Regional electric vehicle charging pricing method and device

Abstract

The invention relates to the technical field of smart power grids, and discloses a regional electric vehicle charging pricing method and device. By analyzing an information interaction mechanism between an agent and an electric vehicle user and a dynamic feedback mechanism under an incompletely symmetrical information condition, a dynamic game structure which takes the agent as a dominant and makes a user follow a response is constructed, and a dynamic relation between a charging behavior of the electric vehicle user and an agent pricing strategy is clarified. And based on a dynamic game structure, constructing a regional electric vehicle charging pricing optimization model, and by taking user cost minimization and agent income maximization as targets, performing game solving by using the regional electric vehicle charging pricing optimization model to obtain an optimal pricing strategy and an optimal charging strategy. The method effectively improves the pricing flexibility of the agent and the charging cost optimization of the user, can achieve the reasonable distribution of charging resources and the optimization management of the operation of a power grid, has a good application value, and can be used for the operation and management of an actual charging network.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and in particular to a method and device for regional electric vehicle charging pricing. Background Art

[0002] With the transformation of the global energy structure and the rapid expansion of the electric vehicle market, the construction and management of electric vehicle charging infrastructure has become an important issue. The widespread popularity of electric vehicles has not only brought revolutionary changes to the transportation sector, but also brought new challenges to the operation of the power system. The high concentration and uncertainty of charging behavior can easily cause an imbalance in the load of the power grid, especially during peak hours, which may have a negative impact on the stability and reliability of the power grid. Therefore, how to reasonably manage and optimize the charging process of electric vehicles has become an urgent problem to be solved.

[0003] In the existing charging management system, pricing strategies are often single and static, and usually do not take into account the behavioral dynamics of electric vehicle users and the fluctuations in market demand. This single pricing mechanism easily leads to the centralization of charging behavior, thereby exacerbating the fluctuations in grid load. In addition, the traditional pricing method lacks consideration of the balance between electric vehicle user preferences and agent revenue, making it difficult to effectively motivate users to charge during favorable time periods, thereby optimizing the operation of the entire power system. In addition, the existing electric vehicle charging market generally has information asymmetry problems. The information between agents and electric vehicle users in terms of price, demand, market supply and demand, etc. is often asymmetric, making it difficult for pricing strategies to accurately reflect the actual market situation. This information asymmetry not only affects the efficiency of the market, but may also lead to reduced user satisfaction and fluctuations in agent revenue.

[0004] Although game theory, as an effective tool for analyzing multi-party decision-making interactions, has been widely used in the study of electric vehicle charging pricing, most studies focus on theoretical analysis and hypothetical scenarios, lacking a systematic approach for practical applications. Summary of the invention

[0005] The present application provides a regional electric vehicle charging pricing method to solve the problems in the prior art, such as a single and static pricing strategy, a lack of consideration of the balance between electric vehicle user preferences and agent profits, and information asymmetry between agents and electric vehicle users.

[0006] Correspondingly, the present application also provides a regional electric vehicle charging pricing device, an electronic device, and a computer-readable storage medium to ensure the implementation and application of the above method.

[0007] In order to solve the above technical problems, the present application discloses a regional electric vehicle charging pricing method, the method comprising:

[0008] Analyze the information interaction mechanism between agents and electric vehicle users and the dynamic feedback mechanism under incompletely symmetrical information conditions, and build a dynamic game structure with agents as the leader and users following and responding;

[0009] Based on the dynamic game structure, a regional electric vehicle charging pricing optimization model is constructed;

[0010] With the goal of minimizing user costs and maximizing agent profits, the regional electric vehicle charging pricing optimization model is used to solve the game and obtain the optimal pricing strategy for agents and the optimal charging strategy for electric vehicle users.

[0011] Preferably, the regional electric vehicle charging pricing optimization model includes an agent model and a user model.

[0012] Preferably, the agent model is:

[0013]

[0014] Among them, f(x) is the agent's objective function, x is the agent's decision variable, and g i (x) and h j (x) is the equality and inequality constraints that the agent needs to satisfy, N eqx With N ieqx are the number of equality constraints and inequality constraints of the agent respectively;

[0015] The optimization goal of the agent model is to maximize the total revenue by formulating the optimal charging pricing strategy.

[0016] Preferably, the agent decision variables include charging electricity price, day-ahead power purchase contract quantity, intraday real-time power purchase quantity, intraday real-time power sales quantity, energy storage charging quantity of energy storage equipment, and energy storage equipment discharging quantity.

