Vehicle network interaction guiding simulation method and system

Abstract

The invention discloses a vehicle network interaction guiding simulation method and system. Comprising the steps that a multi-dimensional factor set of vehicle network interaction is constructed, and user side, charging pile side and road side factors are covered; a vehicle network interaction guiding element set is constructed, user behaviors are optimized through a guiding mechanism, and the electric vehicle charging and discharging efficiency and the power grid operation efficiency are improved; designing a vehicle-pile-road multi-dimensional factor influence quantification mechanism, and extracting influence features of each factor by using a multi-time scale attention mechanism; optimizing a user side response mechanism to obtain an optimal response strategy; based on the optimal response of the user side, selecting a power grid side optimal guide mechanism to ensure stable operation of the power grid; and updating vehicle network interaction multi-dimensional factor influence related parameters, and adjusting a guide strategy to adapt to a dynamic power grid and user behaviors. The method can improve the charging and discharging efficiency of the electric vehicle, optimizes the operation and stability of the power grid, improves the weighted optimization of the electric quantity anxiety value, the travel efficiency and the income of the user, and provides two-way optimization decision support for the power grid and the user.

Description

Technical Field

[0001] The present invention relates to the field of smart grid and traffic technology, and in particular to a vehicle-grid interaction guidance simulation method and system. Background Art

[0002] Electric vehicles (EVs) have received widespread attention as a clean energy means of transportation. The promotion of EVs not only helps reduce dependence on traditional fossil fuels and greenhouse gas emissions, but also brings profound changes to the transportation sector. However, the rapid development of EVs has also brought new challenges and demands to the operation and management of power systems, especially smart grids.

[0003] In traditional power systems, the operation of the power grid mainly depends on the stable balance between power generation and power consumption. The charging demand and quantity changes of electric vehicles will have a great impact on the load distribution of the power grid. At the same time, the imbalance between the energy demand of electric vehicles during charging and the load of the power grid may cause the load fluctuation of the power grid, affecting the operational stability and safety of the power grid. Therefore, how to effectively manage the interaction between electric vehicles and the power grid has become an important issue in the current power grid operation management.

[0004] Vehicle-to-Grid (V2G) technology is an emerging solution that allows two-way energy flow between electric vehicles and the power grid. Electric vehicles can charge when the grid load is low and feed back electricity to the grid when the load is high to provide energy storage services. This two-way energy exchange not only helps balance the grid load, improve the peak-shaving capacity and stability of the grid, but also brings economic benefits to electric vehicle users, such as compensation for participating in demand response projects. In addition, vehicle-to-grid interaction can also improve the energy efficiency of electric vehicles, reduce the operating costs of the power system, and increase the proportion of renewable energy use.

[0005] However, the effective implementation of vehicle-grid interaction technology faces many challenges, especially in the field of vehicle-grid interaction simulation. Although current simulation technology can simulate the interaction process between electric vehicles and the power grid, there are still some key issues:

[0006] Limitations of single-factor models: Existing vehicle-grid interaction simulation technologies usually only consider the impact of a single factor on the system, and fail to comprehensively consider the coupling effects of multi-dimensional factors (such as grid load, electric vehicle location, charging pile status, user behavior, etc.) on vehicle-grid interaction. Traditional methods lack accurate modeling based on multi-time scale attention mechanisms and cannot fully quantify the time-varying influence of these factors at different time scales. Therefore, these simulation methods cannot meet the changes in electric vehicle charging demand, nor can they achieve the overall optimization of the vehicle-grid interaction system, resulting in insufficient accuracy and adaptability of simulation results.

[0007] Lack of dual-perspective optimization: Traditional vehicle-grid interaction simulation methods mostly focus on a single perspective of the power grid or user side, while ignoring the interactive perspective conversion between the user and the power grid. The optimization of the vehicle-grid interaction system is not only a problem of load balancing of the power grid, but also involves the personalized needs and economic benefits of electric vehicle users during the charging process. Therefore, the lack of a multi-perspective optimization process that integrates the user-side response mechanism and the grid-side guidance mechanism makes it impossible to achieve collaborative optimization between the power grid and users.

[0008] Lack of deviation between theoretical and actual performance: Current vehicle-grid interaction simulation methods generally ignore the deviation update mechanism based on theoretical optimization performance values ​​and actual optimization performance values. The behavior of the power grid and users is highly dynamic, and simple theoretical optimization performance calculations cannot fully reflect the complex situation in actual operation. Therefore, the lack of real-time update of mechanism parameters makes the actual performance of the simulation system deviate greatly from user needs or power grid operation status, making it impossible to make timely adjustments and adaptations. Summary of the invention

[0009] In view of the defects of the prior art, the present invention provides a vehicle-grid interaction guidance simulation method and system. It is necessary not only to comprehensively consider the mutual influence between multi-dimensional factors, but also to be able to perform two-way optimization between the power grid and the user, and adjust the strategy in real time to adapt to dynamic environmental changes. The present invention can more accurately and comprehensively reflect the interactive relationship between electric vehicles and the power grid, and provides a new solution for realizing the overall optimization of the vehicle-grid interaction system.

