Electric vehicle charging pricing method oriented to space-time cooperative guidance scene

Abstract

The invention provides an electric vehicle charging pricing method for a space-time cooperative guidance scene, and belongs to the field of electric vehicle scheduling, and the method comprises the steps: obtaining power distribution network line information and node position information of each charging station in a power distribution network topology and a traffic network topology, and taking the information as space coupling information data; acquiring charging demand information of the electric vehicle in one day in the area; establishing an electric vehicle constraint and a power distribution network operation constraint by taking the lowest comprehensive cost as an objective function, and constructing an electric vehicle charging pricing model oriented to a space-time cooperative guidance scene; and solving the model by adopting an optimization algorithm to obtain an electric vehicle charging pricing scheme. Compared with a traditional guiding scene, the electric vehicle charging pricing method oriented to the space-time collaborative guiding scene is more in line with the actual charging decision behavior of the electric vehicle user, and a better charging pricing result can be obtained.

Description

Technical Field

[0001] The present invention belongs to the field of electric vehicle scheduling, and specifically relates to an electric vehicle charging pricing method for a spatiotemporal collaborative guidance scenario. Background Art

[0002] With the rapid development of the electric vehicle industry and a surge in electricity demand, existing chaotic charging models and charging guidance strategies are increasingly unable to adapt to the diverse interests of all parties in this new landscape. However, the existing EV charging pricing mechanism simply adds electricity prices and service fees, failing to account for the impact of price factors on user charging behavior. This makes it difficult to effectively guide the location and time of EV access through pricing, resulting in significant differences in the utilization of different charging piles across space and significant peak charging load issues across time.

[0003] In the current competitive electricity market, electricity prices fluctuate significantly. Traditional time-of-use pricing strategies struggle to keep pace with changing load characteristics. Furthermore, using traditional time-of-use pricing strategies to guide electric vehicles in demand response can easily lead to new charging load peaks during periods of low electricity prices. Therefore, developing dynamic pricing signals that reflect temporal and spatial differences in supply and demand, proactively and accurately guiding user charging behavior, addressing charging resource mismatches and uneven load distribution, and ensuring stable charging network operation are crucial in practical engineering contexts. Summary of the Invention

[0004] Firstly, in response to the deficiencies of the prior art, the present invention aims to provide an electric vehicle charging pricing method for spatiotemporal collaborative guidance scenarios, which solves the technical problems raised by the background technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An electric vehicle charging pricing method for a spatiotemporal coordinated guidance scenario includes the following steps:

[0007] 1) Obtain distribution network line information and node location information of each charging station in the distribution network topology and transportation network topology as spatial coupling information data;

[0008] 2) Obtaining charging demand information of electric vehicles in the region during the day, including the charging start time, charging duration, charging end time, battery power before charging, and location when charging demand is generated, as charging demand information data for electric vehicles;

[0009] 3) Taking spatial coupling information data and electric vehicle charging demand information data as input, and taking the lowest comprehensive cost as the objective function, considering distribution network flow constraints, user station selection constraints, battery status constraints, and charging price upper and lower limits, an electric vehicle charging pricing model for spatiotemporal collaborative guidance scenarios is constructed;

[0010] The comprehensive cost is the sum of network loss cost and electric vehicle user charging fee;

[0011] 4) Solve the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario and obtain the electric vehicle charging price at each charging station in each time period;

[0012] The electric vehicle charging price at each charging station in each time period serves as a pricing scheme for electric vehicle charging in each time period.

[0013] Furthermore, the objective function of the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario is:

[0014]

[0015] Where C represents the comprehensive cost of the model, the first item is the network loss conversion cost, and the second item is the charging fee for electric vehicle users; c l is the unit conversion cost of network loss, T is the total number of time periods in a day, Ω l is the set of all lines in the distribution network, is the active network loss of the lth distribution network line in the tth period, Δt is the unit time period, i is the first section node of the lth distribution network line, j is the first section node of the lth distribution network line, P ij,t is the active power of the lth distribution network line in the tth period, Q ij,t is the reactive power of the lth distribution network line in the tth period, R ij is the resistance of the lth distribution network line, U j is the voltage amplitude of node j; N cr is the number of electric vehicles with charging needs, c n is the charging price of the nth electric vehicle, B is the battery capacity of the electric vehicle, S le,n is the battery state of charge of the nth electric vehicle when it leaves the charging station, S ar,n is the battery state of charge of the nth electric vehicle when it arrives at the charging station.

