A charging station pricing method and system considering maximum carrying capacity of a power distribution network

Abstract

The application discloses a charging station pricing method and system considering the maximum load capacity of a power distribution network, comprising: predicting the space-time distribution of electric vehicle charging load. The starting point and destination of the electric vehicle are determined by using an OD matrix, and a vehicle route is constructed by using a Dijkstra algorithm. According to a road network model and a path planning model, the space-time distribution of the electric vehicle charging load is calculated. Finally, a dynamic pricing scheme for electric vehicles is designed. Taking the maximum load capacity of the power distribution network as an objective function, a dynamic electricity pricing scheme is designed to realize the orderly charging of electric vehicles, improve the charging efficiency and guarantee the safe operation of the power grid. The traffic network in the actual situation is fully considered, and the driving path of the electric vehicle is accurately simulated, so that the charging station electricity pricing is performed under the condition that the load capacity of the power grid is the largest, and the economic and safe operation of the power distribution network and the planning of the traffic system have certain reference significance.

Description

Technical Field

[0001] This invention relates to the field of power grid assessment technology, specifically to a pricing method and system for charging stations that takes into account the maximum carrying capacity of the distribution network. Background Technology

[0002] Electric vehicles and other new energy vehicles have shown great potential in addressing ever-increasing energy demands and the worsening greenhouse effect, thus gaining significant attention worldwide in recent years. Electric vehicle charging load exhibits specific spatiotemporal distribution characteristics, significantly influenced by urban road network layout and user travel behavior, and playing a decisive role in the efficiency of the power distribution network and the planning of charging infrastructure. Simultaneously, limited by the existing urban charging infrastructure layout, the collective charging demand of electric vehicles is difficult to meet, further highlighting the need for effective charging management and control strategies. Effectively guiding and controlling charging behavior, while ensuring user needs are met, can mitigate the negative impact of concentrated charging on the power system and further improve the operational efficiency of charging stations. Therefore, there is an urgent need for a charging station pricing strategy that considers the maximum carrying capacity of the power distribution network, thereby increasing the grid's carrying capacity through reasonable pricing and better meeting the electricity needs of vehicle owners. Summary of the Invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by this invention is that the existing charging station pricing calculation method has optimization problems such as large power consumption loss and inaccurate pricing.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a charging station pricing method considering the maximum carrying capacity of the distribution network, comprising:

[0006] Based on the actual urban traffic conditions, a dynamic urban traffic network model is constructed to simulate urban traffic information.

[0007] Build a vehicle model for electric vehicles, including modeling the vehicle type, travel time, and battery capacity.

[0008] A path planning model for electric vehicles is constructed, the origin and destination of electric vehicles are determined using the OD matrix, and the Dijkstra algorithm is used to construct the vehicle travel route.

[0009] Based on the urban dynamic traffic network model and the electric vehicle path planning model, the spatiotemporal distribution of electric vehicle charging load is calculated;

[0010] The energy value is dynamically evaluated with the objective function of maximizing the load that the distribution network can carry.

[0011] As a preferred embodiment of the charging station pricing method considering the maximum carrying capacity of the power distribution network described in this invention, the urban dynamic transportation network model includes a ring grid layout.

[0012] The topological characteristics and inherent relationships of road networks are explained using graph theory methods; the mathematical model of the road network topology is shown below:

[0013]

[0014] Where G represents the transportation network; N represents the nodes in the network; L represents the road segments in the network; H represents the set of time periods divided within a day; W represents the set of impedances formed by road segment congestion and node waiting time; n represents the total number of transportation network nodes in the region; i and j represent the specific node numbers in the region; h represents the specific time period; and w represents the magnitude of the road impedance between node i and node j during time period k.

[0015] The adjacency matrix Z is used to represent the segment impedance of the road network topology, where the elements Z of the matrix Z are... ij Assignment follows:

[0016]

[0017] Where inf represents node n i With n j The passage is impassable.

