Power distribution network pricing method and device based on electric vehicle demand response and cooperative game, equipment and medium

Abstract

The invention discloses a power distribution network pricing method and device based on electric vehicle demand response and cooperative game, equipment and a medium. The method comprises the following steps: acquiring a charging price, a charging station state and a road network state every first preset time; calculating travel time and travel distance from the electric vehicle to the charging station based on the road network state; based on the charging price, the state of the charging station, the travel time and the travel distance, the attraction force of the charging station to the charging vehicle is calculated, and based on the attraction force, the distribution result of the electric vehicle distributed to the charging station is determined; and determining the power load of the charging station based on the distribution result, and updating the charging price based on the power load and the charging income of the first preset time. According to the method, the power distribution network node marginal electricity price (DLMP) is used for real conduction of the electric energy price, the operation service fee is optimized under the cooperative game framework, and the road network travel cost and the attraction function are combined to construct the user charging selection model; on the premise of satisfying operation constraints of the power distribution network, problems of voltage out-of-limit, branch overload, network loss rising and the like which are easy to occur in a rapid peak period are improved, and the electric energy quality of the power distribution network is improved.

Description

Technical Field

[0001] This application relates to the fields of power system automation and charging control technology, and in particular to a distribution network pricing method, device, equipment and medium based on electric vehicle demand response and cooperative game theory. Background Technology

[0002] The large-scale integration of electric vehicles and charging infrastructure has brought a series of new challenges to the operation and scheduling of power distribution networks. On the one hand, electric vehicles (EVs) can be in the "grid-to-vehicle (G2V)" mode, and may also participate in "vehicle-to-grid (V2G)" in specific scenarios, exhibiting significant mobility and uncertainty in time and space. On the other hand, the high penetration rate of EVs has affected the normal operation of the power grid. In particular, the centralized access of fast charging stations (FCS) changes the original load characteristics, which can easily cause problems such as voltage exceeding limits, branch overload and network loss increase during peak hours, thereby affecting the power quality of the power distribution network.

[0003] To alleviate these pressures, EV participation in demand response has become an important means. Existing technologies typically start from the power system side, setting charging prices or incentive mechanisms to guide users to adjust charging times and locations. However, these methods often ignore the impact of road network operation on user travel and station selection behavior, making it difficult to accurately reflect the linkage between "price—travel costs—station selection—load," resulting in problems such as voltage exceeding limits, branch line overload, and increased network losses still occurring during peak hours.

[0004] Therefore, the existing technology still needs to be improved and enhanced. Summary of the Invention

[0005] The technical problem to be solved by this application is to provide a distribution network pricing method, device, equipment and medium based on electric vehicle demand response and cooperative game theory, which addresses the shortcomings of the existing technology.

[0006] To address the aforementioned technical problems, the first aspect of this application provides a distribution network pricing method based on electric vehicle demand response and cooperative game theory, wherein the method includes: The charging price is obtained at a first preset time interval, and the charging station status and road network status are obtained at a second preset time interval. Calculate the travel time and distance of electric vehicles to the charging station based on the road network conditions; The attractiveness of a charging station to a charging vehicle is calculated based on the charging price, charging station status, travel time, and travel distance, and the allocation result of electric vehicles to the charging station is determined based on the attractiveness. The power load of the charging station is determined based on the allocation result, and the charging price is updated based on the power load and the charging revenue for a first preset time.

[0007] The aforementioned distribution network pricing method based on electric vehicle demand response and cooperative game theory, wherein updating the charging price based on the power load and charging revenue over a first preset time specifically includes: Calculate the marginal electricity price of the distribution network node for each charging station access node; The current charging price is determined based on the charging revenue of each charging station within the first preset time period, the marginal electricity price of the distribution network node, and the power load. The charging price is updated using the current charging price.

[0008] The aforementioned distribution network pricing method based on electric vehicle demand response and cooperative game theory, wherein determining the current charging price based on the charging revenue of each charging station within a first preset time period and the marginal electricity price of the distribution network node specifically includes: The relationship between the charging stations is regarded as a cooperative alliance, and the Shapley value of each charging station is calculated based on the charging revenue of each charging station in the first preset time period. Based on the Shapley value, the marginal electricity price of the distribution network node, and the power load, a charging price optimization model is constructed with the goal of maximizing the charging price. Solve the charging price optimization model to obtain the current charging price.