[0010] In order to achieve the above invention object, the technical solution adopted by the present invention is as follows:

[0011] A vehicle-grid interaction guidance simulation method comprises the following steps:

[0012] S1: Construct a multi-dimensional factor set for vehicle-grid interaction, considering user-side factors, charging pile-side factors and road-side factors respectively. User-side factors include departure place, destination, vehicle SoC, battery anxiety value, travel efficiency and benefits; charging pile-side factors include charging pile power, idle status and service fee price; road-side factors include traffic volume, number of lanes and lane width.

[0013] S2: Constructing a set of guiding elements for vehicle-network interaction G o The guiding elements include the unit price of auxiliary service revenue, charging and swapping service fees, and demand response revenue. User behavior is guided through different guiding mechanisms to optimize the charging and discharging behavior of electric vehicles and the operating efficiency of the power grid.

[0014] S3: Design a mechanism to quantify the influence of multi-dimensional factors in the vehicle-pile-road network. Use a multi-dimensional function model to model the influence of user-side, pile-side, and road-side factors. Use a multi-time-scale attention mechanism to extract influence features and obtain the influence function of each factor at different time scales.

[0015] S4: Under the influence of multi-dimensional factors, the user-side response mechanism is optimized according to different guidance mechanisms to obtain the user's optimal response strategy and achieve the weighted optimality of power anxiety value, travel efficiency and benefits.

[0016] S5: Based on the optimal response mechanism on the user side, the grid side selects the optimal guidance mechanism to minimize network losses and source-load imbalance rate to ensure the stable operation and economy of the grid.

[0017] S6: Based on the selected optimal guidance mechanism, update the relevant parameters of the influence of multi-dimensional factors of vehicle-grid interaction, calculate the theoretical optimal performance values ​​of the power grid and users under the guidance mechanism, and adjust the guidance strategy to adapt to the dynamic power grid operation status and user behavior.

[0018] Furthermore, in step 3, the single factor influence function of user side factors, pile side factors and road side factors is fitted based on historical data and expert experience.

[0019] Based on the single factor influence function, a multi-time scale attention mechanism is adopted to obtain the influence function of user side, pile side and road side factors under the influence of multi-dimensional factors.

[0020] For the influence at each time scale, the calculated attention is processed by weighted average to obtain the comprehensive influence of each factor at different time scales.

[0021] Furthermore, the single factor influence functions in step 3 are expressed as:

[0022]

[0023] In the formula, They are the single factor influence functions of the i-th user-side factor, the j-th pile-side factor and the k-th road-side factor respectively. is the parameter matrix of the single factor influence function fitting model of the i-th user-side factor, the j-th pile-side factor and the k-th road-side factor, It is the single factor influence function fitting model of the i-th user side factor, the j-th pile side factor and the k-th road side factor.

[0024] Influence function of user side, pile side and road side factors It is expressed as:

[0025]

[0026] In the formula, are the influence functions of the i-th user-side factor at the year, month, day, hour, and minute levels, respectively. Ye,U 、V Mo,U 、V Da,U 、V Ho,U 、V Mi,U They are the time factors corresponding to the influence of user-side factors at the year, month, day, hour, and minute levels. are the influence functions of the jth pile side factor at the year, month, day, hour, and minute levels, respectively. Ye,P 、V Mo,P 、V Da,P 、V Ho,P 、V Mi,P They are the time factors corresponding to the influence of pile side factors at the year, month, day, hour and minute levels respectively. are the influence functions of the kth roadside factor at the year, month, day, hour, and minute levels, respectively. Ye,R 、V Mo,R 、V Da,R 、V Ho,R 、V Mi,R They are the time factors corresponding to the influence of roadside factors at the year, month, day, hour and minute levels respectively.

[0027] The comprehensive influence of each factor at different time scales is specifically expressed as:

[0028] The comprehensive influence function of user-side factors at multiple time scales:

[0029]

[0030] The comprehensive influence function of pile side factors at multiple time scales:

[0031]

[0032] Comprehensive influence function of roadside factors at multiple time scales:

[0033]

[0034] In the formula, Multi-timescale attention weights representing user-side factors. Representing multi-timescale attention weights of pile-side factors. Representing multi-timescale attention weights of roadside factors. It represents the influence of the user-side factor and other factors b (such as other time scales or the interaction between different factors) at time point t. Represents the influence of pile side factors and other factors b at time point t.

[0035] Represents the influence of roadside factors and other factors b at time point t.

[0036] Furthermore, the user optimal response mechanism in step 4 is based on the multi-dimensional influence function of user-side factors. By solving the optimization problem, a set of optimal response mechanisms under different guidance mechanisms is obtained to achieve the weighted optimality of the user's personalized power anxiety value, travel efficiency and benefits.

[0037] The optimal response mechanism guides users to charge when the grid load is low by dynamically adjusting charging prices or demand response incentives based on the response strategy selected by the user, thereby reducing the peak load of the grid and improving the benefits of the grid and users.

[0038] Furthermore, the optimal guiding mechanism on the grid side in step 5 selects the optimal guiding mechanism by minimizing the grid loss and the source-load imbalance rate, and adjusts the operation strategy of the grid according to the mechanism.

