Trustworthy shared data-driven traffic-grid fusion charging scheduling method and system

Abstract

The present application relates to the technical field of power system dispatching, and particularly relates to a trusted shared data driven traffic-power grid fusion charging dispatching method and system, which comprises the following steps: considering vehicle passing and charging queuing, fusing BPR function and M / M / c / K queuing model, and constructing a dynamic traffic network model based on node-section set; combining the dynamic traffic network model and time-varying O-D combination to simulate the space-time distribution of user travel demand, designing an incentive price differentiation response mechanism, constructing a charging time transfer probability model and a user response degree proportion model based on a joint distribution Copula function; based on the charging time transfer probability model and the user response degree proportion model, establishing a total cost target function of distribution network containing distributed power supply, electricity purchase cost, and V2G compensation of power supply from electric vehicles to power grid, constructing a charging optimization dispatching model, and introducing an elite genetic algorithm to obtain a power grid-traffic collaborative charging dispatching scheme.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching technology, and in particular to a trusted shared data-driven transportation-grid integrated charging dispatching method and system. Background Technology

[0002] In recent years, the electric vehicle industry has experienced explosive growth, with its ownership continuing to climb over the past five years and an average annual growth rate remaining at a high level. This trend has not only driven the energy transition in the transportation sector but has also played a significant role in energy conservation and environmental protection. However, as electric vehicles are connected to the power grid, the connection between the transportation network and the distribution network becomes closer, posing challenges to the safe and efficient operation of both. Similar usage patterns among residents lead to a large number of electric vehicles choosing the same charging routes and charging at the same times, resulting in a continuous increase in the peak-valley difference between the transportation network and the power grid, and even causing traffic congestion and power grid overload, posing a serious threat to their safety.

[0003] Against this backdrop, constructing a collaborative scheduling framework based on shared traffic and power grid information has become crucial for solving the aforementioned challenges. By integrating multi-source data such as real-time traffic flow, road conditions, power grid load, and charging station usage, the scheduling system can achieve precise guidance and optimized control of electric vehicle charging behavior. This includes not only incentivizing users to participate in off-peak charging and optimizing power allocation among charging stations, but also balancing power grid load, alleviating traffic congestion, and reducing users' overall energy costs through dynamic electricity pricing and route recommendation strategies, thus providing technical support for the sustainable development of the electric vehicle industry.

[0004] As flexible load units with both charging and discharging capabilities, electric vehicles, based on information sharing and interoperability, can further participate in grid interaction through incentive policies to achieve coordinated optimization of charging and discharging behavior and system demand. Currently, the main factors influencing user response dispatch strategies are still concentrated at the economic level, such as electricity price structure, charging costs, and incentive compensation mechanisms. However, existing dispatch models often lack high-precision, dynamically updated traffic and grid interaction data, making it difficult to accurately depict the spatiotemporal evolution of traffic flow, resulting in discrepancies between charging resource allocation and actual demand.

[0005] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] This invention provides a transportation-grid integrated charging scheduling method and system based on blockchain-based trusted shared data, thereby effectively solving the problems in the background technology.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: a trusted shared data-driven transportation-grid integrated charging scheduling method, comprising the following steps:

[0008] By using blockchain for information sharing, considering vehicle passage and charging queuing, and integrating the traffic passage time BPR function with the service desk hybrid M / M / c / K queuing model, a dynamic traffic network model based on node-segment set is constructed.

[0009] Combining the dynamic traffic network model and time-varying OD combination to simulate the spatiotemporal distribution of user travel demand, and based on the influence of price and work and rest on charging behavior, an incentive-based differentiated electricity price response mechanism is designed. A charging time transition probability model and a user response rate ratio model are constructed based on the joint distribution Copula function.

[0010] Based on the charging time transition probability model and the user responsiveness ratio model, a total cost objective function for the distribution network is established, including distributed power sources, electricity purchase costs, and V2G rewards for electric vehicles supplying power to the grid. A charging optimization scheduling model is constructed, and an elite genetic algorithm is introduced to solve it, thereby obtaining a grid-transportation collaborative charging scheduling scheme.

[0011] Furthermore, the information sharing via blockchain specifically includes:

[0012] Real-time shared road segment traffic data is used as input to the BPR function to calculate travel time;

[0013] Real-time shared charging station queuing information is used to combine with the OD (Operation Demand) to simulate the spatiotemporal distribution of user travel demand.

[0014] Real-time sharing of dynamic electricity prices and user activity data serves as the quantization basis for the Copula function;

[0015] Real-time sharing of V2G compensation and subsidy information to determine the incentive electricity price differentiation response mechanism;

[0016] Real-time shared distributed power output, upstream grid electricity purchase price, and real-time load of each charging station are used to establish the objective function for the total cost of the distribution network.

[0017] Furthermore, the construction of the dynamic traffic network model based on the node-segment set includes:

[0018] The transportation network is viewed as a network composed of several nodes and road segments; using Represents a set of nodes, Represents a set of road segments. and These represent the sets of starting and ending nodes in a transportation network, respectively. Each pair of starting and ending nodes is called an OD combination. Each OD combination can be connected by different paths, and the paths are represented by... express, , s is the origin node, e is the destination node, and the travel demand between OD combinations is used This indicates that in a transportation network considering the penetration rate of electric vehicles and V2G vehicles, routes can be divided into ordinary routes. The charging path is and V2G path The road segment set can be divided into ordinary road segment sets. Collection of charging sections and V2G road segment collection ;

[0019] To describe the aggregation relationship of vehicles on a road segment and the dynamic process of inflow from the starting node and outflow from the destination node, the following equations are established:

[0020] ;

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] In the formula: , , These represent the inflow, outflow, and status traffic flow on road segment a for the k-th path with s as the starting node and e as the destination node at time t. , , These represent the inflow, outflow, and aggregated traffic flow on road segment a at time t; Let a be the set of road segments a leading to the destination node e; Let t be the arrival traffic flow between the start and destination nodes for se; The cumulative arrival traffic flow between the start and destination nodes for the vehicle se up to time t; Select the k-th path from the cumulative arrival traffic volume;

[0027] Use the BPR function to describe the travel time of a vehicle on a regular road segment:

[0028] ;

[0029] In the formula: Indicates that road segment a has Travel time when vehicles are flowing in; Let a be the traffic capacity of road segment a. The average travel time for vehicles entering road segment a;

[0030] Charging section and V2G section An M / M / c / K queuing model is used to describe the charging queuing behavior of vehicles at charging stations:

[0031] ;

[0032] ;

[0033] In the formula: and Average charging / V2G time for the vehicle; and The maximum queuing time for the site's capacity; and The configured capacity for charging stations / V2G stations.

