Mobile and fixed hybrid charging scheduling method and system and storage medium

Abstract

The invention provides a mobile and fixed hybrid charging scheduling method and system, and a storage medium. The method comprises the steps of obtaining a charging request of at least one target vehicle; a space-time distribution model of a hybrid charging station is constructed according to the charging request, the hybrid charging station comprises a fixed charging pile FCP and a mobile charging pile MCV, and the total cost of the same target vehicle in the mobile charging mode and the total cost of the same target vehicle in the fixed charging mode are obtained according to the space-time distribution model; generating a target vehicle charging selection scheme based on a principle of lowest target vehicle charging cost; and if the target vehicle charging selection scheme is a mobile charging mode, constructing a target function by taking the minimum total operation cost of the hybrid charging station as an optimization target, and solving the target function to obtain an optimal scheduling scheme for selecting the mobile charging mode in the next day. According to the invention, the most economical charging selection scheme can be pushed to the customer, so that the user cost can be reduced, and the optimal scheduling of the MCV can be completed under the condition of selecting the mobile charging mode.

Description

Technical Field

[0001] The present invention relates to the technical field of charging facility optimization, and in particular to a scheduling method, system and storage medium for mobile and fixed hybrid charging. Background Art

[0002] As the number of electric vehicles continues to increase, society's demand for charging infrastructure continues to grow. Users are faced with challenges such as long waiting times for charging and inconvenient charging locations. In the context of diverse user charging needs, fixed charging stations are limited by budget, site, and grid infrastructure. The number is insufficient to provide charging services, or the utilization rate is low, which harms the operator's benefits. Mobile charging, as an effective supplement to fixed charging methods, can provide charging services when and where users need them, alleviate the pressure on the grid caused by electric vehicle charging during peak hours, and provide greater flexibility for the system. In existing technologies, fixed charging and mobile charging are rarely considered together, ignoring the impact of user choices on the configuration and scheduling of mobile charging stations. Summary of the invention

[0003] The purpose of the present invention is to provide a scheduling method, system and storage medium for mobile and fixed hybrid charging, so as to propose a scheduling method that takes into account both mobile and fixed charging modes, and then push the most economical charging option to customers, thereby reducing user costs and improving user satisfaction; and when the mobile charging mode is selected, the total operating cost of the hybrid charging station is minimized as the objective function, and the optimal scheduling of MCV is completed, so as to achieve a win-win situation for users and operators.

[0004] In a first aspect, the present invention provides a scheduling method for mobile and fixed hybrid charging, the method comprising:

[0005] Obtaining a charging request of at least one target vehicle, the charging request including a desired charging start time of the target vehicle, energy required for charging the target vehicle, a travel start point coordinate of the target vehicle, and a travel end point coordinate of the target vehicle;

[0006] Constructing a spatiotemporal distribution model of a hybrid charging station according to the charging request, wherein the hybrid charging station includes a fixed charging pile FCP and a mobile charging pile MCV, and obtaining the total cost of the same target vehicle in a mobile charging mode and a fixed charging mode respectively according to the spatiotemporal distribution model;

[0007] Generate a charging option for the target vehicle based on the principle of lowest charging cost for the target vehicle;

[0008] If the target vehicle charging option is mobile charging, an objective function is constructed with the minimum total operating cost of the hybrid charging station as the optimization goal, and the objective function is solved to obtain the optimal scheduling plan within one day when the mobile charging method is selected.

[0009] Further, the spatiotemporal distribution model includes a target vehicle cost model, a time cost model, and a target vehicle charging and discharging cost model;

[0010] The step of constructing a spatiotemporal distribution model of a hybrid charging station according to the charging request, wherein the hybrid charging station includes a fixed charging pile FCP and a mobile charging pile MCV, and obtaining the total cost of the same target vehicle in a mobile charging mode and a fixed charging mode respectively according to the spatiotemporal distribution model includes:

[0011] Assume that there are n target vehicles, define |I|=n, and obtain the EV of each target vehicle according to the following formula i ∈I’s target vehicle cost model:

[0012]