[0009] The aforementioned distribution network pricing method based on electric vehicle demand response and cooperative game theory, wherein the road network state includes a static road network state description matrix and a dynamic road network state description matrix; the calculation of the travel time and distance of electric vehicles to charging stations based on the road network state specifically includes: The travel distance from the electric vehicle to the charging station is calculated based on the static road network state description matrix. The travel time from the electric vehicle to the charging station is calculated based on the dynamic road network state description matrix.

[0010] The aforementioned distribution network pricing method based on electric vehicle demand response and cooperative game theory, wherein the specific formula for calculating the attractiveness is as follows: , in, Indicates time Charging station electric vehicles Its appeal. Indicates charging station The total number of charging guns owned. It is a charging station The rated power of the charging gun in the middle, yes charging station The number of available charging stations, Indicates charging station Parking fees, This is the weighting coefficient for parking fees. Based on the benchmark parking rate, Indicates electric vehicles To the charging station Travel time, Indicates electric vehicles To the charging station The travel distance.

[0011] The aforementioned distribution network pricing method based on electric vehicle demand response and cooperative game theory, wherein determining the allocation result of electric vehicles allocated to charging stations based on the attractiveness specifically includes: Calculate the selection probability of each charging station based on its attractiveness. The amount of charging allocated to a charging station is determined based on the selection probability and the charging demand of electric vehicles. The number of electric vehicles allocated is determined based on the amount of electricity, and the number of rechargeable electric vehicles is determined based on the number of allocated electric vehicles, so as to obtain the allocation result of electric vehicles allocated to the charging station.

[0012] The aforementioned distribution network pricing method based on electric vehicle demand response and cooperative game theory, wherein, after determining the power load of the charging station based on the allocation result and updating the charging price based on the power load and the charging revenue over a first preset time, the method further includes: The charging station status is updated based on the allocation result at second preset time intervals, wherein the second preset time interval is less than the first preset time interval.

[0013] A second aspect of this application provides a distribution network pricing device based on electric vehicle demand response and cooperative game theory, wherein the device includes: The acquisition module is used to acquire the charging price at a first preset time interval and the charging station status and road network status at a second preset time interval. The calculation module is used to calculate the travel time and distance of electric vehicles to the charging station based on the road network conditions. The determination module is used to calculate the attractiveness of the charging station to the charging vehicle based on the charging price, charging station status, travel time and travel distance, and determine the allocation result of the electric vehicle to the charging station based on the attractiveness. An update module is used to determine the power load of the charging station based on the allocation result, and update the charging price based on the power load and the charging revenue for a first preset time.

[0014] A third aspect of this application provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps in the distribution network pricing method based on electric vehicle demand response and cooperative game theory as described above.

[0015] A fourth aspect of this application provides a terminal device, which includes: a processor and a memory; The memory stores a computer-readable program that can be executed by the processor; When the processor executes the computer-readable program, it implements the steps in the distribution network pricing method based on electric vehicle demand response and cooperative game theory as described above.

[0016] Beneficial Effects: Compared with existing technologies, this application provides a distribution network pricing method, apparatus, equipment, and medium based on electric vehicle demand response and cooperative game theory. The method includes acquiring charging prices, charging station status, and road network status at first preset time intervals; calculating the travel time and distance of electric vehicles to charging stations based on the road network status; calculating the attractiveness of charging stations to charging vehicles based on the charging prices, charging station status, travel time, and travel distance, and determining the allocation result of electric vehicles allocated to charging stations based on the attractiveness; determining the power load of charging stations based on the allocation result, and updating the charging price based on the power load and charging revenue over the first preset time interval. This application uses the distribution network node marginal price (DLMP) to truly transmit electricity prices and optimizes operating service fees under a cooperative game theory framework. Simultaneously, it constructs a user charging choice model by combining road network travel costs and attractiveness functions. Under the premise of satisfying distribution network operation constraints, it improves problems such as voltage exceeding limits, branch overload, and increased network losses that easily occur during rapid peak periods, thereby improving the power quality of the distribution network. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a power distribution network pricing method based on electric vehicle demand response and cooperative game theory, provided for embodiments of this application.

[0019] Figure 2 A schematic diagram of a power distribution network pricing device based on electric vehicle demand response and cooperative game theory provided in this application embodiment.