[0034] Furthermore, the simulated spatiotemporal distribution of user travel demand includes:

[0035] The following user travel cost calculation method is used to simulate the actual application scenario when users choose charging stations:

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] In the formula: Cost per unit of time; Let be the unit charging electricity price at the charging station on road segment a at time t; and Average charging / V2G power; and Average charging / V2G time; Let V2G station on segment a at time t be the unit discharge reward of V2G station at time t. A one-time subsidy for the V2G station on road segment a at time t; Let be the travel cost of the k-th path starting at time t and ending at point s and destination e. This indicates that segment b is the selected path. All road segments before middle segment a, that is, in the calculation The method used is the toll cost at the time of inflow for each road segment; The 0-1 parameters are used to determine the correspondence between road segments and paths. If path k passes through road segment a, then... If not through .

[0041] Furthermore, the charging time transition probability model is as follows:

[0042] ;

[0043] The probability distribution function of charging time transition affected by stimulus compensation is:

[0044] ;

[0045] In the formula: The incentive compensation price difference at time t; Compensation for price differences due to time period And the probability of shifting charging time, This represents the maximum probability that a vehicle will shift its charging time due to the price difference between time periods. The minimum inter-period compensation price difference that would cause a shift in vehicle charging time; This represents the increase in charging time transition probability for each additional unit of electricity compensation price difference; To maximize the price difference in vehicle transfer charging time; , , This was determined through a survey of electric vehicle users. This is a function representing the user's responsiveness to charging price incentives.

[0046] The probability of charging time transition considering the user's daily routine is:

[0047] ;

[0048] In the formula: Let t be the probability that the charging time of the vehicle will shift at time t due to whether the owner is asleep. This is for the car owner's bedtime;

[0049] Charging time transition probability considering the effects of compensation and work / rest schedule for:

[0050] ;

[0051] Among the parameters It can be obtained by finding the maximum log-likelihood function of the probability density function:

[0052] In the formula, This represents the number of random data point samples generated based on the marginal distribution.

[0053] Furthermore, the user response rate ratio model is as follows:

[0054] Percentage of vehicles dispatched to V2G by users:

[0055] ;

[0056] Percentage of vehicles whose users do not participate in V2G scheduling and choose the initial charging path:

[0057] ;

[0058] In the formula: For electric vehicle users' battery level; and The battery level at time t is respectively The percentage of vehicles whose users choose V2G route / normal route; Select factors that influence unit discharge reward for V2G differentiation; The V2G differentiation choice is influenced by a one-time subsidy. The minimum V2G unit discharge reward.

[0059] Furthermore, the charging optimization scheduling model is as follows:

[0060] Objective function:

[0061] ;

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] In the formula: The total cost of the distribution network system; The cost of purchasing electricity from the upper-level power grid; Cost of distributed power generation; Total revenue for the charging station; Total V2G compensation paid for the distribution network; This refers to the load shedding losses in the distribution network. Time-of-use pricing; For time span; The charging electricity price is used as a reference price; if the price is higher than the reference price, there will be a profit, and if the price is lower, there will be a loss. For load shedding losses of distribution network units, Let t be the power of the upstream grid. The power of the distributed power source at time t; The inflow traffic volume on road segment a at time t is calculated as charging power × charging time × inflow traffic volume at time t. Power used for V2G participation × V2G participation time Let be the unit electricity price for segment a participating in v2g at time t; A one-time subsidy for the V2G station on road segment a at time t;

[0068] Constraints:

[0069] Constraints on the output of distributed renewable energy sources:

[0070] ;

[0071] In the formula: Power output for distributed renewable energy sources This represents the maximum expected output power of distributed renewable energy sources.

[0072] Dynamic traffic segment aggregation flow constraints:

[0073] ;

[0074] In the formula, Let be the traffic flow on road segment a for the k-th path with starting node s and destination node e at time t+1. Let be the inflow traffic volume on road segment a for the k-th path with starting node s and destination node e at time t. Let be the outflow traffic volume on road segment a for the kth path with s as the starting node and e as the destination node at time t.

[0075] Dynamic traffic node flow conservation constraints:

[0076] ;

[0077] In the formula: Let a represent the set of road segments a starting from node j. Let a represent the set of road segments a leading to node j;

[0078] EV driving battery limit:

[0079] ;

[0080] In the formula: This is the EV energy consumption coefficient, expressed as the electricity consumption per 100 kilometers. This represents the distance 's' of each EV from the charging station; , These are the upper and lower limits of the state of charge of each EV battery;

[0081] EV state of charge constraints:

[0082] ;

[0083] In the formula: , These are the upper and lower limits of the EV's battery capacity, respectively.

[0084] Price fluctuation constraints:

[0085] ;

[0086] ;

[0087] ;

[0088] In the formula: This serves as the lower limit for price fluctuations. This is the upper limit for price fluctuations; , , , , and These are the lower and upper limits for charging electricity price, V2G discharging electricity price, and V2G one-time subsidy, respectively.

[0089] Node power constraints:

[0090] ;

[0091] ;

[0092] In the formula: , and , These are the lower and upper limits of active and reactive power transmitted from the upstream power grid of node i, respectively. , and , These represent the lower and upper limits of the active and reactive power output of the distributed power source at node i, respectively.

[0093] The present invention also includes a trusted shared data-driven transportation-grid integrated charging scheduling system, using the method described above, wherein the system comprises:

[0094] The dynamic traffic network model unit is used for information sharing via blockchain, taking into account vehicle passage and charging queuing, integrating the traffic passage time BPR function and the service desk hybrid M / M / c / K queuing model, and constructing a dynamic traffic network model based on node-segment set;

[0095] The charging response decision unit is used to combine the dynamic traffic network model and time-varying OD combination to simulate the spatiotemporal distribution of user travel demand, design an incentive price differentiation response mechanism based on the influence of charging behavior on price and work and rest, and construct a charging time transition probability model and a user response rate ratio model based on the joint distribution Copula function.

[0096] The charging scheduling optimization unit is used to establish a total cost objective function of the distribution network, including distributed power sources, electricity purchase costs, and V2G rewards for electric vehicles supplying electricity to the grid, based on the charging time transition probability model and the user responsiveness ratio model. It then constructs a charging optimization scheduling model and introduces an elite genetic algorithm to solve it, thereby obtaining a grid-transportation collaborative charging scheduling scheme.

[0097] The present invention also includes a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described above.