[0013] in, Indicates the target vehicle EV i The target vehicle total cost, Indicates the target vehicle EV i The time cost, Indicates the target vehicle EV i The charging cost is: when j=1, it is a fixed charging method, and when j=2, it is a mobile charging method;

[0014] The time cost model is constructed according to the following formula:

[0015]

[0016] Where W represents the average hourly wage of the target vehicle, Indicates the target vehicle EV i The driving time required to obtain charging service, Indicates the target vehicle EV i Charging time;

[0017] The target vehicle charging cost model is constructed according to the following formula:

[0018]

[0019] in, represents the unit charging cost of electric vehicles. When j = 1, η j Represents the charging efficiency of a fixed charging pile. When j = 2, η j Indicates the charging efficiency of the mobile charging pile, ΔΕ i Indicates the target vehicle EV i When users choose a mobile charging station, they need to pay an additional delivery fee for the mobile charging station, which is CM Indicates. i,j is a decision variable. When j=2, X i,j =1, when j=1, X i,j =0.

[0020] Furthermore, the target vehicle EV is obtained according to the following formula: i The distance required to obtain charging service:

[0021]

[0022] Among them, (x0, y0) is the location coordinate of the hybrid charging station, (x 1,i ,y 1,i ) is the target vehicle EV i The coordinates of the starting point of travel, (x 2,i ,y 2,i ) is the target vehicle EV i The coordinates of the destination point, V i Target vehicle EV i driving speed.

[0023] Furthermore, the target vehicle EV is obtained according to the following formula: i Charging time:

[0024]

[0025] When j = 1, P charge，j represents the charging power of a fixed charging pile. When j=2, P charge，j Indicates the charging power of the mobile charging station.

[0026] Furthermore, the target vehicle EV is obtained according to the following formula: i Energy required for charging:

[0027] ΔE i =E i ×(0.9-SOC i )

[0028] Among them, E i Indicates the target vehicle EV i Battery capacity, SOC i Indicates the target vehicle EV i state of charge.

[0029] Furthermore, if the target vehicle charging option is a mobile charging method, the steps of constructing an objective function with the minimum total operating cost of the hybrid charging station as the optimization target, and solving the objective function to obtain the optimal scheduling plan within one day of selecting the mobile charging method include:

[0030] Assuming that the hybrid charging station includes m mobile charging piles, define m=|K|, and construct the objective function according to the following formula:

[0031]

[0032] C total represents the total operating cost of the hybrid charging station in one day, γ represents the daily amortized cost of purchasing each mobile charging pile, represents the charging and discharging cost of the kth mobile charging pile, represents the battery degradation cost of the kth mobile charging station per 1 kW of charging and discharging;

[0033] The constraints of the objective function are constructed according to the following formula:

[0034]

[0035] ∑ t∈T ∑ i∈I ∑ k∈K X i,k,t =m

[0036]

[0037] X i,k,t , Y k,t , Z k,t are decision variables representing the MCV state, and are all binary variables. k,i,t =1 represents that the kth mobile charging station charges the target vehicle EV at time t i Provide charging service; k,t =1 represents that at time t, the kth mobile charging pile provides charging service in the photovoltaic power station or the upper power grid; Z k,t =1 represents that the kth mobile charging pile is in motion or parking state at time t.

[0038] T represents the total time in a day. represents the charging power of the kth mobile charging pile at time t, Indicates the upper limit of MCV charging power. represents the discharge power of the kth mobile charging pile at time t, Indicates the upper limit of discharge power.

[0039] Furthermore, the charging and discharging cost of the kth mobile charging pile is obtained according to the following formula:

[0040]

[0041] in, represents the discharge power of the photovoltaic power station, δ represents the unit charging price set by the photovoltaic power station on that day based on the predicted discharge amount, represents the discharge power of the upper grid, α t represents the unit electricity price of the upper power grid at time t, Indicates the target vehicle EV i Charging costs in mobile charging mode;

[0042] The battery degradation cost of the kth mobile charging pile is obtained according to the following formula:

[0043]

[0044] Among them, p i,k,t represents the discharge power of the kth mobile charging pile at time t, E k represents the battery capacity of k mobile charging piles, C Γ represents the battery replacement cost, and Γ represents the battery degradation factor.

