issues

[0153] 1. Information transmission mechanism between agents and users

[0154] As an intermediary between the power system and electric vehicle users, the agent collects information on power grid electricity prices and user charging time, sets charging prices, and supplies electricity to electric vehicles in the community. Figure 2 shown.

[0155] The above embodiments of the present invention aim to balance the charging demand between agents and electric vehicles to maximize charging benefits and improve the satisfaction of electric vehicle users. Therefore, according to the charging habits and charging price feedback of electric vehicle users, combined with the agent's recent electricity sales, a charging price game optimization model is established to optimize the user's charging price and improve the utilization rate of charging piles to meet the agent's benefits.

[0156] 2. Agent operation mode

[0157] Based on the above information transmission mechanism, the agent operation model is as follows:

[0158] (1) The user reports to the agent the charging time and power demand of the electric vehicle for the next day. The agent forms a table of available charging time based on the report, determines the power demand, and completes the equivalent aggregation.

[0159] (2) The agent signs a power purchase contract in the day-ahead market, determines the amount of electricity to be purchased in each time period, and releases the electricity price information for each time period of the next day to users. Furthermore, it can be stipulated here that the retail electricity price of the agent shall not be higher than the real-time market electricity price. In order to further reduce the market power of the agent, the upper limit of the average retail electricity price can be set to ensure the interests of users.

[0160] (3) The pricing plan is provided to electric vehicle users, who can provide feedback based on factors such as charging time and price to the agent, who will then optimize the price based on the feedback and provide it to the users.

[0161] (4) Users can automatically control the charging of electric vehicles and pay for charging through smart terminals in real time; at the same time, agents can also sell and / or purchase electricity in the real-time market.

[0162] In the above mode, the key problem that the agent needs to solve is to formulate the electricity price for each time period of the next day. Unlike traditional optimization problems, the income of the agent depends on the charging strategy of the electric vehicle, and the charging of the electric vehicle is not directly controlled by the agent, but depends on the electricity price set by the agent. According to the price agreement, the daily average electricity price is fixed. If the agent raises the electricity price in a certain period of time, the electricity price in other periods will inevitably be lower than the average. Users can provide charging feedback or charging response according to their own needs. It can be seen that an evolving relationship is naturally formed between agents and users. In addition, in the market structure based on the embodiment of the present invention, the power grid company is responsible for organizing energy transactions in the power market, and does not directly participate in the energy management of the retail market or profit from it. The interests of the power grid company are reflected in the following aspects: the charging behavior of a large number of electric vehicles is manifested as a deterministic load demand on the power grid through the power purchase contract of the agent, which reduces the difficulties caused by random charging and discharging behavior to the power grid dispatch.

[0163] 2. Evolutionary Game Model of Agent Pricing

[0164] 1. Description of the charging business model

[0165] The pricing evolution game is a game between agents and user demand that is played multiple times until a price satisfactory to both parties is reached. The game structure is as follows: Figure 3 shown.

[0166] The agent's optimization problem is as follows. Assuming that there is only one charging station agent in a certain area, and users cannot choose the charging strategy of other agents, in order to meet the charging demand of each feedback, only the charging price provided by the agent is evolved and iterated, then the agent's objective function can be expressed as:

[0167]

[0168] Where: a t is the charging price during period t; p it A is the charging power of the i-th electric vehicle in period t; t The contracted electricity purchase amount for the period t in the day-ahead market; is the amount of electricity purchased and sold from the real-time market during period t; is the day-ahead contract electricity price; is the electricity price purchased and sold from the real-time market during time period t;

[0169] In the above formula, the objective function (1) maximizes the interests of the agent. The interests of the agent consist of four parts, of which the first item represents the income from selling electricity to users; the second item is the income from selling electricity to the real-time market; the third item is the cost of purchasing electricity in the day-ahead market; and the fourth item is the cost of purchasing electricity in the real-time market.

