Electric vehicle space-time load characteristic modeling method considering differentiated travel modes and electronic equipment

Abstract

The invention relates to an electric vehicle space-time load characteristic modeling method considering differentiated travel modes and electronic equipment. The method comprises the following steps: establishing a traffic network-power distribution network coupling model; constructing an OD matrix considering a differentiated travel mode; a path with the shortest passing time of each electric vehicle is searched and obtained, a dynamically updated driving parameter model and an electric quantity parameter model are established, and seasonal factors are considered in the construction of the electric quantity parameter model; and when the charging demand of each electric vehicle is triggered, corresponding slow charging or fast charging is carried out, and each charging load is superposed so as to describe the space-time load characteristic of the electric vehicle. Compared with the prior art, the time-space load characteristic modeling of the electric vehicle is carried out based on the traffic network-power distribution network coupling model, and the influence of seasonal factors is considered when the power consumption of the electric vehicle is modeled, so that the time-space load characteristic of the electric vehicle can be described more accurately, the actual situation is fit, and a more reliable basis is provided for subsequent load analysis.

Description

Technical Field

[0001] This invention belongs to the field of power distribution network load characteristic modeling technology, and relates to a method for modeling electric vehicle charging load characteristics, and in particular to a method and electronic device for modeling the spatiotemporal load characteristics of electric vehicles that considers differentiated travel patterns. Background Technology

[0002] With the increasing urgency of global energy structure transformation and environmental protection, electric vehicles (EVs), with their clean and efficient characteristics, have become an important development direction in the transportation sector. As a new generation of transportation, they have significant advantages in energy conservation and emission reduction. To alleviate the energy crisis and improve the ecological environment, countries worldwide are actively promoting the development of the EV industry. On the one hand, EVs possess mobility and flexible charging and discharging capabilities, which, through reasonable guidance, can be transformed into a high-quality resource for grid operation and regulation. On the other hand, the strong randomness of EV loads and the high-power charging demand under large-scale access also pose serious challenges to distribution network planning and operation. The spatiotemporal distribution characteristics of EV charging loads are comprehensively influenced by multiple factors, including different travel modes, charging access modes, and the dynamic characteristics of the transportation network. Modeling these spatiotemporal load characteristics is a key issue for subsequent assessment of their impact on the distribution network and for achieving vehicle-grid coordinated optimization.

[0003] However, existing studies often only model the spatiotemporal load characteristics of electric vehicles (EVs) based on a single EV travel mode and charging access mode, failing to fully consider the differentiated travel modes of different types of EVs such as private cars, taxis, and buses. This makes it difficult to accurately analyze the impact of large-scale EV integration on power distribution network flow and node voltage. How to achieve more accurate and refined modeling of the spatiotemporal load characteristics of EVs under increasingly large-scale integration is a pressing technical problem in this field. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and electronic device for modeling the spatiotemporal load characteristics of electric vehicles that takes into account differentiated travel patterns, which can more accurately describe the spatiotemporal load characteristics of electric vehicles.

[0005] The objective of this invention can be achieved through the following technical solutions: A method for modeling the spatiotemporal load characteristics of electric vehicles considering differentiated travel patterns, characterized by the following steps: Establish a transportation network-distribution network coupling model that incorporates multiple types of electric vehicles; Randomly generate driving parameters and battery parameters for each electric vehicle, and construct an OD matrix that takes into account differentiated travel modes; Based on the traffic network-distribution network coupling model and the constructed OD matrix, the shortest travel time path for each electric vehicle is searched, and a dynamically updated driving parameter model and power parameter model are established. The power parameter model takes into account the influence of the vehicle air conditioning load. Based on the dynamic update results of driving parameters and power parameters, the charging demand of each electric vehicle is judged. When the charging demand is triggered, the corresponding slow charging or fast charging is carried out. The charging loads are superimposed on the traffic nodes in the traffic network-distribution network coupling model to realize the spatiotemporal load characteristics of electric vehicles.