[0045] In a second aspect, the present invention provides a scheduling system for mobile and fixed hybrid charging, the system comprising:

[0046] A charging request acquisition module, used to acquire a charging request of at least one target vehicle, wherein the charging request includes the target vehicle's desired charging start time, the energy required for charging the target vehicle, the target vehicle's travel start point coordinates, and the target vehicle's travel end point coordinates;

[0047] A model building module is used to build a spatiotemporal distribution model of a hybrid charging station according to the charging request, wherein the hybrid charging station includes a fixed charging pile FCP and a mobile charging pile MCV, and obtain the total cost of the same target vehicle in a mobile charging mode and a fixed charging mode respectively according to the spatiotemporal distribution model;

[0048] A charging mode selection module is used to generate a charging selection plan for a target vehicle based on the principle of lowest charging cost for the target vehicle;

[0049] The objective function construction module is used to construct an objective function with the minimum total operating cost of the hybrid charging station as the optimization goal if the target vehicle charging option is the mobile charging method, and solve the objective function to obtain the optimal scheduling plan within one day when the mobile charging method is selected.

[0050] In a third aspect, the present invention provides a storage medium storing one or more programs, which, when executed by a processor, implement the above-mentioned scheduling method for mobile and fixed hybrid charging.

[0051] In a fourth aspect, the present invention provides an electronic device, the electronic device comprising a memory and a processor, wherein:

[0052] The memory is used to store computer programs;

[0053] When the processor is used to execute the computer program stored in the memory, the above-mentioned scheduling method for mobile and fixed hybrid charging is implemented.

[0054] Compared with the prior art, the present invention has the following advantages:

[0055] 1. The present invention constructs a spatiotemporal distribution model of charging demand for hybrid charging stations to comprehensively consider fixed charging and mobile charging methods, and generates user charging selection plans based on the user's lowest cost, thereby effectively reducing the user's charging cost, and taking the minimum total operating cost of the hybrid charging station as the objective function, completes the optimal scheduling of MCV, and further achieves a win-win situation for users and operators.

[0056] 2. Compare the real-time charging plan costs of users and select the charging plan with the lowest overall cost, which can effectively reduce user costs and improve user satisfaction.

[0057] 3. By effectively obtaining the temporal and spatial distribution of charging demand of MCV and FCP, and the output forecast of photovoltaic power station, the optimal time period for MCV to charge at photovoltaic power station and charge users is determined. The scheduling scheme can reduce the system operation cost while achieving the effect of smoothing the load curve and absorbing photovoltaic power.

[0058] 4. The present invention comprehensively considers the interests of users and operators to achieve a win-win situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flowchart of a method for scheduling mobile and fixed hybrid charging proposed in one embodiment of the present invention;

[0060] Figure 2 A schematic diagram of the structure of a mobile and fixed hybrid charging scheduling system proposed in one embodiment of the present invention.

[0061] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be understood by people with general skills in the field to which the present invention belongs. "Including" and similar words used in this article mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0063] like Figure 1 As shown, an embodiment of the present application further proposes a scheduling method for mobile and fixed hybrid charging, the method comprising steps S101 to S104, wherein:

[0064] Step S101: obtaining a charging request of at least one target vehicle, wherein the charging request includes the target vehicle's desired charging start time, the energy required for charging the target vehicle, the target vehicle's travel start point coordinates, and the target vehicle's travel end point coordinates;

[0065] It should be noted that when a user has a charging demand, a charging request Q will be sent to the hybrid charging system through the corresponding target vehicle. i , the charging request includes In addition, since each user corresponds to a target vehicle, in the following description of the embodiments, the user is equivalent to the target vehicle.

[0066] in, Indicates EV i Desired start time of charging, ΔE i Indicates EV i Charging Energy charged into the battery (energy required for charging), P 1,i Indicates EV i Coordinates of the starting point of travel (x 1,i ,y 1,i ), P 2,i Indicates EV i The coordinates of the end point of travel (x 2,i ,y 2,i ), in addition, j represents the charging method, and the charging method includes two types: mobile charging method and fixed charging method. It should be pointed out that in this step, j in the quintuple is still unknown at this time.