[0017] Preferably, the user model is:

[0018]

[0019] Among them, f(y) is the objective function of electric vehicle users, y is the decision variable of electric vehicle users, and g i (y) and h j (y) is the equality constraint and inequality constraint that electric vehicle users need to satisfy, N eqy With N ieqy are the number of equality constraints and inequality constraints for electric vehicle users, respectively;

[0020] The optimization goal of the user model is to meet the charging needs of electric vehicles at the lowest charging cost.

[0021] Preferably, the electric vehicle user decision variable is the electric vehicle charging load.

[0022] Preferably, the dynamic feedback mechanism under the condition of incompletely symmetric information is:

[0023] Agents release pricing information to users based on real-time electricity prices, users' historical charging data, and market supply and demand conditions. Pricing information includes charging prices and available charging periods for each time period, and pricing information is updated daily.

[0024] Before charging, users obtain the latest electricity price information through the smart platform and submit charging demand forecast data to the agent based on the electricity price information; the charging demand forecast data includes the expected charging amount and time preference;

[0025] Agents adjust electricity purchasing and pricing strategies based on users’ charging demand forecasts and actual charging behaviors;

[0026] Among them, the agent's day-ahead charging price and charging time period are visible to users after they are released, but the user's intraday charging behavior is random, and the agent does not know it in advance. The resulting information asymmetry will bring certain intraday electricity purchasing risks to the agent.

[0027] The present application also discloses a regional electric vehicle charging pricing device, the device comprising:

[0028] The game structure construction module is used to analyze the information interaction mechanism between agents and electric vehicle users and the dynamic feedback mechanism under incompletely symmetric information conditions, and to construct a dynamic game structure with agents as the leader and users following and responding;

[0029] A model building module, which is used to build a regional electric vehicle charging pricing optimization model based on a dynamic game structure;

[0030] The pricing optimization module is used to minimize user costs and maximize agent profits, and use the regional electric vehicle charging pricing optimization model to solve the game, so as to obtain the optimal pricing strategy for agents and the optimal charging strategy for electric vehicle users.

[0031] The present application also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, one or more methods described in the present application are implemented.

[0032] The present application also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, one or more methods described in the present application are implemented.

[0033] In this application, by analyzing the information interaction mechanism between agents and electric vehicle users and the dynamic feedback mechanism under incompletely symmetric information conditions, a dynamic game structure dominated by agents and followed by users is constructed, and the dynamic connection between the charging behavior of electric vehicle users and the pricing strategy of agents is clarified. Based on the dynamic game structure, a regional electric vehicle charging pricing optimization model is constructed. With the goal of minimizing user costs and maximizing agent revenue, the regional electric vehicle charging pricing optimization model is used to solve the game and obtain the optimal pricing strategy of agents and the optimal charging strategy of electric vehicle users. The iterative solution method of dynamic game is used to effectively improve the pricing flexibility of agents and the optimization of users' charging costs. This method can realize the reasonable allocation of charging resources and the optimal management of power grid operation, and has good application value and can be used for the operation and management of actual charging networks.

[0034] This application also has the following technical effects:

[0035] By introducing an interactive mechanism of asymmetric information, the information gap between agents and electric vehicle users is effectively reduced, which optimizes the decision-making process of agents while ensuring that users can charge at the best price under different market conditions, achieving a dual balance between user costs and agent profits.

[0036] The regional electric vehicle charging pricing optimization model built based on a dynamic game structure has good adaptability and can flexibly adjust electricity prices according to changes in demand from electric vehicle users and market supply and demand fluctuations. It overcomes the problem of rigid electricity prices, greatly improves resource utilization, and effectively avoids unbalanced grid load.

[0037] This application ensures that users' charging needs are met while taking into account the stability of grid operation, providing a highly flexible pricing mechanism for the electric vehicle market. It has strong practical application value and has certain reference value for the intelligent charging management of regional power grids containing electric vehicles.

[0038] Additional aspects and advantages of the present application will be given in the following description, which will become apparent from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0040] Figure 1 A flow chart of a regional electric vehicle charging pricing method provided in an embodiment of the present application;

[0041] Figure 2 A rechargeable distributed segment diagram provided for an embodiment of the present application;

[0042] Figure 3 The agent energy optimization result diagram provided in the embodiment of the present application;

[0043] Figure 4 A graph showing the aggregation results of charging loads of three types of electric vehicles provided in an embodiment of the present application;

[0044] Figure 5 The optimal pricing result diagram for regional agents provided in the embodiment of the present application;

[0045] Figure 6 A schematic diagram of the structure of a regional electric vehicle charging pricing device provided in an embodiment of the present application;

[0046] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present application.