[0016] Furthermore, the distribution network flow constraint of the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario is:

[0017]

[0018] in, is the generator power of the jth node in the tth period, Ω j is the set of all lines starting with node j, is the active load of the jth node in the tth period, is the charging load of the jth node in the tth period, is the reactive load of the jth node in the tth period, I ij,t is the current of the lth distribution network line in the tth period, Z ij is the impedance of the lth distribution network line, U min is the lower limit of the node voltage amplitude, U max is the upper limit of the node voltage amplitude.

[0019] Furthermore, the user station selection constraints of the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario are as follows:

[0020]

[0021]

[0022] Among them, ia is the nearest charging station to the nth electric vehicle, ib is the charging station with the lowest charging service fee at the tth moment, is the probability that the nth electric vehicle accepts the guidance of the charging station, which can be calculated by the electric vehicle user decision function f. is the random boot probability of the nth electric car, L ib,n is the guidance distance between the nth electric vehicle and the charging station ib, is the difference in charging prices between charging stations ia and ib at the tth moment, N cs is the number of charging stations, u m,n is the selection sign of the nth electric car for charging station m, is the time when the nth electric vehicle arrives at the designated electric vehicle charging station, is the time when the nth electric car leaves the charging station, is the charging flag of the mth electric vehicle in the nth electric vehicle charging station at time t. These variables are all Boolean variables.

[0023] Furthermore, the battery state constraint of the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario is:

[0024]

[0025] Among them, S max is the upper limit of the state of charge level of electric vehicles, S min is the lower limit of the state of charge level of electric vehicles, μ cis the charging efficiency of the charging pile, P c is the charging power of the charging pile.

[0026] Furthermore, the upper and lower limits of the charging price of the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario are as follows:

[0027] c min ≤c n ≤c max

[0028] Among them, c max The upper limit of the charging price for electric vehicles, c min Setting a lower limit on the price of charging electric vehicles.

[0029] Secondly, in response to the deficiencies of the existing technology, the purpose of the present invention is to provide an electric vehicle charging pricing system for spatiotemporal collaborative guidance scenarios, which solves the technical problems raised by the background technology.

[0030] The purpose of the present invention can be achieved through the following technical solutions:

[0031] An electric vehicle charging pricing system for spatiotemporal collaborative guidance scenarios, including:

[0032] A distribution network line information acquisition module is used to obtain distribution network line information and the node location information of each charging station in the distribution network topology and transportation network topology as spatial coupling information data;

[0033] The electric vehicle charging demand information acquisition module is used to obtain the charging start time, charging duration, charging end time, battery power before charging, and charging demand information of the location where the charging demand is generated for electric vehicles in the area during the day as the charging demand information data of the electric vehicles;

[0034] Construct an electric vehicle charging pricing model module for spatiotemporal collaborative guidance scenarios, which uses spatial coupling information data and electric vehicle charging demand information data as input, takes the lowest comprehensive cost as the objective function, and considers distribution network flow constraints, user station selection constraints, battery status constraints, and upper and lower limits of charging prices to construct an electric vehicle charging pricing model for spatiotemporal collaborative guidance scenarios. The comprehensive cost is the sum of network loss costs and electric vehicle user charging fees.

[0035] The model solving module is used to solve the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario, and obtain the electric vehicle charging price of each charging station in each time period; the electric vehicle charging price of each charging station in each time period is used as the pricing plan for electric vehicle charging in each time period.

[0036] Thirdly, in view of the deficiencies in the prior art, the present invention aims to provide a computer storage medium that solves the technical problems raised by the background art.

[0037] The purpose of the present invention can be achieved through the following technical solutions:

[0038] A computer storage medium stores a readable program, characterized in that when the program is run, it can execute an electric vehicle charging pricing method for a spatiotemporal collaborative guidance scenario according to the first aspect.

[0039] Fourthly, in view of the deficiencies in the prior art, the present invention aims to provide an electronic device that solves the technical problems raised by the background art.

[0040] The purpose of the present invention can be achieved through the following technical solutions:

[0041] An electronic device comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0042] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the electric vehicle charging pricing method for a spatiotemporal collaborative guidance scenario in the first aspect.

[0043] Fifthly, in view of the deficiencies in the prior art, the present invention aims to provide a computer program product that solves the technical problems raised by the background art.

[0044] The purpose of the present invention can be achieved through the following technical solutions:

[0045] A computer program product includes computer instructions, which instruct a computing device to execute operations corresponding to an electric vehicle charging pricing method for a spatiotemporal collaborative guidance scenario in a first aspect.