[0067] Step S102: constructing a spatiotemporal distribution model of a hybrid charging station according to the charging request, wherein the hybrid charging station includes a fixed charging pile FCP and a mobile charging pile MCV, and obtaining the total cost of the same target vehicle in a mobile charging mode and a fixed charging mode respectively according to the spatiotemporal distribution model;

[0068] In some embodiments, the spatiotemporal distribution model includes a target vehicle cost model, a time cost model, and a target vehicle charging and discharging cost model.

[0069] Specifically, assuming that there is a group of n electric vehicle users, referred to as EVs, denoted as I, |I| = n, a spatiotemporal distribution model of electric vehicle charging demand is constructed. EVs data is generated by the probability density function of the electric vehicle departure time and charge state, and the starting and ending points of EVs are determined by the traffic volume method to generate the EVs charging demand model.

[0070] For each EV i For the charging demand of ∈I, the charging scheme is selected based on the principle of minimum user charging cost. The target vehicle cost model is specifically constructed as follows:

[0071]

[0072] in, Indicates the target vehicle EV i The target vehicle total cost, Indicates the target vehicle EV i The time cost, Indicates the target vehicle EV i The charging cost is affected by the charging method. i ∈I, when j=1, it is a fixed charging mode, and when j=2, it is a mobile charging mode.

[0073] The time cost model is constructed according to the following formula:

[0074]

[0075] Where W represents the average hourly wage of the target vehicle, Indicates the target vehicle EV i The driving time required to obtain charging service, Indicates the target vehicle EV i Charging time;

[0076] The target vehicle charging cost model is constructed according to the following formula:

[0077]

[0078] in, represents the unit charging cost of electric vehicles. When j = 1, η j Represents the charging efficiency of a fixed charging pile. When j = 2, η j Indicates the charging efficiency of the mobile charging pile, ΔΕ i Indicates the target vehicle EV i When users choose a mobile charging station, they need to pay an additional delivery fee for the mobile charging station, which is C M Indicates. i,j is a decision variable. When j=2, X i,j =1, when j=1, X i,j =0.

[0079] In addition, in order to obtain charging services at FCS, users must change their original driving trajectory and pass through the FCS location. Changing the original driving trajectory will increase the user's driving time, thereby increasing the user's time cost. When the user chooses MCS charging, there is no need to change the driving trajectory. Instead, MCS moves to the user's driving destination to charge it. Based on this, the target vehicle EV is obtained according to the following formula i The distance required to obtain charging service:

[0080]

[0081] Among them, (x0, y0) is the location coordinate of the hybrid charging station, (x 1,i ,y 1,i ) is the target vehicle EV i The coordinates of the starting point of travel, (x 2,i ,y 2,i ) is the target vehicle EV i The coordinates of the destination point, V i Target vehicle EV i driving speed.

[0082] The target vehicle EV is obtained according to the following formula i Charging time:

[0083]

[0084] When j = 1, P charge，j represents the charging power of a fixed charging pile. When j=2, P charge，j Indicates the charging power of the mobile charging station.

[0085] The target vehicle EV is obtained according to the following formula i Energy required for charging:

[0086] ΔE i =E i ×(0.9-SOC i )

[0087] Among them, E i Indicates the target vehicle EV i Battery capacity, SOC i Indicates the target vehicle EV i In this step, it is defined that the battery is charged to SOC=0.9.

[0088] Step S103: generating a charging option for the target vehicle based on the principle of lowest charging cost for the target vehicle;

[0089] It should be pointed out that the above steps respectively obtain the target vehicle total cost under the fixed charging mode and the target vehicle total cost under the mobile charging mode, and then based on the principle of lowest charging cost of the target vehicle, the charging mode with the lowest total cost is selected as the charging option for the target vehicle.

[0090] Step S104: If the target vehicle charging option is a mobile charging method, an objective function is constructed with the minimum total operating cost of the hybrid charging station as the optimization goal, and the objective function is solved to obtain the optimal scheduling plan within one day when the mobile charging method is selected.

[0091] It should be noted that when the target charging plan is a fixed charging method, cost minimization has been achieved and no further optimization is required. When the target charging plan is a mobile charging method, the scheduling of mobile charging piles is also involved. At this time, an MCS scheduling model will be constructed, that is, the minimum total operating cost of the hybrid charging station is used as the objective function to complete the coordinated optimization scheduling of MCS between charging services and peak-valley arbitrage.