[0170] The sales price of the agent satisfies the following relationship:

[0171]

[0172]

[0173] In the formula, T is the total number of time periods; the sales electricity price must be within a reasonable range to ensure that the sales average remains unchanged, that is, to meet the fluctuations of time-of-use electricity prices.

[0174] The power purchased by the agent under the contract meets the following requirements:

[0175]

[0176] In the formula, F is a sufficiently large positive constant, and F can also be taken as the sum of the maximum charging power of all power-consuming devices. It should be pointed out that under normal circumstances, since the real-time market electricity price is higher than the day-ahead market electricity price in the same period, the agent will not purchase electricity directly from the real-time market in the optimal charging strategy. However, there are various uncertainties in reality, such as some electric vehicles temporarily leaving or requiring temporary charging services. When the charging strategy is implemented in a rolling optimization manner, user information will be updated in each period, and the purchase and sale of electricity in the real-time market provides retailers with a means to deal with various uncertainties.

[0177] The electricity purchase and sales must meet the following requirements:

[0178]

[0179]

[0180] Where: is the discharge and charge amount of the energy storage device during period t; xt is the working state of the energy storage device during period t; when x t =0, the energy storage device is in working state; x t = 1, the energy storage device is in a stopped state. Constraint (6) stipulates that the charging power of the car and the charging and discharging power of the energy storage device are always equal to the sum of the day-ahead purchase power and the real-time sales and purchase power.

[0181] The constraints of the energy storage device are:

[0182]

[0183] M 1 =M t =M 0 (8)

[0184] Where M t is the capacity of the energy storage device at time t; η + , η - is the charging and discharging efficiency of the energy storage device. Constraints (7) and (8) are the state of charge equations of the energy storage device, and the state of charge of the energy storage device in the last period is equal to the initial state of charge, making the state of charge (SOC) of the energy storage device periodic. In the agent optimization problem, {p it} is the charging power of electric vehicle i in period t, corresponding to the optimal charging strategy of the electric vehicle.

[0185] 2. Description of the car user charging model

[0186] For car users, when charging in a community, they do not need to consider issues such as time cost and charging speed, so they only need to care about the feedback of charging prices. The user's objective function is:

[0187] R i =argmin∑ t a t p it (9)

[0188] The total demand for electric vehicle charging is:

[0189]

[0190] In the formula, is the battery capacity of the i-th electric vehicle at the end of charging; is the initial battery capacity of the i-th electric vehicle;

[0191]

[0192]

[0193] in, is the maximum charging power of the i-th car; T a is the charging period of the car. In the above formula, the objective function (9) represents minimizing the charging cost of the car under the electricity price given by the agent; the constraint (10) indicates that the charging amount of the electric car should make the battery reach the corresponding charge level; the constraint (11) is the limit on the charging speed; the constraint (12) indicates that the charging power is 0 during the unavailable charging period.

[0194] 3. Community Electric Vehicle Charging Target Optimization

[0195] Through the ultimate optimal pricing, user charging behavior is changed and arranged, and the number and distribution of charging piles, charging speed and / or grid load are dynamically adjusted, thereby reducing the grid load while meeting the lowest charging cost for electric vehicles.

[0196] The technical solution provided by the above embodiment of the present invention is further described in detail below in conjunction with a specific application example.

[0197] In this specific application example, the community electric vehicle charging electricity price pricing method based on evolutionary game involves the following two parts:

[0198] Analyze the agent's profit matrix and the car owner's charging needs;

[0199] Provide charging solutions to car owners based on the current electricity price and receive feedback. The dealer will optimize the electricity price solution again and then iterate to the optimal electricity price.

[0200] In order to verify the applicability of the community electric vehicle charging target optimization method provided by the above embodiment of the present invention, a community test area in Shanghai is selected as an example for verification. The number of electric vehicles in the community is 210, the power of the charging column is 7Kw, and there is no fast charging pile. In order to facilitate the display of the calculation results, the electric vehicles can be divided into 3 groups after being aggregated according to their charging time periods, and each group has N = [135, 43, 32] electric vehicles. The charging time of each group of electric vehicles is fixed. At the same time, the electricity price purchased in real time is generally higher than the electricity price purchased a day ago. In this example, it is assumed that the real-time electricity price fluctuation does not exceed 20%.