[0048] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0049] Those skilled in the art will appreciate that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the field to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0050] The solution provided in the embodiments of the present application can be executed by any electronic device, such as a terminal device or a server, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected via wired or wireless communication, and this application does not limit this. With respect to the technical problems existing in the prior art, the regional electric vehicle charging pricing method and device provided in this application is intended to solve at least one of the technical problems of the prior art.

[0051] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0052] The present application embodiment provides a possible implementation method, such as Figure 1 As shown, a flowchart of a regional electric vehicle charging pricing method is provided, which can be executed by any electronic device, and optionally, can be executed on a server or terminal device.

[0053] like Figure 1 As shown in , the method may include the following steps:

[0054] Step 101, analyzing the information interaction mechanism between agents and electric vehicle users and the dynamic feedback mechanism under the condition of incompletely symmetric information, and constructing a dynamic game structure with agents as the leader and users following and responding.

[0055] In the embodiment of the present application, the regional electric vehicle agent, as the core role connecting the power grid and users, is responsible for managing the pricing and power distribution of electric vehicle charging. The agent not only needs to obtain changes in market electricity prices in a timely manner, but also needs to dynamically grasp the charging needs of users to ensure a balance between electricity prices and demand. In the embodiment of the present application, the agent and the user communicate in real time through the smart terminal and the cloud platform. The agent adjusts the charging price based on the real-time data, and the user chooses the optimal charging time based on the price.

[0056] The information exchange mechanism between agents and electric vehicle users includes:

[0057] 1) User demand forecast: Users report the next day's charging demand forecast data, including charging amount and charging time period, through smart terminals every day. After receiving the user's charging demand forecast data, the agent evaluates the overall demand situation through load aggregation technology and compares it with the power supply capacity of the power grid.

[0058] 2) Adjustment and release of electricity prices: Agents release dynamic electricity price strategies based on the charging demand forecast data received from users and the market power supply situation; specifically, agents predict the available charging time periods based on the collected charging demand forecast data, and purchase electricity in advance in the electricity wholesale market to determine the amount of electricity purchased for each charging time period, and dynamically adjust the charging price for each time period based on market conditions. Charging prices are affected by market fluctuations and user demand, and are updated daily and pushed to user terminals. At the same time, agents must ensure that charging prices do not exceed the upper limit of market electricity prices to protect user interests.

[0059] 3) Intelligent charging management: Users use intelligent terminal devices to automatically calculate the optimal charging strategy based on the charging prices of each time period released by the agent, and automatically execute the optimal charging strategy after authorization. Charging and fee settlement can be automatically completed through intelligent devices. This process reduces the direct intervention of agents in the charging process, allowing users to reduce the burden on the power grid while completing charging.

[0060] Users and agents reach game equilibrium through multiple dynamic interactions:

[0061] 1) User-reported demand: Users report the next day’s charging demand forecast data through smart terminals every day, including preferences for charging amount and charging time period.

[0062] 2) Agents’ power purchase and pricing: Agents use big data to analyze users’ historical charging behaviors and market electricity price trends, assess overall charging demand, and purchase the required electricity in the electricity wholesale market based on their estimates of user demand to ensure that there is sufficient electricity supply at all times. Then, agents set charging prices for each time period, which fluctuate according to real-time market electricity prices and are dynamically adjusted according to different time periods to ensure the agent’s profit target.

[0063] 3) User-executed charging strategy: After the agent releases pricing information, the user will choose the optimal charging time period based on personal needs and electricity prices, and start the automatic charging process through the intelligent platform. This intelligent process enables users to charge during periods with lower prices, thereby optimizing charging costs.

[0064] In the embodiment of the present application, the agent's profit depends on the user's charging behavior pattern, and the user's charging choice is guided by the agent's pricing strategy; if the agent increases the electricity price in a certain period of time, due to the limitation of the average electricity price, the user will tend to choose a period of time with lower costs to charge; this interaction forms a dynamic game relationship, the agent as the dominant party guides the user's charging behavior, and the user flexibly adjusts the charging decision according to the price strategy.

[0065] In the embodiment of the present application, the dynamic feedback mechanism under the condition of incompletely symmetric information is:

[0066] Agents release pricing information to users based on real-time electricity prices, users' historical charging data, and market supply and demand conditions. Pricing information includes charging prices and available charging periods for each time period, and pricing information is updated daily.

[0067] Before charging, users obtain the latest electricity price information through the smart platform and submit charging demand forecast data to the agent based on the electricity price information; the charging demand forecast data includes the expected charging amount and the charging time period preference;

[0068] Agents adjust electricity purchasing and pricing strategies based on users’ charging demand forecast data and their actual charging behaviors;

[0069] Among them, the agent's day-ahead charging price and charging time period are visible to users after they are released, but the user's intraday charging behavior is random, and the agent does not know it in advance. The resulting information asymmetry will bring certain intraday electricity purchasing risks to the agent.

