New energy automobile and power grid fusion interaction potential assessment method and system

Abstract

The invention discloses a new energy vehicle and power grid fusion interaction potential assessment method and system, and the method comprises the steps: obtaining a first parameter of a new energy vehicle, defining a user utility function and an agent profit function according to the first parameter, and constructing an interaction strategy behavior between an electric vehicle user and a charging station agent; based on the interactive strategy behavior, designing an evolutionary game model between the agent and the user, and searching strategy equilibrium between the user and the agent through an iterative algorithm; based on strategy equalization, calculating the shortest arrival time of the user responding to the charging and discharging demand by using a Wardrop first balance principle; and establishing a scheduling potential evaluation model under the condition of comprehensively considering the user activity type and the historical credit influence. The method optimizes the operation efficiency of the power grid, promotes the deep fusion of the new energy vehicle and the power system, improves the overall stability and economic benefits of the system, can provide a scientific basis for an electric power company to formulate reasonable scheduling measures, and enhances the capability of the power grid to cope with the access of large-scale electric vehicles.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method and system for evaluating the potential of integrated interaction between new energy vehicles and power grids. Background Art

[0002] With the transformation of the global energy structure and the enhancement of environmental protection awareness, the development of new energy vehicles (NEVs) has become an important trend in the automotive industry. New energy vehicles can not only reduce dependence on fossil fuels, but also significantly reduce greenhouse gas emissions, which is of great significance for improving urban air quality and promoting sustainable development. However, with the rapid growth in the number of new energy vehicles, their interaction with the power system has also received increasing attention. On the one hand, the large number of new energy vehicle charging demands will cause load pressure on the power grid, especially during peak hours, which may cause local power grid overload; on the other hand, through reasonable scheduling strategies, new energy vehicles can also participate in power grid regulation as distributed energy storage resources, improving the flexibility and stability of power grid operation.

[0003] At present, in order to achieve the effective integration of new energy vehicles and the power grid, it is necessary to evaluate the potential for interaction between the two, including but not limited to: vehicle-to-grid (V2G) capabilities, the impact of charging and discharging behaviors on the power grid, and how to use smart charging strategies to balance the power grid load. Some existing studies mainly focus on a single or a few key factors, such as only considering the charging demand forecast or battery capacity of electric vehicles, but lack a comprehensive and integrated approach to evaluate the overall potential of interaction between new energy vehicles and the power grid. In addition, existing evaluation methods are often based on static data analysis and fail to fully consider dynamic factors in actual applications, such as changes in user travel patterns and fluctuations in power grid load. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a method for evaluating the potential of integration and interaction between new energy vehicles and power grids to solve the problem that the prior art lacks a comprehensive method for evaluating the potential of integration and interaction between new energy vehicles and power grids, and fails to fully consider dynamic change factors.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for evaluating the potential of interaction between new energy vehicles and power grids, comprising:

[0008] Obtaining a first parameter of a new energy vehicle, defining a user utility function and an agent profit function based on the first parameter, and constructing an interactive strategy behavior between an electric vehicle user and a charging station agent;

[0009] Based on the interactive strategic behavior, an evolutionary game model between agents and users is designed, and the strategic equilibrium between users and agents is found through an iterative algorithm;

[0010] Based on the strategic equilibrium, the shortest arrival time for users to respond to charging and discharging demands is calculated by applying the Wardrop first equilibrium principle;

[0011] Establish a scheduling potential evaluation model that comprehensively considers user activity types and historical credit influences.

[0012] As a preferred solution of the method for evaluating the potential of interaction between new energy vehicles and power grids described in the present invention, the user utility function includes:

[0013] The electric vehicle user's personal electricity consumption preference and satisfaction are expressed by a quadratic utility function, which is the user utility function. The user utility function is expressed by the formula:

[0014]

[0015] Among them, ω i,k Represents the value brought by charging and discharging for different electric vehicle users; α k It is expressed to reflect the utility generated by the charging and discharging of electric vehicle users; Q -i,k It is represented as the charging or discharging strategy set of all electric vehicle users except i.

[0016] As a preferred solution of the method for evaluating the potential of interaction between new energy vehicles and power grids described in the present invention, the agent profit function is expressed as follows:

[0017] R j,k (p j,k ,s j,k )=p j,k s j,k -pL j,k

[0018] Among them, s j,k It is represented by the amount of electricity sold by agent j in the retail market during period k; D j,k represents the total electricity demand of electricity users served by agent j, L j,k It is represented by the amount of electricity purchased by the agent in the wholesale market during period k.