[0039] The optimal guidance mechanism selection on the grid side is based on the optimal response mechanism set. o To minimize the grid loss and source-load imbalance rate of the power grid, so as to achieve the optimal operation of the power grid.

[0040] Furthermore, the update of the influence function in step 6 includes the following steps: * Input into the scenario generation module to calculate the theoretical optimization performance values ​​of the user side and the grid side The theoretical optimization performance value is used to update the parameters of the vehicle-pile-road multi-dimensional factor influence quantification mechanism, which is expressed as:

[0041]

[0042] In the formula, is the gradient function. Represents the deviation sample set Ω relative to the user side, pile side, and road side User ,Ω Pile ,Ω Road gradient.

[0043] The present invention also discloses a vehicle-grid interaction guidance simulation system, comprising: an environment input module, a simulation optimization module, a vehicle-grid interaction guidance simulation module and a power grid management and control system interface docking module;

[0044] The environment input module is used to input the environmental information of the status and distribution of electric vehicle users, road topology, and charging pile data to build a vehicle-grid interaction simulation scenario;

[0045] The simulation optimization module calculates the optimal response and optimization performance under different guidance mechanisms based on the data provided by the environmental input module.

[0046] The vehicle-grid interaction guidance simulation system recommends the optimal response mechanism to the user based on the simulation optimization results, thereby optimizing the user's power anxiety value, travel efficiency and benefits.

[0047] The power grid control system interface docking module is used to connect the simulation results with the actual power grid system and update the simulation optimization performance value according to the real power grid data.

[0048] Further, the simulation optimization module includes: a guidance mode selection module, a response mechanism selection module, and a simulation scenario generation module;

[0049] A guidance mode selection module, used to select different guidance mechanisms and input them into the system to guide the decision-making of electric vehicle users;

[0050] A response mechanism selection module is used to select the optimal response mechanism for electric vehicle users under different guidance mechanisms;

[0051] The simulation scene generation module generates the corresponding simulation scene according to the environmental data provided by the environmental input module;

[0052] Furthermore, the vehicle-grid interaction guidance simulation module includes: a user-station-pile-road multi-dimensional influencing factor set construction submodule, a vehicle-grid interaction guidance element set construction submodule, a multi-dimensional vehicle-pile-road factor influence quantification submodule and a user-side response mechanism optimization submodule, a grid-side optimal guidance mechanism selection submodule and an influence quantification mechanism parameter update submodule;

[0053] The user-charging pile-road multi-dimensional influencing factor set construction submodule constructs the influencing set of multi-dimensional factors of users, charging piles and roads according to the input data provided by the environmental input module.

[0054] The vehicle-grid interaction guiding element set construction submodule is responsible for constructing the vehicle-grid interaction guiding element set.

[0055] The multi-dimensional vehicle-pile-road factor influence quantification submodule constructs the corresponding influence function based on the influence of multi-dimensional factors such as vehicles, piles, roads, etc. on different time scales.

[0056] The user-side response mechanism optimization submodule calculates the optimal response mechanism from the user-side perspective.

[0057] The grid-side optimal guidance mechanism selection submodule is used to evaluate the response effects of electric vehicle users under different guidance mechanisms, quantify the impact of these responses on grid operation, and select the guidance mechanism that best suits grid needs.

[0058] Influence quantification mechanism parameter update submodule: By connecting to the actual power grid control system interface, the deviation between the theoretical optimization performance value obtained in the simulation and the actual optimization performance value is calculated, and the parameters of the multidimensional influence function are updated according to the deviation value.

[0059] Compared with the prior art, the advantages of the present invention are:

[0060] 1. The present invention effectively improves the intelligence level of the vehicle-grid interaction system through a method for quantifying the time-varying influence of multi-dimensional factors of vehicle-pile-road based on multi-time scale attention. This method can dynamically capture and quantify the time-varying influence of various factors such as vehicles, piles, roads, etc. at different time scales, so that the simulation system can adapt to changes in factors such as grid load, traffic flow and user behavior in real time, and enhance the adaptability and prediction accuracy of the simulation model to environmental changes. In this way, the vehicle-grid interaction system can provide more accurate optimization results, help electric vehicle users and power grids achieve collaborative optimization, and optimize the scheduling and utilization efficiency of power resources.

[0061] 2. The guidance mechanism optimization method proposed in the present invention is based on the dual perspectives of users and power grids, taking into account the personalized needs of the user side and the operational needs of the power grid side, and can effectively coordinate the relationship between the two. Through the comprehensive optimization of the user-side response mechanism and the power grid-side guidance mechanism, electric vehicle users can make the best decisions in terms of travel, charging, demand response, etc., while the power grid can be most effectively regulated in terms of power load, network security, etc. This two-way optimization method not only improves the load balancing ability of the power grid, but also provides users with a better electricity experience and economic benefits.