[0070] Step 102: construct a regional electric vehicle charging pricing optimization model based on a dynamic game structure.

[0071] Step 103, with the goal of minimizing user costs and maximizing agent revenues, a regional electric vehicle charging pricing optimization model is used to perform game solving to obtain the agent's optimal pricing strategy and the electric vehicle user's optimal charging strategy.

[0072] The embodiments of the present application can achieve a balance of interests between agents and users through game solving under dynamic conditions, ensuring that users choose charging time under the premise of minimizing costs while agents maximize their profits.

[0073] In the embodiment of the present application, by analyzing the information interaction mechanism between agents and electric vehicle users and the dynamic feedback mechanism under the condition of incompletely symmetric information, a dynamic game structure dominated by agents and followed by users is constructed, and the dynamic connection between the charging behavior of electric vehicle users and the pricing strategy of agents is clarified. Based on the dynamic game structure, a regional electric vehicle charging pricing optimization model is constructed. With the goal of minimizing user costs and maximizing agent revenue, the regional electric vehicle charging pricing optimization model is used to solve the game and obtain the optimal pricing strategy of agents and the optimal charging strategy of electric vehicle users. The iterative solution method of dynamic game is used to effectively improve the pricing flexibility of agents and the optimization of users' charging costs. This method can realize the reasonable allocation of charging resources and the optimal management of power grid operation, and has good application value and can be used for the operation and management of actual charging networks.

[0074] In an optional embodiment, the regional electric vehicle charging pricing optimization model includes an agent model and a user model.

[0075] In the electric vehicle charging pricing system, agents, as the dominant party in price setting, are responsible for regulating electricity prices, while users, as responders, make charging choices based on the prices. The main challenge for agents is how to set reasonable prices for different time periods every day. Their income is not entirely controlled by themselves, but depends on the user's choice of charging time period. Because user charging behavior is affected by electricity price fluctuations, agents need to balance the pricing of each time period to ensure that their interests are maximized. Although the power grid company does not directly participate in the pricing of the retail market, through the agent contract, it can convert the load demand of a large number of electric vehicles into a predictable and stable load, reducing the uncertainty of power grid dispatch. Reasonable pricing by agents not only guides users to optimize charging time periods, but also provides stability and efficiency improvement for power grid operation. Based on this, an agent model is constructed in the embodiment of the present application to balance the pricing of each time period to ensure that its interests are maximized; a user model is constructed to maximize the use of lower-priced charging time periods within the available time period, while avoiding charging during periods with higher electricity prices to avoid increasing costs.

[0076] In an optional embodiment, the agent model is:

[0077]

[0078] Among them, f(x) is the agent's objective function, x is the agent's decision variable, and g i (x) and h j (x) is the equality and inequality constraints that the agent needs to satisfy, N eqx With N ieqx are the number of equality constraints and inequality constraints of the agent respectively.

[0079] The optimization goal of the agent model is to maximize total revenue by formulating the optimal charging pricing strategy. Under the premise of maintaining the stability of the power grid, the agent sets the charging prices for different time periods according to the charging needs of users and the fluctuations in market electricity prices, while minimizing the cost of purchasing electricity. The constraints are that the electricity price in each time period must meet the constraints between the upper and lower limits of the market price and must not be higher than the highest electricity price in the real-time market; the amount of electricity purchased must be consistent with the charging needs of users, and the cost of purchasing electricity should be controlled as much as possible below the market average price to ensure maximum profit; the allocation of power resources must be balanced between time periods to avoid over-reliance on purchasing electricity during peak hours, which will lead to excessive load on the power grid; the charging and discharging capacity of energy storage equipment limits the amount of electricity transactions that agents can conduct, so when setting electricity prices, agents also need to consider the charging and discharging efficiency and capacity of the energy storage system.

[0080] In an optional embodiment, the agent decision variables include charging electricity price, day-ahead power purchase contract quantity, intra-day real-time power purchase quantity, intra-day real-time power sales quantity, energy storage charging quantity of energy storage equipment, and energy storage equipment discharging quantity.

