New energy automobile dynamic charging-sharing coupling scheduling method and system

Abstract

The invention relates to a new energy vehicle dynamic charging-sharing coupling scheduling method and system, and the method comprises the steps: dividing a target region into a plurality of sub-regions, and obtaining the related parameters and vehicle scheduling constraints of each sub-region; calculating travel benefits of the sharing platform; calculating the regional fairness benefit of each sub-region; calculating the photovoltaic power generation utilization rate of each sub-region in each time period; and taking the shared platform travel benefit, the regional fairness benefit and the photovoltaic power generation utilization rate as objective functions, calculating an evaluation value of each objective function by using a Pareto optimal solution method, and generating a vehicle charging scheduling strategy based on the evaluation values. Compared with the prior art, photovoltaic power generation is taken into consideration of vehicle charging scheduling, and vehicle sharing charging scheduling is comprehensively optimized from sharing platform travel benefits, regional fairness benefits and photovoltaic power generation utilization rates.

Description

Technical Field

[0001] The present invention relates to the field of vehicle scheduling and energy, and in particular to a new energy vehicle dynamic charging-sharing coupling scheduling method and system. Background Art

[0002] As a new type of user-side flexible resource that has developed rapidly in recent years, the rapid growth of new energy vehicles has led to a sharp increase in charging load, which has become an important factor that cannot be ignored in the operation of the power grid. Especially in the context of the evening peak electricity consumption in the urban power grid, the charging behavior of electric vehicles during the evening peak period has further aggravated the burden on the power grid, making the already significant "duck curve" effect more prominent. Although the disorderly charging of electric vehicles does bring many challenges, it also contains huge scheduling potential given the randomness and flexibility of its charging time and location. However, the charging scheduling problem for electric vehicles in the prior art is more from the perspective of shared travel platforms and traditional urban power grids, ignoring the energy benefits brought by new power generation; for the charging-sharing problem, it is generally optimized and scheduled locally from the vehicle level from the aspects of driving distance after picking up and dropping off orders and charging, charging waiting time, passenger delays, and preferential policies in sharing, and it is impossible to achieve fully optimized charging-sharing scheduling. Therefore, providing a method that can fully optimize the charging-sharing scheduling of electric vehicles is a problem that needs to be solved. Summary of the invention

[0003] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a new energy vehicle dynamic charging-sharing coupling scheduling method and system.

[0004] The purpose of the present invention can be achieved by the following technical solutions:

[0005] According to a first aspect of the present invention, a new energy vehicle dynamic charging-sharing coupling scheduling method is provided, the method comprising:

[0006] Divide the target area into multiple sub-areas, and obtain relevant parameters and vehicle dispatch constraints for each sub-area; the relevant parameters include regional power grid load data, regional photovoltaic power generation data, online car-hailing specifications, order data, and regional charging pile data;

[0007] The travel benefits of the sharing platform are calculated based on order data, regional photovoltaic power generation data and vehicle dispatch constraints; the order data includes the benefits generated by vehicle pick-up and drop-off orders, the costs generated by vehicle pick-up and drop-off orders, and the costs generated by vehicles going to charge; the regional photovoltaic power generation data includes the regional photovoltaic power generation subsidy coefficient and the regional photovoltaic power generation power;

[0008] The order data also includes the order quantity, obtaining the order quantity of each sub-region in a preset time period, predicting the order quantity of a future preset time period based on the order quantity of the preset time period, and calculating the regional fairness benefit of each sub-region based on the order quantity of the future preset time period and the vehicle scheduling constraint;

[0009] Calculate the photovoltaic power generation utilization rate in each sub-area and each time period based on vehicle scheduling constraints;

[0010] The travel benefit of the sharing platform, the regional fairness benefit and the utilization rate of photovoltaic power generation are taken as objective functions, the evaluation value of each objective function is calculated using the Pareto optimal solution method, and a vehicle charging scheduling strategy is generated based on the evaluation value.