[0006] Furthermore, the transportation network-distribution network coupling model is expressed as follows: In the formula: It is the set of edges formed by the power distribution network and the transportation network; For the respective network layers of the power distribution network and the transportation network; This is the coupling number; if it is 1, there is coupling, otherwise there is no coupling. , For layer index, , For node indexing.

[0007] Furthermore, the driving parameters include the origin and destination points, travel time, and return time; The power parameters include battery capacity, initial state of charge, and power consumption.

[0008] Furthermore, the various types of electric vehicles include electric private cars, electric taxis, and electric buses.

[0009] Furthermore, when assessing the charging needs of various electric vehicles, for private electric vehicles, the criterion is the current remaining battery power. Unable to meet the charging needs triggered during the next trip ,Right now In the formula: Indicates the current location of the electric vehicle Distance to destination The distance; Indicates power consumption per unit distance; Indicates the total power consumption over the distance traveled; Power of the air conditioning unit in an electric vehicle; This indicates the average traffic speed of the road segment.

[0010] Furthermore, the power of the electric vehicle's onboard air conditioning is determined based on vehicle factors, including vehicle model and manufacturer.

[0011] Furthermore, the shortest travel time path for each electric vehicle is searched, and the OD matrix is ​​used to model the entire daily travel process of the electric vehicles from departure to destination. The modeling for different types of electric vehicles is as follows: For electric private cars In the formula: PEV refers to electric private vehicles; For electric private vehicles, the 24-hour OD matrix; The range of values ​​for the number of electric private cars; m Number of traffic nodes; element Indicates time t and t +1 between nodes of daily commute i Drive to the node j The number of electric private cars; For electric private cars in time t and t +1 between nodes of daily commute i Drive to the node j The probability of; For electric taxis In the formula: ET represents an electric taxi; A 24-hour OD matrix for electric taxis; The range of values ​​for the number of electric taxis; m Number of traffic nodes; element Indicates time t and t Between +1, each node that can potentially carry passengers. i Drive to the node j The number of electric taxis; For electric taxis in time t and t Between +1, each node that can potentially carry passengers. i Drive to the node j The probability of; For electric buses In the formula: EB represents an electric bus; A 24-hour OD matrix for electric buses; The range of values ​​for the number of electric buses; m Number of traffic nodes; element Indicates time t and t Between +1, nodes in the planned bus route i Drive to the node j The number of electric buses; For electric buses in time t and t Between +1, nodes in the planned bus route i Drive to the node j The probability of.

[0012] Furthermore, the shortest travel time path for each electric vehicle is obtained by using the dynamic Dijkstra algorithm.

[0013] The present invention also provides a computer-readable storage medium including one or more programs executable by one or more processors of an electronic device, said one or more programs including instructions for performing the method for modeling the spatiotemporal load characteristics of electric vehicles considering differentiated travel patterns as described above.

[0014] The present invention also provides an electronic device including one or more processors, a memory, and one or more programs stored in the memory, said one or more programs including instructions for executing the method for modeling the spatiotemporal load characteristics of electric vehicles that takes into account differentiated travel patterns as described above.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention establishes a spatiotemporal load characteristic model of electric vehicles based on a transportation network-distribution network coupling model, which can better reflect the mutual influence between the transportation network and the power grid, thereby obtaining more accurate spatiotemporal load characteristic description results of electric vehicles.

[0016] 2. This invention considers the impact of seasonal factors on the onboard air conditioning load when modeling the power consumption of electric vehicles. Seasonal temperature changes cause the onboard air conditioning of electric vehicles to start cooling / heating, resulting in significant differences in power consumption among different types of electric vehicles under varying driving speeds and travel times. Considering the differentiated power characteristics of onboard air conditioning allows the model to better reflect the power consumption under the influence of seasonal factors, making the final spatiotemporal load characteristics of electric vehicles more realistic and providing a more reliable basis for subsequent load analysis.