[0092] In some embodiments, the specific process of constructing the objective function is as follows:

[0093] Assume that there is a group of m MCS in a park (mobile charging pile activity area), denoted as K, m = |K|. The mathematical model for minimizing the total cost is as follows:

[0094]

[0095] C total represents the total operating cost of the hybrid charging station in one day, γ represents the daily amortized cost of purchasing each mobile charging pile, represents the charging and discharging cost of the kth mobile charging pile, It represents the battery degradation cost of the k-th mobile charging station per 1 kW of charging and discharging.

[0096] Furthermore, the charging and discharging cost of the kth mobile charging pile is obtained according to the following formula:

[0097]

[0098] in, represents the discharge power of the photovoltaic power station, δ represents the unit charging price set by the photovoltaic power station on that day based on the predicted discharge amount, represents the discharge power of the upper grid, α t represents the unit electricity price of the upper power grid at time t, Indicates the target vehicle EV i Charging costs in mobile charging mode.

[0099] It should be pointed out that during the dispatch process, each MCV has three possible states: charging at a PV plant or the upper grid, charging an EV, moving, or parked, so the following constraints are constructed:

[0100]

[0101] The restriction is that the MCV has only one state at each time.

[0102]

[0103] The restriction in the formula is that each MCV can only charge one EV customer at a time.

[0104]

[0105] The limitation in the formula is that each EV customer can only be charged by one MCV at a time.

[0106] ∑ t∈T ∑ i∈I,j＝2 ∑ k∈K X i,k,t =m

[0107] The constraint ensures that the charging demand of each EV is met during the day.

[0108]

[0109] In the formula, the charging and discharging power of each MCV is limited not to exceed the upper limit.

[0110] In addition, in some embodiments, the battery degradation cost of the kth mobile charging pile is obtained according to the following formula:

[0111]

[0112] Among them, p i,k,t represents the discharge power of the kth mobile charging pile at time t, E k represents the battery capacity of k mobile charging piles, C Γrepresents the battery replacement cost, Γ represents the battery degradation factor, and the remaining battery SOC at the end of life is set to 80%.

[0113] Finally, the objective function is solved to obtain the scheduling plan with the minimum total operating cost.

[0114] In summary, the above-mentioned scheduling method for mobile and fixed hybrid charging has the following advantages:

[0115] 1. The present invention constructs a spatiotemporal distribution model of charging demand for hybrid charging stations to comprehensively consider fixed charging and mobile charging methods, and generates user charging selection plans based on the user's lowest cost, thereby effectively reducing the user's charging cost, and taking the minimum total operating cost of the hybrid charging station as the objective function, completes the optimal scheduling of MCV, and further achieves a win-win situation for users and operators.

[0116] 2. Compare the real-time charging plan costs of users and select the charging plan with the lowest overall cost, which can effectively reduce user costs and improve user satisfaction.

[0117] 3. By effectively obtaining the temporal and spatial distribution of charging demand of MCV and FCP, and the output forecast of photovoltaic power station, the optimal time period for MCV to charge at photovoltaic power station and charge users is determined. The scheduling scheme can reduce the system operation cost while achieving the effect of smoothing the load curve and absorbing photovoltaic power.

[0118] 4. The present invention comprehensively considers the interests of users and operators to achieve a win-win situation.

[0119] like Figure 2 As shown, an embodiment of the present invention further provides a scheduling system for mobile and fixed hybrid charging, the system comprising:

[0120] A charging request acquisition module 10 is used to acquire a charging request of at least one target vehicle, wherein the charging request includes the desired charging start time of the target vehicle, the energy required for charging the target vehicle, the travel start point coordinates of the target vehicle, and the travel end point coordinates of the target vehicle;

[0121] A model building module 20 is used to build a spatiotemporal distribution model of a hybrid charging station according to the charging request, wherein the hybrid charging station includes a fixed charging pile FCP and a mobile charging pile MCV, and obtain the total cost of the same target vehicle in a mobile charging mode and a fixed charging mode according to the spatiotemporal distribution model;

[0122] A charging mode selection module 30, for generating a charging selection scheme for a target vehicle based on the principle of lowest charging cost for the target vehicle;

[0123] The objective function construction module 40 is used to construct an objective function with the minimum total operating cost of the hybrid charging station as the optimization goal if the target vehicle charging selection scheme is the mobile charging method, and solve the objective function to obtain the optimal scheduling plan within one day when the mobile charging method is selected.