[0018] A speed-flow model is introduced to calculate the driving speed and travel time of an electric vehicle. The driving speed of the electric vehicle is v. ij The expression is:

[0019]

[0020] Among them, v ij,max C represents the maximum speed of road ij. ij The traffic capacity of road ij is determined by its road classification; Q ij (t) represents the electric vehicle traffic flow on road ij at time t; Q ij (t) and C ij The ratio is the saturation of the road segment at time t; w is an intermediate parameter in the calculation; a, b, and γ are the adaptive coefficients of the road, which are determined by the road grade.

[0021] As a preferred embodiment of the charging station pricing method considering the maximum carrying capacity of the power distribution network described in this invention, the vehicle model of the electric vehicle includes all introduced electric vehicle numbers, where i = 1, 2, ..., N, and each vehicle type corresponds to:

[0022]

[0023] Among them, commuter private cars are designated as 1, taxis as 2, and other shared vehicles as 3;

[0024] The probability density function of daily mileage is obtained by fitting historical data:

[0025]

[0026] Where s is the number of kilometers the electric vehicle travels per day, and μ D and σ D These are the expected deviation and standard deviation in the function, respectively, and D represents the curve fitted to the travel data;

[0027] The probability density function of a vehicle is approximated using a gamma distribution:

[0028]

[0029]

[0030] Where, α t With β t Let be the gamma distribution parameters at time t. Let be the battery capacity of the p-th vehicle of type i at time t, where t is the specific time period and p represents the vehicle number.

[0031] As a preferred embodiment of the charging station pricing method considering the maximum carrying capacity of the power distribution network described in this invention, the electric vehicle route planning model includes: calculating the OD matrix using traffic flow, constructing a travel matrix by analyzing the traffic flow of road segments, and updating the OD matrix in real time using different traffic conditions.

[0032] Let X ij N represents the traffic volume between two traffic nodes. v R represents the traffic flow in the road segment. v This represents the probability of traffic passing through the road segment;

[0033]

[0034] Where Ψ represents the number of road segments in the selected area, O represents the number of nodes, v represents, and Ψ represents;

[0035] The Dijkstra algorithm is used to gradually determine the length of the shortest path until the target node is found.

[0036] As a preferred embodiment of the charging station pricing method considering the maximum carrying capacity of the power distribution network described in this invention, the spatiotemporal distribution of electric vehicle charging load includes analyzing the start time of different electric vehicle categories through the probability density function obtained by the vehicle model of the electric vehicle, and determining the start time and return time of each vehicle.

[0037] The OD matrix is ​​used to determine the starting point and destination of the trip;

[0038] Based on the road network structure, Dijkstra's algorithm is used to plan the shortest path, and the road resistance model is used to determine the required travel time.

[0039] Based on the electric vehicle model, the energy consumption model is used to update the vehicle's battery level after the vehicle arrives at its destination and set this destination as the starting point for the next trip. By repeatedly sampling the OD probability matrix of the vehicle at different time periods in Monte Carlo, the driving trajectory of the electric vehicle for a whole day and 24 hours is simulated.

[0040] During the journey, according to the electric vehicle charging model, if the vehicle's remaining battery power does not support its journey to the next destination, it will select the nearest charging station to charge and record the resulting charging load, thereby capturing the charging behavior and impact at different locations and times.

[0041] As a preferred embodiment of the charging station pricing method considering the maximum carrying capacity of the distribution network described in this invention, the objective function includes calculating the charging load threshold that each charging station can access with the maximum charging load connected to the distribution network as the objective.

[0042]

[0043] in, This represents the maximum value of the grid charging load connected to the k-th node out of all n nodes during the complete time period t;

[0044] By combining a dynamic road network model with Dijkstra's algorithm, the battery status and driving location of each electric vehicle in the road network can be obtained.

[0045] When the electric vehicle's battery level drops below the expected level, the electric vehicle will head to the nearest charging station to recharge.