[0201] Under the premise of ensuring that the total number of electric vehicles in the three groups is constant, changing the proportion of electric vehicles in each group will result in different electricity prices. The results are as follows: Figure 4 As shown. It can be seen that the optimal price is not the same under different proportions of electric vehicles, which is mainly affected by the proportion of electric vehicles of each charging type. Under the condition that the charging electricity price remains unchanged, the proportion of different types of electric vehicles is considered respectively, and the agent profit and optimization result are compared. Figure 5 shown.

[0202] It can be seen that by adopting the method provided by the above embodiment of the present invention, the agent's profit becomes larger, and the following information can be obtained at the same time: when the types of electric vehicles are concentrated (such as N = [210, 0, 0], N = [0, 0, 210]), the agent's profit is the highest, and as the number of the three types is evenly distributed, the agent's profit gradually decreases. This is because when a single type is dominant, the agent sets the price of the rechargeable / non-rechargeable period of this type to the highest / lowest and meets the mean to obtain the maximum profit; when the proportion of the three types is close, due to the limitation of the mean, the agent's charging price in each period cannot be set too high, which will reduce its total profit.

[0203] If the car owner is not satisfied with the pricing result after the first game, he can give feedback on the charging demand and conduct a second game, such as Figure 6 As shown in the figure, after the second optimization, the charging companies have greater profits, and for car owners, the charging prices drop in certain time periods, and they can adjust the charging according to their own needs.

[0204] After multiple rounds of feedback optimization, the charging price tends to converge and gradually stabilize, reaching the optimal value. Figure 7 shown.

[0205] At this point, the agent's profit is maximized. For car users, the charging cost is still lower than the cost of purchasing electricity directly from the real-time market, and the charging price is reduced during some periods, so from this perspective, the cost of car owners is reduced. Figure 8 shown.

[0206] The final pricing based on the optimized charging price can change and arrange the user charging behavior, dynamically adjust the number and distribution of charging piles, charging speed and / or grid load, and thus reduce the grid load while meeting the lowest charging cost for electric vehicles.

[0207] Through the above verification, it can be seen that the community electric vehicle charging electricity price pricing method based on evolutionary game provided in the above embodiment of the present invention determines the agent's electricity price which is related to the user's demand, and the pricing strategy involved has better flexibility than adopting a constant electricity price. Through the final pricing, the number and distribution of charging piles can be better optimized, the charging speed can be improved, and the grid load can be balanced and stabilized, thereby improving the charging station revenue and user satisfaction.

[0208] Based on the same inventive concept, an embodiment of the present invention also provides a community electric vehicle charging station target optimization system based on evolutionary game.

[0209] Specifically, Fig. 9 As shown, the community electric vehicle charging station target optimization system based on evolutionary game provided in this embodiment may include the following modules:

[0210] The next day electricity price setting module is used to establish an information transmission mechanism between agents and users, obtain the agent operation model based on the information transmission mechanism, and set the electricity price for each period of the next day;

[0211] Evolutionary game model module, which is used to establish an evolutionary game model between agents and user demand based on the electricity price at each time period of the next day and user charging demand feedback;

[0212] Charging electricity price optimization module, which is used to optimize the target of the evolutionary game model and obtain the optimal community electric vehicle charging electricity price;

[0213] The charging station target optimization module is used to dynamically adjust the number and distribution of charging piles, charging speed and / or grid load according to the optimal community electric vehicle charging electricity price.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] The community electric vehicle charging station target optimization method and system based on evolutionary game provided by the above-mentioned embodiment of the present invention overcomes the defect of complete rationality of participants in classical game theory through the optimal pricing strategy based on evolutionary game, and constructs an evolutionary game model of EV charging and discharging scheduling on the basis of limited rationality and limited information, characterizes the game behavior of EV users in actual decision-making scenarios, avoids the idealization of game conclusions, and obtains more realistic and effective charging station target optimization results; constructs an EV scheduling evolution 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; through evolutionary game, the proportion of electric vehicle users participating is increased, the situation dominated by charging companies is avoided, and the frequency of electric vehicle users participating in vehicle-grid interaction is increased. While optimizing the number and distribution of charging piles, charging speed and grid load of charging stations, the revenue of charging companies is increased, and the satisfaction of electric vehicle users is improved.