[0017] 3. This invention considers the travel patterns of different types of electric vehicles, establishes a spatiotemporal load characteristic model of electric vehicles, and simulates the spatiotemporal load distribution of electric vehicles in a refined manner under the transportation network-distribution network coupling system, which is conducive to improving the scientific nature of distribution network planning and voltage support schemes. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a topology diagram of a 32-node transportation network system. Figure 3 A graph showing the number of electric vehicles connected to each traffic node; Figure 4 A graph showing the total charging demand over 24 hours; Figure 5 This is a distribution diagram of the spatiotemporal load characteristics of electric vehicles. Figure 6 The diagram shows the voltage distribution at each node of the distribution network under three case scenarios in the embodiment. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0020] like Figure 1 As shown, this embodiment provides a method for modeling the spatiotemporal load characteristics of electric vehicles considering differentiated travel patterns, including the following steps: Step S1: Establish a transportation network-distribution network coupling model that incorporates multiple types of electric vehicles, including electric private cars, electric taxis, and electric buses. Step S2: Randomly generate driving parameters and battery parameters for each electric vehicle, and construct an OD matrix that considers differentiated travel modes. The driving parameters include origin and destination points, travel time and return time, and the battery parameters include battery capacity, initial state of charge and power consumption. Step S3: Based on the traffic network-distribution network coupling model and the constructed OD matrix, search for the shortest travel time path for each electric vehicle, and establish a dynamically updated driving parameter model and power parameter model. The construction of the power parameter model takes into account the influence of the vehicle air conditioning load. Specifically, the influence of the vehicle air conditioning load is considered when modeling the power consumption. Step S4: Based on the dynamic update results of driving parameters and power parameters, the charging demand of each electric vehicle is judged. When the charging demand is triggered, the corresponding slow charging or fast charging is carried out. The charging loads are superimposed on the traffic nodes of the traffic network-distribution network coupling model to realize the spatiotemporal load characteristics of electric vehicles.

[0021] The above method fully considers the travel modes of different types of electric vehicles, including electric private cars, electric taxis, and electric buses, in the modeling of the spatiotemporal load characteristics of electric vehicles. It also introduces dynamic traffic information and constructs a spatiotemporal load characteristic model of electric vehicles that considers differentiated travel modes in the transportation network-distribution network coupling model. Furthermore, when modeling the power consumption of electric vehicles, it considers the impact of seasonal factors on the on-board air conditioning load, so that the model can better reflect the power consumption under the influence of seasonal factors, making the final spatiotemporal load characteristics of electric vehicles more in line with reality.

[0022] The specific technical features of the above method are described below.

[0023] 1. Transportation Network-Distribution Network Coupling Model In this embodiment, a coupling model of the urban transportation network and distribution network is established based on models of the transportation network, road segment impedance, node impedance, and distribution network. The models are represented as follows: 1) Transportation network model (taking a small 6-node network as an example) (1) (2) In the formula: For nodes i and nodes j The road impedance between them; inf indicates that the two nodes are not adjacent, that is, there is no direct path connecting them.

[0024] 2) Road segment impedance model (3) Where: saturation , Q Traffic flow on the road segment C For traffic capacity; Zero-traffic trip time; , This is the impedance influence factor.

[0025] 3) Nodal impedance model (4) In the formula: c The signal period; For green credit ratio; q This refers to the vehicle arrival rate on the road segment.

[0026] 4) Urban road resistance model (5) (6) In the formula: Represents the node impedance model; This represents the impedance model of the road segment.

[0027] 5) Distribution network model (7) In the formula: Represents a power distribution network model; It is a set of distribution network nodes; n The number of distribution network nodes; A collection of distribution network branches; It is the collection of branch impedances; k The number of distribution network branches; It is the set of active and reactive power of each node.

[0028] 6) Coupling model of transportation network and distribution network (8) (9) In the formula: It is the set of edges formed by the power distribution network and the transportation network; For the respective network layers of the power distribution network and the transportation network; This is the coupling number; if it is 1, there is coupling, otherwise there is no coupling. , For layer index, , For node indexing.