[0011] As a preferred technical solution, the vehicle dispatch constraints include power constraints, charging requirement constraints, quantity constraints, order time constraints and carpooling constraints;

[0012] The power constraint is that the current power of the vehicle must be sufficient to complete the current order or complete the journey to the target charging area; the charging requirement constraint is that the vehicle is not allowed to charge when the current power is higher than the upper limit of charging, and must be charged when the current power is lower than the lower limit of charging. When the current power of the vehicle is between the upper limit and the lower limit of charging, whether to charge is determined based on the power constraint, and the vehicle charging power cannot be higher than the power supply upper limit; the quantity constraint includes that a vehicle can only receive one order at a time, an order can only be received by one vehicle, and a vehicle can only go to one charging pile at a time; the order time constraint is that the actual pick-up time and the actual delivery time should meet the time window of the passenger; the carpooling constraint includes time carpooling constraint and space carpooling constraint.

[0013] As a preferred technical solution, the method for calculating the travel benefits of the sharing platform is:

[0014]

[0015] Among them, F1 represents the travel benefits of the sharing platform. represents the set of online ride-hailing vehicles participating in the dispatch, represents the set of travel requests involved in scheduling, Indicates the benefits generated by vehicle pick-up and drop-off orders, Represents the cost of vehicle pick-up and drop-off orders, represents the regional photovoltaic power generation subsidy coefficient, represents the cost of the vehicle going to charge, represents the set of charging stations participating in the scheduling, k represents the charging station number, x ij represents the decision variable for vehicle i to pick up order j, y ik represents the decision variable for vehicle i to go to charging station k, Ppv (t) represents photovoltaic power generation, P max (t) represents the vehicle charging power.

[0016] As a preferred technical solution, the benefits generated by the vehicle pick-up and drop-off order are obtained by the weighted sum of the commission on the total order amount and the commission on labor fees for the remaining amount excluding the commission.

[0017] As a preferred technical solution, the cost generated by the vehicle pick-up and drop-off order is obtained by multiplying the current position of the vehicle and the idle running loss, and the idle running loss is calculated based on the hourly wage of the driver in the target area and the platform order efficiency.

[0018] As a preferred technical solution, the cost incurred by the vehicle going to charge includes the empty running cost of the vehicle going to the target charging area, the waiting time cost and the vehicle charging cost.

[0019] As a preferred technical solution, the method for calculating the fairness benefit is:

[0020]

[0021] Among them, p represents a sub-region, P represents a sub-region set, SAV p represents the number of vehicles in area p after scheduling, TOTAL SAV Represents the total number of vehicles involved in the dispatch, RE p Indicates the predicted number of requests from region p to this sub-region, TOTAL RE represents the total number of requests; the number of vehicles in area p after scheduling includes the original number of vehicles in area p, the number of vehicles going to area p due to orders, and the number of vehicles going to area p due to charging.

[0022] As a preferred technical solution, the method for calculating the photovoltaic power generation utilization rate in each time period of the target charging area is:

[0023]

[0024] Among them, F3 represents the utilization rate of photovoltaic power generation, T represents the target period, P pv (t) represents photovoltaic power generation, P sav (t) represents the vehicle charging power.

[0025] As a preferred technical solution, the step of calculating the evaluation value of each objective function includes:

[0026] The travel benefit of the shared platform, the regional fairness benefit and the utilization rate of photovoltaic power generation are normalized, and the expression is:

[0027]

[0028] in, represents the normalized function of the ζth objective function in the kth Pareto solution, represents the ζth objective function in the kth Pareto solution, represents the minimum value of the ζth objective function, represents the maximum value of the ζth objective function; ζ represents the objective function type, and when ζ=1, it represents the travel benefit of the sharing platform; when ζ=2, it represents the regional fairness benefit; when ζ=3, it represents the photovoltaic power generation utilization rate;

[0029] The evaluation value is calculated based on the normalized function of each objective function, and its expression is:

[0030]

[0031] Among them, μ k represents the evaluation value of the kth Pareto solution, ω ζ represents the weight value of the ζth objective function;

[0032] A group of Pareto solutions with the largest evaluation value is selected as the optimal solution for the scheduling strategy.

[0033] According to a second aspect of the present invention, a new energy vehicle dynamic charging-sharing coupling scheduling system is provided, and the system is used to implement the above method.