[0124] Another aspect of the present invention further provides a storage medium on which one or more programs are stored, and when the programs are executed by a processor, the above-mentioned scheduling method for mobile and fixed hybrid charging is implemented.

[0125] On the other hand, the present invention further proposes an electronic device, including a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the above-mentioned scheduling method for mobile and fixed hybrid charging.

[0126] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may 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 purposes of this specification, "computer-readable medium" may 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.

[0127] 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.

[0128] 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 a combination thereof: 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.

[0129] Although the embodiments of the present invention are described in detail above, it is obvious to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as described in the claims. Moreover, the present invention described herein may have other embodiments and may be implemented or realized in a variety of ways.

Claims

1. A scheduling method for mobile and fixed hybrid charging, characterized in that: The method comprises: Obtaining a charging request of at least one target vehicle, the charging request including a desired charging start time of the target vehicle, energy required for charging the target vehicle, a travel start point coordinate of the target vehicle, and a travel end point coordinate of the target vehicle; Constructing a spatiotemporal distribution model of a hybrid charging station according to the charging request, wherein the hybrid charging station includes a fixed charging pile FCP and a mobile charging pile MCV, and obtaining the total cost of the same target vehicle in a mobile charging mode and a fixed charging mode respectively according to the spatiotemporal distribution model; Generate a charging option for the target vehicle based on the principle of lowest charging cost for the target vehicle; If the target vehicle charging option is mobile charging, an objective function is constructed with the minimum total operating cost of the hybrid charging station as the optimization goal, and the objective function is solved to obtain the optimal scheduling plan within one day when the mobile charging method is selected.

2. The method for scheduling mobile and fixed hybrid charging according to claim 1, characterized in that: The spatiotemporal distribution model includes a target vehicle cost model, a time cost model, and a target vehicle charging and discharging cost model; The step of constructing a spatiotemporal distribution model of a hybrid charging station according to the charging request, wherein the hybrid charging station includes a fixed charging pile FCP and a mobile charging pile MCV, and obtaining the total cost of the same target vehicle in a mobile charging mode and a fixed charging mode respectively according to the spatiotemporal distribution model includes: Assume that there are n target vehicles, define |I|=n, and obtain the EV of each target vehicle according to the following formula i ∈I’s target vehicle cost model: in, Indicates the target vehicle EV i The target vehicle total cost, Indicates the target vehicle EV i The time cost, Indicates the target vehicle EV i The charging cost is: when j=1, it is a fixed charging method, and when j=2, it is a mobile charging method; The time cost model is constructed according to the following formula: Where W represents the average hourly wage of the target vehicle, Indicates the target vehicle EV i The driving time required to obtain charging service, Indicates the target vehicle EV i Charging time; The target vehicle charging cost model is constructed according to the following formula: in, represents the unit charging cost of electric vehicles. When j = 1, η j Represents the charging efficiency of a fixed charging pile. When j = 2, η j Indicates the charging efficiency of the mobile charging pile, ΔΕ i Indicates the target vehicle EV i The energy required for charging. When users choose a mobile charging station, they need to pay an additional delivery fee for the mobile charging station. M Indicates that X i,j is a decision variable. When j=2, X i,j =1, when j=1, X i,j =0.

3. The method for scheduling mobile and fixed hybrid charging according to claim 2, characterized in that: The target vehicle EV is obtained according to the following formula i The distance required to obtain charging service: Among them, (x0, y0) is the location coordinate of the hybrid charging station, (x 1,i ,y 1,i ) is the target vehicle EV i The coordinates of the starting point of travel, (x 2,i ,y 2,i ) is the target vehicle EV i The coordinates of the destination point, V i Target vehicle EV i driving speed.