[0098] The present invention also includes a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0099] The beneficial effects of this invention are as follows: By constructing a dynamic traffic network model, an incentive-based electricity price response mechanism, and an elite genetic algorithm solution model, a dynamic traffic network model and a charging station selection model under information sharing are proposed to quantify user travel costs and route selection behavior. Simultaneously, by combining the BPR function and a queuing model, the process of vehicle passage and charging queuing is characterized. A sharing mechanism based on incentive-based electricity prices is established, revealing the response characteristics of users to different incentive levels during off-peak and peak charging periods. This enables refined classification and guidance of user charging behavior, dynamically adjusting incentive strategies based on real-time load conditions, and effectively balancing charging station load in the spatiotemporal dimension. A charging time transition probability model based on the Copula joint distribution is designed, fully considering the interactive influence of price incentives and user work-rest habits on charging time selection. The effectiveness of this model in peak shaving and valley filling and cost control is verified, optimizing the operating costs of the distribution network and providing strong support for the economical and efficient operation of the transportation-grid coordinated system. Attached Figure Description

[0100] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0101] Figure 1 This is a flowchart of the method in Example 1;

[0102] Figure 2 This is a schematic diagram of the system structure in Example 1;

[0103] Figure 3 This is a flowchart illustrating the transportation-power grid collaborative optimization process under information sharing in Example 2.

[0104] Figure 4 This refers to the response rate of users during off-peak charging times in Example 2;

[0105] Figure 5 This refers to the user response rate during peak charging times in Example 2;

[0106] Figure 6 This refers to the genetic algorithm optimization process in Example 2;

[0107] Figure 7 This is a schematic diagram of the structure of the computer device of the present invention. Detailed Implementation

[0108] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0109] Example 1:

[0110] like Figure 1 As shown: A trusted shared data-driven transportation-grid integrated charging scheduling method includes the following steps:

[0111] By using blockchain for information sharing, considering vehicle passage and charging queuing, and integrating the traffic passage time BPR function with the service desk hybrid M / M / c / K queuing model, a dynamic traffic network model based on node-segment set is constructed.

[0112] By combining dynamic traffic network models and time-varying OD combinations to simulate the spatiotemporal distribution of user travel demand, and based on the influence of price and work / rest schedule on charging behavior, an incentive-based differentiated electricity price response mechanism is designed. A charging time transition probability model and a user response rate ratio model are constructed based on the joint distribution Copula function.

[0113] Based on the charging time transition probability model and the user responsiveness ratio model, a total cost objective function for the distribution network is established, including distributed power sources, electricity purchase costs, and V2G rewards for electric vehicles supplying power to the grid. A charging optimization scheduling model is constructed, and an elite genetic algorithm is introduced to solve it, thereby obtaining a grid-transportation collaborative charging scheduling scheme.

[0114] By constructing a dynamic traffic network model, an incentive-based electricity price response mechanism, and an elite genetic algorithm solution model, a dynamic traffic network model and a charging station selection model under information sharing are proposed to quantify user travel costs and route selection behavior. Simultaneously, by combining the BPR function and a queuing model, the process of vehicle passage and charging queuing is depicted. A sharing mechanism based on incentive-based electricity prices is established, revealing the response characteristics of users to different incentive levels during off-peak and peak charging periods. This enables refined classification and guidance of user charging behavior, dynamically adjusting incentive strategies based on real-time load conditions, and effectively balancing charging station load in the spatiotemporal dimensions. A charging time transition probability model based on the Copula joint distribution is designed, fully considering the interactive influence of price incentives and user work-rest habits on charging time selection. The effectiveness of this model in peak shaving and valley filling and cost control is verified, optimizing the operating costs of the distribution network and providing strong support for the economical and efficient operation of the transportation-grid coordinated system.

[0115] In this embodiment, information sharing is achieved through blockchain, specifically as follows:

[0116] Real-time shared road segment traffic data is used as input to the BPR function to calculate travel time;

[0117] Real-time shared charging station queuing information can be used in conjunction with OD (Operational Location) to simulate the spatiotemporal distribution of user travel demand;

[0118] Real-time sharing of dynamic electricity prices and user activity data serves as the quantization basis for Copula functions;

[0119] Real-time sharing of V2G compensation and subsidy information to determine a differentiated incentive electricity price response mechanism;

[0120] Real-time sharing of distributed power generation output, upstream grid electricity purchase price, and real-time load of each charging station is used to establish the objective function for the total cost of the distribution network.

[0121] The construction of a dynamic traffic network model based on node-road segment sets includes:

[0122] The transportation network is viewed as a network composed of several nodes and road segments; using Represents a set of nodes, Represents a set of road segments. and These represent the sets of starting and ending nodes in a transportation network, respectively. Each pair of starting and ending nodes is called an OD combination. Each OD combination can be connected by different paths, and the paths are represented by... express, , s is the origin node, e is the destination node, and the travel demand between OD combinations is used This indicates that in a transportation network considering the penetration rate of electric vehicles and V2G vehicles, routes can be divided into ordinary routes. The charging path is and V2G path The road segment set can be divided into ordinary road segment sets. Collection of charging sections and V2G road segment collection ;

[0123] To describe the aggregation relationship of vehicles on a road segment and the dynamic process of inflow from the starting node and outflow from the destination node, the following equations are established:

[0124] ;

[0125] ;

[0126] ;

[0127] ;

[0128] ;

[0129] ;

[0130] In the formula: , , These represent the inflow, outflow, and status traffic flow on road segment a for the k-th path with s as the starting node and e as the destination node at time t. , , These represent the inflow, outflow, and aggregated traffic flow on road segment a at time t; Let a be the set of road segments a leading to the destination node e; Let t be the arrival traffic flow between the start and destination nodes for se; The cumulative arrival traffic flow between the start and destination nodes for the vehicle se up to time t; Select the k-th path from the cumulative arrival traffic volume;

[0131] Use the BPR function to describe the travel time of a vehicle on a regular road segment:

[0132] ;

[0133] In the formula: Indicates that road segment a has Travel time when vehicles are flowing in; Let a be the traffic capacity of road segment a. The average travel time for vehicles entering road segment a;

[0134] Charging section and V2G section An M / M / c / K queuing model is used to describe the charging queuing behavior of vehicles at charging stations:

[0135] ;

[0136] ;

[0137] In the formula: and Average charging / V2G time for the vehicle; and The maximum queuing time for the site's capacity; and The configured capacity for charging stations / V2G stations.

