Charging station site selection method and system based on economic operation of power distribution network

Abstract

The invention discloses a charging station site selection method and system based on economic operation of a power distribution network. The method comprises the following steps: establishing a dynamic traffic network model based on travel information of an electric vehicle; modeling the traffic flow running speed in the dynamic traffic network model, and calculating the running speed of the electric vehicle in each time period; calculating the charging load of the electric vehicle at each traffic node according to the charging demand parameter of each traffic node in combination with the distribution condition and the position information of the charging stations; and based on the charging load of the electric vehicle in the power distribution network, carrying out power flow optimization by taking the minimization of the cost of purchasing electricity from the power grid as a target, and selecting an optimal charging station position. According to the invention, the accuracy of electric vehicle driving path simulation is improved, and the load prediction is improved. By optimizing the position of the charging station, the active power consumption of the power distribution network is reduced, the energy is saved, and the cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system distribution network planning, and in particular to a charging station site selection method and system based on the economic operation of the distribution network. Background Art

[0002] As a large-capacity, random load, large-scale, unplanned charging of electric vehicles may cause a variety of problems in the distribution network, such as power congestion, voltage anomalies, and increased economic losses. Reasonable planning of electric vehicle charging stations can effectively manage the distribution of charging loads and play a positive role in the economic operation of the distribution network. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a charging station site selection method and system based on economic operation of distribution network to solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for selecting a charging station site based on economic operation of a distribution network, comprising: establishing a dynamic traffic network model based on travel information of electric vehicles;

[0008] Modeling the vehicle flow speed in the dynamic traffic network model and calculating the driving speed of electric vehicles in each time period;

[0009] Combined with the distribution and location information of charging stations, the charging load of electric vehicles at each traffic node is calculated according to the charging demand parameters of each traffic node;

[0010] Based on the charging load of electric vehicles in the distribution network, power flow optimization is performed with the goal of minimizing the cost of purchasing electricity from the power grid, and the optimal location of the charging station is selected.

[0011] As a preferred solution of the charging station site selection method based on the economic operation of the distribution network described in the present invention, wherein: based on the travel information of electric vehicles, establishing a dynamic traffic network model includes: updating the traffic flow according to the change of the travel time of electric vehicles; the dynamic traffic network model is expressed as:

[0012]

[0013] Among them, G is the traffic network set, V is the set of all nodes in the network, E is the set of road sections in the network, H is the set of time periods, K is the weight of the road section set, and v i is the i-th node in the network, v ij is the road segment connecting the i-th node and the j-th node, k ij is the road section v in the tth time period ij The weight of .

[0014] As a preferred solution of the charging station site selection method based on the economic operation of the distribution network described in the present invention, wherein: calculating the driving speed of the electric vehicle in each time period includes: introducing a speed-flow model to calculate the driving speed and driving time of the electric vehicle, the driving speed v of the electric vehicle ij for:

[0015]

[0016] Among them, v ij,max is the maximum speed of road ij, C ij is the traffic capacity of road ij, 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, and a, b, γ are the adaptive coefficients of the road.

[0017] As a preferred solution of the charging station site selection method based on economic operation of the distribution network described in the present invention, the charging demand parameters include: charging request quantity and vehicle parking time.

[0018] As a preferred solution of the charging station site selection method based on the economic operation of the distribution network described in the present invention, wherein: based on the charging load of electric vehicles in the distribution network, the power flow optimization is performed with the goal of minimizing the cost of purchasing electricity from the power grid, and the optimal charging station location is selected, including: based on the charging load of the corresponding node of the charging station, the objective function and power flow distribution of the distribution network are set as:

[0019]

[0020]

[0021]

[0022]

[0023] Among them, P DGi,t and Q DGi,tThey represent the active power and reactive power of the distributed generation at node i in time period t, respectively. Li,t and Q Li,t They represent the active load and reactive load at node i in time period t, respectively, ij,t and Q ij,t They represent the active power and reactive power of line ij in time period t respectively; ij 、r ij and x ij They represent the square of the current, resistance and inductance of the ij line, respectively, and v j,t represents the square of the voltage at node j during time period t, v i,t P represents the square of the voltage at node i during 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.