[0029] 2. Driving and charging characteristic models of various types of electric vehicles The travel patterns and charging characteristics of different types of electric vehicles can be described as follows: Electric private cars: The origin and destination of this type of vehicle are mainly the place of residence and workplace, the driving route is relatively fixed, the charging time is long and the charging location is relatively fixed; Electric taxis: The origin and destination of this type of vehicle are more random, the number of trips is more frequent, the driving route is not fixed, the charging time is short and the charging location is not fixed; Electric buses: This type of vehicle has fixed driving routes and dedicated charging spaces, and its charging characteristics are less affected by traffic factors compared with the other two types of vehicles.

[0030] The search identifies the shortest travel time path for each electric vehicle. In urban transportation networks, residents' daily travel patterns typically follow a set pattern. Therefore, each electric vehicle can be assigned an initial location. Then, an OD matrix is ​​used to model the entire daily travel process of each electric vehicle from departure to destination. The modeling for different types of electric vehicles is as follows: (1) Electric private cars (10) (11) In the formula: PEV refers to electric private vehicles; For electric private vehicles, the 24-hour OD matrix; The range of values ​​for the number of electric private cars; m Number of traffic nodes; element Indicates time t and t +1 between nodes of daily commute i Drive to the node j The number of electric private cars; For electric private cars in time t and t +1 between nodes of daily commute i Drive to the node j The probability of.

[0031] This type of vehicle primarily operates between its residence and workplace, with relatively fixed routes, long charging times, and relatively fixed charging locations. These vehicles typically travel between their residence and workplace, and upon arrival at their destination, the remaining battery power... Unable to meet the charging needs triggered during the next trip ,Right now (12) (13) In the formula: Indicates the current location of the electric vehicle Distance to destination distance, This represents the electricity consumption per unit distance, taking into account the impact of seasonal factors on the vehicle's air conditioning load. Indicates the total power consumption over the distance traveled; Power of the air conditioning unit in an electric vehicle; This indicates the average travel speed of the road segment. A charging request is generated when the remaining battery power is insufficient to reach the destination; if the destination can be reached directly, slow charging will be used upon arrival.

[0032] (2) Electric taxis (14) (15) In the formula: ET represents an electric taxi; A 24-hour OD matrix for electric taxis; The range of values ​​for the number of electric taxis; m Number of traffic nodes; element Indicates time t and t Between +1, each node that can potentially carry passengers.i Drive to the node j The number of electric taxis; For electric taxis in time t and t Between +1, each node that can potentially carry passengers. i Drive to the node j The probability of.

[0033] This type of vehicle has highly random origin and destination points, frequent trips, and unpredictable routes. It also has short charging times and unpredictable charging locations. Given the high degree of randomness in its travel and the predominantly fast-charging method, the current remaining battery power... Battery level below the set threshold When the charging demand is triggered, that is (16) In the formula: The value is 0.25~0.35, meaning that when a charging demand is triggered, the nearest emergency fast charging method is used.

[0034] (3) Electric buses (17) (18) In the formula: EB represents an electric bus; A 24-hour OD matrix for electric buses; The range of values ​​for the number of electric buses; m Number of traffic nodes; element Indicates time t and t Between +1, nodes in the planned bus route i Drive to the node j The number of electric buses; For electric buses in time t and t Between +1, nodes in the planned bus route i Drive to the node j The probability of.

[0035] This type of vehicle has a fixed driving route and dedicated charging location, and its charging characteristics are less affected by traffic factors compared to the other two types of vehicles. This type of vehicle is characterized by multiple trips, and charging is triggered when the remaining battery power is met. When the current battery level is insufficient to reach the destination, the vehicle will use a slow charging method upon arrival at the destination. If the charging triggering conditions are not met, i.e., the current battery level is insufficient to reach the destination during the journey, the vehicle will use an emergency fast charging method.