[0081] In the embodiment of the present application, the agent's objective function f(x) is:

[0082]

[0083] Various equality constraints that agents need to satisfy g i (x) and the inequality constraint h j (x) The conditions are:

[0084]

[0085] In the formula, p t is the charging price during period t; P i,t is the charging load of the i-th electric vehicle in period t; They are the real-time market electricity purchase price and electricity sales price during period t respectively; is the day-ahead market contract electricity price during period t; E is the contracted electricity volume in the day-ahead market during period t; t , The amount of electricity purchased and sold from the real-time market during period t; is the lowest and highest electricity price during period t; p av is the daily average electricity price, is the corresponding constant const; P ES,ch,t , P ES,dch,t is the charge and discharge amount of the energy storage device during period t; are the maximum charging power and discharging power of energy storage respectively; ν ES,ch,t and ν ES,dch,tis a 0-1 variable for energy storage charging and discharging during period t, 1 for working and 0 for not working; z t is a 0-1 variable representing the energy transaction status during period t; S ES,t S is the storage capacity of the energy storage device in period t; ES,0 is the initial capacity of the energy storage device; σ ES is the energy storage self-loss rate; η ES,ch , η ES,dch Charging and discharging efficiency of energy storage devices; are the maximum, minimum and initial capacity proportions of energy storage respectively; ES is the maximum capacity of energy storage; T is the total number of scheduling periods, which is 24; M is a sufficiently large positive number.

[0086] The goal of the agent model is to maximize the profit of equation (2). In the embodiment of the present application, the market rules assume that the contract electricity price in the day-ahead market is lower than the electricity price in the real-time market. Specifically, constraint (3) specifies the range limit of the electricity price, while constraint (4) sets the limit of the daily average electricity price. In addition, constraints (5) to (7) are used to ensure power balance, and constraint (8) involves constraints on energy storage devices. 0-1 variables are used to strictly control the operating logic of energy storage devices to ensure that the energy storage devices can continue to operate within a scheduling cycle, where the initial state of charge must be consistent with the state of charge in the last period.

[0087] In an optional embodiment, the user model is:

[0088]

[0089] Among them, f(y) is the objective function of electric vehicle users, y is the decision variable of electric vehicle users, and g i (y) and h j (y) is the equality constraint and inequality constraint that electric vehicle users need to satisfy, N eqy With N ieqy are the number of equality constraints and inequality constraints for electric vehicle users, respectively.

[0090] The optimization goal of the user model is to meet the charging needs of electric vehicles at the lowest charging cost. They will flexibly adjust their charging behavior according to the pricing strategy released by the agent and choose the optimal charging time period to reduce costs. The constraints are: users must complete charging within a limited time period, and the charging behavior must ensure that the minimum charge requirement of the electric vehicle battery is met; the charging power limits the amount of electricity that users can obtain in each time period, and users need to maximize the use of lower-priced charging time periods within the available time period; users need to complete the necessary charging amount within a certain charging time period, and avoid charging during periods with higher electricity prices to avoid increasing costs.

[0091] In an optional embodiment, the electric vehicle user decision variable is the electric vehicle charging load.

[0092] In the embodiment of the present application, the specific objective function f(y) of the electric vehicle user i is as follows, and the decision variables are {P i,t}:

[0093]

[0094] Various equality constraints that electric vehicle users need to satisfy i (y) and the inequality constraint h j (y) is:

[0095]

[0096] In the formula, p t is the charging price during period t; P i,t is the charging load of the i-th electric vehicle in period t; is the maximum capacity of the electric vehicle battery of user i; α max The maximum capacity for safely charging electric vehicles; The charging starting power of user i; The maximum charging power of user i’s electric vehicle; T a is the charging period of electric vehicles; N is the total number of electric vehicle users.

[0097] Formula (10) describes how electric vehicle users can minimize their own charging costs under the charging electricity price set by the regional agent. Constraint (11) ensures that the charging capacity of the electric vehicle reaches the safe charge state of the battery capacity. Constraint (12) limits the power range of electric vehicle charging, and constraint (13) stipulates that the charging power must be zero during the unavailable charging period.

[0098] In summary, the regional agent problems (2)-(8) and the electric vehicle user problems (10)-(13) constitute an incompletely symmetric information limited dynamic game structure. According to the non-cooperative game theory, at the game equilibrium point, both agents and electric vehicle users can maximize their respective interests, and neither party can increase the interests of both parties at the same time by unilaterally changing their strategy.

[0099] In the embodiment of the present application, the regional electric vehicle charging pricing optimization model is modeled and solved using the Matlab language.

[0100] For example, after the regional electric vehicle charging pricing method designed in the embodiment of the present application is implemented based on an appropriate programming language, the following example scenario is set: Assume that after equal value aggregation, electric vehicles can be divided into 3 groups, each with 50 electric vehicles. The charging time of each group of electric vehicles is as follows: Figure 2As shown (1 means rechargeable, 0 means non-rechargeable). The user types include 3 groups, the first group represents "early to go out and late to come back"; the second group represents "normal work and rest type"; the third group represents "night shift type". The electric vehicle parameters after equivalent aggregation are shown in Table 1. The day-ahead market electricity price is shown in Table 2. The real-time market electricity purchase price is generally higher than the day-ahead market electricity purchase price. In the present invention, the real-time electricity price is set to 1.38 times the day-ahead electricity price, the upper limit of the retail electricity price is the upper limit of the real-time market electricity price, and the lower limit of the retail electricity price is 0.83 times the day-ahead electricity price. The daily average electricity price is set to 0.65 yuan / (kW·h), and the energy storage equipment parameters are shown in Table 3.