[0046] Beneficial effects of the present invention:

[0047] This method constructs a user charging decision-making model based on the bounded rational charging choice behavior of electric vehicle users. By considering the interests of both distribution network operation and user satisfaction, it can fully tap the guiding role of price measures on the access location and access time of electric vehicles, effectively exert its regulatory potential as a flexible resource, solve the problems of charging resource mismatch and uneven load distribution, and ensure the stable operation of the charging network. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0049] Figure 1 is a flow chart of the method of the present invention;

[0050] Figure 2 is the charging price curve of each charging station within one day in the embodiment;

[0051] Figure 3 is the charging power of each charging station in one day in the embodiment;

[0052] Figure 4 is the charging station utilization rate of each charging station in one day in the embodiment;

[0053] Figure 5 is the charging power of each charging station in one day in the comparison ratio;

[0054] Figure 6 It is the utilization rate of charging piles at each charging station in the comparison within one day. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0056] An electric vehicle charging pricing method for a spatiotemporal coordinated guidance scenario includes the following steps:

[0057] 1) Obtain distribution network line information and node location information of each charging station in the distribution network topology and transportation network topology as spatial coupling information data;

[0058] 2) Obtaining charging demand information of electric vehicles in the region during the day, including the charging start time, charging duration, charging end time, battery power before charging, and location when charging demand is generated, as charging demand information data for electric vehicles;

[0059] 3) Taking spatial coupling information data and electric vehicle charging demand information data as input, and taking the lowest comprehensive cost as the objective function, considering distribution network flow constraints, user station selection constraints, battery status constraints, and charging price upper and lower limits, an electric vehicle charging pricing model for spatiotemporal collaborative guidance scenarios is constructed;

[0060] The comprehensive cost is the sum of network loss cost and electric vehicle user charging fee;

[0061] 4) Solve the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario and obtain the electric vehicle charging price at each charging station in each time period;

[0062] The electric vehicle charging price at each charging station in each time period serves as a pricing scheme for electric vehicle charging in each time period.

[0063] Furthermore, the objective function of the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario is:

[0064]

[0065] Where C represents the comprehensive cost of the model, the first item is the network loss conversion cost, and the second item is the charging fee for electric vehicle users; c l is the unit conversion cost of network loss, T is the total number of time periods in a day, Ω l is the set of all lines in the distribution network, is the active network loss of the lth distribution network line in the tth period, Δt is the unit time period, i is the first section node of the lth distribution network line, j is the first section node of the lth distribution network line, P ij,t is the active power of the lth distribution network line in the tth period, Q ij,t is the reactive power of the lth distribution network line in the tth period, R ij is the resistance of the lth distribution network line, U j is the voltage amplitude of node j; N cr is the number of electric vehicles with charging needs, c n is the charging price of the nth electric vehicle, B is the battery capacity of the electric vehicle, S le,n is the battery state of charge of the nth electric vehicle when it leaves the charging station, S ar,n is the battery state of charge of the nth electric vehicle when it arrives at the charging station.

[0066] Furthermore, the distribution network flow constraint of the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario is:

[0067]

[0068]

[0069] in, is the generator power of the jth node in the tth period, Ω j is the set of all lines starting with node j, is the active load of the jth node in the tth period, is the charging load of the jth node in the tth period, is the reactive load of the jth node in the tth period, I ij,t is the current of the lth distribution network line in the tth period, Z ij is the impedance of the lth distribution network line, U min is the lower limit of the node voltage amplitude, U max is the upper limit of the node voltage amplitude.

[0070] Furthermore, the user station selection constraints of the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario are as follows:

[0071]

[0072] Among them, ia is the nearest charging station to the nth electric vehicle, ib is the charging station with the lowest charging service fee at the tth moment, is the probability that the nth electric vehicle accepts the guidance of the charging station, which can be calculated by the electric vehicle user decision function f. is the random boot probability of the nth electric car, L ib,n is the guidance distance between the nth electric vehicle and the charging station ib, is the difference in charging prices between charging stations ia and ib at the tth moment, N cs is the number of charging stations, u m,n is the selection sign of the nth electric car for charging station m, is the time when the nth electric vehicle arrives at the designated electric vehicle charging station, is the time when the nth electric car leaves the charging station, is the charging flag of the mth electric vehicle in the nth electric vehicle charging station at time t. These variables are all Boolean variables.

