Charging station pricing method and system considering maximum bearing capacity of power distribution network

Abstract

The invention discloses a charging station pricing method and system considering the maximum bearing capacity of a power distribution network. The method comprises the following steps: carrying out space-time distribution prediction on the charging load of an electric vehicle; and determining a starting place and a destination of the electric vehicle by using the OD matrix, and constructing a vehicle passing route by using a Dijkstra algorithm. And according to the road network model and the path planning model, calculating the charging load space-time distribution condition of the electric vehicle. And finally, designing an electric vehicle dynamic pricing scheme. And a dynamic electricity price scheme is designed by taking the maximum load capacity borne by the power distribution network as an objective function so as to realize ordered charging of the electric vehicle, improve the charging efficiency and guarantee safe operation of the power grid. The method fully considers various complex conditions of a traffic network under actual conditions, accurately simulates the driving path of the electric vehicle, prices the electricity price of the charging station under the condition that the bearable load of a power grid is maximum, and has certain reference significance for economic and safe operation of the power distribution network and planning of a traffic system.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid assessment, and in particular to a charging station pricing method and system that considers the maximum carrying capacity of a distribution network. Background Art

[0002] New energy vehicles such as electric vehicles have shown great potential in responding to the growing energy demand and the increasingly serious greenhouse effect, and have therefore received much attention from countries around the world in recent years. The charging load of electric vehicles shows specific spatiotemporal distribution characteristics, which are significantly affected by the layout of urban road networks and user travel behavior, and have a decisive effect on the efficiency of distribution networks and the planning of charging infrastructure. At the same time, limited by the layout of existing urban charging infrastructure, the collective charging demand of electric vehicles is difficult to meet, which further highlights the need for effective charging management and regulation strategies. Effectively guiding and controlling charging behavior can reduce the negative impact of centralized charging on the power system while ensuring that user needs are met, and further improve the operating efficiency of charging stations. Therefore, there is an urgent need for a charging station pricing strategy that takes into account the maximum carrying capacity of the distribution network, improves the carrying capacity of the power grid through reasonable prices, and thus better meets the electricity needs of car 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 the present invention is that the existing charging station pricing calculation method has optimization problems such as large power consumption and inaccurate pricing.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a charging station pricing method considering the maximum carrying capacity of the distribution network, comprising:

[0006] According to the actual situation of urban traffic, a dynamic urban traffic network model is constructed to simulate urban traffic information;

[0007] Build a vehicle model for electric vehicles, and model the vehicle type, travel time, and battery capacity of electric vehicles;

[0008] Construct a path planning model for electric vehicles, use the OD matrix to determine the starting point and destination of the electric vehicle, and use the Dijkstra algorithm to construct the vehicle route;

[0009] Calculating the spatiotemporal distribution of the electric vehicle charging load according to the urban dynamic traffic network model and the electric vehicle path planning model;

[0010] The energy value is dynamically evaluated by taking the maximum load that the distribution network can bear as the objective function.

[0011] As a preferred solution of the charging station pricing method considering the maximum carrying capacity of the distribution network described in the present invention, wherein: the urban dynamic transportation network model includes, through a ring grid layout;

[0012] The topological characteristics of the road network and its internal correlation are explained by using graph theory methods; the mathematical model of the road network topology is shown below:

[0013]

[0014] Among them, G is the traffic network; N is the node in the network; L is the road section in the network; H represents the set of time periods divided in a day; W is the set of impedances formed by road congestion and node waiting time; n is the specific total number of traffic network nodes in the region; i and j are the specific node numbers in the region; h is the specific time period; w is the road impedance between node i and node j in time period k;

[0015] The adjacency matrix Z is used to represent the road impedance of the road network topology, where the element Z of the matrix Z is ij The assignment follows:

[0016]

[0017] Among them, inf represents the node n i With n j The room is impassable;

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

[0019]

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

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

[0022]

[0023] Among them, commuter private cars are set to 1, taxi types are set to 2, and other shared cars are set to 3;

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

[0025]

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

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

[0028]

[0029]

[0030] Among them, α t With β t is the gamma distribution parameter at time t, is the battery capacity of the pth vehicle of the i-th category at time t, where t is the specific time period and p is the vehicle number.