[0019] As a preferred solution of the method for evaluating the potential of interaction between new energy vehicles and power grids described in the present invention, the evolutionary game model between the design agent and the user includes:

[0020] Considering electric vehicle users as a population, each individual user will make charging and discharging choices based on the electricity prices announced by various agents, and adjust their own replication dynamic equations by continuously learning from others' electricity usage strategies. By designing appropriate replication factors, the evolutionary equilibrium among electric vehicle users can be gradually achieved.

[0021] The evolutionary equilibrium is determined by the agent’s electricity utility and the average of the utilities of all agents.

[0022] As a preferred solution of the method for evaluating the potential of interaction between new energy vehicles and power grids described in the present invention, the method of finding the strategic equilibrium between users and agents through an iterative algorithm includes:

[0023] The iterative algorithm is expressed as:

[0024]

[0025] Among them, s represents the number of iterations, σ1 represents the iteration step size, and y j,k It is represented by the probability that the electric vehicle user chooses agent j in time period k, π j,k Expressed as the electricity utility of the agent.

[0026] As a preferred solution of the method for evaluating the potential of interaction between new energy vehicles and power grids described in the present invention, the calculation of the shortest arrival time for users to respond to charging and discharging demands includes:

[0027] Considering that there is a certain relationship between the driving time of electric vehicles and traffic flow, the shortest driving time of electric vehicles is calculated using the Wardrop first equilibrium principle. The traffic network can be modeled as a graph set G = (N R ,A R ), parallel connections are allowed in the graph as long as they do not form a closed loop;

[0028] Among them, N R and A R are the set of nodes and links of the transportation network, respectively. The transportation demand is modeled based on the origin-destination demand pairs (OD pairs); each OD pair is connected by a set of paths in the transportation network;

[0029] Define W as the set of all OD pairs, w as one of the OD pairs, the running time on a route is the sum of the running times of the links that make up the route, and the running time of each section can be estimated by a section performance function. The section performance function is a monotonic function of traffic flow and can be expressed as:

[0030]

[0031] Among them, t α (x α ) represents the travel time of the electric vehicle on link α; It is expressed as the free flow time of link α; C α It is expressed as the capacity of link α; x α It is represented as the traffic flow of link α; α and β are the coefficients of link performance function, α is set to 4, β is set to 0.15;

[0032] Since charging stations hope to make electric vehicles respond to charging and discharging needs in the shortest time possible, when evaluating the maximum scheduling potential, the traffic flow will reach a stable equilibrium state called user equilibrium, which is expressed as:

[0033]

[0034] The equilibrium state is subject to the segment performance function and the following constraints, which are expressed as:

[0035]

[0036] in, — is expressed as an association coefficient, which is 1 if link a belongs to path P, and 0 otherwise; R w ——represented as a set of paths w; P——represented as any path in the transportation network; ——represents the traffic flow of route p allocated to the travel demand of OD pair w; d w —travel demand represented by w; —expressed as the running time of route p;

[0037] The non-negative constraints of the equilibrium state should satisfy:

[0038]

[0039] When solving the traffic flow distribution problem, in order to obtain the minimum area sum, the integral sum or area sum of the resistance functions of different road sections must be equal, so as to ensure that the traffic flow distribution result that meets the shortest time cost is obtained.

[0040] As a preferred solution of the method for evaluating the potential of interaction between new energy vehicles and power grids described in the present invention, the scheduling potential evaluation model taking into account the user activity type and historical credit influence includes:

[0041] When users need to carry out necessary activities, the impact of activity types on the final charging and discharging decision can be described by the relative value of the electric vehicle user's idle time and the time required to participate in charging and discharging. The idle time of electric vehicle user i is defined as:

[0042]

[0043] Among them, T si Represents the idle time of user i; TS i ——represents the start time of the activity of user i; TA i ——represented as the estimated arrival time of user i;

[0044] The idle time of electric vehicle user i, the influence coefficient of the travel activity type and historical credit of electric vehicle user on the final charging and discharging decision of electric vehicle user i and

[0045] According to the influence of user activity attributes and credit, the expected charging and discharging power of electric vehicle users is obtained. By superimposing the charging power of electric vehicles in each period, the charging and discharging scheduling potential of charging station j at time t is obtained.