[0223] All matters not covered in the above embodiments of the present invention are well known in the art.

[0224] 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 community electric vehicle charging station target optimization method based on evolutionary game, characterized in that: include: Establishing an information transmission mechanism between agents and users, obtaining the agent operation model based on the information transmission mechanism, and formulating the electricity price for each time period of the next day; According to the electricity prices at different time periods of the next day and the user's charging demand feedback, an evolutionary game model between the agent and the user's demand is established; Optimizing the target of the evolutionary game model to obtain the optimal community electric vehicle charging electricity price; According to the optimal community electric vehicle charging electricity price, the number and distribution of charging piles, charging speed and / or grid load are dynamically adjusted.

2. The community electric vehicle charging station target optimization method based on evolutionary game according to claim 1 is characterized in that: The information transmission mechanism between the agent and the user is established, including: The agent is used as an intermediary between the power system and electric vehicle users. By obtaining the power price of the power grid and the user's charging time information, the charging price is set to supply power to electric vehicle users. At the same time, the power purchase demand is output to the power system, and the information transmission mechanism between the agent and the user is obtained. Based on the information transmission mechanism, the agent operation mode is obtained, and the electricity price for each time period of the next day is formulated, including: The user reports the next day's charging time and power demand information of the electric vehicle to the agent. The agent forms the available charging time information based on the user information, determines the power demand, and completes the equal value aggregation; The agent determines the amount of electricity to be purchased in each time period and releases the electricity price information for each time period of the next day to users based on the day-ahead electricity price; Users provide feedback to agents based on factors such as charging time and charging price. Agents optimize electricity price information for each time period of the next day based on user feedback and provide it to users again; Users automatically control the charging of electric vehicles through smart terminals in real-time operation and pay for charging; at the same time, agents sell and / or buy electricity in the real-time market.

3. The community electric vehicle charging station target optimization method based on evolutionary game according to claim 2 is characterized in that: The agent forms available charging time period information based on user information, determines power demand, and completes equal value aggregation, including: Using the K-Means clustering algorithm, in the scenario of time periods, the distance between the data point and each cluster center is calculated according to the time difference, and the data point is assigned to the cluster with the closest cluster center. Then the cluster center is updated and the above assignment process is repeated until the cluster center no longer changes significantly or the preset number of iterations is reached. The clustering function expression is: x i =(x i1 ,x i2 ,x i3 …x in ) and j (and j1 ,and j2 ,and j3 …and jn ) Where: x i is the data point, i.e., the time point; y i is the cluster center; n is the dimension of the data; d is the distance from the electric vehicle charging time period to the cluster center.

4. The community electric vehicle charging station target optimization method based on evolutionary game according to claim 2 is characterized in that: The electricity price information for each time period of the next day is formulated in the following manner: The price fluctuations of the power purchase contract and the sales contract are used as the upper and lower limits, and the electricity prices for each time period of the next day are formulated in combination with the power purchase cost and user elasticity demand.