[0062] 3. By comparing the theoretical optimization performance value with the actual optimization performance value and updating the parameters based on the deviation, the present invention significantly improves the actual adaptability of the simulation system. By continuously adjusting the influence function of the multi-dimensional factors of vehicle-pile-road, the simulation system can be more closely connected with the actual power grid and traffic operation status, making the simulation results closer to the actual operation, ensuring that the system can still provide efficient guidance and optimization solutions under the dynamic power grid environment and changing user behavior.

[0063] 4. The vehicle-grid interaction guidance simulation method provided by the present invention can not only help the power grid to better manage loads and dispatch resources, but also encourage users to charge, travel or participate in demand response at reasonable time periods, thereby optimizing the energy distribution of the power grid and reducing power grid fluctuations and load conflicts. At the same time, users can make decisions based on the simulation results to obtain the best economic benefits or charging experience. Through this synergistic effect, the operating efficiency of vehicle-grid interaction has been greatly improved, further promoting the coordinated development of electric vehicles and smart grids.

[0064] 5. The present invention improves the flexibility and dynamic adjustment capability of the vehicle-grid interaction guidance strategy through guidance mechanism optimization and parameter update method. The simulation system can not only cope with the current power grid and traffic status, but also adjust according to future change trends to ensure that the strategy remains effective in a variety of situations. This flexible strategy adjustment mechanism provides a guarantee for the long-term stability and adaptability of the vehicle-grid interaction system in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic diagram of the structure of the vehicle-grid interactive guidance simulation system according to an embodiment of the present invention.

[0066] Figure 2 It is a flow chart of the vehicle-grid interactive guidance simulation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0068] The present invention proposes a vehicle-grid interactive guidance simulation system, such as Figure 1 As shown, it includes an environment input module, a simulation optimization module and a power grid management and control system interface docking module.

[0069] Environmental input module: In the environmental input module, simulation users can input vehicle-charging pile-road multi-dimensional factors to build a simulation environment. According to the actual situation, input the status and distribution of electric vehicle users, including the location and travel needs of electric vehicle users, to simulate the behavior of electric vehicle users; simulation users can input the topological structure of the road, including the connection relationship of the road, the number of lanes, the lane width and other information to simulate the actual road network environment; simulation users can input the location, power, idle status and other data of charging piles to provide basic charging facility information for simulation.

[0070] Simulation optimization module: It includes four parts: guidance mode selection module, response mechanism selection module, simulation scenario generation module and vehicle-grid interaction guidance simulation module.

[0071] The guidance mode selection module and the response mechanism selection module are the operation interfaces of the simulation user in the process of vehicle-grid interactive guidance simulation. The simulation optimization module can simulate according to the preset guidance mechanism. The simulation user can choose guidance mechanisms such as coupon issuance, electricity price adjustment, and demand response revenue adjustment. The simulation scenario generation module can configure the corresponding simulation scenario based on the environment input by the simulation user. The simulation optimization module uses the proposed vehicle-grid interactive guidance simulation method to calculate the optimal response results and theoretical optimization performance values ​​of electric vehicle users under each mechanism. Then, the system can recommend the optimal response mechanism under the current guidance mechanism to the user based on the simulation optimization results to help electric vehicle users make decisions, and users can also choose by themselves.

[0072] The vehicle-grid interaction guidance simulation module includes a sub-module for constructing a multi-dimensional influencing factor set of users, piles and roads, a sub-module for constructing a set of vehicle-grid interaction guidance elements, a sub-module for quantifying the influence of multi-dimensional vehicle-pile-road factors, a sub-module for optimizing the response mechanism on the user side, a sub-module for selecting the optimal guidance mechanism on the grid side and a sub-module for updating the parameters of the influence quantification mechanism.

[0073] Sub-module for constructing a set of multi-dimensional influencing factors of user-station-road: Constructs a set of multi-dimensional influencing factors of vehicle-network interaction user-station-road based on the environment input module.

[0074] The sub-module for constructing a set of guiding elements for vehicle-grid interaction is to construct a set of guiding elements for vehicle-grid interaction, including guiding mechanisms such as adopting charging and swapping electricity price subsidies on the grid side and increasing demand response benefits.

[0075] Multi-dimensional vehicle-pile-road factor influence quantification submodule: Construct a multi-dimensional function model to describe the single factor influence distribution of vehicle-pile-road factors at different time points. Based on the influence mean, change trend and change range of each factor at large and small time scales, calculate the attention of each factor at large and small time scales. Use multi-time scale attention for feature extraction to obtain the influence function of user-side, pile-side and road-side factors under the influence of multi-dimensional factors.

[0076] User-side response mechanism optimization submodule: Calculate the optimal response mechanism under different guidance mechanisms from the user side, obtain the theoretical optimization performance value, and use it as the theoretical vehicle-grid interaction effect. Under different guidance mechanisms, by selecting different response mechanisms, the weighted optimization of the user-side personalized power anxiety value, travel efficiency, and benefits can be achieved.

[0077] Grid-side optimal guidance mechanism selection submodule: evaluates the user response under different guidance mechanisms, quantifies the impact of different guidance mechanisms on grid operation, and selects the optimal guidance mechanism.

[0078] Influence quantification mechanism parameter update submodule: The parameters of the multidimensional influence function are updated according to the deviation calculated based on the actual optimization performance value obtained by the power grid control system interface docking module.