[0020] Figure 3 A schematic block diagram of the terminal device provided in the embodiments of this application. Detailed Implementation

[0021] This application provides a distribution network pricing method, apparatus, equipment, and medium based on electric vehicle demand response and cooperative game theory. To make the objectives, technical solutions, and effects of this application clearer and more explicit, the following detailed description, with reference to the accompanying drawings and embodiments, further illustrates this application. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0022] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated 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 groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0023] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0024] It should be understood that the sequence number and size of each step in this embodiment do not imply the order of execution. The execution order of each process is determined by its function and internal logic, and should not constitute any limitation on the implementation process of this application embodiment.

[0025] The application content will be further explained below with reference to the accompanying drawings and the description of the embodiments.

[0026] This embodiment provides a distribution network pricing method based on electric vehicle demand response and cooperative game theory, such as... Figure 1 As shown, the method includes: S10. Obtain the charging price at a first preset time interval, and obtain the charging station status and road network status at a second preset time interval.

[0027] Specifically, the charging price is updated every first preset time interval, and the charging station status and road network status are updated every second preset time interval. The first and second preset times are pre-set. In this embodiment, the first preset time is longer than the second preset time; the first preset time is on the hourly level, and the second preset time is on the minute level, for example, the first preset time is 1 hour, and the second preset time is 15 minutes. That is, every hour, the charging price is updated based on the power load and the charging revenue over the first preset time; every 15 minutes, the charging station status and road network status are updated. Then, the power load of the charging station is re-determined based on the updated charging price, charging station status, and road network status, thus forming a closed-loop relationship between charging price configuration and demand response. This can improve the power quality of the distribution network by addressing issues such as voltage exceeding limits, branch overload, and increased network losses during rapid peak hours, while meeting the constraints of the distribution network operation.

[0028] Furthermore, the charging price includes electricity costs and a charging service fee. The electricity cost is the price charged by the distribution network, and the charging service fee is the service fee collected by the operator. The electricity cost is determined based on the marginal electricity price at each node of the distribution network, while the charging service fee is determined based on a cooperative game mechanism for distributing profits among operators.

[0029] In one embodiment, the process of determining the charging price may include: Calculate the marginal electricity price of the distribution network node for each charging station access node; The current charging price is determined based on the charging revenue of each charging station within the first preset time period, the marginal electricity price of the distribution network node, and the power load. The charging price is updated using the current charging price.

[0030] Specifically, the distribution network node marginal price (DLMP) reflects the operating status of the distribution network. In other words, the DLMP is determined based on the operating status of the distribution network, and the charging price for the distribution network is determined accordingly. This application uses voltage index, voltage, and power loss as influencing factors in calculating the DLMP, and determines the DLMP based on these factors. The distribution network node marginal price can be expressed as: , , , in, Indicates distribution network node The marginal electricity price at the distribution network node Represent the Lagrange function, Indicates the generator index. Indicates the number of generators. Indicates the unit cost of power generation. This indicates the generator's active power output. This indicates that the generator has no reactive power output. and Both refer to shadow prices. Indicates the distribution network node index. and These are active load and reactive load, respectively. Indicates the active power loss of the distribution network. This indicates the reactive power loss in the distribution network. and This represents the active power deviation coefficient. and This represents the reactive power deviation coefficient. and Indicates the voltage deviation coefficient; This represents the maximum value of the active power output, and This represents the minimum value of active power output; This represents the maximum value of reactive power output. This represents the minimum value of reactive power output; Indicates distribution network node The voltage amplitude; This indicates the maximum value of the voltage amplitude. This represents the minimum value of the voltage amplitude. This indicates the maximum value of the voltage fluctuation limit. This indicates the minimum value for voltage fluctuation limits.

[0031] Furthermore, after obtaining the marginal electricity price of the distribution network node, each charging station is formed into a cooperative alliance, and all charging stations in the alliance participate in a cooperative game. Based on the cooperative game mechanism, the revenue within the cooperative alliance is rationally allocated to optimize the charging service fees of each charging station in the alliance. Therefore, determining the current charging price based on the charging revenue of each charging station within a first preset time period and the marginal electricity price of the distribution network node specifically includes: The relationship between the charging stations is regarded as a cooperative alliance, and the Shapley value of each charging station is calculated based on the charging revenue of each charging station in the first preset time period. Based on the Shapley value, the marginal electricity price of the distribution network node, and the power load, a charging price optimization model is constructed with the goal of maximizing the charging price. Solve the charging price optimization model to obtain the current charging price.