[0138] As a preferred embodiment of the above, simulating the spatiotemporal distribution of user travel demand includes:

[0139] The following user travel cost calculation method is used to simulate the actual application scenario when users choose charging stations:

[0140] ;

[0141] ;

[0142] ;

[0143] ;

[0144] In the formula: Cost per unit of time; Let be the unit charging electricity price at the charging station on road segment a at time t; and Average charging / V2G power; and Average charging / V2G time; Let V2G station on segment a at time t be the unit discharge reward of V2G station at time t. A one-time subsidy for the V2G station on road segment a at time t; Let be the travel cost of the k-th path starting at time t and ending at point s and destination e. This indicates that segment b is the selected path. All road segments before middle segment a, that is, in the calculation The method used is the toll cost at the time of inflow for each road segment; The 0-1 parameters are used to determine the correspondence between road segments and paths. If path k passes through road segment a, then... If not through .

[0145] The charging time transition probability model is as follows:

[0146] ;

[0147] The probability distribution function of charging time transition affected by stimulus compensation is:

[0148] ;

[0149] In the formula: The incentive compensation price difference at time t; Compensation for price differences due to time period And the probability of shifting charging time, This represents the maximum probability that a vehicle will shift its charging time due to the price difference between time periods. The minimum inter-period compensation price difference that would cause a shift in vehicle charging time; This represents the increase in charging time transition probability for each additional unit of electricity compensation price difference; To maximize the price difference in vehicle transfer charging time; , , This was determined through a survey of electric vehicle users. This is a function representing the user's responsiveness to charging price incentives.

[0150] The probability of charging time transition considering the user's daily routine is:

[0151] ;

[0152] In the formula: Let t be the probability that the charging time of the vehicle will shift at time t due to whether the owner is asleep. This is for the car owner's bedtime;

[0153] Charging time transition probability considering the effects of compensation and work / rest schedule for:

[0154] ;

[0155] Among the parameters It can be obtained by finding the maximum log-likelihood function of the probability density function:

[0156] In the formula, This represents the number of random data point samples generated based on the marginal distribution.

[0157] The user responsiveness ratio model is as follows:

[0158] Percentage of vehicles dispatched to V2G by users:

[0159] ;

[0160] Percentage of vehicles whose users do not participate in V2G scheduling and choose the initial charging path:

[0161] ;

[0162] In the formula: For electric vehicle users' battery level; and The battery level at time t is respectively The percentage of vehicles whose users choose V2G route / normal route; Select factors that influence unit discharge reward for V2G differentiation; The V2G differentiation choice is influenced by a one-time subsidy. The minimum V2G unit discharge reward.

[0163] In this embodiment, the charging optimization scheduling model is as follows:

[0164] Objective function:

[0165] ;

[0166] ;

[0167] ;

[0168] ;

[0169] ;

[0170] ;

[0171] In the formula: The total cost of the distribution network system; The cost of purchasing electricity from the upper-level power grid; Cost of distributed power generation; Total revenue for the charging station; Total V2G compensation paid for the distribution network; This refers to the load shedding losses in the distribution network. Time-of-use pricing; For time span; The charging electricity price is used as a reference price; if the price is higher than the reference price, there will be a profit, and if the price is lower, there will be a loss. For load shedding losses of distribution network units, Let t be the power of the upstream grid. The power of the distributed power source at time t; The inflow traffic volume on road segment a at time t is calculated as charging power × charging time × inflow traffic volume at time t. Power used for V2G participation × V2G participation time Let be the unit electricity price for segment a participating in v2g at time t; A one-time subsidy for the V2G station on road segment a at time t;

[0172] Constraints:

[0173] Constraints on the output of distributed renewable energy sources:

[0174] ;

[0175] In the formula: Power output for distributed renewable energy sources This represents the maximum expected output power of distributed renewable energy sources.

[0176] Dynamic traffic segment aggregation flow constraints:

[0177] ;

[0178] In the formula, Let be the traffic flow on road segment a for the k-th path with starting node s and destination node e at time t+1. Let be the inflow traffic volume on road segment a for the k-th path with starting node s and destination node e at time t. Let be the outflow traffic volume on road segment a for the kth path with s as the starting node and e as the destination node at time t.

[0179] Dynamic traffic node flow conservation constraints:

[0180] ;

[0181] In the formula: Let a represent the set of road segments a starting from node j. Let a represent the set of road segments a leading to node j;

[0182] EV driving battery limit:

[0183] ;

[0184] In the formula: This is the EV energy consumption coefficient, expressed as the electricity consumption per 100 kilometers. This represents the distance 's' of each EV from the charging station; , These are the upper and lower limits of the state of charge of each EV battery;

[0185] EV state of charge constraints:

[0186] ;

[0187] In the formula: , These are the upper and lower limits of the EV's battery capacity, respectively.

[0188] Price fluctuation constraints:

[0189] ;

[0190] ;

[0191] ;

[0192] In the formula: This serves as the lower limit for price fluctuations. This is the upper limit for price fluctuations; , , , , and These are the lower and upper limits for charging electricity price, V2G discharging electricity price, and V2G one-time subsidy, respectively.

[0193] Node power constraints:

[0194] ;

[0195] ;

[0196] In the formula: , and , These are the lower and upper limits of active and reactive power transmitted from the upstream power grid of node i, respectively. , and , These represent the lower and upper limits of the active and reactive power output of the distributed power source at node i, respectively.

[0197] like Figure 2 As shown, this embodiment also includes a trusted shared data-driven transportation-grid integrated charging scheduling system, using the method described above. The system includes:

[0198] The dynamic traffic network model unit is used for information sharing via blockchain, taking into account vehicle passage and charging queuing, integrating the traffic passage time BPR function and the service desk hybrid M / M / c / K queuing model, and constructing a dynamic traffic network model based on node-segment set;

[0199] The charging response decision unit is used to simulate the spatiotemporal distribution of user travel demand by combining dynamic traffic network models and time-varying OD combinations. Based on the influence of price and work and rest on charging behavior, it designs an incentive-based differentiated response mechanism for electricity prices and constructs a charging time transition probability model and a user response rate ratio model based on the joint distribution Copula function.

[0200] The charging scheduling optimization unit is used to establish a total cost objective function for the distribution network, including distributed power sources, electricity purchase costs, and V2G rewards for electric vehicles supplying electricity to the grid, based on the charging time transition probability model and the user responsiveness ratio model. It then constructs a charging optimization scheduling model and introduces an elite genetic algorithm to solve it, thereby obtaining a grid-transportation collaborative charging scheduling scheme.