[0101] Table 1 Electric vehicle parameters after equal value aggregation

[0102]

[0103] Table 2 Market electricity price before

[0104]

[0105] Table 3 Energy storage equipment parameters

[0106]

[0107] (1) Optimal pricing and optimal charging strategy results

[0108] Using the above data, the maximum profit of the regional agent is 967.58 yuan, the total charging cost of electric vehicle users is 2295.3328 yuan, and the agent energy optimization results are as follows: Figure 3 The optimal charging strategy for each group of electric vehicles is shown in Table 4. The total charging power after equal aggregation of the three groups of electric vehicles is shown in Figure 4 As shown, the optimal pricing result is Figure 5 shown.

[0109] Table 4 Optimal charging strategy for a single electric vehicle user

[0110]

[0111] Figure 5 The upper and lower limits of electricity prices in each time period, as well as the optimal electricity price are shown. Figure 5The results show that the agent pricing (optimal electricity price) is close to the upper limit of electricity price during the period of 1:00-5:00, and is relatively low during the period of 6:00-8:00 and 21:00-24:00. The logic behind this pricing strategy is that since the number of electric vehicle users in the three categories is the same, the period from 1:00 to 5:00 is the peak charging period for the first category of electric vehicle users. During this period, the electricity price limit will not be too high, so the agent directly pushes the electricity price close to the upper limit to obtain higher profits; during the daytime period from 8:00 to 20:00, the second and third categories of electric vehicle users complement each other, making the charging demand relatively high throughout the day. Therefore, the agent hopes to increase profits by raising the electricity price during this period of high demand. The reason why the electricity price ceiling is not approached during this period is because of the constraint of the average electricity price. At the same time, the agent also ensures the balance of the average charging price by lowering the electricity price during the period of low demand; for example, during the period from 21:00 to 24:00, the demand is relatively small and the overall upper limit of the electricity price is relatively low. For the agent, this period can be used to meet the constraint of the average electricity price. According to the user demand and corresponding electricity price limits of the current example, the agent hopes to make the electricity price as high as possible during the period of 1:00-7:00 by some means, while the upper limit of the electricity price during the period of 21:00-24:00 is not required to be high. In this way, higher profits can be obtained without violating the constraints of the average electricity price.

[0112] also, Figure 3 The figure shows the day-ahead contract power, real-time power purchase, real-time power sales and energy storage power in each period after the agent's energy optimization. Figure 3 From the results of energy optimization, agents can fully meet the charging needs of electric vehicles through electricity purchases in the day-ahead market, because the electricity purchase price in the real-time market is usually higher, which adds additional costs. Small fluctuations can be smoothed by charging equipment. However, in actual operation, considering that electric vehicle users may need emergency charging due to sudden changes in demand, such as temporary departure or sudden increase in load, the charging demand at this time is relatively large. Even if the energy storage equipment has enough power to meet the demand, due to the limitation of charging and discharging power, agents still need to purchase a certain amount of power from the real-time market. The purpose of this strategy is to ensure that the charging needs of all electric vehicles can still be met under the influence of uncertain factors, so as to avoid user dissatisfaction or loss of agent revenue due to power shortage.

[0113] On the other hand, according to the data analysis in Table 4, under the currently adopted optimal charging strategy, the charging cost of electric vehicle users has reached the lowest value. This shows that under this pricing mechanism, rational electric vehicle users will naturally choose this optimal strategy for charging, because it can maximize the reduction of their charging costs. This strategy effectively regulates the charging behavior of electric vehicles and avoids overcharging or charging at inappropriate times. This not only helps to improve the satisfaction of electric vehicle users, but also optimizes the operating efficiency of the entire power grid to a certain extent.