[0031] As a preferred solution of the charging station pricing method considering the maximum carrying capacity of the distribution network described in the present invention, wherein: the path planning model of the electric vehicle includes calculating the OD matrix using the traffic flow, constructing the travel matrix by analyzing the traffic flow of the road section, and updating the OD matrix in real time using different traffic conditions;

[0032] Let X ij Represents the vehicle traffic volume between two traffic nodes, N v is the flow rate in the road section, R v It represents the travel probability of traffic passing through the road section;

[0033]

[0034] Among them, Ψ is the number of road sections in the selected area, O is 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 solution of the charging station pricing method considering the maximum carrying capacity of the distribution network described in the present invention, wherein: the spatiotemporal distribution of the electric vehicle charging load includes, through the probability density function obtained by the vehicle model of the electric vehicle, analyzing the starting travel time of different electric vehicle categories, and determining the starting travel time and return time of each vehicle;

[0037] Determine the starting point and destination of the trip using the OD matrix;

[0038] According to the structure of the road network, the Dijkstra algorithm is used to plan the shortest path, and the road resistance model is used to determine the required travel time;

[0039] According to the electric vehicle model, through the energy consumption model, after the vehicle arrives at the destination, the vehicle power is updated, and this destination is set as the starting point of the next trip. The OD probability matrix of the vehicle at different time periods is repeatedly sampled by Monte Carlo to simulate the driving trajectory of the electric vehicle for 24 hours a day;

[0040] During driving, according to the electric vehicle charging model, if the remaining power of the vehicle does not support it to reach the next destination, it will choose to charge at the nearest charging station and record the generated charging load, thereby capturing the behavior and impact of charging at different locations and times.

[0041] As a preferred solution of the charging station pricing method considering the maximum carrying capacity of the distribution network described in the present invention, the objective function includes calculating the threshold value of the charging load that can be connected to each charging station with the maximum charging load connected to the distribution network as the goal;

[0042]

[0043] in, It represents the maximum value of the charging load of the grid connected to the node numbered k among all n nodes in the complete t period;

[0044] By combining the dynamic road network model with the Dijkstra algorithm, the power status and driving position of each electric vehicle in the road network are obtained;

[0045] When the electric vehicle's charge level is lower than expected, the electric vehicle goes to the nearest charging station to charge;

[0046] After calculating the charging load of the corresponding node of 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 represents the active power and reactive power of the distributed generation at node i in time period t; P Li,t and Q Li,t represents the active load and reactive load at node i in time period t; P ij,t Q ij,t represents the active power and reactive power of line ij in time period t; l ij 、r ij and x ij They represent the square of the current, resistance and inductance of the ij line respectively; v j,t and v i,t P represents the square of the voltage at node i in time period t; CARi,t When node i is selected as a charging station, P CARi,t The value of is the load size of the electric vehicle during this period; when the i node is not a charging station, P CARi,t =0.

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

[0053]

[0054] In the formula, ζ n is the normal charging electricity price. When the fast charging load of charging station k and the basic load of the node are superimposed on the load threshold of the current period, the charging electricity price is increased; η is the electricity price incentive coefficient; represents the charging load of the kth node connected to the grid in time period t; It represents the maximum value of the charging load of the grid connected to the jth node in time period t.

[0055] A charging station pricing system that takes into account the maximum carrying capacity of the distribution network and adopts the method described in the present invention is characterized by:

[0056] The simulation unit builds a city dynamic traffic network model based on the actual situation of urban traffic and simulates urban traffic information;

[0057] A modeling unit, which constructs a vehicle model of an electric vehicle and models the vehicle type, travel time, and battery capacity of the electric vehicle;

[0058] The path planning unit builds the path planning model of the electric vehicle, uses the OD matrix to determine the starting point and destination of the electric vehicle, and uses the Dijkstra algorithm to build the vehicle route;

[0059] A calculation unit, which calculates the spatiotemporal distribution of the electric vehicle charging load according to the urban dynamic traffic network model and the path planning model of the electric vehicle;

[0060] The evaluation unit dynamically evaluates the energy value by taking the maximum load that the distribution network can bear as the objective function.

[0061] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein: the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0062] A computer-readable storage medium stores a computer program, wherein: when the computer program is executed by a processor, the steps of any one of the methods of the present invention are implemented.