[0032] Specifically, the Shapley value of a charging station is determined by distributing the profits of the cooperative alliance using the Shapley value method. The Shapley value of a charging station can be expressed as: , in, This indicates the charging station index. Indicates the number of charging stations. Indicates charging station Shapley value, Represents the cooperative game combination index. Indicates the first A cooperative game combination, Used to calculate except for the first The revenue of all charging stations other than the one charging station express The probability of.

[0033] Furthermore, after determining the Shapley value of the charging station, a charging price optimization model can be constructed based on the Shapley value, the marginal electricity price of the distribution network nodes, and the power load, with the objective of maximizing the charging price. The charging price optimization model is as follows: , , , , in, Describe the objective function. Indicates charging station Weighted coefficients based on Shapley values Indicates charging station Charging service fee, Indicates charging station The corresponding marginal electricity price at the distribution network node, This indicates the maximum service fee. This represents the minimum service fee. This represents the average service fee. Indicates charging station The charging price, Indicates the number of charging stations. Indicates charging station The amount of charge.

[0034] Then, the charging price optimization model is solved (e.g., through iterative methods, genetic algorithms, ant colony algorithms, etc.) to obtain the charging price. During the solution process, the iteration error can be: , in, Indicates the iteration error. Indicates the number of iterations. Indicates charging station The charging price.

[0035] It should be noted that the charging price is updated at a first preset time interval. This means that at each first preset time interval, the charging load of the charging stations and the revenue of the cooperative alliance formed by the charging stations within that first preset time interval are obtained. Then, based on the charging load and the revenue within the first preset time interval, the current charging price is updated using the above process to obtain the updated charging price. Furthermore, when multiple charging stations are connected to a single distribution network node, the marginal electricity price of the distribution network node corresponding to each charging station is the marginal electricity price of that distribution network node. During the cooperative game process, only the service fee is optimized; the marginal electricity price of the distribution network node remains unchanged.

[0036] S20. Calculate the travel time and distance of the electric vehicle to the charging station based on the road network status.

[0037] Specifically, because traffic conditions on the road are constantly changing, the travel cost for vehicles to reach charging stations also varies. Therefore, in predicting the allocation of electric vehicles to charging stations, this embodiment of the application considers road network conditions as a factor influencing electric vehicle selection. For electric vehicles, road network conditions include two factors: travel time and travel distance. Therefore, road network conditions can include a static road network state description matrix and a dynamic road network state description matrix. The static road network state description matrix mainly consists of nodes and road segments, used to determine the travel distance from the electric vehicle to the charging station. The dynamic road network state description matrix mainly consists of nodes and time, used to determine the travel time from the electric vehicle to the charging station.

[0038] In one embodiment, calculating the travel time and distance of the electric vehicle to the charging station based on the road network conditions specifically includes: The travel distance from the electric vehicle to the charging station is calculated based on the static road network state description matrix. The travel time from the electric vehicle to the charging station is calculated based on the dynamic road network state description matrix.

[0039] Specifically, the static road network state description matrix can be represented as: , , in, This represents the static road network state description matrix. Indicates the number of nodes. Indicates infinity. Indicates the length of the road segment, when When, if node and Direct connection ( ),but It is the length of the road segment. When When, if node and No direct connection ( ),but It is INF. When , It is 0.

[0040] Based on this static road network state description matrix, the travel distance from an electric vehicle to a charging station can be calculated using Dijkstra's algorithm. The travel distance can be expressed as: , in, Indicates travel distance.

[0041] Furthermore, the dynamic road network description matrix can be represented as: , , in, This represents the dynamic road network description matrix. Indicates the number of nodes. Indicates infinity. Indicates the time cost on a road segment, when When, if node and Direct connection ( ),but This refers to the permitted travel time for that section of road. When When, if node and No direct connection ( ),but It is INF. When , It is 0.

[0042] Based on this dynamic road network description matrix, the travel time from an electric vehicle to a charging station can be calculated using Dijkstra's algorithm. The travel time can be expressed as: , in, Indicates travel distance.