[0201] Example 2:

[0202] This embodiment proposes a blockchain-driven dynamic traffic network charging scheduling optimization model. First, considering the actual situation of vehicle traffic and charging queuing, a dynamic traffic network model based on node-segment sets is constructed by integrating the BPR function and the M / M / c / K queuing model. Simultaneously, an OD combination is used to simulate the spatiotemporal distribution of user travel demand, accurately analyzing the vehicle operating status in the traffic network. Then, considering the influence of price and work / rest schedules on vehicle owner charging behavior, a differentiated incentive price response mechanism is designed. A charging time transition probability model and a user responsiveness ratio model are constructed based on the Copula joint distribution to quantify the interactive influence of work / rest habits and price incentives on charging behavior. Finally, focusing on distribution network cost optimization, a total distribution network cost objective function is established, including distributed power sources, electricity purchase costs, and V2G rewards. An elite genetic algorithm is introduced to solve this function, improving convergence efficiency and optimization stability by retaining an elite population, thus achieving coordinated optimization of the power grid and transportation system.

[0203] 1) Information-sharing transportation-grid coordinated charging scheduling implementation framework;

[0204] Information sharing is the core support for dynamic transportation-power grid coordinated charging scheduling. The decentralized, immutable, and secure characteristics of blockchain technology provide technical assurance for cross-domain information interaction between transportation networks and power systems. This embodiment constructs a blockchain-based information sharing mechanism, clarifying the sharing subjects, content, technical architecture, and supporting role for subsequent collaborative optimization models, achieving real-time interconnection and reliable interaction of traffic flow, power flow, and user behavior data. The information sharing mechanism provides key data support for subsequent dynamic transportation network models, charging and discharging response models, and distribution network cost optimization models, serving as the link to achieve "transportation-power grid" coordination. Its specific functions include... Figure 3 As shown.

[0205] Supporting dynamic traffic network models: Real-time shared road segment traffic data provides input parameters for the BPR function, accurately calculating travel time; Charging station queuing information combined with OD combination data enables the traffic network model to more accurately depict user route selection behavior and solve travel planning constraints.

[0206] Supporting the charge / discharge response decision model: The dynamic electricity price and user activity data shared by the blockchain provide a quantitative basis for the Copula joint distribution model, accurately describing the impact of price and activity on the probability of charging time transition; V2G reward and subsidy information directly affect the proportion of users participating in V2G, providing an incentive mechanism basis for the charge / discharge response model.

[0207] Supporting the distribution network cost optimization model: Sharing information such as distributed power generation output, upstream grid electricity purchase price, and real-time load of each charging station enables the distribution network total cost objective function to be solved based on real data. Through elite genetic algorithm, multi-factor collaborative optimization is achieved to reduce operating costs.

[0208] 2) Charging station selection model considering dynamic traffic;

[0209] During EV operation, Dynamic Traffic Assignment (DTA) is a mathematical framework that describes the dynamic changes in traffic flow over time and space. By characterizing the spatiotemporal distribution, operational status, and dynamic evolution of traffic demand within the road network, it provides theoretical support for traffic planning, management, and control. Compared to static models, its core advantage lies in its ability to capture the time-varying characteristics of the traffic system, including sudden changes in traffic flow during peak hours and dynamic processes such as changes in road network conditions caused by traffic accidents.

[0210] This embodiment views the transportation network as a network composed of several nodes and road segments. Represents a set of nodes, Represents a set of road segments. and Let represent the set of starting nodes and the set of ending nodes in the transportation network. Each pair of starting and ending nodes is called an OD combination. Each OD combination can be connected by different paths, and the paths are represented by _____. express, , s is the origin node, e is the destination node, and the travel demand between OD combinations is used This indicates that, in transportation networks considering the penetration rate of electric vehicles and V2G vehicles, paths can be divided into ordinary paths. The charging path is and V2G path The road segment set can be divided into ordinary road segment sets. Collection of charging sections and V2G road segment collection .

[0211] To describe the aggregation relationship of vehicles on a road segment and the dynamic process of inflow from the starting node and outflow from the destination node, the following equations are established:

[0212]

[0213]

[0214]

[0215]

[0216]

[0217]

[0218] In the formula: , , These represent the inflow, outflow, and status traffic flow on road segment a for the k-th path with s as the starting node and e as the destination node at time t. , , These represent the inflow, outflow, and aggregated traffic flow on road segment a at time t; Let a be the set of road segments a leading to the destination node e; Let t be the arrival traffic flow between the start and destination nodes for se; The cumulative arrival traffic flow between the start and destination nodes for the vehicle se up to time t; Select the cumulative arrival traffic flow of the k-th path.

[0219] For ordinary road segments, the Bureau of Public Roads (BPR) function is used to describe the travel time of vehicles on ordinary road segments:

[0220]

[0221] In the formula: Indicates that road segment a has Travel time when vehicles are flowing in; Let a be the traffic capacity of road segment a. The average travel time for vehicles entering road segment a is denoted as .

[0222] Charging section and V2G section An M / M / c / K queuing model is used to describe the charging queuing behavior of vehicles at charging stations:

[0223]

[0224]

[0225] In the formula: and Average charging / V2G time for the vehicle; and The maximum queuing time for the site's capacity; and The configured capacity of the charging station / V2G station is determined by the configuration of the charging / V2G interfaces within the station.

[0226] The multitude of possibilities in user behavior leads to a diversity in charging station selection. To weigh the optimal outcome among multiple charging stations, a method for calculating user travel costs is designed.

[0227] This method comprehensively analyzes and calculates the travel costs of users participating in different charging service options to make a charging service choice, which is more in line with the actual application scenarios when users choose charging stations.

[0228]

[0229]

[0230]

[0231]

[0232] In the formula: Cost per unit of time; Let be the unit charging electricity price at the charging station on road segment a at time t; and Average charging / V2G power; and Average charging / V2G time; Let V2G station on segment a at time t be the unit discharge reward of V2G station at time t. A one-time subsidy for the V2G station on road segment a at time t; Let be the travel cost of the k-th path starting at time t and ending at point s and destination e. This indicates that segment b is the selected path. All road segments before middle segment a, that is, in the calculation The method used is the toll cost at the time of inflow for each road segment; The 0-1 parameters are used to determine the correspondence between road segments and paths. If path k passes through road segment a, then... If not through .

[0233] 3) A charge / discharge response decision model considering price incentives;

[0234] This embodiment incentivizes users to participate in charging scheduling by sharing incentive price information. However, whether users switch charging times is not solely determined by time differences, but also by the vehicle owner's daily routine. For example, users who are early risers are unlikely to participate in the off-peak charging scheduling strategy to save money; even if the time is postponed, their participation probability decreases as the time moves into the late night.