[0063] Beneficial effects of the present invention: The charging station pricing method provided by the present invention that takes into account the maximum carrying capacity of the distribution network fully considers the various complex situations of the transportation network in actual situations, and accurately simulates the driving path of electric vehicles, so as to carry out charging station electricity price pricing when the power grid can carry the maximum load, which has a certain reference significance for the economic and safe operation of the distribution network and the planning of the transportation system. The introduction of the dynamic electricity price model can not only improve the operating income and charging efficiency of the charging station itself, but also encourage users to adjust their charging time and charging location according to the changes in electricity prices, thereby promoting the safe operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0065] Figure 1 An overall flow chart of a charging station pricing method considering the maximum carrying capacity of a distribution network provided in the first embodiment of the present invention;

[0066] Figure 2 A schematic diagram of a road network coupling structure in a charging station pricing method considering the maximum carrying capacity of a distribution network provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0067] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0068] Example 1, reference Figure 1 , 2 , which is an embodiment of the present invention, provides a charging station pricing method considering the maximum carrying capacity of the distribution network, including:

[0069] S1: Based on the actual situation of urban traffic, a dynamic urban traffic network model is constructed to simulate urban traffic information.

[0070] The urban road system mainly includes three structural forms: grid network, radial network and ring network. The grid layout is suitable for towns with flat terrain and relatively common structure. The design of its nodes and roads is conducive to efficient traffic and land use. The radial network extends radially in a limited space. It is especially suitable for cities with compact and highly recognizable central areas, which is conducive to concentrating traffic to surrounding areas. For cities in my country that are still under construction, the ring network architecture is highly favored because it meets the current road traffic needs. In view of the city scale factor, the ring grid layout was selected when constructing the model, and the topological characteristics of the road network and its inherent correlation were explained by using graph theory methods. The mathematical model of road network topology is shown below:

[0071]

[0072] Among them, G is the traffic network; N is the node in the network; L is the road section in the network; H represents the set of time periods divided in a day; W is the set of impedances formed by road congestion and node waiting time; n is the specific total number of traffic network nodes in the region; i and j are the specific node numbers in the region; h is the specific time period; w is the road impedance between node i and node j in time period k;

[0073] The adjacency matrix Z is used to represent the road impedance of the road network topology, where the element Z of the matrix Z is ij The assignment follows:

[0074]

[0075] Among them, inf represents the node n i With n j The room is impassable.

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

[0077]

[0078] For any electric vehicle i, in order to characterize the dynamic driving trajectory of its travel, the starting point and destination are first assigned to it through the OD analysis method, and then the initial time and return time are assigned to it; then, the real-time Dijkstra algorithm is used to search for the shortest path between the starting and ending points for path guidance, and its objective function is shown below.

[0079]

[0080]

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

[0082] Next, we construct a city road resistance model.

[0083] During travel, most electric car owners tend to pay attention to the driving time of the vehicle, which is closely related to the driving distance and driving speed. Generally speaking, the driving distance is easy to obtain after the route is determined and is a static constant; while the driving speed is closely related to factors such as the road capacity and traffic flow, and is a time-varying factor. Therefore, the speed-flow model is introduced to calculate the driving speed and driving time of electric vehicles. The driving speed of electric vehicles v ij The expression is:

[0084]

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

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

[0087] The vehicle types are modeled and all the introduced electric vehicles are numbered, where i = 1, 2, ..., N, and the corresponding vehicle types are shown in the following formula:

[0088]

[0089] Among them, commuter private cars are set to 1, taxi types are set to 2, and other shared cars are set to 3.

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

[0091]

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

[0093] In the MERGE project database, statistics on the UK electric vehicle market divide electric vehicles into four types based on how they are used and how much they carry. These types include L7e (mostly small electric vehicles), M1 (most cars in daily life are of this type, with the same structure as gasoline vehicles), N1 (large passenger cars that can carry multiple people) and N2 (freight vehicles, generally used for transportation). According to these types, taxis are mainly used to carry passengers, corresponding to the M1 type, while private cars generally include the L7e type of small electric vehicles that can carry two people, which are commonly used by the elderly, and most M1 type vehicles. Public cars may belong to N1, N2 or M1 types. The probability density function of the vehicle is approximated using the gamma distribution:

[0094]

[0095]

[0096] Among them, α t With β t is the gamma distribution parameter at time t, is the battery capacity of the pth vehicle of type i at time t, t is the specific time period, and p is the vehicle number. The parameters of 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 Car 100 10.8，0.8 taxi 120 4.5，6.7 Public Car 72 8.7，3.2