[0046] After calculating the charging load of the corresponding nodes at the charging station, the objective function and power flow distribution of the distribution network are set according to Distflow:

[0047]

[0048]

[0049]

[0050]

[0051] Among them, P DGi,t and Q DGi,t P represents the active and reactive power of the distributed power source at node i within time period t; Li,t and Q Li,t P represents the active and reactive loads at node i within time period t; ij,t Q ij,t This represents the active and reactive power of line ij within time period t; ij r ij and x ij Represent the square of the current, resistance, and inductance of line ij, respectively; v j,t and v i,t P represents the square of the voltage at node i within time period t; CARi,t When node i is selected as a charging station, P CARi,t The value of P represents the load size of the electric vehicle during that period; when node i is not a charging station, P CARi,t =0.

[0052] As a preferred embodiment of the charging station pricing method considering the maximum carrying capacity of the distribution network described in this invention, the dynamic evaluation of energy value includes, at time t, the charging price ζ of charging station k for each electric vehicle user and charging station. k,t The calculation uses a dynamic electricity price demand function:

[0053]

[0054] In the formula, ζ n For normal charging electricity price, when the sum of the fast charging load of charging station k and the basic load of the node exceeds the current time period load threshold, the charging electricity price will be increased; η is the electricity price incentive coefficient; This represents the charging load connected to the power grid at the k-th node during time period t; This represents the maximum value of the grid charging load connected to the j-th node during time period t.

[0055] A charging station pricing system that considers the maximum carrying capacity of the distribution network and employs the method described in this invention is characterized by:

[0056] The simulation unit constructs a dynamic urban traffic network model based on the actual urban traffic conditions and simulates urban traffic information.

[0057] The modeling unit constructs a vehicle model of an electric vehicle, modeling the vehicle type, travel time, and battery capacity.

[0058] The path planning unit constructs a path planning model for electric vehicles, uses the OD matrix to determine the starting point and destination of the electric vehicles, and uses Dijkstra's algorithm to construct the vehicle's travel route.

[0059] The calculation unit calculates the spatiotemporal distribution of electric vehicle charging load based on the urban dynamic traffic network model and the electric vehicle path planning model.

[0060] The evaluation unit uses the maximum load that the distribution network can carry as the objective function to dynamically evaluate the value of energy.

[0061] A computer device includes: a memory and a processor; the memory stores a computer program, wherein: when the processor executes the computer program, it implements the steps of the method described in any one of the present invention.

[0062] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of the present invention.

[0063] The beneficial effects of this invention are as follows: The charging station pricing method provided by this invention, which considers the maximum carrying capacity of the distribution network, fully takes into account various complex situations in the actual transportation network and accurately simulates the driving paths of electric vehicles. This allows for pricing of charging station electricity under the condition that the power grid can carry the maximum load, providing valuable reference for the economic and safe operation of the distribution network and the planning of the transportation system. The introduction of a dynamic electricity price model not only improves the operating revenue and charging efficiency of charging stations but also incentivizes users to adjust their charging time and location according to changes in electricity prices, thereby promoting the safe operation of the power grid. Attached Figure Description

[0064] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 A flowchart of a charging station pricing method considering the maximum carrying capacity of the power distribution network, provided in the first embodiment of the present invention;

[0066] Figure 2 This is a schematic diagram of the road network coupling structure in a charging station pricing method that considers the maximum carrying capacity of the distribution network, provided in the first embodiment of the present invention. Detailed Implementation

[0067] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0068] Example 1, referring to Figure 1 , 2 As one embodiment of the present invention, a charging station pricing method considering the maximum carrying capacity of the power distribution network is provided, comprising:

[0069] S1: Based on the actual urban traffic conditions, construct a dynamic urban traffic network model and simulate urban traffic information.