[0079] Grid control system interface docking module: The grid control system interface docking module can be connected to the actual grid control system interface to obtain the actual optimized performance value based on the real-time vehicle-grid interaction scenario.

[0080] The present invention proposes a vehicle-grid interaction guidance simulation method, such as Figure 2 As shown, the following steps are included:

[0081] Step 1: Construction of multi-dimensional influencing factor set of vehicle-network interaction users, station piles, and roads

[0082] In order to more comprehensively understand and predict vehicle-grid interaction behavior, this patent fully considers the multi-dimensional factors in vehicle-grid interaction and constructs the influencing factor sets on the user side, the pile side and the road side respectively. The user side factors consider the departure place, destination, vehicle SoC, user personalized power anxiety value, travel efficiency, and benefits, expressed as U i (t), i = 1, 2, 3, 4, 5, 6, i is the index of the user-side factor. The charging pile-side factors include the charging pile power, idleness, and service fee price, expressed as P j (t), j = 1, 2, 3, j is the index of the pile side factor. Road side factors include traffic volume, number of lanes, and lane width, expressed as R k (t), k = 1, 2, 3, k is the index of the roadside factor. Wherein, t is a time character sequence, t = Year / Month / Day / Hour / Min / Sec, and the simulation stage can be configured by the user during simulation, such as t = 2022 / 3 / 10 / 0 / 0 / 0-2023 / 3 / 10 / 23 / 59 / 59.

[0083] Step 2: Constructing a set of vehicle-grid interaction guidance elements

[0084] The vehicle-grid interaction guidance elements can guide and optimize the charging and discharging behavior of electric vehicles during the vehicle-grid interaction process. Through the joint action of these elements, effective energy and information interaction between the power grid and electric vehicles can be achieved, and the operating efficiency and economy of the power grid can be improved. The vehicle-grid interaction guidance elements considered in this patent include the unit price of auxiliary service revenue, charging and swapping service fees, and demand response revenue. Based on the vehicle-grid interaction guidance elements, the power grid side adopts O guidance mechanisms such as charging and swapping electricity price subsidies and increased demand response revenue. The guidance mechanism set G o Represented as G o ={g1,...,g o ,...,g O}, where g o Indicates that the oth guidance mechanism is adopted.

[0085] Step 3: Design of a mechanism to quantify the influence of multi-dimensional vehicle-pile-road factors

[0086] 1) In order to quantify the influence of factors such as users, piles, and roads over time, the present invention first constructs a multidimensional function model to describe the single factor influence distribution of each factor of vehicle-pile-road at different time points. Based on expert experience, the influence function of each factor of vehicle-pile-road is fitted using historical data, which is expressed as

[0087]

[0088] In the formula, They are the single factor influence functions of the i-th user-side factor, the j-th pile-side factor and the k-th road-side factor respectively. is the parameter matrix of the single factor influence function fitting model of the i-th user-side factor, the j-th pile-side factor and the k-th road-side factor, It is the single factor influence function fitting model of the i-th user side factor, the j-th pile side factor and the k-th road side factor.

[0089] 2) The single factor influence function can describe the changes in the influence of the vehicle-pile-road factor and its influence over time. In order to more accurately reflect the influence of each factor in the multi-dimensional influence environment of vehicle-network interactive users-pile-road, the present invention solves the mutual influence between factors on this basis, obtains the dependence between factors, and combines multi-time scale attention to obtain the influence function of each factor under the multi-factor coupling condition, and quantifies the influence of the multi-dimensional vehicle-pile-road factor.

[0090] In order to accurately measure the relative size of the influence of each factor, the attention of each factor at different time scales is calculated based on the mean, change trend and change range of the influence of each factor at different time scales. Considering the attention of a smaller time scale can more effectively analyze the rapid changes in the influence of factors in the short term from the data characteristics, which is particularly important for application scenarios that require rapid response, such as traffic flow prediction. Attention at a larger time scale can help identify long-term trends and cyclical patterns, which is helpful for long-term planning and strategy formulation.

[0091] The present invention solves multi-time scale attention based on simulation user needs, including minute-level attention Hour-level attention Daily attention Month-level attention Year-level attention Taking the minute-level attention solution as an example, the attention solution process for other levels is similar. The minute-level attention is updated based on the minute Min, and the time window contains T0 data of the current minute. At the minute-level time scale, the minute-level attention of the user side, pile side, and road side factors is calculated based on the mean, change trend, and change range of the influence of a single factor in a time window. It is expressed as:

[0092]

[0093] Where Min_Attention(·) is the small time scale attention mechanism function. is the small time scale attention of the i-th user-side factor, is the small time scale attention of the j-th pile side factor, is the small time scale attention of the kth roadside factor. is the set of data means of the current and previous and next time windows of the i-th user-side factor, is the set of linear fitting slopes of the data in the current and previous time windows of the i-th user-side factor, is the set of extreme values ​​of the data of the i-th user-side factor in the current and previous time windows, is the set of data means of the jth pile side factor in the current and previous and next time windows, is the set of linear fitting slopes of the data in the current and previous time windows of the jth pile side factor, is the set of extreme values ​​of the j-th pile side factor in the current and previous and next time windows, is the set of data means of the current and previous and next time windows of the kth roadside factor, is the set of linear fitting slopes of the data in the current and previous and next time windows of the kth roadside factor, is the set of data ranges of the kth roadside factor in the current and previous and next time windows. Comprehensively considering the mean, slope, and range of the influence function can comprehensively evaluate attention from multiple aspects, including the average state of the influence of a single factor, the direction and rate of change over time, and stability and volatility.