5. The community electric vehicle charging station target optimization method based on evolutionary game according to claim 1 is characterized in that: The evolutionary game model between the agent and the user demand is established based on the electricity price of each period of the next day and the user charging demand feedback, including: The objective function of the constructed agent is: Where: a t is the charging price in period t, i.e. the sales price of electricity of the agent; p it A is the charging power of the i-th electric vehicle in period t; t The contracted electricity purchase amount for the period t in the day-ahead market; The amount of electricity purchased and sold from the real-time market during period t; is the day-ahead contract electricity price; is the purchase and sale price of electricity from the real-time market during period t; Among them, item 1 represents the revenue from selling electricity to users; item 2 represents the revenue from selling electricity to the real-time market; item 3 represents the cost of purchasing electricity in the day-ahead market; item 4 represents the cost of purchasing electricity in the real-time market; The sales price of the agent meets the following constraints: In the formula, T is the total number of time periods; the superscripts 1 and u represent the lowest and highest electricity prices at time t respectively; a av is the average charging price; The agent's contractual purchase of electricity meets the following constraints: In the formula, F is a sufficiently large positive constant, and F can also be taken as the sum of the maximum charging power of all electrical devices; The agent's electricity purchase and sales meet the following constraints: Where: is the discharge and charge amount of the energy storage device during period t; x t is the working state of the energy storage device during period t; when x t =0, the energy storage device is in working state; x t =1, the energy storage device is in a stopped state; Formula (6) stipulates that the charging power of the car and the charging and discharging power of the energy storage device are always equal to the sum of the day-ahead purchase power and the real-time sales and purchase power; The constraints of the energy storage device are: M1=M t =M0 (8) Where M t is the capacity of the energy storage device at time t; η + , η - is the charging and discharging efficiency of the energy storage device; M max is the maximum energy storage capacity; M1 is the capacity of the energy storage device at the initial moment; M0 is the capacity of the energy storage device at the last moment; Formula (7) and Formula (8) are the state of charge equations of the energy storage device, and the state of charge of the energy storage device in the last period is equal to the initial state of charge, making the state of charge (SOC) of the energy storage device periodic; In solving the agent's optimization goal, p it is the charging power of the i-th electric vehicle in period t, corresponding to the optimal charging strategy of the electric vehicle; The objective function of constructing the user is: R i =argmin∑ t a t p it (9) In the formula, R i is the minimum cost for electric vehicle users, p it is the charging power of electric vehicle i in period t; Formula (9) represents the minimization of its own charging cost under the sales electricity price of the agent; The total demand for electric vehicle charging is: In the formula, is the battery capacity of the i-th electric vehicle at the end of charging; is the initial battery power of the i-th electric vehicle; Formula (10) indicates that the charging amount of the electric vehicle should make the battery reach the corresponding charge level; in, is the maximum charging power of the i-th electric vehicle; T a is the charging period of the electric vehicle; Formula (11) represents the restriction on the charging speed; Formula (12) indicates that the charging power is 0 during the unavailable charging period.

6. The community electric vehicle charging station target optimization method based on evolutionary game according to claim 1 is characterized in that: The target optimization of the evolutionary game model to obtain the optimal community electric vehicle charging electricity price includes: Using the mixed integer linear programming optimization method, under the MILP framework, the objective function is a linear function used to measure the goal that the electricity sales operator wants to optimize, then: In the formula, x ij =1 means that in the game stage j chooses the electricity price strategy i; x ij =0 means no pricing strategy game is conducted.

7. The community electric vehicle charging station target optimization method based on evolutionary game according to claim 1 is characterized in that: The dynamically adjusting the number and distribution of charging piles, charging speed and / or grid load according to the optimal community electric vehicle charging electricity price includes: Based on the optimal community electric vehicle charging electricity price obtained, the vehicle charging behavior is scheduled and arranged, and the number and distribution of charging piles, charging speed and / or grid load are dynamically adjusted.

8. A community electric vehicle charging station target optimization system based on evolutionary game, characterized in that: include: The next day electricity price setting module is used to establish an information transmission mechanism between agents and users, obtain the agent operation mode based on the information transmission mechanism, and set the electricity price for each time period of the next day; An evolutionary game model module, which is used to establish an evolutionary game model between agents and user demands based on the electricity prices at different time periods of the next day and user charging demand feedback; A charging electricity price optimization module, which is used to optimize the target of the evolutionary game model to obtain the optimal community electric vehicle charging electricity price; A charging station target optimization module is used to dynamically adjust the number and distribution of charging piles, charging speed and / or grid load according to the optimal community electric vehicle charging electricity price.

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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