[0024] As a preferred solution of the charging station site selection method based on the economic operation of the distribution network described in the present invention, it also includes: establishing the voltage in the distribution network, the flow direction constraints and the constraints of the charging station charging; calculating the economic efficiency of the distribution network for different charging station locations through the established objective function, and selecting the optimal charging station location; the objective function is expressed as:

[0025]

[0026] Among them, c t is the electricity price at time t, P 12,t is the injected power flow of the first node at time t.

[0027] As a preferred solution of the charging station site selection method based on the economic operation of the distribution network described in the present invention, it also includes:

[0028] The node voltage constraint is:

[0029] V n,min ≤V n,real ≤V n,max

[0030] Among them, V n,min is the minimum voltage at node n, V n,max is the maximum voltage at node n, V n,real is the actual voltage at node n;

[0031] The flow direction of the power flow is from the head end to the end of the distribution network, and there is only one generator connected to the head end in the distribution network. The flow direction of the power flow is expressed as:

[0032]

[0033] The constraints of charging at the charging station are expressed as:

[0034]

[0035] in, They are respectively charging station node i at t 1 ,t 2 Time is the amount of electricity charged in the electric vehicle, t 1 For the first half hour of charging time for electric vehicles, t 2 For the second half hour of charging time for electric vehicles, E min and E max are the minimum and maximum charge capacities, E soc This is the initial capacity when charging begins.

[0036] In a second aspect, the present invention provides a charging station site selection system based on economic operation of a distribution network, comprising: a model building module for establishing a dynamic traffic network model based on travel information of electric vehicles;

[0037] A vehicle flow speed calculation module is used to model the vehicle flow speed in the dynamic traffic network model and calculate the electric vehicle speed in each time period;

[0038] The charging load calculation module is used to calculate the charging load of electric vehicles at each traffic node according to the charging demand parameters of each traffic node in combination with the distribution and location information of the charging stations;

[0039] The optimization module is used to optimize the power flow based on the charging load of the electric vehicles in the distribution network with the goal of minimizing the cost of purchasing electricity from the power grid, and select the optimal location of the charging station.

[0040] In a third aspect, the present invention provides a computing device, comprising:

[0041] Memory and processor;

[0042] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the charging station site selection method based on the economic operation of the distribution network are implemented.

[0043] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the charging station site selection method based on the economic operation of the distribution network.

[0044] Compared with the prior art, the present invention has the following beneficial effects: the present invention takes into account the changes in the traffic network at different times of the day, making the simulation of the driving route of electric vehicles more accurate; plans the route of electric vehicles in the shortest time based on actual conditions, making the load forecasting results closer to reality; selects locations with less active power consumption in the distribution network, and operates in the most economical way of the power grid, saving energy and reducing operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0046] Figure 1 A method flow chart of a charging station site selection method and system based on economic operation of a distribution network according to an embodiment of the present invention;

[0047] Figure 2 A schematic diagram of the traffic network structure of a charging station site selection method and system based on economic operation of a distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] 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.

[0049] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0050] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0051] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0052] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0053] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0054] Example 1

[0055] Reference Figure 1-2 , is an embodiment of the present invention, which provides a method for selecting a charging station site based on economic operation of a distribution network, including:

[0056] S100: Establish a dynamic traffic network model based on the travel information of electric vehicles;

[0057] S200: Modeling the vehicle flow speed in the dynamic traffic network model and calculating the driving speed of electric vehicles in each time period;

[0058] S300: Calculate the charging load of electric vehicles at each traffic node according to the charging demand parameters of each traffic node in combination with the distribution and location information of the charging stations;

[0059] S400: Based on the charging load of electric vehicles in the distribution network, power flow optimization is performed with the goal of minimizing the cost of purchasing electricity from the power grid, and the optimal charging station location is selected.

[0060] It should be noted that since large-scale, unplanned charging of electric vehicles can lead to power congestion, voltage anomalies, and increased economic losses in the distribution network, this application combines the owner's car usage habits, vehicle types, and route selection to simulate the driving routes of electric vehicles, and then predict the charging behavior of electric vehicles. Combined with the load of the power grid and the electricity prices in different time periods, the most suitable location of the charging station is selected in combination with the economic operation of the distribution network. Further considering the various complex situations of the transportation network in actual situations, the driving routes of electric vehicles are accurately simulated to predict the spatial load distribution of electric vehicles that is more in line with the actual distribution, which has a certain reference significance for the economic operation of the distribution network and the planning of the transportation system.