[0094] Based on the single factor influence function of user side, pile side and road side factors, multi-time scale attention is used for feature extraction to obtain the influence function of user side, pile side and road side factors under the influence of multi-dimensional factors. Expressed as

[0095]

[0096] In the formula, are the influence functions of the i-th user-side factor at the year, month, day, hour, and minute levels, respectively. Ye,U 、V Mo,U 、V Da,U 、V Ho,U 、V Mi,U They are the time factors corresponding to the influence of user-side factors at the year, month, day, hour, and minute levels. are the influence functions of the jth pile side factor at the year, month, day, hour, and minute levels, respectively. Ye,P 、V Mo,P 、V Da,P 、V Ho,P 、V Mi,P They are the time factors corresponding to the influence of pile side factors at the year, month, day, hour and minute levels respectively. are the influence functions of the kth roadside factor at the year, month, day, hour, and minute levels, respectively. Ye,R 、V Mo,R 、V Da,R 、V Ho,R 、V Mi,R They are the time factors corresponding to the influence of roadside factors at the year, month, day, hour and minute levels respectively.

[0097] The minute-level influence function of the i-th user-side factor Take it as an example, based on the inter-correlation of other factors to factor i, we can solve Expressed as

[0098]

[0099] In the formula, is the mutual influence. The higher the mutual influence between factor i and other factors, the worse the independence of the factor, the more dependent it is on other variables, and the smaller the minute-level influence on the system. The yearly, monthly, daily, and hourly influence functions of other user-side factors are solved in the same way as above.

[0100] Similarly, the minute-level influence function of the jth pile side factor is Take 2 as an example, based on the correlation between other factors and factor j, we can solve Expressed as

[0101]

[0102] The solutions for the influence functions of other pile-side factors at the annual, monthly, daily and hourly levels are the same as above.

[0103] The minute-level influence function of the kth roadside factor Take 2 as an example, based on the correlation between other factors and factor j, we can solve Expressed as

[0104]

[0105] The solutions for the influence functions of other roadside factors at the annual, monthly, daily and hourly levels are the same as above.

[0106] Step 4: Build the optimal response mechanism on the user side.

[0107] In specific implementations, a guidance mechanism can be used to motivate users to take specific actions. For example, by adjusting the charging price or providing demand response incentives, users are guided to charge when the grid load is low, thereby reducing the peak load of the grid. Under each guidance mechanism, users choose the most appropriate response strategy based on their own preferences and actual conditions. The present invention calculates the optimal response mechanism under different guidance mechanisms from the user's perspective based on the influence of multi-dimensional factors such as vehicle-pile-road, obtains the theoretical optimization performance value, and uses it as the theoretical vehicle-grid interaction effect. Under different guidance mechanisms, by selecting different response mechanisms, the weighted optimality of personalized power anxiety value, travel efficiency, and benefits on the user side can be achieved. The optimization problem under different guidance mechanisms is constructed from the user side as follows

[0108]

[0109] In the formula, are the influence functions of the multi-dimensional factors of user personalized power anxiety value, travel efficiency and benefits, To adopt the guidance mechanism o In the case of , the user selects the vth response mechanism. According to the optimization problem constructed on the user side, the optimal response mechanism set under different guidance mechanisms can be obtained in, To adopt the guidance mechanism o τ is the simulation time configured by the simulation user.

[0110] Step 5: Optimal guidance mechanism selection on the grid side

[0111] The goal of the grid side is to minimize network losses and source-load imbalance to ensure the stable operation and economy of the grid. Based on the construction of the optimal response mechanism on the user side, the grid side needs to evaluate the response of users under different guidance mechanisms, quantify the impact of different guidance mechanisms on grid operation, and select the optimal guidance mechanism. The grid side can dynamically adjust the guidance strategy to adapt to the changing grid operation status and user behavior.

[0112] The present invention constructs the optimization problem of selecting the optimal guidance mechanism on the grid side, substitutes the optimal response mechanism selected by the user under different guidance mechanisms, and selects different guidance mechanisms on the grid side to minimize the grid loss and source-load imbalance rate on the grid side. The optimization problem is constructed as follows:

[0113]

[0114] In the formula, Loss(τ) and Ratio(τ) are network loss and source-load imbalance rate, To adopt the best response mechanism The weighted sum of network loss and source-load imbalance rate. The grid side is based on the optimal response mechanism Bring in different guidance mechanisms and select the guidance mechanism g that minimizes the grid side network loss and source-load imbalance rate * .