[0046] In a second aspect, the present invention provides a system for evaluating the potential of interaction between new energy vehicles and power grids, comprising:

[0047] A function definition module, used to obtain a first parameter of the new energy vehicle, and define a user utility function and an agent profit function according to the first parameter;

[0048] Interaction strategy behavior building module, used to build the interaction strategy behavior between electric vehicle users and charging station agents;

[0049] A model design module, used to design an evolutionary game model between agents and users based on the interactive strategy behavior, and to find a strategic equilibrium between users and agents through an iterative algorithm;

[0050] A calculation module, used to calculate the shortest arrival time for users to respond to charging and discharging demands based on the strategy balance and using the Wardrop first equilibrium principle;

[0051] The evaluation module is used to establish a scheduling potential evaluation model that comprehensively considers the user activity type and historical credit impact.

[0052] In a third aspect, the present invention provides a computing device, comprising:

[0053] Memory and processor;

[0054] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for evaluating the potential for integration and interaction between new energy vehicles and power grids are implemented.

[0055] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for evaluating the potential for interaction between new energy vehicles and power grids.

[0056] Compared with the prior art, the present invention has the following beneficial effects: the present invention provides an electric vehicle dispatching potential assessment method that comprehensively considers user behavior, social impact and economic incentive factors, aiming to optimize the grid operation efficiency, promote the deep integration of new energy vehicles and the power system, and improve the overall stability and economic benefits of the system. In practical applications, this technology can provide a scientific basis for power companies to formulate reasonable dispatching measures and enhance the grid's ability to cope with large-scale electric vehicle access. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0058] Figure 1 It is a schematic diagram of the overall process of a method for evaluating the potential of interaction between new energy vehicles and power grids according to an embodiment of the present invention;

[0059] Figure 2 A schematic diagram of the game evolution process of a method for evaluating the potential for interaction between new energy vehicles and power grids according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0063] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0064] At the same time, in the description of the present invention, it should be noted that the orientations or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the system or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0065] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0066] Example 1

[0067] Reference Figure 1 , which is an embodiment of the present invention, provides a method for evaluating the potential of integration and interaction between new energy vehicles and power grids, including:

[0068] S100: Obtaining a first parameter of a new energy vehicle, defining a user utility function and an agent profit function according to the first parameter, and constructing an interactive strategy behavior between an electric vehicle user and a charging station agent;

[0069] In an optional embodiment, the first parameter may include technical parameters of the electric vehicle, such as battery capacity, charging rate, and maximum charging and discharging power; users' electricity usage behaviors and preferences, such as daily travel habits, requirements for charging time, and sensitivity to electricity prices; operating strategies of charging station agents, such as charging station locations, types and quantities of charging equipment, charging services provided, and pricing strategies; real-time electricity prices in the power market; and the ability of electric vehicles to interact with power grids, such as V2G technical support and traffic and power grid congestion. These factors together constitute an important basis for evaluating user utility and agent profits, and provide a comprehensive and accurate consideration for constructing an interactive strategy behavior model between electric vehicle users and charging station agents.

[0070] Preferably, the electric vehicle user's personal electricity consumption preference and satisfaction are expressed by a quadratic utility function, namely, a user utility function, which is expressed by the formula:

[0071]

[0072] Among them, ω i,k Represents the value brought by charging and discharging for different electric vehicle users; α k It is expressed to reflect the utility generated by the charging and discharging of electric vehicle users; Q -i,k It is represented as the set of charging or discharging strategies of all electric vehicle users except i;

[0073] It should be noted that the user utility function has the characteristic of monotony and non-decreasing, which can ensure linear marginal benefits and accurately characterize the degree of satisfaction brought about by charging and discharging for each user.

[0074] In the embodiment of the present application, the charging or discharging strategy set Q -i,k =[Q 1,k ,Q 2,k ,…,Q i-1,k ,Q i+1,k ,…,Q n,k ], and the higher the charge and discharge capacity, the higher the k The corresponding power utility is greater, but it also has an upper limit. After reaching the upper limit, the increase in charging and discharging power will not improve the power satisfaction; for a given charging and discharging power Q i,k , a higher ω i,k and a smaller α k It will increase the upper limit of its satisfaction; and when the charge and discharge power is 0, the satisfaction is also 0.

[0075] In the embodiment of the present application, for the user utility function, when When is a constant, so the preference behavior of electric vehicle users is mainly in Reflected in scope;

[0076] In the embodiment of the present application, based on the above reasons, the utility function describing the electric vehicle user's electricity preference behavior is taken as:

[0077]

[0078] Obviously, this utility function can be expressed in different time periods by Q i,k Different values ​​of are used to obtain the dynamic changes in the charging and discharging demands of electric vehicle users. The decisions of individual users will be affected by the decisions of group users, so the decision benefits of individual users will also be affected by the decisions of group users.