[0061] In the embodiment of the present application, based on the travel information of electric vehicles, establishing a dynamic traffic network model includes: updating the traffic flow according to the change of the travel time of electric vehicles; the dynamic traffic network model is expressed as:

[0062]

[0063] Among them, G is the traffic network set, V is the set of all nodes in the network, E is the set of road sections in the network, H is the set of time periods, K is the weight of the road section set, and v i is the i-th node in the network, v ij is the road segment connecting the i-th node and the j-th node, k ij is the road section v in the tth time period ij The weight of .

[0064] Specifically, the connections between nodes in the traffic network set G are described by the adjacency matrix D. The element d ij It is given by the following formula:

[0065]

[0066] In an optional embodiment, a day is divided into 48 time periods at half-hour intervals, and the traffic flow is updated according to time changes.

[0067] In an optional embodiment, the daily mileage and travel probability distribution curve of the electric vehicle are established based on the travel situation of the electric vehicle, wherein the daily mileage reflects the power consumption of the electric vehicle and also affects the prediction of the destination; the probability density function of the daily mileage can be obtained by fitting historical data:

[0068]

[0069] Among them, s is the number of kilometers traveled by the electric vehicle per day, μ D and σ D are the expected deviation and standard deviation in the function, respectively.

[0070] For example, since the travel of electric online ride-hailing vehicles is mainly concentrated in the daytime, the period with the largest usage is 8:00-21:00. Therefore, the peak travel time is set between 6:00-8:00. The distribution probability function of electric vehicle travel time is expressed as:

[0071]

[0072] Where T is the parking time of electric online taxis, μ is the mean, σ is the standard deviation, and λ is the parking time of electric online taxis. 1 =0.389,α 1 =7.046, β 1 =1.086,λ 2 =0.066, α 2 =15.610,β 2 =9.667.

[0073] It should be noted that 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.

[0074] In the embodiment of the present application, calculating the driving speed of the electric vehicle in each time period includes: introducing a speed-flow model to calculate the driving speed and driving time of the electric vehicle, the driving speed v of the electric vehicle ij for:

[0075]

[0076] Among them, v ij,max is the maximum speed of road ij, C ij is the traffic capacity of road ij, 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, and a, b, γ are the adaptive coefficients of the road.

[0077] It should be noted that the road capacity and adaptability coefficient are determined by the road grade.

[0078] In the embodiment of the present application, the charging demand parameters include: charging request amount and vehicle parking time.

[0079] In the embodiment of the present application, based on the charging load of electric vehicles in the distribution network, the power flow optimization is performed with the goal of minimizing the cost of purchasing electricity from the power grid, and the optimal charging station location is selected, including: based on the charging load of the corresponding node of the charging station, the objective function and power flow distribution of the distribution network are set as:

[0080]

[0081]

[0082]

[0083]

[0084] Among them, P DGi,t and Q DGi,t They represent the active power and reactive power of the distributed generation at node i in time period t, respectively. Li,t and Q Li,t They represent the active load and reactive load at node i in time period t, respectively, ij,t and Q ij,t They represent the active power and reactive power of line ij in time period t respectively; ij 、r ij and x ij They represent the square of the current, resistance and inductance of the ij line, respectively, and v j,t represents the square of the voltage at node j during time period t, v i,t P represents the square of the voltage at node i during 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.

[0085] In an optional embodiment, by combining the dynamic road network model with the Floyd algorithm, the power status and driving position of each electric vehicle in the road network can be obtained. When the power of the electric vehicle is lower than the owner's expectation, that is, when the power is lower than 20%, the electric vehicle chooses to charge and needs to go to the nearest charging station to charge.

[0086] In the embodiment of the present application, it also includes: establishing the voltage in the distribution network, the flow direction constraints and the constraints of the charging station charging; calculating the economic efficiency of the distribution network for different charging station locations through the established objective function, and selecting the optimal charging station location; the objective function is expressed as:

[0087]

[0088] Among them, c t is the electricity price at time t, P12,t is the injected power flow of the first node at time t.