[0073] Furthermore, the battery state constraint of the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario is:

[0074]

[0075] Among them, S max is the upper limit of the state of charge level of electric vehicles, S min is the lower limit of the state of charge level of electric vehicles, μ c is the charging efficiency of the charging pile, P c is the charging power of the charging pile.

[0076] Furthermore, the upper and lower limits of the charging price of the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario are as follows:

[0077] c min ≤c n ≤c max

[0078] Among them, c max The upper limit of the charging price for electric vehicles, c min Setting a lower limit on the price of charging electric vehicles.

[0079] Example

[0080] like Figure 1 As shown, a charging pricing method for electric vehicles in a spatiotemporal collaborative guidance scenario includes the following steps:

[0081] 1) Obtain distribution network line information and node location information of each charging station in the distribution network topology and transportation network topology as spatial coupling information data;

[0082] 2) Using the Monte Carlo method, we obtain charging demand information for electric vehicles in the region, including charging start time, charging duration, charging end time, battery level before charging, and location when charging demand arises. A day is divided into 96 time periods with a step size of 15 minutes. The start time of each time period is recorded as the start time of the time period. The battery capacity of each electric vehicle is 100 kWh.

[0083] 3) Based on the data obtained in steps 1)-2), with the goal of minimizing the overall cost as the objective function, considering the distribution network flow constraints, user station selection constraints, battery status constraints, and upper and lower limits of charging prices, an electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario is constructed;

[0084] The comprehensive cost is the sum of network loss cost and electric vehicle user charging fee, and the parameter values ​​included are as follows: c l =0.07￥ / kWh; In this system, the line parameters of the distribution network are the IEEE33-node standard example, the geographical topology is an actual geographical area, and U min =0.9, U max =1.1, S min =0.2, S max =0.8, S B =10MW, L max =1km, the number of charging piles at the electric vehicle charging station is 10, the charging power is 60kW, the charging efficiency is 0.9, and the charging price range is [0.65,1.5] yuan.

[0085] 4) In this embodiment, the particle swarm algorithm is used to solve the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario, and the electric vehicle charging price of each charging station in each time period is obtained under the objective function of the lowest comprehensive cost. The charging price curve of each charging station in a day is as follows: Figure 2 As shown in Figure 2, the charging power and utilization rate of each charging station in a day are as follows: Figure 3 、 4 shown.

[0086] Comparative Example

[0087] In order to verify the effectiveness of the method proposed in this invention, a comparative example is given. In the comparative example, a time-of-use electricity price strategy is adopted to compare the charging power and utilization rate of each charging station. Figure 5 、 6 As shown;

[0088] As can be seen from the figure, the adoption of the dynamic pricing model in this paper effectively alleviates the uneven utilization of charging piles, fully taps the guiding role of price measures on the access location and access time of electric vehicles, and effectively plays its regulatory potential as a flexible resource.

[0089] The method of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a special-purpose processor or programmable or special-purpose hardware (such as an ASIC or FPGA). It will be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown here, the execution of the code converts the general-purpose computer into a special-purpose computer for executing the method shown here.

[0090] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A method for pricing electric vehicle charging for spatiotemporal collaborative guidance scenarios, characterized in that: The following steps are involved: 1) Obtain distribution network line information and node location information of each charging station in the distribution network topology and transportation network topology as spatial coupling information data; 2) Obtaining charging demand information of electric vehicles in the region during the day, including the charging start time, charging duration, charging end time, battery power before charging, and location when charging demand is generated, as charging demand information data for electric vehicles; 3) Taking spatial coupling information data and electric vehicle charging demand information data as input, and taking the lowest comprehensive cost as the objective function, considering distribution network flow constraints, user station selection constraints, battery status constraints, and charging price upper and lower limits, an electric vehicle charging pricing model for spatiotemporal collaborative guidance scenarios is constructed; The comprehensive cost is the sum of network loss cost and electric vehicle user charging fee; 4) Solve the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario and obtain the electric vehicle charging price at each charging station in each time period; The electric vehicle charging price at each charging station in each time period serves as a pricing scheme for electric vehicle charging in each time period.

2. The electric vehicle charging pricing method for spatiotemporal collaborative guidance scenarios according to claim 1 is characterized in that: The objective function of the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario is: Where C represents the comprehensive cost of the model, the first item is the network loss conversion cost, and the second item is the charging fee for electric vehicle users; c l is the unit conversion cost of network loss, T is the total number of time periods in a day, Ω l is the set of all lines in the distribution network, is the active network loss of the lth distribution network line in the tth period, Δt is the unit time period, i is the first section node of the lth distribution network line, j is the first section node of the lth distribution network line, P ij,t is the active power of the lth distribution network line in the tth period, Q ij,t is the reactive power of the lth distribution network line in the tth period, R ij is the resistance of the lth distribution network line, U j is the voltage amplitude of node j; N cr is the number of electric vehicles with charging needs, c n is the charging price of the nth electric vehicle, B is the battery capacity of the electric vehicle, S le,n is the battery state of charge of the nth electric vehicle when it leaves the charging station, S ar,n is the battery state of charge of the nth electric vehicle when it arrives at the charging station.