[0070] Urban road systems mainly comprise three structural forms: grid networks, radial networks, and ring networks. Grid layouts are suitable for towns with flat terrain and relatively common structures, where the design of nodes and roads facilitates efficient traffic flow and land use. Radial networks extend radially within a limited space, particularly suitable for cities with compact and easily identifiable central areas, facilitating the centralized diversion of traffic to surrounding areas. For cities in my country still under construction, ring network architectures are favored due to their suitability for current road traffic needs. Considering the city's scale, a ring grid layout was selected for model construction, and graph theory methods were used to explain the topological characteristics and inherent relationships of the road network. The mathematical model of the road network topology is shown below:

[0071]

[0072] Where G represents the transportation network; N represents the nodes in the network; L represents the road segments in the network; H represents the set of time periods divided within a day; W represents the set of impedances formed by road segment congestion and node waiting time; n represents the total number of transportation network nodes in the region; i and j represent the specific node numbers in the region; h represents the specific time period; and w represents the magnitude of the road impedance between node i and node j during time period k.

[0073] The adjacency matrix Z is used to represent the segment impedance of the road network topology, where the elements Z of the matrix Z are... ij Assignment follows:

[0074]

[0075] Where inf represents node n i With n j The passage is impassable.

[0076] The adjacency matrix Z is ultimately expressed in the following form:

[0077]

[0078] For any electric vehicle i, in order to characterize its dynamic travel trajectory, we first assign it a starting point and a destination using the OD analysis method, and then assign it an initial time and a return time. Then, we use the real-time Dijkstra algorithm to search for the shortest path between the starting point and the destination for path guidance. The objective function is shown below.

[0079]

[0080]

[0081] In the formula, when n ij Included in the actual driving path d i When it is in (i), its value is set to 1; otherwise, it is set to 0.

[0082] The following section constructs a road resistance model for urban roads.

[0083] During travel, most electric vehicle owners tend to focus on the vehicle's travel time, which is closely related to both distance traveled and speed. Generally, travel distance is relatively easy to obtain once the route is determined, and is considered a static constant; while speed is closely related to factors such as road capacity and traffic flow, and is considered a time-varying factor. Therefore, a speed-flow model is introduced to calculate the electric vehicle's speed and travel time. Electric vehicle speed v ij The expression is:

[0084]

[0085] Among them, v ij,max C represents the maximum speed of road ij. ij The traffic capacity of road ij is determined by its road classification; Q ij (t) represents the electric vehicle traffic flow on road ij at time t; Q ij (t) and C ij The ratio is the saturation of the road segment at time t; w is an intermediate parameter in the calculation; a, b, and γ are the adaptive coefficients of the road, which are determined by the road grade.

[0086] S2: Build a vehicle model for electric vehicles, including modeling the vehicle type, travel time, and battery capacity.

[0087] Modeling the vehicle types involves assigning numbers to all introduced electric vehicles, where i = 1, 2, ..., N. The corresponding vehicle types are shown in the following formula:

[0088]

[0089] Among them, commuter private cars are designated as 1, taxis as 2, and other shared vehicles as 3.

[0090] Daily mileage reflects the energy consumption of an electric vehicle and also affects destination prediction. The probability density function of daily mileage is obtained by fitting historical data:

[0091]

[0092] Where s is the number of kilometers the electric vehicle travels per day, and μ D and σ D These are the expected deviation and standard deviation in the function, respectively, and D represents the curve fitted to the travel data.

[0093] In the MERGE project database, UK electric vehicle market statistics categorize electric vehicles into four types based on their usage and load capacity. These types include L7e (mostly small electric vehicles), M1 (the most common type of everyday car, with a similar structure to gasoline vehicles), N1 (large passenger vehicles, capable of carrying multiple passengers), and N2 (cargo vans, generally used for transport). Based on these types, taxis are primarily for passenger transport and correspond to the M1 type, while private cars generally include the L7e type (small electric vehicles carrying two passengers), often used by the elderly, and most M1 vehicles. Public vehicles may belong to the N1, N2, or M1 types. The probability density function of the vehicles is approximated using a gamma distribution.