[0079] In the embodiment of the present application, when m agents announce the price vector p in time period k k =[p 1,k ,p 2,k ,…,p j,k ,…,p m,k ], if electric car user i chooses agent j, the payment is p j,k Q i,k . So the social welfare function of user i is as follows:

[0080]

[0081] Q i,k(min) ≤Q i,k ≤Q i,k(max)

[0082] Considering electric vehicle charging as a demand resource, the charging behavior of electric vehicles can affect the price of electricity; in order to prevent grid congestion, the price of electricity will rise as the charging load increases, which can be described by the sensitivity of the price of electricity to demand. The discharge of electric vehicles is a supply resource. In order to prevent traffic and grid congestion, the incentive price decreases as the "response" increases;

[0083] The effective incentive price is proposed in the following formula. When the response to charging and discharging measures is low, a higher incentive price will be used to attract more electric vehicles. As the number of responding vehicles increases, the incentive price will decrease considering congestion and the cost of charging stations. The function of incentive price and response can be fitted by an exponential function. The final fitting result is the first-order coefficient function of Taylor expansion, expressed as:

[0084]

[0085] in, ——Expressed as the charging baseline incentive price coefficient; ——Expressed as the discharge baseline incentive price coefficient; ——Expressed as charging incentive price sensitivity coefficient; ——Expressed as the discharge incentive price sensitivity coefficient; N c ——Represents the total number of electric vehicles that responded to the incentives.

[0086] In the embodiment of the present application, the cost C of purchasing real-time electricity from the wholesale electricity market by agent j in period k is j,k Denoted as:

[0087] C j,k =pL k (5)

[0088] Where p represents the wholesale electricity price, which is set as a constant here; L j,k It is represented by the amount of electricity purchased by the agent in the wholesale market during period k;

[0089] Preferably, the agent profit function is expressed as:

[0090] R j,k (p j,k ,s j,k )=p j,k s j,k -pL j,k

[0091] Among them, s j,k ——represented as the amount of electricity sold by agent j in the retail market during period k; D j,k ——represents the total electricity demand of electricity users served by agent j, L j,k It is represented by the amount of electricity purchased by the agent in the wholesale market during period k.

[0092] It should be noted that the present invention accurately depicts the changes in satisfaction of electric vehicle users under different charging and discharging strategies and the profit optimization of agents by defining user utility functions and agent profit functions. It also introduces a dynamic electricity price and incentive price mechanism, which is adjusted with the charging load and the number of responding vehicles to effectively prevent congestion. It constructs a comprehensive interactive strategy behavior model between electric vehicle users and charging station agents, and provides a comprehensive evaluation and optimization method for the integrated interaction of new energy vehicles and power grids.

[0093] S102: Based on interactive strategic behaviors, an evolutionary game model between agents and users is designed, and a strategic equilibrium between users and agents is found through an iterative algorithm;

[0094] Preferably, electric vehicle users are regarded as a population, and each individual user will make charging and discharging choices based on the electricity prices announced by various agents, and adjust their own replication dynamic equations by continuously learning from others' electricity usage strategies, and gradually achieve evolutionary equilibrium among electric vehicle users by designing appropriate replication factors;

[0095] In the embodiment of the present application, in the real-time electricity price pricing problem, the evolutionary model can be used to analyze the electricity consumption competition problem among electric vehicle users. The strategy of each electric vehicle user to select an agent for charging and discharging is a hybrid strategy. If the probability that an electric vehicle user selects agent j in time period k is y j,k ∣0≤y j,k ≤1,∑ m y j,k =1), then the population state can be expressed as y k =[y 1,k ,y 2,k ,…,y j,k ,…,y m,k ]express.

[0096] In the embodiment of the present application, when m agents announce the incentive price vector p in time period k k =[p 1,k ,p 2,k ,…,p j,k ,…,p m,k ], if electric car user i chooses agent j to charge, the price he pays is p j,k Q i,k , so the charging social welfare function of user i is as follows:

[0097]

[0098] Q ij,k(min) ≤Q ij,k ≤Q ij,k(max)

[0099] In the embodiment of the present application, if electric vehicle user i chooses agent j to discharge, the discharge income is p j, k Q i,k , so the discharge social welfare function of user i is as follows:

[0100]

[0101] Q ij,k(min) ≤Q ij,k ≤Q ij,k(max)

[0102] Among them, Q ij,k(min) It is represented as the minimum amount of electricity that user i charges and discharges from agent j in time period k; Q ij,k(max) ——Represents the maximum amount of electricity charged and discharged by user i from agent j in time period k.