[0036] 3. Electricity Model for Electric Vehicles Each electric vehicle travels along a starting and ending path determined by the dynamic Dijkstra algorithm, and its energy parameters are updated in real time to obtain charging characteristic parameters. According to the statistical data, the initial state of charge of the electric vehicle follows a normal distribution. By combining this with the electric vehicle's battery capacity, the initial charge level can be obtained. If the power consumption per kilometer increases linearly with the mileage traveled, then... t Remaining battery level at all times for (19) In the formula: This is the energy consumption coefficient, with a value ranging from 0.9 to 1; Indicates power consumption per unit distance; for t Remaining battery power at moment 1; for t 1 to t Time of the first i The distance traveled by the vehicle; and the impact of seasonal factors on the vehicle's air conditioning load on power consumption. For the power of the electric vehicle's onboard air conditioning, This indicates the average traffic speed of the road segment.

[0037] Specifically, this embodiment considers the impact of seasonal factors on the onboard air conditioning load when modeling the power consumption of electric vehicles. Seasonal temperature changes cause the onboard air conditioning of electric vehicles to start cooling / heating, resulting in significant differences in power consumption among different types of electric vehicles under varying driving speeds and travel times. The specific power value of the onboard air conditioning of electric vehicles varies depending on factors such as vehicle model and manufacturer. The air conditioning power of electric private cars is relatively small, so it is set to 1.5kW in the model. Electric taxis, due to their service characteristics, need to meet stronger cooling / heating demands, so their air conditioning power is slightly higher than that of electric private cars, set to 2kW. Electric buses have the largest air conditioning power, needing to meet the temperature control requirements of large passenger compartments, so it is set to 15kW. Since air conditioning power consumption directly affects the energy consumption and charging demand of electric vehicles, considering the differentiated characteristics of onboard air conditioning power allows the model to better reflect the power consumption under the influence of seasonal factors, making the final spatiotemporal load characteristics of electric vehicles more realistic. The total mileage power consumption considering the onboard air conditioning load is expressed as: (20) In the formula: Indicates the total power consumption over the distance traveled; This refers to the power output of the air conditioning system in an electric vehicle.

[0038] Establish a power consumption model per unit mileage for electric vehicles on urban roads of level three and above. (twenty one) In the formula: , and The power consumption per unit mileage for electric vehicles in road grades 1, 2 and 3, respectively; This indicates the average traffic speed of the road segment.

[0039] Charging time can be expressed as (twenty two) In the formula: This indicates the charging efficiency, with a value ranging from 0.8 to 0.9. This indicates the charging power, which is determined by the actual power of slow or fast charging.

[0040] The above model can be simulated using MATLAB to obtain a spatiotemporal load characteristic model of electric vehicles that considers differentiated travel patterns. The total load of the nodes is obtained by superimposing the electric vehicle charging demand load of each node with the base load, so as to analyze the impact of the charging demand load.

[0041] 4. Case Analysis 4.1 Test System The relevant parameter settings for the simulation system in this embodiment are as follows: Regarding the urban road traffic network, the road topology of some arterial roads in a certain urban area is modeled, such as... Figure 2 As shown in the diagram, there are 32 nodes and 53 road segments. Regarding electric vehicles, a certain proportion of electric vehicles have been introduced to each traffic node, including 217 electric private cars, 473 electric taxis, and 310 electric buses. The number of vehicles at each traffic node is as follows: Figure 3 As shown. Regarding charging stations, vehicles requiring charging are supplied with energy through both slow and fast charging, with slow charging power set at 12kW and fast charging power at 48kW. For the urban power distribution network, the IEEE 33-node standard system is used as a test case. Subsequently, based on the aforementioned urban road network and power distribution network, road network nodes are individually assigned to power distribution network nodes through the coupling relationship between the power distribution network and the road network. Simultaneously, comparative cases are set up: one with only conventional loads in the power distribution network, and another considering only electric private cars in the traditional method. In both cases, the transportation network-power distribution network coupling systems are identical, and the number of electric private cars is 1000. The specific comparison scenarios are as follows.