[0235] The community is functionally divided into three zones: residential (H), work (W), and commercial / leisure (C). The H plot where car owners reside, the W plot where they work, and the C plot where they engage in consumption and entertainment are referred to as the associated plots for their respective vehicles. Since charging takes several hours, vehicles typically generate charging demand when they approach an associated plot. Specifically, residents in zone H usually generate charging demand based on remaining battery power when they arrive near their associated plot after get off work, potentially delaying charging until late at night during off-peak electricity prices to take advantage of lower rates. Users in zones W and C have parking times and durations significantly influenced by work and activities, and their charging time is generally not shifted due to incentive compensation.

[0236] Based on the above conclusions, we can conclude that users' responses to incentive compensation have a certain range: when the compensation price is very low, vehicles will not switch charging times due to unwillingness to wait; when the compensation price reaches a threshold, car owners will consider switching charging times, and the probability of switching increases with the increase of the compensation price; when the compensation price reaches a certain value, the number of vehicles switching charging times will tend to saturate, and not all vehicles will switch. Therefore, the charging time switching probability model can be constructed as follows:

[0237]

[0238] Therefore, the probability distribution function of charging time transition affected by stimulus compensation is:

[0239]

[0240] In the formula: The incentive compensation price difference at time t; The probability that a vehicle will shift its charging time due to the price difference between time periods; The minimum inter-period compensation price difference that would cause a shift in vehicle charging time; This represents the increase in charging time transition probability for each additional unit of electricity compensation price difference; To maximize the price difference in vehicle transfer charging time. , , This was determined through a survey of electric vehicle users. This is a function for the user's responsiveness to charging price incentives.

[0241] Based on users' sleep habits, they do not wake up after falling asleep to switch charging times. Therefore, the probability of switching charging times under the influence of users' sleep patterns is:

[0242]

[0243] In the formula: Let t be the probability that the charging time of the vehicle will shift at time t due to whether the owner is asleep. This is the time for car owners to go to sleep.

[0244] From a price perspective, users tend to shift their charging time to off-peak hours. However, from a daily routine perspective, the probability of user response decreases over time. Therefore, these two factors are negatively correlated. Thus, to combine these two factors and influence the probability of shifting charging time... and Described as:

[0245]

[0246] Among the parameters It is obtained by finding the maximum log-likelihood function of the probability density function, i.e.

[0247]

[0248] In the above formula, The number of random data point samples generated based on the marginal distribution is 100 in this embodiment. The Copula joint distribution determines the relationship between the probability of vehicle charging time transitions during the day and time within the community.

[0249] This embodiment proposes using incentive compensation to guide electric vehicle (EV) charging during both off-peak and peak charging periods, achieving load balancing at charging stations across time and space through price control strategies. When EV users generate charging demand, they can autonomously decide whether to respond to the mechanism based on shared incentive compensation price information, thus forming responsive and non-responsive clusters. For responsive clusters, the study assumes that this group will fully respond to incentive compensation, i.e., participate in the distribution network's charging scheduling; while non-responsive clusters indicate that users have low sensitivity to compensation prices and will maintain their original charging choices. This differentiated response mechanism provides charging station operators with the possibility of fine-tuning load distribution.

[0250] When faced with charging guidance, a user's willingness to respond will vary due to different incentive discounts. This embodiment uses responsiveness to describe a user's willingness to respond. Figure 4 and Figure 5 It represents the relationship between the user's response to different levels of incentives.

[0251] from Figure 4 This shows that the incentive level starts from 0 and increases until it reaches... Previously, user responsiveness gradually increased from 0, with upper and lower limits. When the incentive reached... The rate of response has changed and continues to rise. Response rate Even if the incentive is increased again, the responsiveness will not exceed this upper limit. The yellow area represents the fluctuation range of user response under different incentives, reflecting the stimulation and fluctuation range of user response during the trough.

[0252] and Figure 4 compared to, Figure 5 Incentives must be achieved at the beginning Only then is a user response triggered, followed by incentives. Response rate increased to Subsequent incentives do not increase responsiveness further. The yellow area represents the response fluctuation range corresponding to peak incentives. During peak hours, users have a threshold for activating incentives, and after reaching a certain level, the incentive effect tends to saturate, and the response fluctuation range differs from that at the trough.

[0253] Percentage of vehicles dispatched to V2G by users:

[0254]

[0255] Percentage of vehicles whose users do not participate in V2G scheduling and choose the initial charging path:

[0256]

[0257] In the formula: For electric vehicle users' battery level; and The battery level at time t is respectively The percentage of vehicles whose users choose V2G route / normal route; Select factors that influence unit discharge reward for V2G differentiation; The V2G differentiation choice is influenced by a one-time subsidy. The minimum V2G unit discharge reward.

[0258] 4) Charging optimization scheduling model oriented towards distribution network operation costs;

[0259] As the core carrier of the power system, the modern power distribution network exhibits multi-source and dynamic operating costs. With the high penetration rate of new energy sources and the large-scale integration of electric vehicles, system operating costs need to shift from the traditional single power purchase model to a multi-factor collaborative optimization approach. Its specific cost structure can be broken down into the following five core modules: power purchase cost from the upstream grid, distributed generation cost, V2G payment cost, charging station electricity sales revenue, and load shedding losses, as shown below:

[0260]

[0261] The calculation formulas for each part are as follows:

[0262]

[0263]

[0264]

[0265]

[0266]

[0267] In the formula: The total cost of the distribution network system; The cost of purchasing electricity from the upper-level power grid; Cost of distributed power generation; Total revenue for the charging station; Total V2G compensation paid for the distribution network; This refers to the load shedding losses in the distribution network. Time-of-use pricing; For time span; The charging electricity price is used as a reference price; if the price is higher than the reference price, there will be a profit, and if the price is lower, there will be a loss. For load shedding losses of distribution network units, Let t be the power of the upstream grid. The power of the distributed power source at time t; The inflow traffic volume on road segment a at time t is calculated as charging power × charging time × inflow traffic volume at time t. Power used for V2G participation × V2G participation time Let be the unit electricity price for segment a participating in v2g at time t; This is a one-time subsidy for the V2G station on road segment a at time t.

[0268] Constraints on the output of distributed renewable energy sources:

[0269] Since distributed renewable energy generation is owned by charging stations and has extremely low marginal generation costs, its output costs are not considered in economic optimization over a shorter timescale.

[0270] The output of renewable energy sources is volatile and unstable. The constraints related to the output of renewable energy sources and the total power are as follows:

[0271]

[0272] In the formula: Power output for distributed renewable energy sources This represents the maximum expected output power of distributed renewable energy sources.