[0103] In the embodiment of the present application, considering Q ij,k(min) ≤Q ij,k≤Q ij,k(max) , the optimal charging and discharging power requirement of user i is:

[0104]

[0105] In the embodiment of the present application, the optimal amount of electricity for electric vehicle user i to charge and discharge from agent j in time period k is shown as follows:

[0106]

[0107] In the embodiment of the present application, the total amount of electricity charged or discharged by the electric vehicle user from the agent j in the time period k is:

[0108]

[0109] In the embodiment of the present application, and L at a certain moment in the evolution process j,k Once announced by the agent, it remains unchanged. In this case, the agent's electricity utility is π j,k It is expressed as:

[0110]

[0111] In the embodiment of the present application, the dynamic selection behavior design of the population is expressed as:

[0112]

[0113] Wherein, t represents time; ——Expressed as the average value of agent utility.

[0114] Preferably, the evolutionary equilibrium is determined by the agent's electricity utility and the average utility of all agents, which can be expressed as:

[0115]

[0116] So ∑y i,k The replication dynamics are:

[0117]

[0118] In the process of evolution m y j,k = 1 holds, and the evolutionary equilibrium is recorded as Its convergence can be ensured by Lyapunov method.

[0119] Preferably, the iterative algorithm is expressed as:

[0120]

[0121] Among them, s represents the number of iterations, σ1 represents the iteration step size, and y j,k It is represented by the probability that the electric vehicle user chooses agent j in time period k, π j,k It is expressed as the electricity utility of the agent;

[0122] In the embodiment of the present application, the iteration termination criterion is:

[0123]

[0124] Here, ε is represented by an arbitrarily small positive number.

[0125] The specific network generation flowchart of the iterative algorithm is shown in Figure 2 As shown, the specific steps are:

[0126] Step 1: Set relevant parameters and initialize the power consumption strategy;

[0127] Step 2: Each user will compare his / her own income with the income of the neighboring network users with social connections. If both are lower than his / her own income, then his / her next iteration strategy will not change. If there is a neighboring network user with more income than him / her, he / she will select the neighbor with the largest income to imitate and use its charging and discharging strategy as the strategy in the next iteration.

[0128] Step 3: All individuals update their strategies simultaneously;

[0129] Step 4: Calculate the utility π of agent j after charging and discharging from agent j j,k(s) , after obtaining the utility of all agents, calculate their average value;

[0130] Step 5: Calculate the probability y j,k(s) The amount of electricity charged and discharged from agent j;

[0131] Step 6: Repeat the above process until the iteration termination criteria are met to complete the strategy optimization.

[0132] It should be noted that the present invention is based on the evolutionary game model between agents and users designed based on interactive strategic behavior, and effectively finds the strategic equilibrium between users and agents through an iterative algorithm. The model not only takes into account the charging and discharging choices and learning and adjustment process of electric vehicle users, but also incorporates the electricity price and power consumption decisions of agents, realizing the dynamic evolution of the strategies of both parties. By simulating competition and cooperative learning among users, as well as the utility maximization behavior of agents, the model promotes the integrated interaction between new energy vehicles and the power grid, and improves overall efficiency and stability.

[0133] S104: Based on the strategic equilibrium, the shortest arrival time for the user to respond to the charging and discharging demand is calculated by using the Wardrop first equilibrium principle;

[0134] In the embodiment of the present application, considering that there is a certain relationship between the driving time of electric vehicles and the traffic flow, the shortest driving time of electric vehicles is calculated using the Wardrop first equilibrium principle. The traffic network can be modeled as a graph set G = (N R ,A R ), parallel connections are allowed in the graph as long as they do not form a closed loop;

[0135] Among them, N R and A R are the set of nodes and links of the transportation network, respectively. The transportation demand is modeled based on the origin-destination demand pairs (OD pairs); each OD pair is connected by a set of paths in the transportation network;

[0136] In the embodiment of the present application, W is defined as a set of all OD pairs, w is one of the OD pairs, the running time on a route is the sum of the running times of the links constituting the route, and the running time of each section can be estimated by a section performance function. The section performance function is a monotonic function of the traffic flow and can be expressed as:

[0137]

[0138] Among them, t α (x α ) represents the travel time of the electric vehicle on link α; It is expressed as the free flow time of link α; C α It is expressed as the capacity of link α; x α It is represented as the traffic flow of link α; α and β are the coefficients of link performance function, α is set to 4, β is set to 0.15;