[0042] 4.2 Comparison and Analysis of Simulation Results To verify the effectiveness of the method of this invention, the following three cases are set up for comparative analysis: Case 1: Considering only the conventional load of the distribution network; Case 2: Modeling and impact analysis of the spatiotemporal load characteristics of electric vehicles considering three electric vehicle travel modes, with a total of 1000 electric vehicles in total (using the method of this invention). Case 3: Modeling and impact analysis of the spatiotemporal load characteristics of electric vehicles with only electric private cars connected, where the number of electric private cars is 1000.

[0043] The simulation time was set to a typical 24-hour day. Figure 4 The overall charging demand for each traffic node throughout the day is calculated. The overall charging demand indicates that charging is likely to occur around 6-10 and 18-22, forming peak-hour charging demand for electric vehicles.

[0044] Subsequently, the spatiotemporal distribution of electric vehicle charging demand was obtained through simulation, and the spatiotemporal load characteristic distribution of electric vehicles in the distribution network nodes was calculated based on the charging power accumulation method. The results are as follows: Figure 5 As shown in the figure, the spatiotemporal load characteristics of electric vehicles reveal that the charging load at distribution network nodes is mainly concentrated at nodes 6, 15, 18, 26, and 32. The temporal distribution exhibits a double-peak superposition pattern corresponding to charging demand, with peak charging demand occurring in the morning and evening. Furthermore, the obtained spatiotemporal load characteristics show a significant spatiotemporal differential distribution, indicating substantial differences in charging load across different spatial regions and uneven distribution across different time spans.

[0045] The large-scale integration of electric vehicles (EVs) will have a certain impact on the power distribution network. Based on the spatiotemporal characteristics of EV charging load, this study examines its impact on grid load and voltage. Using the nodal loads of the distribution network nodes in the aforementioned coupled system as the base load, and combining the spatiotemporal distribution characteristics of charging pile nodes in the transportation network and EV charging load, the power flow of EVs is calculated. The voltage curves of each grid node before and after EV integration, as well as those in the traditional method when only electric private cars are connected, are compared to analyze the impact of EV integration on the power grid.

[0046] like Figure 6The three case studies illustrate the voltage distribution at various nodes of the distribution network before and after the integration of electric vehicles, and the voltage distribution at nodes 6-18 and 28-33 in the traditional method, respectively. Simulation results show that after the integration of the three types of electric vehicles, the voltage at the coupled nodes 6-18 and 28-33 of the transportation network and distribution network generally decreased. During peak hours, the voltage drop at nodes with higher loads (such as node 18) reached 0.024 pu, and the peak-to-valley load difference increased by 64%. For nodes with voltage drops, comprehensive voltage management can be achieved through multi-dimensional technological collaboration, such as deploying dynamic reactive power compensation devices and battery energy storage devices at the corresponding nodes to provide voltage support. However, when only the same number of electric private vehicles are integrated, even during peak hours, the voltage drop at node 18, with a higher load, is only 0.002 pu, and the peak-to-valley load difference increases by only 5%. In this case, the impact on the distribution network is relatively small and cannot reflect the true impact of various types of electric vehicle integration on the distribution network in real-world scenarios. This may lead to weak voltage support measures in the distribution network and even threaten the safe and reliable operation of the distribution network.

[0047] If the above methods are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0048] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0049] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for modeling the spatiotemporal load characteristics of electric vehicles considering differentiated travel patterns, characterized in that, Includes the following steps: Establish a transportation network-distribution network coupling model that incorporates multiple types of electric vehicles; Randomly generate driving parameters and battery parameters for each electric vehicle, and construct an OD matrix that takes into account differentiated travel modes; Based on the traffic network-distribution network coupling model and the constructed OD matrix, the shortest travel time path for each electric vehicle is searched, and a dynamically updated driving parameter model and power parameter model are established. The power parameter model takes into account the influence of the vehicle air conditioning load. Based on the dynamic update results of driving parameters and power parameters, the charging demand of each electric vehicle is judged. When the charging demand is triggered, the corresponding slow charging or fast charging is carried out. The charging loads are superimposed on the traffic nodes in the traffic network-distribution network coupling model to realize the spatiotemporal load characteristics of electric vehicles.