[0273] Dynamic traffic segment aggregation flow constraints:

[0274] Considering that the aggregated traffic flow on the road segment does not exceed the road's capacity, and to avoid traffic congestion affecting the accessibility of charging activities:

[0275]

[0276] In the formula, Let be the traffic flow on road segment a for the k-th path with starting node s and destination node e at time t+1. Let be the inflow traffic volume on road segment a for the k-th path with starting node s and destination node e at time t. Let be the outflow traffic volume on road segment a for the kth path with s as the starting node and e as the destination node at time t.

[0277] Dynamic traffic node flow conservation constraints:

[0278]

[0279] In the formula: Let a represent the set of road segments a starting from node j. Let a represent the set of road segments a leading to node j;

[0280] EV driving battery limit:

[0281] When choosing a charging station, EV users should consider selecting one within the range of their remaining battery power to prevent the battery from running out of power.

[0282]

[0283] In the formula: The EV energy consumption coefficient is affected by factors such as weather, road conditions, and user driving habits, and is expressed as the electricity consumption per 100 kilometers. This represents the distance 's' of each EV from the charging station; , These represent the upper and lower limits of the state of charge of each EV battery.

[0284] EV state of charge constraints:

[0285]

[0286] In the formula: , These represent the upper and lower limits of the EV's battery capacity.

[0287] Price fluctuation constraints:

[0288]

[0289]

[0290]

[0291] In the formula: This serves as the lower limit for price fluctuations. This is the upper limit for price fluctuations; , , , , and These are the lower and upper limits for charging electricity price, V2G discharging electricity price, and V2G one-time subsidy, respectively.

[0292] Node power constraints:

[0293]

[0294]

[0295] In the formula: , and , These are the lower and upper limits of active and reactive power transmitted from the upstream power grid of node i, respectively. , and , These represent the lower and upper limits of the active and reactive power output of the distributed power source at node i, respectively.

[0296] This embodiment utilizes a solution model based on an elite genetic algorithm, which boasts a faster convergence speed compared to traditional genetic algorithms. The core idea of ​​this algorithm is to select elite individuals from the previous generation to form an elite population. During the iteration process of the new generation, individuals with lower fitness in the original population are replaced by the elite population, thereby strengthening the population's ability to retain superior genes. The optimization process of the genetic algorithm is as follows: Figure 6 As shown.

[0297] This embodiment addresses the power system scheduling challenges arising from the large-scale integration of electric vehicles, proposing a blockchain-driven charging scheduling optimization framework. By constructing a dynamic traffic network model, an incentive-based electricity price response mechanism, and an elite genetic algorithm to solve the model, the following core conclusions are drawn:

[0298] This embodiment proposes a dynamic traffic network model and a charging station selection model under information sharing to quantify user travel costs and route selection behavior; at the same time, it combines the BPR function and queuing model to characterize the process of vehicle passage and charging queuing.

[0299] This embodiment establishes a sharing mechanism based on incentive-based electricity pricing, and obtains the response characteristics of users to different incentive levels during off-peak and peak charging periods. It realizes refined classification and guidance of user charging behavior, dynamically adjusts incentive strategies according to real-time load conditions, and effectively balances the load of charging stations in the spatiotemporal dimensions.

[0300] This embodiment designs a charging time transition probability model based on the Copula joint distribution, fully considering the interactive influence of price incentives and user work-rest habits on charging time selection. The effectiveness of this model in peak shaving and valley filling and cost control is verified, achieving optimization of distribution network operating costs and providing strong support for the economical and efficient operation of the transportation-power grid coordinated system.

[0301] Please see Figure 7The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0302] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0303] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0304] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0305] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0306] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0307] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0308] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0309] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0310] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0311] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A trusted shared data-driven transportation-grid integrated charging scheduling method, characterized in that, Includes the following steps: By using blockchain for information sharing, considering vehicle passage and charging queuing, and integrating the traffic passage time BPR function with the service desk hybrid M / M / c / K queuing model, a dynamic traffic network model based on node-segment set is constructed. Combining the dynamic traffic network model and time-varying OD combination to simulate the spatiotemporal distribution of user travel demand, and based on the influence of price and work and rest on charging behavior, an incentive-based differentiated electricity price response mechanism is designed. A charging time transition probability model and a user response rate ratio model are constructed based on the joint distribution Copula function. Based on the charging time transition probability model and the user responsiveness ratio model, a total cost objective function for the distribution network is established, including distributed power sources, electricity purchase costs, and V2G rewards for electric vehicles supplying power to the grid. A charging optimization scheduling model is constructed, and an elite genetic algorithm is introduced to solve it, thereby obtaining a grid-transportation collaborative charging scheduling scheme. The charging time transition probability model is as follows: ； The probability distribution function of charging time transition affected by stimulus compensation is: ； In the formula: The incentive compensation price difference at time t; Compensation for price differences due to time period And the probability of shifting charging time, This represents the maximum probability that a vehicle will shift its charging time due to the price difference between time periods. The minimum inter-period compensation price difference that would cause a shift in vehicle charging time; This represents the increase in charging time transition probability for each additional unit of electricity compensation price difference; To maximize the price difference in vehicle transfer charging time; , , This was determined through a survey of electric vehicle users. This is a function representing the user's responsiveness to charging price incentives. The probability of charging time transition considering the user's daily routine is: ； In the formula: Let t be the probability that the charging time of the vehicle will shift at time t due to whether the owner is asleep. This is for the car owner's bedtime; Charging time transition probability considering the effects of compensation and work / rest schedule for: ； Among the parameters It can be obtained by finding the maximum log-likelihood function of the probability density function: In the formula, This represents the number of random data point samples generated based on the marginal distribution. The user response rate ratio model is as follows: Percentage of vehicles dispatched to V2G by users: ； Percentage of vehicles whose users do not participate in V2G scheduling and choose the initial charging path: ； In the formula: For electric vehicle users' battery level; and The battery level at time t is respectively The percentage of vehicles whose users choose V2G route / normal route; Select factors that influence unit discharge reward for V2G differentiation; The V2G differentiation choice is influenced by a one-time subsidy. The minimum V2G unit discharge reward.

2. The trusted shared data-driven transportation-grid integrated charging scheduling method according to claim 1, characterized in that, The information sharing via blockchain specifically refers to: Real-time shared road segment traffic data is used as input to the BPR function to calculate travel time; Real-time shared charging station queuing information is used to combine with the OD (Operation Demand) to simulate the spatiotemporal distribution of user travel demand. Real-time sharing of dynamic electricity prices and user activity data serves as the quantization basis for the Copula function; Real-time sharing of V2G compensation and subsidy information to determine the incentive electricity price differentiation response mechanism; Real-time shared distributed power output, upstream grid electricity purchase price, and real-time load of each charging station are used to establish the objective function for the total cost of the distribution network.