[0043] S30. Calculate the attractiveness of the charging station to the charging vehicle based on the charging price, charging station status, travel time and travel distance, and determine the allocation result of the electric vehicle to the charging station based on the attractiveness.

[0044] Specifically, attractiveness reflects the electric vehicle (i.e., user) preference for charging stations. A higher attractiveness indicates a stronger preference for charging stations, and vice versa. The specific formula for calculating attractiveness is as follows: , in, Indicates time Charging station electric vehicles Its appeal. Indicates charging station The total number of charging guns owned. It is a charging station The rated power of the charging gun in the middle, It is time Charging station The number of available charging stations, Indicates charging station Parking fees, This is the weighting coefficient for parking fees. Based on the benchmark parking rate, Indicates electric vehicles To the charging station Travel time, Indicates electric vehicles To the charging station The travel distance.

[0045] Furthermore, after obtaining the attractiveness, the corresponding charging stations for electric vehicles can be directly determined based on the attractiveness. However, in practical applications, when allocating charging stations for electric vehicles, in addition to considering the electric vehicles' preference for charging stations, it is also necessary to consider the number of electric vehicles that the charging station can accommodate, so as to avoid the situation where the charging positions provided by the charging station cannot meet the needs of the electric vehicles allocated to the charging station.

[0046] Based on this, in one embodiment, determining the allocation result of electric vehicles to charging stations based on the attractiveness specifically includes: Calculate the selection probability of each charging station based on its attractiveness. The amount of charging allocated to a charging station is determined based on the selection probability and the charging demand of electric vehicles. The number of electric vehicles allocated is determined based on the amount of electricity, and the number of rechargeable electric vehicles is determined based on the number of allocated electric vehicles, so as to obtain the allocation result of electric vehicles allocated to the charging station.

[0047] Specifically, since attractiveness reflects the electric vehicle's (i.e., the user's) preference for charging stations, the probability of an electric vehicle choosing a charging station can be calculated based on attractiveness, thus obtaining the charging station selection probability. In other words, the charging station selection probability refers to the probability that an electric vehicle will choose a charging station for charging. This selection probability can be expressed as: , in, Indicates time electric vehicles For charging stations The probability of selection.

[0048] Furthermore, regarding the arrival of the vehicle assembly electric vehicles The charging power requirement is In this embodiment, the electric vehicle In time charging demand Randomly generated according to a probability distribution, which is an interval The log-normal distribution within the range, where Single time slot It can replenish the upper limit of energy. Therefore, within the first preset time period, the charging station... The allocated power is: , in, Indicates the charging station within the first preset time period The amount of electricity allocated.

[0049] charging station The maximum available power supply is: , in, Indicates charging station Maximum available power supply Indicates the first preset time period. Indicates the amount of charge. express charging station The number of available charging stations.

[0050] Completed battery level: , in, This indicates the completed charge level.

[0051] Therefore, the charging station can be calculated. The allocated charging amount, of which, charging stations The amount of charge allocated is: , in, Indicates charging station The amount of charge allocated. It is a set of time slots within a first preset time (e.g., 1 hour) and a second preset time (e.g., 15 minutes).

[0052] At the same time, they were assigned to charging stations The formula for calculating the number of vehicles can be: , in, Indicates rounding down. Indicates charging station The number of electric vehicles allocated.

[0053] Furthermore, the number of electric vehicles that can be charged is limited due to the actual number of charging piles within the station. The calculation formula can be: , in, Indicates the number of stakes that are occupied. Indicates charging station The number of charging stations.

[0054] S40. Determine the power load of the charging station based on the allocation result, and update the charging price based on the power load and the charging revenue for a first preset time.

[0055] Specifically, after obtaining the allocation results, the power load of the charging station can be determined based on the allocation results. Then, the charging price is updated based on the power load and the charging revenue for a first preset time. The update process is the same as the charging price acquisition process described above, and will not be elaborated here. In other words, before the start of each cycle, power load can be predicted, and then the charging price for that cycle is set based on the predicted power load and the charging revenue for the first preset time, so as to realize the linkage between "price - travel cost - station selection - load". At the same time, in the pricing process, the distribution network node marginal price (DLMP) is used to truly transmit the electricity price. Under the framework of cooperative game, the operation service is optimized, avoiding the problem of merging the two into a single fee for optimization, or only considering the guiding effect of service fee pricing on users, which would lead to the electricity price not being truly transmitted to the user side according to the distribution network operation status. This improves the operation level of fast charging stations and the user charging experience while ensuring the operation constraints of the distribution network.