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

[0100] The construction of the OD matrix in the traditional sense relies on extensive traffic surveys, which takes a lot of time and effort. However, with the continuous advancement of intelligent technology, some studies have proposed methods to use traffic flow to estimate the OD matrix. This method constructs a travel matrix by analyzing the traffic flow of the road section, and uses different traffic conditions to update the OD matrix in real time. This method accurately presents the current traffic flow distribution and provides an effective reference for traffic planning and management. Theoretical analysis shows that estimating OD distribution through traffic flow is actually the reverse process of traffic distribution. Among them, X ij Represents the vehicle traffic volume between two traffic nodes, N w is the flow rate in the road section, and R w It represents the probability of traffic flow passing through this section of road. This relationship can be expressed as follows:

[0101]

[0102] Among them, Ψ is the number of road sections in the selected area, O is the number of nodes, v represents, and Ψ represents.

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

[0104] Dijkstra algorithm is used for path planning, and the goal is to minimize the travel time.

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

[0106] The core idea of ​​the algorithm is to gradually determine the length of the shortest path until the target node is found. It is achieved 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 the distances of all other points to infinity (indicating that there is no known path between them initially).

[0108] 2. Set division: During the execution of the algorithm, all nodes are divided into two groups, one is the determined node set, and the other is the undetermined node set.

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

[0110] 4 Repeat step 3: Repeat the above process, select a node with the shortest distance from the undetermined node set, and update the distance from this node to other nodes until all nodes are 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 all nodes have been processed.

[0112] The Dijkstra algorithm is very important in the path planning of electric vehicles because it can effectively calculate the shortest time path or shortest distance path from the current location to the charging station or destination, thereby providing the best driving route for electric vehicles and ensuring that electric vehicle users can reach their destination in the shortest time under the premise of sufficient remaining power.

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

[0114] Based on the different starting travel times of different electric vehicle categories mentioned above, the starting travel time and return time of each vehicle are determined according to their characteristic distribution, and the OD matrix is ​​used to determine the starting point and destination of the trip. This can simulate the travel distribution of electric vehicles and provide a basis for further charging demand and path planning analysis.

[0115] Using the itinerary planning obtained in the previous step, the Dijkstra algorithm is used to plan the shortest path according to the structure of the road network, and the road resistance model is used to determine the required travel time. According to the electric vehicle model, through the energy consumption model, after the vehicle arrives at the destination, its power is updated, and this destination is set as the starting point for the next trip. By repeatedly sampling the OD probability matrix of the vehicle at different time periods through Monte Carlo, the driving trajectory of the electric vehicle for 24 hours a day can be simulated.

[0116] During driving, according to the electric vehicle charging model, if the remaining power of the vehicle does not support it to reach the next destination, it will choose to charge at the nearest charging station and record the generated charging load to capture the behavior and impact of charging at different locations and times.

[0117] like Figure 2 This is an example of modeling road network coupling. 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 of the power grid and road network nodes. The power grid and transportation network are connected to each other through the charging load of the charging station.

[0118] S5: Taking the maximum load that the distribution network can bear as the objective function, the energy value is dynamically evaluated.

[0119] Since the charging load threshold that can be connected to the charging station is not only limited by the charging capacity of the charging station itself, but also constrained by the node voltage of the power grid and the thermal stability limit of the line. In order to evaluate the satisfaction of the charging demand of each charging station, this paper calculates the charging load threshold that can be connected to each charging station based on the Distflow power flow model of the distribution network, taking into account the safety constraints of the node voltage and the thermal stability limit of the line, with the maximum charging load connected to the distribution network as the goal.

[0120]

[0121] in, It represents the maximum value of the charging load of the grid connected to the node numbered k among all n nodes in the complete t period.

[0122] By combining the dynamic road network model with the Dijkstra algorithm, the power status and driving position of each electric vehicle in the road network can be obtained. When the power of an electric vehicle is lower than the owner's expectations, the electric vehicle needs to go to the nearest charging station to charge. After calculating the charging load of the corresponding node of 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 represents the active power and reactive power of the distributed generation at node i in time period t; P Li,t and Q Li,t represents the active load and reactive load at node i in time period t; P ij,t Q ij,t represents the active power and reactive power of line ij in time period t; l ij 、r ij and x ij They represent the square of the current, resistance and inductance of the ij line respectively; v j,t and v i,t P represents the square of the voltage at node i in time period t; CARi,t When node i is selected as a charging station, P CARi,t The value of is the load size of the electric vehicle during this period; when the i node is not a charging station, P CARi,t =0.