[0114] The above example analysis can be concluded:

[0115] 1) Under the pricing strategy of the agent, the charging behavior of electric vehicle users is effectively guided, and the electricity price is flexibly adjusted in different time periods, so that users can make reasonable charging choices based on the electricity price, and optimize the management of the grid load. At the same time, the agent maximizes the profit by accurately setting the electricity price during peak hours, and maintains the overall balance by adjusting the electricity price during low demand periods;

[0116] 2) Through reasonable electricity price setting and market power purchasing strategies, agents can effectively respond to the uncertain needs of electric vehicle users, such as sudden charging needs or emergencies. The combination of energy storage equipment and real-time market power purchase ensures continuous and stable charging services when demand fluctuates, thus avoiding problems such as user dissatisfaction or service interruption caused by insufficient power;

[0117] 3) Under the current optimal charging strategy, electric vehicle users can charge during periods of low electricity prices, significantly reducing their overall charging costs. This not only protects the economic interests of users, but also improves the overall operating efficiency of the electric vehicle market and user satisfaction, and promotes the formation of reasonable charging behavior.

[0118] Based on the same principle as the method provided in the embodiment of the present application, the embodiment of the present application also provides a regional electric vehicle charging pricing device, such as Figure 6 As shown, the device comprises:

[0119] The game structure construction module 601 is used to analyze the information interaction mechanism between agents and electric vehicle users and the dynamic feedback mechanism under the condition of incompletely symmetrical information, and to construct a dynamic game structure with agents as the leader and users following and responding;

[0120] A model building module 602 is used to build a regional electric vehicle charging pricing optimization model based on a dynamic game structure;

[0121] The pricing optimization module 603 is used to minimize user costs and maximize agent revenues, and to solve the game using the regional electric vehicle charging pricing optimization model to obtain the agent's optimal pricing strategy and the electric vehicle user's optimal charging strategy.

[0122] In the embodiment of the present application, by analyzing the information interaction mechanism between agents and electric vehicle users and the dynamic feedback mechanism under the condition of incompletely symmetric information, a dynamic game structure dominated by agents and followed by users is constructed, and the dynamic connection between the charging behavior of electric vehicle users and the pricing strategy of agents is clarified. Based on the dynamic game structure, a regional electric vehicle charging pricing optimization model is constructed. With the goal of minimizing user costs and maximizing agent revenue, the regional electric vehicle charging pricing optimization model is used to solve the game and obtain the optimal pricing strategy of agents and the optimal charging strategy of electric vehicle users. The iterative solution method of dynamic game is used to effectively improve the pricing flexibility of agents and the optimization of users' charging costs. This method can realize the reasonable allocation of charging resources and the optimal management of power grid operation, and has good application value and can be used for the operation and management of actual charging networks.

[0123] The regional electric vehicle charging pricing device provided in the embodiment of the present application can achieve Figures 1 to 5 To avoid repetition, the various processes implemented in the method embodiment are not described here.

[0124] The regional electric vehicle charging pricing device of the embodiment of the present application can execute the regional electric vehicle charging pricing method provided in the embodiment of the present application, and its implementation principle is similar. The actions performed by each module and unit in the regional electric vehicle charging pricing device in each embodiment of the present application correspond to the steps in the regional electric vehicle charging pricing method in each embodiment of the present application. For the detailed functional description of each module of the regional electric vehicle charging pricing device, please refer to the description of the corresponding regional electric vehicle charging pricing method shown in the previous text, which will not be repeated here.

[0125] Based on the same principle as the method shown in the embodiment of the present application, the embodiment of the present application also provides an electronic device, which may include but is not limited to: a processor and a memory; a memory for storing a computer program; a processor for executing the regional electric vehicle charging pricing method shown in any optional embodiment of the present application by calling a computer program. Compared with the prior art, the regional electric vehicle charging pricing method provided by the present application, by analyzing the information interaction mechanism between agents and electric vehicle users and the dynamic feedback mechanism under the condition of incompletely symmetric information, constructs a dynamic game structure dominated by agents and followed by users, and clarifies the dynamic connection between the charging behavior of electric vehicle users and the pricing strategy of agents. Based on the dynamic game structure, a regional electric vehicle charging pricing optimization model is constructed, with the goal of minimizing user costs and maximizing agent benefits, and the regional electric vehicle charging pricing optimization model is used to solve the game to obtain the optimal pricing strategy of agents and the optimal charging strategy of electric vehicle users. By using the iterative solution method of dynamic game, the pricing flexibility of agents and the optimization of users' charging costs are effectively improved. The method can realize the reasonable allocation of charging resources and the optimal management of power grid operation, and has good application value, which can be used for the operation and management of actual charging networks.

[0126] In an optional embodiment, an electronic device is also provided, such as Figure 7 As shown, Figure 7 The electronic device 700 shown may be a server, including: a processor 701 and a memory 703. The processor 701 and the memory 703 are connected, such as through a bus 702. Optionally, the electronic device 700 may further include a transceiver 704. It should be noted that in actual applications, the transceiver 704 is not limited to one, and the structure of the electronic device 700 does not constitute a limitation on the embodiments of the present application.