3. The trusted shared data-driven transportation-grid integrated charging scheduling method according to claim 1, characterized in that, The construction of a dynamic traffic network model based on node-road segment sets includes: The transportation network is viewed as a network composed of several nodes and road segments; using Represents a set of nodes, Represents a set of road segments. and These represent the sets of starting and ending nodes in the transportation network, respectively. Each pair of starting and ending nodes is called an OD combination. Each OD combination is connected by different paths, and the paths are represented by... express, , s is the origin node, e is the destination node, and the travel demand between OD combinations is used This indicates that in a transportation network considering the penetration rate of electric vehicles and V2G vehicles, routes can be divided into ordinary routes. The charging path is and V2G path The road segment set is divided into ordinary road segment set. Collection of charging sections and V2G road segment collection ; To describe the aggregation relationship of vehicles on a road segment and the dynamic process of inflow from the starting node and outflow from the destination node, the following equations are established: ； ； ； ； ； ； In the formula: , , These represent the inflow, outflow, and status traffic flow on road segment a for the k-th path with s as the starting node and e as the destination node at time t. , , These represent the inflow, outflow, and aggregated traffic flow on road segment a at time t; Let a be the set of road segments a leading to the destination node e; Let t be the arrival traffic flow between the start and destination nodes for se; The cumulative arrival traffic flow between the start and destination nodes for the vehicle se up to time t; Select the k-th path from the cumulative arrival traffic volume; Use the BPR function to describe the travel time of a vehicle on a regular road segment: ； In the formula: Indicates that road segment a has Travel time when vehicles are flowing in; Let a be the traffic capacity of road segment a. The average travel time for vehicles entering road segment a; Charging section and V2G section An M / M / c / K queuing model is used to describe the charging queuing behavior of vehicles at charging stations: ； ； In the formula: and Average charging / V2G time for the vehicle; and The maximum queuing time for the site's capacity; and The configured capacity for charging stations / V2G stations.

4. The trusted shared data-driven transportation-grid integrated charging scheduling method according to claim 3, characterized in that, The simulated spatiotemporal distribution of user travel demand includes: The following user travel cost calculation method is used to simulate the actual application scenario when users choose charging stations: ； ； ； ； In the formula: Cost per unit of time; Let be the unit charging electricity price at the charging station on road segment a at time t; and Average charging / V2G power; and Average charging / V2G time; Let V2G station on segment a at time t be the unit discharge reward of V2G station at time t. A one-time subsidy for the V2G station on road segment a at time t; Let be the travel cost of the k-th path starting at time t and ending at point s and destination e. This indicates that segment b is the selected path. All road segments before middle segment a, that is, in the calculation The method used is the toll cost at the time of inflow for each road segment; The 0-1 parameters are used to determine the correspondence between road segments and paths. If path k passes through road segment a, then... If not through .

5. The trusted shared data-driven transportation-grid integrated charging scheduling method according to claim 1, characterized in that, The charging optimization scheduling model is as follows: Objective function: ； ； ； ； ； ； In the formula: The total cost of the distribution network system; The cost of purchasing electricity from the upper-level power grid; Cost of distributed power generation; Total revenue for the charging station; Total V2G compensation paid for the distribution network; This refers to the load shedding losses in the distribution network. Time-of-use pricing; For time span; The charging electricity price is used as a reference price; if the price is higher than the reference price, there will be a profit, and if the price is lower, there will be a loss. For load shedding losses of distribution network units, Let t be the power of the upstream grid. The power of the distributed power source at time t; The inflow traffic volume on road segment a at time t is calculated as charging power × charging time × inflow traffic volume at time t. Power used for V2G participation × V2G participation time Let be the unit electricity price for segment a participating in v2g at time t; A one-time subsidy for the V2G station on road segment a at time t; Constraints: Constraints on the output of distributed renewable energy sources: ； In the formula: Power output for distributed renewable energy sources This represents the maximum expected output power of distributed renewable energy sources. Dynamic traffic segment aggregation flow constraints: ； In the formula, Let be the traffic flow on road segment a for the k-th path with starting node s and destination node e at time t+1. Let be the inflow traffic volume on road segment a for the k-th path with starting node s and destination node e at time t. Let be the outflow traffic volume on road segment a for the kth path with s as the starting node and e as the destination node at time t. Dynamic traffic node flow conservation constraints: ； In the formula: Let a represent the set of road segments a starting from node j. This represents the set of road segments a leading to node j; EV driving battery limit: ； In the formula: This is the EV energy consumption coefficient, expressed as the electricity consumption per 100 kilometers. This represents the distance s of each EV from the charging station; , These are the upper and lower limits of the state of charge of each EV battery; EV state of charge constraints: ； In the formula: , These are the upper and lower limits of the EV's battery capacity, respectively. Price fluctuation constraints: ； ； ； In the formula: This serves as the lower limit for price fluctuations. This is the upper limit for price fluctuations; , , , , and These are the lower and upper limits for charging electricity price, V2G discharging electricity price, and V2G one-time subsidy, respectively. Node power constraints: ； ； In the formula: , and , These are the lower and upper limits of active and reactive power transmitted from the upstream power grid of node i, respectively. , and , These represent the lower and upper limits of the active and reactive power output of the distributed power source at node i, respectively.

6. A trusted shared data-driven transportation-grid integrated charging dispatching system, characterized in that, Using the method as described in any one of claims 1 to 5, the system comprises: The dynamic traffic network model unit is used for information sharing via blockchain, taking into account vehicle passage and charging queuing, integrating the traffic passage time BPR function and the service desk hybrid M / M / c / K queuing model, and constructing a dynamic traffic network model based on node-segment set; The charging response decision unit is used to combine the dynamic traffic network model and time-varying OD combination to simulate the spatiotemporal distribution of user travel demand, design an incentive price differentiation response mechanism based on the influence of charging behavior on price and work and rest, and construct a charging time transition probability model and a user response rate ratio model based on the joint distribution Copula function. The charging scheduling optimization unit is used to establish a total cost objective function of the distribution network, including distributed power sources, electricity purchase costs, and V2G rewards for electric vehicles supplying electricity to the grid, based on the charging time transition probability model and the user responsiveness ratio model. It then constructs a charging optimization scheduling model and introduces an elite genetic algorithm to solve it, thereby obtaining a grid-transportation collaborative charging scheduling scheme.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-5.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method as described in any one of claims 1-5.

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