[0089] In the embodiment of the present application, the node voltage constraint is further included as follows:

[0090] V n,min ≤V n,real ≤V n,max

[0091] Among them, V n,min is the minimum voltage at node n, V n,max is the maximum voltage at node n, V n,real is the actual voltage at node n;

[0092] The power flow direction is from the head end to the end of the distribution network, and there is only one generator connected to the head end in the distribution network. The power flow direction is expressed as:

[0093]

[0094] The constraints of charging at charging stations are expressed as:

[0095]

[0096] in, They are respectively charging station node i at t 1 ,t 2 Time is the amount of electricity charged in the electric vehicle, t 1 For the first half hour of charging time for electric vehicles, t 2 For the second half hour of charging time for electric vehicles, E min and E max are the minimum and maximum charge capacities, E soc This is the initial capacity when charging begins.

[0097] In an optional embodiment, considering that the charging time of electric vehicles is one hour, these cars will not set off within this hour. In order to meet the normal operation of electric network taxis, the power at the end of charging is required to be at least 90% of the total storage capacity.

[0098] It should be noted that this application establishes a dynamic traffic network model to accurately simulate the travel path of electric vehicles, and uses the Floyd algorithm to determine the shortest path between two nodes in the road network. Secondly, combined with the transportation model and location of the charging station, the charging load of electric vehicles at each node is calculated according to the charging demand and parking time of each node. Finally, considering the charging load of electric vehicles in the distribution network, the power flow optimization is carried out with the goal of minimizing the cost of purchasing electricity from the power grid.

[0099] It should also be noted that this application makes the simulation of the driving path of electric vehicles more accurate by considering the changes in the traffic network at different times of the day, which plays a beneficial role in improving the prediction. Using the shortest path as the path planning method, the path of the electric vehicle is planned in the shortest time in combination with the actual situation, so that the load forecast result is closer to reality. This application can select locations where the active power consumption of the distribution network is low, which is very helpful for saving energy. At the same time, it operates in the most economical way of the power grid, saving energy and reducing operating costs.

[0100] Example 2

[0101] The above embodiment is a schematic scheme of a charging station site selection method based on the economic operation of the distribution network. It should be noted that the technical scheme of the charging station site selection system based on the economic operation of the distribution network and the technical scheme of the charging station site selection method based on the economic operation of the distribution network belong to the same concept. The details not described in detail in the technical scheme of the charging station site selection system based on the economic operation of the distribution network in this embodiment can be referred to the description of the technical scheme of the charging station site selection method based on the economic operation of the distribution network.

[0102] In this embodiment, a charging station site selection system based on economic operation of a distribution network includes:

[0103] Model building module, used to build a dynamic transportation network model based on electric vehicle travel information;

[0104] The vehicle flow speed calculation module is used to model the vehicle flow speed in the dynamic traffic network model and calculate the electric vehicle speed in each time period;

[0105] The charging load calculation module is used to calculate the charging load of electric vehicles at each traffic node according to the charging demand parameters of each traffic node in combination with the distribution and location information of the charging stations;

[0106] The optimization module is used to optimize the power flow based on the charging load of electric vehicles in the distribution network, with the goal of minimizing the cost of purchasing electricity from the power grid, and select the optimal charging station location.

[0107] This embodiment further provides a computing device, which is applicable to the case of a charging station site selection method based on economic operation of a distribution network, and includes:

[0108] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the charging station site selection method based on the economic operation of the distribution network as proposed in the above embodiment.

[0109] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for selecting a charging station site based on economic operation of a distribution network as proposed in the above embodiment is implemented.

[0110] The storage medium proposed in this embodiment and the charging station site selection method for realizing economic operation of the distribution network proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0111] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0112] 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 site selection method based on economic operation of distribution network, characterized in that: include: Based on the travel information of electric vehicles, a dynamic transportation network model is established; Modeling the vehicle flow speed in the dynamic traffic network model and calculating the driving speed of electric vehicles in each time period; Combined with the distribution and location information of charging stations, the charging load of electric vehicles at each traffic node is calculated according to the charging demand parameters of each traffic node; Based on the charging load of electric vehicles in the distribution network, power flow optimization is performed with the goal of minimizing the cost of purchasing electricity from the power grid, and the optimal location of the charging station is selected.