[0139] In the embodiment of the present application, since the charging station hopes to make the electric vehicle respond to the charging and discharging demand in the shortest time possible, when evaluating the maximum scheduling potential, the traffic flow will reach a stable equilibrium state called user equilibrium, and the equilibrium state is expressed as:

[0140]

[0141] The equilibrium state is subject to the segment performance function and the following constraints, which are expressed as:

[0142]

[0143] in, — is expressed as an association coefficient, which is 1 if link a belongs to path P, and 0 otherwise; R w ——represented as a set of paths w; P——represented as any path in the transportation network; ——represents the traffic flow of route p allocated to the travel demand of OD pair w; d w —travel demand represented by w; —expressed as the running time of route p;

[0144] The non-negative constraints of the equilibrium state should satisfy:

[0145]

[0146] In an embodiment of the present application, when solving the traffic flow distribution problem, in order to obtain the minimum area sum, the integral sum or area sum of the resistance functions of different road sections must be equal, so as to ensure that the traffic flow distribution result that meets the shortest time cost is obtained.

[0147] It should be noted that the present invention is based on strategic equilibrium and uses the Wardrop first equilibrium principle to calculate the shortest arrival time for users to respond to charging and discharging needs. This approach not only optimizes the driving path of electric vehicles and the service efficiency of charging stations, but also ensures the stable operation of the transportation network during peak traffic periods. By accurately evaluating the driving time and capacity of each link, the model effectively guides electric vehicle users to choose the optimal path, reduces congestion and waiting time, and thus improves operating efficiency and user experience, providing strong support for the popularization of new energy vehicles and interactive scheduling of power grids.

[0148] S106: Establishing a scheduling potential evaluation model that comprehensively considers the user activity type and historical credit impact;

[0149] Preferably, when the user needs to perform necessary activities, the impact of the activity type on the final charging and discharging decision can be described by the relative value of the electric vehicle user's idle time and the time required to participate in charging and discharging. The idle time of electric vehicle user i is defined as:

[0150]

[0151] Among them, T si Represents the idle time of user i; TS i ——represents the start time of the activity of user i; TA i ——represented as the estimated arrival time of user i;

[0152] The influence coefficients of the travel activity type and historical credit of electric vehicle users on the final charging and discharging decision of electric vehicle user i are and The travel mode chosen by users is determined by the types of activities they participate in, which will affect their final charging and discharging decisions;

[0153] Preferably, the idle time of electric vehicle user i, the influence coefficient of the travel activity type and historical credit of the electric vehicle user on the final charging and discharging decision of the electric vehicle user i and

[0154] In the embodiments of the present application, It can be expressed as:

[0155]

[0156] Among them, T d represents the time required for electric vehicle user i to participate in charging or discharging, including the travel time from the response point to the charging station and the charging or discharging time; from the above formula, it can be seen that the shorter the time required for charging and discharging is relative to the idle time, the smaller the impact of the activity on the final decision of charging and discharging; when the user activity is a flexible activity, the user's participation in the activity will not affect his or her final charging and discharging choice, that is,

[0157] In the embodiment of the present application, the charging and discharging behavior of the electric vehicle is ultimately controlled by the user. If the electric vehicle user participating in the dispatch needs to use the electric vehicle due to an emergency or accident during the dispatch period, even if the battery charging and discharging effect does not reach the expected value, it will be forcibly disconnected from the power grid and unilaterally violate the agreement;

[0158] Therefore, the concept of user credit is introduced, and the impact of user credit on charging and discharging capacity is expressed by the participation of electric vehicles in the scheduling strategy within a certain period of time.

[0159] In the embodiments of the present application, The expression of is shown as follows:

[0160]

[0161] Among them, X represents the total number of times electric vehicle user i participates in scheduling within a certain period of time; Indicates the start time of the user's xth scheduled participation; It represents the expected time of leaving the network for the user’s xth scheduled participation; It represents the actual off-network time of the user’s xth scheduled participation.

[0162] Preferably, the expected charging and discharging power of the electric vehicle user is obtained according to the influence of the user's activity attributes and credit, and the charging and discharging scheduling potential of the charging station j at time t is obtained by superimposing the charging power of the electric vehicle in each time period;

[0163] In the embodiment of the present application, the expected charge and discharge power of electric vehicle user i is expressed as:

[0164]

[0165] In the embodiment of the present application, the charge and discharge scheduling potential calculation formula is:

[0166]

[0167] Among them, N t It is represented by the total number of responsive electric vehicles with dispatchable capacity in the charging station at time t.