[0094]

[0095]

[0096] Where, α t With β t Let be the gamma distribution parameters at time t. Let be the battery capacity of the p-th vehicle of type i at time t, where t is the specific time period and p represents the vehicle number. Parameters for different types are shown in Table 1 below:

[0097] Table 1. Gamma distribution parameters and battery capacity of different types of electric vehicles

[0098] Vehicle type Battery capacity / kW·h <![CDATA[α t 、b t ]]> private cars 100 10.8，0.8 taxi 120 4.5，6.7 public vehicles 72 8.7，3.2

[0099] S3: Construct a path planning model for electric vehicles, use the OD matrix to determine the starting point and destination of the electric vehicles, and use Dijkstra's algorithm to construct the vehicle travel route.

[0100] Traditionally, constructing an OD matrix relies on extensive traffic surveys, which is time-consuming and labor-intensive. However, with the continuous advancement of intelligent technologies, some studies have proposed methods to extrapolate the OD matrix using traffic flow. This method constructs a travel matrix by analyzing traffic flow on road segments and updates the OD matrix in real time using different traffic conditions. This method accurately presents the current traffic flow distribution, providing a valuable reference for traffic planning and management. Theoretical analysis shows that extrapolating OD distribution through traffic flow is essentially the reverse process of traffic assignment. Where X... ij N represents the traffic volume between two traffic nodes. w R represents the traffic flow within the road segment. w This represents the probability of traffic passing through this road segment, and this relationship can be expressed by the following formula:

[0101]

[0102] Where Ψ represents the number of road segments in the selected area, O represents the number of nodes, v represents Ψ represents .

[0103] The Dijkstra algorithm is used to gradually determine the length of the shortest path until the target node is found.

[0104] The Dijkstra algorithm is used for path planning, with the goal of minimizing the travel time.

[0105] It is mainly used to solve the shortest path problem in graph theory and is suitable for handling graphs with non-negative weights.

[0106] The core idea of ​​the algorithm is to progressively determine the length of the shortest path until the target node is found. It achieves this through the following steps:

[0107] 1. Initialization: First, set the distance of the starting point to 0 (because the distance from the starting point to itself is 0), and set the distance of all other points to infinity (indicating that there is no known path between them initially).

[0108] 2. Set partitioning: During the algorithm execution, all nodes are divided into two groups: one is the set of nodes that have been determined, and the other is the set of nodes that have not been determined.

[0109] 3. Path update: Select the node closest to the starting point from the unvisited set and add it to the visited set. Then consider whether using this node as a transit point can shorten the distance to other nodes. If so, update the shortest path length of these nodes.

[0110] 4. Repeat step 3: Repeat the above process, select the node with the shortest distance from the undetermined set of nodes, update the distance of that node to other nodes, until all nodes have been visited, that is, the visited set contains all nodes.

[0111] 5. Termination: The algorithm terminates when the target node is added to the visited set, or when all nodes have been processed.

[0112] Dijkstra's algorithm is crucial for path planning in electric vehicles because it can effectively calculate the shortest time or shortest distance path from the current location to a charging station or destination, thus providing the optimal driving route for electric vehicles and ensuring that electric vehicle users can reach their destination in the shortest time, provided that there is still enough battery power remaining.

[0113] S4: Calculate the spatiotemporal distribution of electric vehicle charging load based on the urban dynamic traffic network model and the electric vehicle path planning model.

[0114] Based on the different start times of various electric vehicle categories mentioned above, the start and return times of each vehicle are determined according to their characteristic distribution, and the origin-destination (OD) matrix is ​​used to determine the starting point and destination of the trip. This simulates the trip distribution of electric vehicles, providing a foundation for further analysis of charging demand and route planning.