4. The method for scheduling mobile and fixed hybrid charging according to claim 2, characterized in that: The target vehicle EV is obtained according to the following formula i Charging time: When j = 1, P charge，j represents the charging power of a fixed charging pile. When j=2, P charge，j Indicates the charging power of the mobile charging station.

5. The method for scheduling mobile and fixed hybrid charging according to claim 4, characterized in that: The target vehicle EV is obtained according to the following formula i Energy required for charging: ΔE i =E i ×(0.9-SOC i ) Among them, E i Indicates the target vehicle EV i Battery capacity, SOC i Indicates the target vehicle EV i state of charge.

6. The method for scheduling mobile and fixed hybrid charging according to claim 5, characterized in that: If the target vehicle charging option is a mobile charging method, the steps of constructing an objective function with the minimum total operating cost of the hybrid charging station as the optimization target, solving the objective function, and obtaining the optimal scheduling plan within one day of selecting the mobile charging method include: Assuming that the hybrid charging station includes m mobile charging piles, define m=|K|, and construct the objective function according to the following formula: C total represents the total operating cost of the hybrid charging station in one day, γ represents the daily amortized cost of purchasing each mobile charging pile, represents the charging and discharging cost of the kth mobile charging pile, represents the battery degradation cost of the kth mobile charging station per 1 kW of charging and discharging; The constraints of the objective function are constructed according to the following formula: ∑ t∈T ∑ i∈I ∑ k∈K X i,k,t =m X i,k,t , Y k,t , Z k,t are decision variables representing the MCV state, and are all binary variables, X k,i,t =1 represents that the kth mobile charging station charges the target vehicle EV at time t i Provide charging service; k,t =1 represents that at time t, the kth mobile charging pile provides charging service in the photovoltaic power station or the upper power grid; Z k,t =1 means that the kth mobile charging pile is in motion or parking state at time t; T represents the total time in a day. represents the charging power of the kth mobile charging pile at time t, Indicates the upper limit of MCV charging power. represents the discharge power of the kth mobile charging pile at time t, Indicates the upper limit of discharge power.

7. The method for scheduling mobile and fixed hybrid charging according to claim 6, characterized in that: The charging and discharging cost of the kth mobile charging pile is obtained according to the following formula: in, represents the discharge power of the photovoltaic power station, δ represents the unit charging price set by the photovoltaic power station on that day based on the predicted discharge amount, represents the discharge power of the upper grid, α t represents the unit electricity price of the upper power grid at time t, Indicates the target vehicle EV i Charging costs in mobile charging mode; The battery degradation cost of the kth mobile charging pile is obtained according to the following formula: Among them, p i,k,t represents the discharge power of the kth mobile charging pile at time t, E k represents the battery capacity of k mobile charging piles, C Γ represents the battery replacement cost, and Γ represents the battery degradation factor.

8. A dispatching system for mobile and fixed hybrid charging, characterized in that: The system comprises: A charging request acquisition module, used to acquire a charging request of at least one target vehicle, wherein the charging request includes the target vehicle's desired charging start time, the energy required for charging the target vehicle, the target vehicle's travel start point coordinates, and the target vehicle's travel end point coordinates; A model building module is used to build a spatiotemporal distribution model of a hybrid charging station according to the charging request, wherein the hybrid charging station includes a fixed charging pile FCP and a mobile charging pile MCV, and obtain the total cost of the same target vehicle in a mobile charging mode and a fixed charging mode respectively according to the spatiotemporal distribution model; A charging mode selection module is used to generate a charging selection plan for a target vehicle based on the principle of lowest charging cost for the target vehicle; The objective function construction module is used to construct an objective function with the minimum total operating cost of the hybrid charging station as the optimization goal if the target vehicle charging option is the mobile charging method, and solve the objective function to obtain the optimal scheduling plan within one day when the mobile charging method is selected.

9. A storage medium, characterized in that: The storage medium stores one or more programs, which, when executed by a processor, implement the scheduling method for mobile and fixed hybrid charging as described in any one of claims 1 to 7.

10. An electronic device, comprising a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, it implements the scheduling method for mobile and fixed hybrid charging as described in any one of claims 1 to 7.

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