[0168] It should be noted that the dispatch potential evaluation model that comprehensively considers the user activity type and historical credit effectively evaluates the user's charging and discharging potential in each time period by accurately analyzing the electric vehicle user's idle time, activity type and historical credit. This model not only improves the dispatch efficiency and energy utilization of charging stations, but also ensures the stable operation of the power grid. At the same time, by introducing the concept of user credit, the model further enhances the enthusiasm and reliability of users to participate in dispatch.

[0169] The above is a schematic scheme of a method for evaluating the potential for interaction between new energy vehicles and power grids in this embodiment. It should be noted that the technical scheme of the system for evaluating the potential for interaction between new energy vehicles and power grids and the technical scheme of the method for evaluating the potential for interaction between new energy vehicles and power grids described above belong to the same concept. For details not described in detail in the technical scheme of the system for evaluating the potential for interaction between new energy vehicles and power grids in this embodiment, please refer to the description of the technical scheme of the method for evaluating the potential for interaction between new energy vehicles and power grids described above.

[0170] The new energy vehicle and power grid integration interaction potential assessment system in this embodiment includes:

[0171] A function definition module, used to obtain a first parameter of the new energy vehicle, and define a user utility function and an agent profit function according to the first parameter;

[0172] Interaction strategy behavior building module, used to build the interaction strategy behavior between electric vehicle users and charging station agents;

[0173] The model design module is used to design an evolutionary game model between agents and users based on interactive strategic behaviors, and to find the strategic equilibrium between users and agents through an iterative algorithm;

[0174] The calculation module is used to calculate the shortest arrival time for users to respond to charging and discharging demands based on policy balance and the Wardrop first equilibrium principle;

[0175] The evaluation module is used to establish a scheduling potential evaluation model that comprehensively considers the user activity type and historical credit impact.

[0176] This embodiment also provides a computing device suitable for evaluating the potential of interaction between new energy vehicles and power grids, including:

[0177] Memory and processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the method for evaluating the potential for integration and interaction between new energy vehicles and power grids as proposed in the above embodiment.

[0178] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for evaluating the potential for integration and interaction between new energy vehicles and power grids proposed in the above embodiment is implemented.

[0179] The storage medium proposed in this embodiment and the method for evaluating the potential for integrated interaction between new energy vehicles and power grids proposed in the above embodiment belong to the same inventive concept. For technical details not described in detail in this embodiment, please refer to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0180] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0181] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for evaluating the potential of interaction between new energy vehicles and power grids, characterized in that: include: Obtaining a first parameter of a new energy vehicle, defining a user utility function and an agent profit function based on the first parameter, and constructing an interactive strategy behavior between an electric vehicle user and a charging station agent; Based on the interactive strategic behavior, an evolutionary game model between agents and users is designed, and the strategic equilibrium between users and agents is found through an iterative algorithm; Based on the strategic equilibrium, the shortest arrival time for users to respond to charging and discharging demands is calculated by applying the Wardrop first equilibrium principle; Establish a scheduling potential evaluation model that comprehensively considers user activity types and historical credit influences.

2. The method for evaluating the potential of interaction between new energy vehicles and power grids as claimed in claim 1, characterized in that: The user utility function includes, The electric vehicle user's personal electricity consumption preference and satisfaction are expressed by a quadratic utility function, which is the user utility function. The user utility function is expressed by the formula: Among them, ω i,k Represents the value brought by charging and discharging for different electric vehicle users; α k It is expressed to reflect the utility generated by the charging and discharging of electric vehicle users; Q -i,k It is represented as the charging or discharging strategy set of all electric vehicle users except i.

3. The method for evaluating the potential of interaction between new energy vehicles and power grids according to claim 1 or 2, characterized in that: The agent profit function is expressed as: R j,k (p j,k ,s j,k )=p j,k s j,k -pL j,k Among them, s j,k ——represented as the amount of electricity sold by agent j in the retail market during period k; D j,k ——represents the total electricity demand of electricity users served by agent j, L j,k It is represented by the amount of electricity purchased by the agent in the wholesale market during period k.

4. The method for evaluating the potential of interaction between new energy vehicles and power grids as claimed in claim 3, characterized in that: The evolutionary game model between design agents and users includes: Considering electric vehicle users as a population, each individual user will make charging and discharging choices based on the electricity prices announced by various agents, and adjust their own replication dynamic equations by continuously learning from others' electricity usage strategies. By designing appropriate replication factors, the evolutionary equilibrium among electric vehicle users can be gradually achieved. The evolutionary equilibrium is determined by the agent's electricity utility and the average of the utilities of all agents.