[0115] Using the trip plan obtained in the previous step, Dijkstra's algorithm is employed to plan the shortest path based on the road network structure, and a road resistance model is used to determine the required travel time. Based on the electric vehicle model, and through an energy consumption model, the vehicle's battery level is updated after reaching its destination, and this destination is set as the starting point for the next trip. By repeatedly sampling the vehicle's OD probability matrix at different times using Monte Carlo methods, the driving trajectory of the electric vehicle over a full 24 hours can be simulated.

[0116] During the journey, according to the electric vehicle charging model, if the vehicle's remaining battery power does not support its journey to the next destination, it will select the nearest charging station to charge and record the resulting charging load, thereby capturing the charging behavior and impact at different locations and times.

[0117] like Figure 2 This is a diagram illustrating the coupling modeling of a road network. The upper layer is the topology of the road network, where the nodes represent road intersections in the transportation network. The lower layer represents the topology of the power grid, and the connecting lines represent the coupling parts between the power grid and the road network nodes. The power grid and the transportation network are interconnected through the charging load of charging stations.

[0118] S5: Using the maximum load that the distribution network can carry as the objective function, dynamically evaluate the value of energy.

[0119] Since the charging load threshold that a charging station can access is limited not only by the charging capacity of the charging station itself, but also by the grid node voltage and line thermal stability limits, this paper calculates the charging load threshold that each charging station can access based on the Distflow power flow model of the distribution network, taking into account the node voltage and line thermal stability limit safety constraints, with the goal of maximizing the charging load accessed by the distribution network.

[0120]

[0121] in, This represents the maximum value of the grid charging load connected to the k-th node out of all n nodes during the complete time period t.

[0122] By combining a dynamic road network model with Dijkstra's algorithm, the battery status and location of each electric vehicle in the road network can be obtained. When the battery level of an electric vehicle is lower than the owner's expectation, the vehicle needs to go to the nearest charging station to charge. After calculating the charging load of the corresponding nodes at the charging station, the objective function and power flow distribution of the distribution network are set according to Distflow:

[0123]

[0124]

[0125]

[0126]

[0127] Among them, P DGi,t and Q DGi,t P represents the active and reactive power of the distributed power source at node i within time period t; Li,t and Q Li,t P represents the active and reactive loads at node i within time period t; ij,t Q ij,t This represents the active and reactive power of line ij within time period t; ij r ij and x ij Represent the square of the current, resistance, and inductance of line ij, respectively; v j,t and v i,t P represents the square of the voltage at node i within time period t; CARi,t When node i is selected as a charging station, P CARi,t The value of P represents the load size of the electric vehicle during that period; when node i is not a charging station, P CARi,t =0.

[0128] At time t, for each electric vehicle user and charging station, the charging price ζ for charging station k is... k,t The calculation is shown in the following formula. Where ζ n P represents the normal charging price that has not been changed by regulatory strategies. ch Let be the charging load within charging station k at time t. The dynamic electricity price demand function used is shown in the following equation:

[0129]

[0130] In the formula, ζ n The normal charging price is determined when the sum of the fast charging load of charging station k and the basic load of the node exceeds the current time period load threshold. η is the price incentive coefficient, which ranges from 1 to 1.5. This represents the charging load connected to the power grid at the k-th node during time period t; This represents the maximum value of the grid charging load connected to the j-th node during time period t.

[0131] The normal charging price is the base price of a charging station before any price incentives, and it is usually a fixed value. If the charging load is high at a certain moment, exceeding the charging station's threshold, a dynamic charging price model can calculate the actual charging price, which is determined by the base price and the excess value. This price is the charging station's electricity sales price. The charging station's electricity purchase price is not considered in this paper because it can adjust its charging price based on the electricity purchase price from the distribution network at different times. The introduction of the dynamic pricing model can not only improve the charging station's own revenue and charging efficiency, but also incentivize users to adjust their charging time and location according to changes in electricity prices, thereby promoting the safe operation of the power grid.

[0132] If the above functions 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.

[0133] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced 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.

[0134] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), 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). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because 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 computer memory.

[0135] 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.