2. The method for selecting a charging station site based on economic operation of a distribution network according to claim 1, characterized in that: Based on the travel information of electric vehicles, the establishment of a dynamic traffic network model includes: updating the traffic flow according to the change of electric vehicle travel time; the dynamic traffic network model is expressed as: Among them, G is the traffic network set, V is the set of all nodes in the network, E is the set of road sections in the network, H is the set of time periods, K is the weight of the road section set, and v i is the i-th node in the network, v ij is the road segment connecting the i-th node and the j-th node, k ij is the road section v in the tth time period ij The weight of .

3. The method for selecting a charging station site based on economic operation of a distribution network according to claim 2, characterized in that: Calculating the driving speed of electric vehicles in each period includes: introducing the speed-flow model to calculate the driving speed and driving time of electric vehicles, the driving speed v of electric vehicles ij for: Among them, v ij,max is the maximum speed of road ij, C ij is the traffic capacity of road ij, 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, and a, b, γ are the adaptive coefficients of the road.

4. The method for selecting a charging station site based on economic operation of a distribution network according to claim 3, characterized in that: Charging demand parameters include: charging request amount and vehicle stop time.

5. The method for selecting a charging station site based on economic operation of a distribution network according to claim 4, characterized in that: Based on the charging load of electric vehicles in the distribution network, the power flow optimization is performed with the goal of minimizing the cost of purchasing electricity from the power grid, and the optimal charging station location is selected. Based on the charging load of the corresponding node of the charging station, the objective function and power flow distribution of the distribution network are set as follows: Among them, P DGi,t and Q DGi,t They represent the active power and reactive power of the distributed generation at node i in time period t, respectively. Li,t and Q Li,t They represent the active load and reactive load at node i in time period t, respectively, ij,t and Q ij,t They represent the active power and reactive power of line ij in time period t respectively; ij 、r ij and x ij They represent the square of the current, resistance and inductance of the ij line, respectively, and v j,t represents the square of the voltage at node j during time period t, v i,t P represents the square of the voltage at node i during 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.

6. The method for selecting a charging station site based on economic operation of a distribution network according to claim 5, characterized in that: Also includes: Establish the voltage and power flow constraints in the distribution network and the constraints of charging stations; The economic efficiency of the distribution network is calculated for different charging station locations through the established objective function, and the optimal charging station location is selected; The objective function is expressed as: Among them, c t is the electricity price at time t, P 12,t is the injected power flow of the first node at time t.

7. The method for selecting a charging station site based on economic operation of a distribution network according to claim 6, characterized in that: Also includes: The node voltage constraint is: In n,min ≤V n,real ≤V n,max Among them, V n,min is the minimum voltage at node n, V n,max is the maximum voltage at node n, V n,real is the actual voltage at node n; The flow direction of the power flow is from the head end to the end of the distribution network, and there is only one generator connected to the head end in the distribution network. The flow direction of the power flow is expressed as: The constraints of charging at the charging station are expressed as: in, are the power charged by charging station node i for electric vehicles at time t1 and t2, t1 is the first half hour of charging time for electric vehicles, t2 is the second half hour of charging time for electric vehicles, E min and E max are the minimum and maximum charge capacities, E soc This is the initial capacity when charging begins.

8. A charging station site selection system based on economic operation of distribution network, characterized in that: include: Model building module, used to build a dynamic transportation network model based on electric vehicle travel information; A vehicle flow speed calculation module is used to model the vehicle flow speed in the dynamic traffic network model and calculate the electric vehicle speed in each time period; The charging load calculation module is used to calculate the charging load of electric vehicles at each traffic node according to the charging demand parameters of each traffic node in combination with the distribution and location information of the charging stations; The optimization module is used to optimize the power flow based on the charging load of the electric vehicles in the distribution network with the goal of minimizing the cost of purchasing electricity from the power grid, and select the optimal location of the charging station.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the charging station site selection method based on the economic operation of the distribution network as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for site selection of charging stations based on economic operation of distribution network as described in any one of claims 1 to 7.

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