[0128] At time t, for each electric vehicle user and charging station, the charging electricity price ζ of charging station k k,t The calculation of is shown in the following formula. n represents the normal charging electricity price without any change in the regulation strategy, P ch is the charging load in charging station k at time t. The dynamic electricity price demand function used is shown in the following formula:

[0129]

[0130] In the formula, ζ n is the normal charging electricity price. When the superposition of the fast charging load of the charging station k and the basic load of the node is greater than the load threshold of the current period, the charging electricity price is increased; η is the electricity price incentive coefficient, and its value range is 1 to 1.5. represents the charging load of the kth node connected to the grid in time period t; It represents the maximum value of the charging load of the grid connected to the jth node in time period t.

[0131] The normal charging price is the basic price of the charging station when there is no electricity price stimulus, which is usually a fixed value. If the charging load at a certain moment is large and exceeds the threshold of the charging station, the dynamic charging price model can be used to calculate the actual charging price determined by the basic price and the excess value. This price is the selling price of the charging station. The purchase price of the charging station is not considered in this article because it can use the purchase price of electricity from the distribution network at different times to adjust its own charging price. The introduction of the dynamic price model can not only improve the operating income and charging efficiency of the charging station, but also encourage 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 in the form of 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 the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0133] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0134] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0135] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0136] Embodiment 2 is an embodiment of the present invention, which provides a charging station pricing system considering the maximum carrying capacity of the distribution network, and includes:

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

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

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

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

[0141] The evaluation unit dynamically evaluates the energy value by taking the maximum load that the distribution network can bear as the objective function.

[0142] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A charging station pricing method considering the maximum carrying capacity of the distribution network, characterized in that: include: According to the actual situation of urban traffic, a dynamic urban traffic network model is constructed to simulate urban traffic information; Build a vehicle model for electric vehicles, and model the vehicle type, travel time, and battery capacity of electric vehicles; Construct a path planning model for electric vehicles, use the OD matrix to determine the starting point and destination of the electric vehicle, and use the Dijkstra algorithm to construct the vehicle route; Calculating the spatiotemporal distribution of the electric vehicle charging load according to the urban dynamic traffic network model and the electric vehicle path planning model; The energy value is dynamically evaluated by taking the maximum load that the distribution network can bear as the objective function.

2. The charging station pricing method considering the maximum carrying capacity of the distribution network as claimed in claim 1, characterized in that: The urban dynamic transportation network model includes, through a ring grid layout; The topological characteristics of the road network and its internal correlation are explained by using graph theory methods; the mathematical model of the road network topology is shown below: Among them, G is the traffic network; N is the node in the network; L is the road section in the network; H represents the set of time periods divided in a day; W is the set of impedances formed by road congestion and node waiting time; n is the specific total number of traffic network nodes in the region; i and j are the specific node numbers in the region; h is the specific time period; w is the road impedance between node i and node j in time period k; The adjacency matrix Z is used to represent the road impedance of the road network topology, where the element Z of the matrix Z is ij The assignment follows: Among them, inf represents the node n i With n j The room is impassable; The speed-flow model is introduced to calculate the driving speed and driving time of electric vehicles. The driving speed v i The expression of j is: Among them, v ij,max is the maximum speed of road ij; C ij is the traffic capacity of road ij, which is determined by the road grade; Q ij (t) is the electric vehicle flow rate on road ij at time t; Q ij (t) and C ij The ratio is the saturation of the road section at time t; w is the intermediate parameter in the calculation; a, b, 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 claimed in claim 2, characterized in that: The vehicle model of the electric vehicle includes numbering all the introduced electric vehicles, where i=1, 2, ..., N, and each vehicle type corresponds to: Among them, commuter private cars are set to 1, taxi types are set to 2, and other shared cars are set to 3; The probability density function of daily mileage is obtained by fitting historical data: Where s is the number of kilometers driven by the electric vehicle per day, μ D and σ D are the expected deviation and standard deviation in the function, respectively, and D represents the curve fitted by the travel data; The probability density function of the vehicle is approximated using a gamma distribution: Among them, α t With β t is the gamma distribution parameter at time t, is the battery capacity of the pth vehicle of the i-th category at time t, where t is the specific time period and p is the vehicle number.