3. The electric vehicle charging pricing method for spatiotemporal collaborative guidance scenarios according to claim 1 is characterized in that: The distribution network flow constraints of the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario are: in, is the generator power of the jth node in the tth period, Ω j is the set of all lines starting with node j, is the active load of the jth node in the tth period, is the charging load of the jth node in the tth period, is the reactive load of the jth node in the tth period, I ij,t is the current of the lth distribution network line in the tth period, Z ij is the impedance of the lth distribution network line, U min is the lower limit of the node voltage amplitude, U max is the upper limit of the node voltage amplitude.

4. The electric vehicle charging pricing method for spatiotemporal collaborative guidance scenarios according to claim 1 is characterized in that: The user station selection constraints of the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario are: Among them, ia is the nearest charging station to the nth electric vehicle, ib is the charging station with the lowest charging service fee at the tth moment, is the probability that the nth electric vehicle accepts the guidance of the charging station, which can be calculated by the electric vehicle user decision function f. is the random boot probability of the nth electric car, L ib,n is the guidance distance between the nth electric vehicle and the charging station ib, is the difference in charging prices between charging stations ia and ib at the tth moment, N cs is the number of charging stations, u m,n is the selection sign of the nth electric car for charging station m, is the time when the nth electric vehicle arrives at the designated electric vehicle charging station, is the time when the nth electric car leaves the charging station, is the charging flag of the mth electric vehicle in the nth electric vehicle charging station at time t. These variables are all Boolean variables.

5. The electric vehicle charging pricing method for spatiotemporal collaborative guidance scenarios according to claim 1 is characterized in that: The battery state constraint of the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario is: Among them, S max is the upper limit of the state of charge level of electric vehicles, S min is the lower limit of the state of charge level of electric vehicles, μ c is the charging efficiency of the charging pile, P c is the charging power of the charging pile.

6. The electric vehicle charging pricing method for spatiotemporal collaborative guidance scenarios according to claim 1 is characterized in that: The upper and lower limits of the charging price of the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario are: c min ≤c n ≤c max Among them, c max The upper limit of the charging price for electric vehicles, c min Setting a lower limit on the price of charging electric vehicles.

7. An electric vehicle charging pricing system for spatiotemporal collaborative guidance scenarios, characterized by: include: A distribution network line information acquisition module is used to obtain distribution network line information and the node location information of each charging station in the distribution network topology and transportation network topology as spatial coupling information data; The electric vehicle charging demand information acquisition module is used to obtain the charging start time, charging duration, charging end time, battery power before charging, and charging demand information of the location where the charging demand is generated for electric vehicles in the area during the day as the charging demand information data of the electric vehicles; Construct an electric vehicle charging pricing model module for spatiotemporal collaborative guidance scenarios, which uses spatial coupling information data and electric vehicle charging demand information data as input, takes the lowest comprehensive cost as the objective function, and considers distribution network flow constraints, user station selection constraints, battery status constraints, and upper and lower limits of charging prices to construct an electric vehicle charging pricing model for spatiotemporal collaborative guidance scenarios. The comprehensive cost is the sum of network loss costs and electric vehicle user charging fees. The model solving module is used to solve the electric vehicle charging pricing model for the spatiotemporal collaborative guidance scenario, and obtain the electric vehicle charging price of each charging station in each time period; the electric vehicle charging price of each charging station in each time period is used as the pricing plan for electric vehicle charging in each time period.

8. A computer storage medium storing a readable program, characterized in that: When the program is running, it can execute the electric vehicle charging pricing method for a spatiotemporal collaborative guidance scenario as described in any one of claims 1-6.

9. An electronic device comprising: A processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the electric vehicle charging pricing method for a spatiotemporal collaborative guidance scenario as described in any one of claims 1 to 6.

10. A computer program product comprising computer instructions, wherein the computer instructions instruct a computing device to execute operations corresponding to the electric vehicle charging pricing method for a spatiotemporal collaborative guidance scenario as described in any one of claims 1 to 6.

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