[0056] In one embodiment, within each pricing cycle, the charging station status and road network status are updated every second preset time interval to obtain more accurate charging station and road network status when setting charging prices. The road network status can be updated by acquiring real-time traffic conditions. When updating the charging station status, the allocation result of charging stations can be calculated every second preset time interval, and the charging station status is updated based on the charging result. The charging station status includes the number of available charging piles, which is represented as follows: , , in, This indicates the number of vehicles that have completed charging. This indicates the number of rechargeable electric vehicles. Indicates the number of stakes that are occupied. Indicates charging station The number of charging stations. express The number of piles occupied at that time express The number of charging stations available at any time.

[0057] In summary, this embodiment provides a distribution network pricing method based on electric vehicle demand response and cooperative game theory. The method includes acquiring charging prices, charging station status, and road network status at first preset time intervals; calculating the travel time and distance of electric vehicles to charging stations based on the road network status; calculating the attractiveness of charging stations to charging vehicles based on the charging prices, charging station status, travel time, and travel distance, and determining the allocation result of electric vehicles to charging stations based on the attractiveness; determining the power load of charging stations based on the allocation result, and updating the charging price based on the power load and charging revenue over the first preset time interval. This application uses the distribution network node marginal price (DLMP) to truly transmit electricity prices and optimizes operating service fees within a cooperative game framework. Simultaneously, it constructs a user charging choice model by combining road network travel costs and an attractiveness function. Under the premise of satisfying distribution network operation constraints, it improves problems such as voltage exceeding limits, branch overload, and increased network losses that easily occur during rapid peak hours, thereby improving the power quality of the distribution network.

[0058] Based on the aforementioned distribution network pricing method based on electric vehicle demand response and cooperative game theory, this embodiment provides a distribution network pricing device based on electric vehicle demand response and cooperative game theory, such as... Figure 2 As shown, the device includes: The acquisition module 100 is used to acquire the charging price at a first preset time interval and to acquire the charging station status and road network status at a second preset time interval. The calculation module 200 is used to calculate the travel time and distance of electric vehicles to the charging station based on the road network status. The determining module 300 is used to calculate the attractiveness of the charging station to the charging vehicle based on the charging price, charging station status, travel time and travel distance, and to determine the allocation result of the electric vehicle to the charging station based on the attractiveness. The update module 400 is used to determine the power load of the charging station based on the allocation result, and update the charging price based on the power load and the charging revenue for a first preset time.

[0059] Based on the above-described distribution network pricing method based on electric vehicle demand response and cooperative game theory, this embodiment provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps in the distribution network pricing method based on electric vehicle demand response and cooperative game theory as described in the above embodiment.

[0060] Based on the aforementioned distribution network pricing method based on electric vehicle demand response and cooperative game theory, this application also provides a terminal device, such as... Figure 3 As shown, it includes at least one processor 20; a display screen 21; and a memory 22, and may also include a communications interface 23 and a bus 24. The processor 20, display screen 21, memory 22, and communications interface 23 can communicate with each other via the bus 24. The display screen 21 is configured to display a preset user guide interface in the initial setup mode. The communications interface 23 can transmit information. The processor 20 can invoke logical instructions in the memory 22 to execute the methods described in the above embodiments.

[0061] Furthermore, the logical instructions in the aforementioned memory 22 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0062] The memory 22, as a computer-readable storage medium, can be configured to store software programs, computer-executable programs, such as program instructions or modules corresponding to the methods in the embodiments of this disclosure. The processor 20 executes functional applications and data processing by running the software programs, instructions, or modules stored in the memory 22, thereby implementing the methods in the above embodiments.

[0063] The memory 22 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 22 may include high-speed random access memory (RAM) and non-volatile memory. Examples include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, as well as transient storage media.

[0064] Furthermore, the specific process of loading and executing multiple instruction processors in the aforementioned storage medium and terminal device has been described in detail in the above method, and will not be repeated here.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A distribution network pricing method based on electric vehicle demand response and cooperative game theory, characterized in that, The method includes: The charging price is obtained at a first preset time interval, and the charging station status and road network status are obtained at a second preset time interval. Calculate the travel time and distance of electric vehicles to the charging station based on the road network conditions; The attractiveness of a charging station to a charging vehicle is calculated based on the charging price, charging station status, travel time, and travel distance, and the allocation result of electric vehicles to the charging station is determined based on the attractiveness. The power load of the charging station is determined based on the allocation result, and the charging price is updated based on the power load and the charging revenue for a first preset time.