4. The charging station pricing method considering the maximum carrying capacity of the distribution network as claimed in claim 3, characterized in that: The path planning model of the electric vehicle includes calculating the OD matrix using the traffic flow, constructing a travel matrix by analyzing the traffic flow of the road section, and updating the OD matrix in real time using different traffic conditions; Let X ij Represents the vehicle traffic volume between two traffic nodes, N v is the flow rate in the road section, R v It represents the travel probability of traffic passing through the road section; Among them, Ψ is the number of road sections in the selected area, O is 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 claimed in claim 4, characterized in that: The spatiotemporal distribution of the electric vehicle charging load includes analyzing the start travel time of different electric vehicle categories through the probability density function obtained by the vehicle model of the electric vehicle to determine the start travel time and return time of each vehicle; Determine the starting point and destination of the trip using the OD matrix; According to the structure of the road network, the Dijkstra algorithm is used to plan the shortest path, and the road resistance model is used to determine the required travel time; According to the electric vehicle model, through the energy consumption model, after the vehicle arrives at the destination, the vehicle power is updated, and this destination is set as the starting point of the next trip. The OD probability matrix of the vehicle at different time periods is repeatedly sampled by Monte Carlo to simulate the driving trajectory of the electric vehicle for 24 hours a day; During driving, according to the electric vehicle charging model, if the remaining power of the vehicle does not support it to reach the next destination, it will choose to charge at the nearest charging station and record the generated charging load, thereby capturing the behavior and impact of charging at different locations and times.

6. The charging station pricing method considering the maximum carrying capacity of the distribution network as claimed in claim 5, characterized in that: The objective function includes calculating the threshold of charging load that can be connected to each charging station with the maximum charging load connected to the distribution network as the goal; in, It represents the maximum value of the charging load of the grid connected to the node numbered k among all n nodes in the complete t period; By combining the dynamic road network model with the Dijkstra algorithm, the power status and driving position of each electric vehicle in the road network are obtained; When the electric vehicle's charge level is lower than expected, the electric vehicle goes to the nearest charging station to charge; After calculating the charging load of the corresponding node of 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 represents the active power and reactive power of the distributed generation at node i in time period t; P Li,t and Q Li,t represents the active load and reactive load at node i in time period t; P ij,t Q ij,t represents the active power and reactive power of line ij in time period t; l ij 、r ij and x ij They represent the square of the current, resistance and inductance of the ij line respectively; v j,t and v i,t P represents the square of the voltage at node i in time period t; CARi,t When node i is selected as a charging station, P CARi,t The value of is the load size of the electric vehicle during this period; when the i node is not a charging station, P CARi,t =0.

7. The charging station pricing method considering the maximum carrying capacity of the distribution network as claimed in claim 6, characterized in that: The dynamic evaluation of energy value includes, at time t, for each electric vehicle user and charging station, the charging electricity price ζ of charging station k k,t The calculation uses the dynamic electricity price demand function: In the formula, ζ n is the normal charging electricity price. When the fast charging load of charging station k and the basic load of the node are superimposed on the load threshold of the current period, the charging electricity price is increased; η is the electricity price incentive coefficient; represents the charging load of the kth node connected to the grid in time period t; It represents the maximum value of the charging load of the grid connected to the jth node in time period t.

8. A charging station pricing system that takes into account the maximum carrying capacity of the distribution network using the method as described in any one of claims 1 to 7, characterized in that: The simulation unit builds a city dynamic traffic network model based on the actual situation of urban traffic and simulates urban traffic information; A modeling unit, which constructs a vehicle model of an electric vehicle and models the vehicle type, travel time, and battery capacity of the electric vehicle; The path planning unit builds the path planning model of the electric vehicle, uses the OD matrix to determine the starting point and destination of the electric vehicle, and uses the Dijkstra algorithm to build the vehicle route; A calculation unit, which calculates the spatiotemporal distribution of the electric vehicle charging load according to the urban dynamic traffic network model and the path planning model of the electric vehicle; The evaluation unit dynamically evaluates the energy value by taking the maximum load that the distribution network can bear as the objective function.

9. A computer device comprising: A memory and a processor; the memory stores a computer program, wherein the processor implements the steps of any method as claimed in claim 1 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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