[0115] Step 6: Update the parameters of the quantification mechanism for the influence of multiple factors on vehicle, charging pile and road

[0116] 1) Adopting a guidance mechanism * , the boot mechanism g * Input to the scenario generation module to generate a simulation scenario, and calculate the theoretical optimization performance values ​​of the user side and the grid side according to formula (5) in the simulation scenario At the same time, the guidance mechanism g * The power grid management and control system generates real scenarios in the real world network, and then connects with the simulation system to calculate the actual optimized performance values ​​on the user side and the power grid side. and determine the deviation;

[0117] 2) Calculate the deviation between the theoretical optimization performance value and the actual optimization performance value, and set the relative deviation greater than the threshold The samples are included in the set to be updated Ω User ,Ω Pile and Ω Road In, when When , the corresponding user personalized power anxiety value, travel efficiency and theoretical and actual deviation of benefits are put into the set Ω User is used for updating the function parameters on the user side, where |·| is the absolute value function. When the corresponding travel efficiency and benefit are theoretically and practically deviated, they are put into the set Ω Pile It is used to update the parameters of the pile-side function. When the corresponding network loss and the theoretical and actual deviation of the source-load imbalance rate are put into the set Ω Load Used for roadside function parameter update.

[0118] 3) Based on the deviation samples in the set to be updated, update the parameters of the quantification mechanism for the influence of the multi-dimensional factors of vehicle-pile-road, expressed as:

[0119]

[0120] In the formula, is the gradient function.

[0121] The method according to the present invention described above can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded through a network and will be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor or programmable or dedicated hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, a vehicle-grid interactive guidance simulation method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the processing shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the processing shown herein.

[0122] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the implementation methods of the present invention, and should be understood that the protection scope of the present invention is not limited to such special statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A vehicle-grid interactive guidance simulation method, characterized in that: The following steps are involved: S1: Construct a multi-dimensional factor set for vehicle-grid interaction, considering user-side factors, charging pile-side factors, and road-side factors. User-side factors include departure point, destination, vehicle SoC, battery anxiety value, travel efficiency, and revenue. Charging pile-side factors include charging pile power, idle conditions, and service fee prices. Road-side factors include traffic volume, number of lanes, and lane width. S2: Constructing a set of guiding elements for vehicle-network interaction G o The guiding elements include the unit price of ancillary service revenue, charging and swapping service fees, and demand response revenue. Different guiding mechanisms are used to guide user behavior and optimize electric vehicle charging and discharging behavior and grid operation efficiency. S3: Design a mechanism to quantify the influence of multi-dimensional factors on vehicle-pile-road, use a multi-dimensional function model to model the influence of factors on the user side, pile side, and road side, and extract influence features through a multi-time scale attention mechanism to obtain the influence function of each factor at different time scales; S4: Under the influence of multi-dimensional factors, the user-side response mechanism is optimized according to different guidance mechanisms to obtain the user's optimal response strategy and achieve the weighted optimal value of power anxiety value, travel efficiency and benefits; S5: Based on the optimal response mechanism on the user side, the grid side selects the optimal guidance mechanism to minimize network losses and source-load imbalance rate to ensure stable operation and economy of the grid; S6: Based on the selected optimal guidance mechanism, update the relevant parameters of the influence of multi-dimensional factors of vehicle-grid interaction, calculate the theoretical optimal performance values ​​of the power grid and users under the guidance mechanism, and adjust the guidance strategy to adapt to the dynamic power grid operation status and user behavior.

2. The vehicle-grid interaction guidance simulation method according to claim 1, characterized in that: In step 3, the single factor influence function of user side factors, pile side factors and road side factors is fitted based on historical data and expert experience. Based on the single factor influence function, a multi-time scale attention mechanism is adopted to obtain the influence function of user side, pile side and road side factors under the influence of multi-dimensional factors. For the influence at each time scale, the calculated attention is processed by weighted average to obtain the comprehensive influence of each factor at different time scales.

3. The vehicle-grid interaction guidance simulation method according to claim 2, characterized in that: The single factor influence functions in step 3 are expressed as: In the formula, are the single factor influence functions of the i-th user side factor, the j-th pile side factor and the k-th road side factor respectively; is the parameter matrix of the single factor influence function fitting model of the i-th user-side factor, the j-th pile-side factor and the k-th road-side factor, Fitting model of single factor influence function of the i-th user side factor, the j-th pile side factor and the k-th road side factor; Influence function of factors on the user side, pile side, and road side It is expressed as: In the formula, are the influence functions of the i-th user-side factor at the year, month, day, hour, and minute levels respectively; V Ye,U 、V Mo,U 、V Da,U 、V Ho,U 、V Mi,U The time factors corresponding to the influence of user-side factors at the year, month, day, hour, and minute levels; are the influence functions of the jth pile side factor at the year, month, day, hour, and minute levels respectively; V Ye,P 、V Mo,P 、V Da,P 、V Ho,P 、V Mi,P The time factors corresponding to the influence of pile side factors at the year, month, day, hour and minute levels respectively; are the influence functions of the kth roadside factor at the year, month, day, hour, and minute levels respectively; V Ye,R 、V Mo,R 、V Da,R 、V Ho,R 、V Mi,R The time factors corresponding to the influence of roadside factors at the year, month, day, hour, and minute levels; The comprehensive influence of each factor at different time scales is specifically expressed as: The comprehensive influence function of user-side factors at multiple time scales: The comprehensive influence function of pile side factors at multiple time scales: Comprehensive influence function of roadside factors at multiple time scales: In the formula, Multi-timescale attention weights representing user-side factors; Multi-timescale attention weights representing pile-side factors; Representing the multi-timescale attention weights of roadside factors; It represents the influence of user-side factors and other factors b at time point t; It represents the influence of pile side factors and other factors b at time point t; Represents the influence of roadside factors and other factors b at time point t.