5. The method for evaluating the potential of interaction between new energy vehicles and power grids as claimed in claim 4, characterized in that: Finding the strategic equilibrium between users and agents through iterative algorithms includes, The iterative algorithm is expressed as: Among them, s represents the number of iterations, σ1 represents the iteration step size, and y j,k It is represented by the probability that the electric vehicle user chooses agent j in time period k, π j,k Expressed as the electricity utility of the agent.

6. The method for evaluating the potential of interaction between new energy vehicles and power grids as claimed in claim 5, characterized in that: Calculating the shortest arrival time for users to respond to charging and discharging needs includes: Considering that there is a certain relationship between the driving time of electric vehicles and traffic flow, the shortest driving time of electric vehicles is calculated using the Wardrop first equilibrium principle. The traffic network can be modeled as a graph set G = (N R ,A R ), parallel connections are allowed in the graph as long as they do not form a closed loop; Among them, N R and A R are the set of nodes and links of the transportation network, respectively. The transportation demand is modeled based on the origin-destination demand pairs (OD pairs); each OD pair is connected by a set of paths in the transportation network; Define W as the set of all OD pairs, w as one of the OD pairs, the running time on a route is the sum of the running times of the links that make up the route, and the running time of each section can be estimated by a section performance function. The section performance function is a monotonic function of traffic flow and can be expressed as: Among them, t α (x α ) represents the travel time of the electric vehicle on link α; Expressed as the free flow time of link α; C α It is expressed as the capacity of link α; x α It is represented as the traffic flow of link α; α and β are the coefficients of link performance function, α is set to 4, β is set to 0.15; Since charging stations hope to make electric vehicles respond to charging and discharging needs in the shortest time possible, when evaluating the maximum scheduling potential, the traffic flow will reach a stable equilibrium state called user equilibrium, which is expressed as: The equilibrium state is subject to the segment performance function and the following constraints, which are expressed as: in, — is expressed as an association coefficient, which is 1 if link a belongs to path P, and 0 otherwise; R w ——represented as a set of paths w; P——represented as any path in the transportation network; ——represents the traffic flow of route p allocated to the travel demand of OD pair w; d w —travel demand represented by w; —expressed as the running time of route p; The non-negative constraints of the equilibrium state should satisfy: When solving the traffic flow distribution problem, in order to obtain the minimum area sum, the integral sum or area sum of the resistance functions of different road sections must be equal, so as to ensure that the traffic flow distribution result that meets the shortest time cost is obtained.

7. The method for evaluating the potential of interaction between new energy vehicles and power grids as claimed in claim 6, characterized in that: The scheduling potential evaluation model that comprehensively considers the user activity type and historical credit includes: When users need to carry out necessary activities, the impact of activity types on the final charging and discharging decision can be described by the relative value of the electric vehicle user's idle time and the time required to participate in charging and discharging. The idle time of electric vehicle user i is defined as: Among them, T si Represents the idle time of user i; TS i ——represents the start time of the activity of user i; TA i ——represented as the estimated arrival time of user i; The idle time of electric vehicle user i, the influence coefficient of the travel activity type and historical credit of electric vehicle user on the final charging and discharging decision of electric vehicle user i and According to the influence of user activity attributes and credit, the expected charging and discharging power of electric vehicle users is obtained. By superimposing the charging power of electric vehicles in each period, the charging and discharging scheduling potential of charging station j at time t is obtained.

8. A system for evaluating the potential of interaction between new energy vehicles and power grids, characterized in that: include, A function definition module, used to obtain a first parameter of the new energy vehicle, and define a user utility function and an agent profit function according to the first parameter; Interaction strategy behavior building module, used to build the interaction strategy behavior between electric vehicle users and charging station agents; A model design module, used to design an evolutionary game model between agents and users based on the interactive strategy behavior, and to find a strategic equilibrium between users and agents through an iterative algorithm; A calculation module, used to calculate the shortest arrival time for users to respond to charging and discharging demands based on the strategy balance and using the Wardrop first equilibrium principle; The evaluation module is used to establish a scheduling potential evaluation model that comprehensively considers the user activity type and historical credit impact.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for evaluating the potential for integration and interaction between new energy vehicles and power grids as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for evaluating the potential for interaction between new energy vehicles and power grids as described in any one of claims 1 to 7.

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