[0136] Example 2, an embodiment of the present invention, provides a charging station pricing system that considers the maximum carrying capacity of the power distribution network, comprising:

[0137] The simulation unit constructs a dynamic urban traffic network model based on the actual urban traffic conditions and simulates urban traffic information.

[0138] The modeling unit constructs a vehicle model of the electric vehicle, modeling the vehicle type, travel time, and battery capacity.

[0139] The path planning unit constructs a path planning model for electric vehicles, uses the OD matrix to determine the starting point and destination of the electric vehicles, and uses Dijkstra's algorithm to construct the vehicle's travel route.

[0140] The calculation unit calculates the spatiotemporal distribution of electric vehicle charging load based on the urban dynamic traffic network model and the electric vehicle path planning model.

[0141] The evaluation unit uses the maximum load that the distribution network can carry as the objective function to dynamically evaluate the value of energy.

[0142] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A pricing method for charging stations that considers the maximum carrying capacity of the power distribution network, characterized in that, include: Based on the actual urban traffic conditions, a dynamic urban traffic network model is constructed to simulate urban traffic information. Build a vehicle model for electric vehicles, including modeling the vehicle type, travel time, and battery capacity. A path planning model for electric vehicles is constructed, the origin and destination of electric vehicles are determined using the OD matrix, and the Dijkstra algorithm is used to construct the vehicle travel route. Based on the urban dynamic traffic network model and the electric vehicle path planning model, the spatiotemporal distribution of electric vehicle charging load is calculated; The energy value is dynamically evaluated with the objective function of maximizing the load that the distribution network can carry. The objective function includes calculating the threshold of the charging load that each charging station can access with the maximum charging load connected to the distribution network as the objective. in, This represents the maximum value of the grid charging load connected to the k-th node out of all n nodes during the complete time period t; By combining a dynamic road network model with Dijkstra's algorithm, the battery status and driving location of each electric vehicle in the road network can be obtained. When the electric vehicle's battery level drops below expected, the electric vehicle will head to the nearest charging station to recharge. After calculating the charging load of the corresponding nodes at the charging station, the objective function and power flow distribution of the distribution network are set according to Distflow: Among them, P DGi,t and Q DGi,t P represents the active and reactive power of the distributed power source at node i within time period t; Li,t and Q Li,t P represents the active and reactive loads at node i within time period t; ij,t Q ij,t This represents the active and reactive power of line ij within time period t; ij r ij and x ij Represent the square of the current, resistance, and inductance of line ij, respectively; v i,t P represents the square of the voltage at node i within time period t; CARi,t When node i is selected as a charging station, P CARi,t The value of P represents the load size of the electric vehicle during that period; when node i is not a charging station, P CARi,t =0; The dynamic assessment of energy value includes, at time t, the charging price ζ for each electric vehicle user and charging station k. k,t The calculation uses a dynamic electricity price demand function: In the formula, ζ n For normal charging electricity price, when the sum of the fast charging load of charging station k and the basic load of the node exceeds the current time period load threshold, the charging electricity price will be increased; η is the electricity price incentive coefficient; This represents the charging load connected to the power grid at the k-th node during time period t; This represents the maximum value of the grid charging load connected to the j-th node during time period t.

2. The charging station pricing method considering the maximum carrying capacity of the distribution network as described in claim 1, characterized in that: The urban dynamic transportation network model includes a ring grid layout; The topological characteristics and inherent relationships of road networks are explained using graph theory methods; the mathematical model of the road network topology is shown below: Where G represents the transportation network; N represents the nodes in the network; L represents the road segments in the network; H represents the set of time periods divided within a day; W represents the set of impedances formed by road segment congestion and node waiting time; n represents the total number of transportation network nodes in the region; i and j represent the specific node numbers in the region; and h represents the specific time period. The magnitude of the road impedance between node i and node j over time k. The adjacency matrix Z is used to represent the segment impedance of the road network topology, where the elements Z of the matrix Z are... ij Assignment follows: Where inf represents node n i With n j The passage is impassable. A speed-flow model is introduced to calculate the driving speed and travel time of an electric vehicle. The driving speed of the electric vehicle is v. ij The expression is: Among them, v ij.max C represents the maximum speed of road ij. ij The traffic capacity of road ij is determined by its road classification; Q ij (t) represents the electric vehicle traffic flow on road ij at time t; Q ij (t) and C ij The ratio is the saturation of the road segment at time t; w is an intermediate parameter in the calculation; a, b, and γ are the adaptive coefficients of the road, which are determined by the road grade.