2. The distribution network pricing method based on electric vehicle demand response and cooperative game theory as described in claim 1, characterized in that, The process of updating the charging price based on the power load and the charging revenue over a first preset time specifically includes: Calculate the marginal electricity price of the distribution network node for each charging station access node; The current charging price is determined based on the charging revenue of each charging station within the first preset time period, the marginal electricity price of the distribution network node, and the power load. The charging price is updated using the current charging price.

3. The distribution network pricing method based on electric vehicle demand response and cooperative game theory according to claim 2, characterized in that, The determination of the current charging price based on the charging revenue of each charging station within a first preset time period and the marginal electricity price of the distribution network node specifically includes: The relationship between the charging stations is regarded as a cooperative alliance, and the Shapley value of each charging station is calculated based on the charging revenue of each charging station in the first preset time period. Based on the Shapley value, the marginal electricity price of the distribution network node, and the power load, a charging price optimization model is constructed with the goal of maximizing the charging price. Solve the charging price optimization model to obtain the current charging price.

4. The distribution network pricing method based on electric vehicle demand response and cooperative game theory according to claim 1, characterized in that, The road network status includes a static road network status description matrix and a dynamic road network status description matrix; the calculation of the travel time and distance of electric vehicles to the charging station based on the road network status specifically includes: The travel distance from the electric vehicle to the charging station is calculated based on the static road network state description matrix. The travel time from the electric vehicle to the charging station is calculated based on the dynamic road network state description matrix.

5. The distribution network pricing method based on electric vehicle demand response and cooperative game theory according to claim 1, characterized in that, The specific formula for calculating the attractive force is as follows: ， in, Indicates time Charging station electric vehicles The attraction Indicates charging station The total number of charging guns owned. It is a charging station The rated power of the charging gun in the middle, yes charging station The number of available charging stations, Indicates charging station Parking fees, This is the weighting coefficient for parking fees. Based on the benchmark parking fee rate, Indicates electric vehicles to charging station Travel time, Indicates electric vehicles to charging station The travel distance.

6. The distribution network pricing method based on electric vehicle demand response and cooperative game theory according to claim 1, characterized in that, The allocation results of electric vehicles to charging stations based on the attractiveness specifically include: Calculate the selection probability of each charging station based on its attractiveness. The amount of charging allocated to a charging station is determined based on the selection probability and the charging demand of electric vehicles. The number of electric vehicles allocated is determined based on the amount of electricity, and the number of rechargeable electric vehicles is determined based on the number of allocated electric vehicles, so as to obtain the allocation result of electric vehicles allocated to the charging station.

7. The distribution network pricing method based on electric vehicle demand response and cooperative game theory according to claim 1, characterized in that, After determining the power load of the charging station based on the allocation result, and updating the charging price based on the power load and the charging revenue for a first preset time, the method further includes: The charging station status is updated based on the allocation result at second preset time intervals, wherein the second preset time interval is less than the first preset time interval.

8. A distribution network pricing device based on electric vehicle demand response and cooperative game theory, characterized in that, The device includes: The acquisition module is used to acquire the charging price at a first preset time interval and the charging station status and road network status at a second preset time interval. The calculation module is used to calculate the travel time and distance of electric vehicles to the charging station based on the road network conditions. The determination module is used to calculate the attractiveness of the charging station to the charging vehicle based on the charging price, charging station status, travel time and travel distance, and determine the allocation result of the electric vehicle to the charging station based on the attractiveness. An update module is used to determine the power load of the charging station based on the allocation result, and update the charging price based on the power load and the charging revenue for a first preset time.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs, which can be executed by one or more processors to implement the steps in the distribution network pricing method based on electric vehicle demand response and cooperative game theory as described in any one of claims 1-7.

10. A terminal device, characterized in that, include: Processor and memory; The memory stores a computer-readable program that can be executed by the processor; When the processor executes the computer-readable program, it implements the steps in the distribution network pricing method based on electric vehicle demand response and cooperative game theory as described in any one of claims 1-7.

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