4. The vehicle-grid interaction guidance simulation method according to claim 1, characterized in that: The user optimal response mechanism in step 4 is based on the multi-dimensional influence function of user-side factors. By solving the optimization problem, the optimal response mechanism set under different guidance mechanisms is obtained to achieve the weighted optimal user personalized power anxiety value, travel efficiency and benefits. The optimal response mechanism guides users to charge when the grid load is low by dynamically adjusting charging prices or demand response incentives based on the response strategy selected by the user, thereby reducing the peak load of the grid and improving the benefits of the grid and users.

5. The vehicle-grid interaction guidance simulation method according to claim 1, characterized in that: The optimal guidance mechanism selection on the grid side in step 5 selects the optimal guidance mechanism by minimizing the grid loss and source-load imbalance rate, and adjusts the grid operation strategy according to the mechanism; The optimal guidance mechanism selection on the grid side is based on the optimal response mechanism set. o To minimize the grid loss and source-load imbalance rate of the power grid, so as to achieve the optimal operation of the power grid.

6. The vehicle-grid interaction guidance simulation method according to claim 1, characterized in that: The update of the influence function in step 6 includes the following steps: * Input into the scenario generation module to calculate the theoretical optimization performance values ​​of the user side and the grid side The theoretical optimization performance value is used to update the parameters of the vehicle-pile-road multi-dimensional factor influence quantification mechanism, which is expressed as: In the formula, is the gradient function; Represents the deviation sample set Ω relative to the user side, pile side, and road side User ,Ω Pile ,Ω Road gradient.

7. A vehicle-grid interactive guidance simulation system, characterized by: The system is used to implement the vehicle-grid interaction guidance simulation method described in any one of claims 1 to 6, and the system includes: an environment input module, a simulation optimization module, a vehicle-grid interaction guidance simulation module, and a power grid management and control system interface docking module; The environment input module is used to input the environmental information of the status and distribution of electric vehicle users, road topology, and charging pile data to build a vehicle-grid interaction simulation scenario; The simulation optimization module calculates the optimal response and optimization performance under different guidance mechanisms based on the data provided by the environmental input module; The vehicle-grid interaction guidance simulation system recommends the optimal response mechanism to the user based on the simulation optimization results, thereby optimizing the user's power anxiety value, travel efficiency and benefits; The power grid control system interface docking module is used to connect the simulation results with the actual power grid system and update the simulation optimization performance value according to the real power grid data.

8. The vehicle-grid interactive guidance simulation system according to claim 7, characterized in that: A simulation optimization module, including: a guidance mode selection module, a response mechanism selection module, and a simulation scenario generation module; A guidance mode selection module, used to select different guidance mechanisms and input them into the system to guide the decision-making of electric vehicle users; A response mechanism selection module is used to select the optimal response mechanism for electric vehicle users under different guidance mechanisms; The simulation scene generation module generates the corresponding simulation scene according to the environmental data provided by the environmental input module.

9. The vehicle-grid interactive guidance simulation system according to claim 7, characterized in that: The vehicle-grid interaction guidance simulation module includes: a user-station-road multi-dimensional influencing factor set construction submodule, a vehicle-grid interaction guidance element set construction submodule, a multi-dimensional vehicle-station-road factor influence quantification submodule and a user-side response mechanism optimization submodule, a grid-side optimal guidance mechanism selection submodule and an influence quantification mechanism parameter update submodule; The user-charging pile-road multi-dimensional influencing factor set construction submodule constructs the influencing set of multi-dimensional factors of users, charging piles and roads according to the input data provided by the environment input module; The vehicle-grid interaction guiding element set construction submodule is responsible for constructing the vehicle-grid interaction guiding element set; The multi-dimensional vehicle-pile-road factor influence quantification submodule builds the corresponding influence function based on the influence of multi-dimensional factors such as vehicles, piles, roads, etc. at different time scales; The user-side response mechanism optimization submodule calculates the optimal response mechanism from the user-side perspective; The grid-side optimal guidance mechanism selection submodule is used to evaluate the response effects of electric vehicle users under different guidance mechanisms, quantify the impact of these responses on grid operation, and select the guidance mechanism that best suits grid needs. Influence quantification mechanism parameter update submodule: By connecting to the actual power grid control system interface, the deviation between the theoretical optimization performance value obtained in the simulation and the actual optimization performance value is calculated, and the parameters of the multidimensional influence function are updated according to the deviation value.

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