3. The charging station pricing method considering the maximum carrying capacity of the distribution network as described in claim 2, characterized in that: The vehicle model of the electric vehicle includes all introduced electric vehicle numbers, where i = 1, 2, ..., N, and each vehicle type corresponds to: Among them, commuter private cars are designated as 1, taxis as 2, and other shared vehicles as 3; The probability density function of daily mileage is obtained by fitting historical data: Where s is the number of kilometers the electric vehicle travels per day, and μ D and σ D These are the expected deviation and standard deviation in the function, respectively, and D represents the curve fitted to the travel data; The probability density function of a vehicle is approximated using a gamma distribution: Where, α t With β t Let be the gamma distribution parameters at time t. Let be the battery capacity of the p-th vehicle of type i at time t, where t is the specific time period and p represents the vehicle number.

4. The charging station pricing method considering the maximum carrying capacity of the distribution network as described in claim 3, characterized in that: The electric vehicle's route planning model includes calculating the OD matrix using traffic flow, constructing a travel matrix by analyzing the traffic flow of road segments, and updating the OD matrix in real time using different traffic conditions. Let X ij N represents the traffic volume between two traffic nodes. v R represents the traffic flow in the road segment. v This represents the probability of traffic passing through the road segment; Where Ψ represents the number of road segments in the selected area, O represents the number of nodes, v represents, and Ψ represents; The Dijkstra algorithm is used to gradually determine the length of the shortest path until the target node is found.

5. The charging station pricing method considering the maximum carrying capacity of the distribution network as described in claim 4, characterized in that: The spatiotemporal distribution of electric vehicle charging load includes analyzing the start time of different electric vehicle categories using the probability density function obtained from the vehicle model of the electric vehicle, and determining the start time and return time of each vehicle. The OD matrix is ​​used to determine the starting point and destination of the trip; Based on the structure of the road network, Dijkstra's algorithm is used to plan the shortest path, and the road resistance model is used to determine the required travel time. Based on the electric vehicle model, the energy consumption model is used to update the vehicle's battery level after the vehicle arrives at its destination and set this destination as the starting point for the next trip. By repeatedly sampling the OD probability matrix of the vehicle at different time periods in Monte Carlo, the driving trajectory of the electric vehicle for a whole day and 24 hours is simulated. During the journey, according to the electric vehicle charging model, if the vehicle's remaining battery power does not support its journey to the next destination, it will select the nearest charging station to charge and record the resulting charging load, thereby capturing the charging behavior and impact at different locations and times.

6. A charging station pricing system that considers the maximum carrying capacity of the distribution network and employs the method described in any one of claims 1-5, characterized in that: The simulation unit constructs a dynamic urban traffic network model based on the actual urban traffic conditions and simulates urban traffic information. The modeling unit constructs a vehicle model of an electric vehicle, modeling the vehicle type, travel time, and battery capacity. The path planning unit constructs a path planning model for electric vehicles, uses the OD matrix to determine the starting point and destination of the electric vehicles, and uses Dijkstra's algorithm to construct the vehicle's travel route. The calculation unit calculates the spatiotemporal distribution of electric vehicle charging load based on the urban dynamic traffic network model and the electric vehicle path planning model. The evaluation unit uses the maximum load that the distribution network can carry as the objective function to dynamically evaluate the value of energy.

7. A computer device, comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-5.

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

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