[0127] Processor 701 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 701 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0128] The bus 702 may include a path to transmit information between the above components. The bus 702 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 702 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0129] The memory 703 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0130] The memory 703 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 701. The processor 701 is used to execute the application code stored in the memory 703 to implement the contents shown in the above method embodiment.

[0131] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0132] The server provided in this application can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected by wired or wireless communication, and this application does not limit this.

[0133] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0134] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0135] It should be noted that the computer-readable storage medium mentioned above in the present application can also be a computer-readable signal medium or a combination of a computer-readable storage medium and a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0136] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0137] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0138] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the regional electric vehicle charging pricing method and apparatus provided in the above-mentioned various optional implementations.

[0139] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0140] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0141] The modules involved in the embodiments described in this application can be implemented by software or hardware. The name of the module does not limit the module itself in some cases. For example, the game structure building module can also be described as "a game structure building module for analyzing the information interaction mechanism between agents and electric vehicle users and the dynamic feedback mechanism under incompletely symmetric information conditions, and building a dynamic game structure with agents as the leader and users following the response".

[0142] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A regional electric vehicle charging pricing method, characterized in that: The method comprises: Analyze the information interaction mechanism between agents and electric vehicle users and the dynamic feedback mechanism under incompletely symmetrical information conditions, and build a dynamic game structure with agents as the leader and users following and responding; Based on the dynamic game structure, a regional electric vehicle charging pricing optimization model is constructed; With the goal of minimizing user costs and maximizing agent profits, the regional electric vehicle charging pricing optimization model is used to solve the game and obtain the optimal pricing strategy for agents and the optimal charging strategy for electric vehicle users.

2. The regional electric vehicle charging pricing method according to claim 1, characterized in that: The regional electric vehicle charging pricing optimization model includes an agent model and a user model.

3. The regional electric vehicle charging pricing method according to claim 2, characterized in that: The agent model is: Among them, f(x) is the agent's objective function, x is the agent's decision variable, and g i (x) and h j (x) is the equality and inequality constraints that the agent needs to satisfy, N eqx With N ieqx are the number of equality constraints and inequality constraints of the agent respectively; The optimization goal of the agent model is to maximize the total revenue by formulating the optimal charging pricing strategy.

4. The regional electric vehicle charging pricing method according to claim 3 is characterized in that: The agent decision variables include charging electricity price, day-ahead power purchase contract quantity, intraday real-time power purchase quantity, intraday real-time power sales quantity, energy storage charging quantity of energy storage equipment, and energy storage equipment discharging quantity.

5. The regional electric vehicle charging pricing method according to claim 2, characterized in that: The user model is: Among them, f(y) is the objective function of electric vehicle users, y is the decision variable of electric vehicle users, and g i (y) and h j (y) is the equality constraint and inequality constraint that electric vehicle users need to satisfy, N eqy With N ieqy are the number of equality constraints and inequality constraints for electric vehicle users, respectively; The optimization goal of the user model is to meet the charging demand of electric vehicles at the lowest charging cost.

6. The regional electric vehicle charging pricing method according to claim 5, characterized in that: The electric vehicle user decision variable is the electric vehicle charging load.

7. The regional electric vehicle charging pricing method according to claim 1, characterized in that: The dynamic feedback mechanism under the incompletely symmetric information condition is: The agent releases pricing information to users based on real-time electricity prices, users' historical charging data and market supply and demand conditions; the pricing information includes charging prices and available charging periods for each time period, and the pricing information is updated daily; Before charging, the user obtains the latest electricity price information through the smart platform and submits charging demand forecast data to the agent based on the electricity price information; the charging demand forecast data includes the expected charging amount and time preference; Agents adjust electricity purchasing and pricing strategies based on users’ charging demand forecasts and actual charging behaviors; Among them, the agent's day-ahead charging price and charging time period are visible to users after they are released, but the user's intraday charging behavior is random, and the agent does not know it in advance. The resulting information asymmetry will bring certain intraday electricity purchasing risks to the agent.

8. A regional electric vehicle charging pricing device, characterized in that: The device comprises: The game structure construction module is used to analyze the information interaction mechanism between agents and electric vehicle users and the dynamic feedback mechanism under incompletely symmetric information conditions, and to construct a dynamic game structure with agents as the leader and users following and responding; A model building module, used to build a regional electric vehicle charging pricing optimization model based on the dynamic game structure; The pricing optimization module is used to minimize user costs and maximize agent revenues, and to solve the game using the regional electric vehicle charging pricing optimization model to obtain the agent's optimal pricing strategy and the electric vehicle user's optimal charging strategy.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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