2. The method for modeling the spatiotemporal load characteristics of electric vehicles considering differentiated travel patterns according to claim 1, characterized in that, The transportation network-distribution network coupling model is represented as follows: In the formula: It is the set of edges formed by the power distribution network and the transportation network; For the respective network layers of the power distribution network and the transportation network; This is the coupling number; if it is 1, there is coupling, otherwise there is no coupling. , For layer index, , For node indexing.

3. The method for modeling the spatiotemporal load characteristics of electric vehicles considering differentiated travel patterns according to claim 1, characterized in that, The driving parameters include the origin and destination points, departure time, and return time; The power parameters include battery capacity, initial state of charge, and power consumption.

4. The method for modeling the spatiotemporal load characteristics of electric vehicles considering differentiated travel patterns according to claim 1, characterized in that, The various types of electric vehicles include electric private cars, electric taxis, and electric buses.

5. The method for modeling the spatiotemporal load characteristics of electric vehicles considering differentiated travel patterns according to claim 4, characterized in that, When assessing the charging needs of various electric vehicles, for electric private cars, the criterion is the current remaining battery power. Unable to meet the charging needs triggered during the next trip ,Right now In the formula: Indicates the current location of the electric vehicle Distance to destination The distance; Indicates power consumption per unit distance; Indicates the total power consumption over the distance traveled; Power of the air conditioning unit in an electric vehicle; This indicates the average traffic speed of the road segment.

6. The method for modeling the spatiotemporal load characteristics of electric vehicles considering differentiated travel patterns according to claim 5, characterized in that, The power of the electric vehicle's onboard air conditioning is determined based on vehicle factors, including vehicle model and manufacturer.

7. The method for modeling the spatiotemporal load characteristics of electric vehicles considering differentiated travel patterns according to claim 4, characterized in that, The shortest travel time path for each electric vehicle is searched, and the OD matrix is ​​used to model the entire daily travel process of the electric vehicles from departure to destination. The modeling for different types of electric vehicles is as follows: For electric private cars In the formula: PEV refers to electric private vehicles; For electric private vehicles, the 24-hour OD matrix; The range of values ​​for the number of electric private cars; m Number of traffic nodes; element Indicates time t and t +1 between nodes of daily commute i Drive to the node j The number of electric private cars; For electric private cars in time t and t +1 between nodes of daily commute i Drive to the node j The probability of; For electric taxis In the formula: ET represents an electric taxi; A 24-hour OD matrix for electric taxis; The range of values ​​for the number of electric taxis; m Number of traffic nodes; element Indicates time t and t Between +1, each node that can potentially carry passengers. i Drive to the node j The number of electric taxis; For electric taxis in time t and t Between +1, each node that can potentially carry passengers. i Drive to the node j The probability of; For electric buses In the formula: EB represents an electric bus; A 24-hour OD matrix for electric buses; The range of values ​​for the number of electric buses; m Number of traffic nodes; element Indicates time t and t Between +1, nodes in the planned bus route i Drive to the node j The number of electric buses; For electric buses in time t and t Between +1, nodes in the planned bus route i Drive to the node j The probability of.

8. The method for modeling the spatiotemporal load characteristics of electric vehicles considering differentiated travel patterns according to claim 1, characterized in that, The shortest travel time path for each electric vehicle is obtained by using the dynamic Dijkstra algorithm.

9. A computer-readable storage medium, characterized in that, Includes one or more programs that are executed by one or more processors of an electronic device, the one or more programs including instructions for executing the method for modeling the spatiotemporal load characteristics of electric vehicles that takes into account differentiated travel patterns as described in any one of claims 1-8.

10. An electronic device, characterized in that, It includes one or more processors, memory, and one or more programs stored in the memory, said one or more programs including instructions for executing the electric vehicle spatiotemporal load characteristic modeling method considering differentiated travel patterns as described in any one of claims 1-8.

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