[0034] Compared with the existing technology, the present invention incorporates the benefits of photovoltaic power generation into the consideration of car sharing charging by calculating the photovoltaic utilization rate, and then conducts comprehensive scheduling optimization for car sharing charging based on the travel benefits of the shared platform and the regional fairness benefits of the charging area. It involves the interests of multiple entities such as shared travel platforms, photovoltaic power generation companies, traditional power grids, and car drivers, and fits the real problems well, and can perform more accurate charging scheduling according to local requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0037] In order to make up for the shortcomings of the existing scheduling model, this embodiment proposes a dynamic charging-sharing coupling scheduling model that takes into account regional fairness and photovoltaic power generation. By setting the photovoltaic utilization rate, the benefits of photovoltaic power generation are integrated into the model, and a dynamic sharing-charging coupling scheduling model under the background of photovoltaic power generation is constructed. At the same time, the present invention incorporates regional fairness into the model considerations. By setting the regional fairness benefit function in the target, vehicles are attracted to charge or pick up orders in places with high demand in the next 30 minutes, which reduces the waiting time of passengers and the empty running distance of vehicles, and increases the model efficiency and the overall benefits of the shared travel platform.

[0038] In order to verify the feasibility of the method provided in this embodiment, a simulation model for realizing dynamic sharing-charging coupling scheduling in the context of photovoltaic power generation is constructed, and the detailed steps are as follows:

[0039] A1. Analyze the main body in the car sharing charging scheduling problem:

[0040] A11. This embodiment uses a certain city as a sample city to construct a simulation model, wherein the subjects in the model include online car-hailing, shared travel platforms, traditional power grid companies, photovoltaic power generation companies and passengers, and clarifies the relationship between the various model subjects.

[0041] A12. Determine the data generated by each subject of the simulation model, including photovoltaic power generation data, grid load data, online car-hailing specification data, order data and charging pile data of the sample city.

[0042] A2. Build a simulation model: determine the modeling principles of vehicles, orders, and charging stations, as well as the update logic and matching principles of vehicles and orders;

[0043] A21. Vehicle modeling principles:

[0044] The vehicle studied is SAV (Shared Autonomous Vehicle). It can ensure that the vehicle runs all day within 24 hours and obeys the platform's deployment 100%. At the beginning of the model, all vehicles are unloaded and their positions are randomly assigned in the study area. A waiting place for a vehicle is arranged in each district within the study area. If the vehicle is not assigned a pick-up task and a charging task during this operation cycle, the idle vehicle will go to the waiting place in the current district to wait for assignment. This embodiment adopts an incomplete charging model, that is, the vehicle does not need to be fully charged to pick up passengers. Some vehicles may interrupt the current charging to pick up orders, but too short a charging time will significantly increase the charging frequency, which will affect the overall benefit of the model and is not in line with reality. Therefore, in the model, once charging starts, the duration must be more than 10 minutes.

[0045] The SAV selection behavior in the model can be divided into pick-up and drop-off behavior, charging behavior, and waiting behavior in the idle state. According to the status of the task, each task is divided into deterministic tasks and non-deterministic tasks. Deterministic tasks include vehicle pick-up and drop-off tasks and charging tasks when the battery is too low. These tasks must be completed and cannot be interrupted. In other words, if the vehicle does not complete the current deterministic task before the start of the next cycle, it cannot participate in the model deployment of the next cycle; and non-deterministic tasks include waiting behaviors and charging behaviors when the battery is sufficient. These behaviors can be interrupted at any time, that is, before the start of the next simulation, even if the vehicle currently has a non-deterministic task, it can still participate in the next model deployment.

[0046] During the simulation, there are five states of SAV, including "idle", "charging (available)", "charging (unavailable)", "picking up passengers (one order)" and "picking up passengers (two orders)". The specific settings of each state and whether the vehicle participates in the scheduling of the next cycle when in this state are shown in Table 1:

[0047] Table 1 Vehicle status information table

[0048]

[0049] A22. Order Modeling:

[0050] In this embodiment, orders are used instead of the main passengers in the model for simulation. The simulation of orders is mainly divided into two parts. First, in the first part, the passenger sends a request to the shared travel platform and provides information such as the departure place. The travel platform allocates a suitable vehicle to pick up the order according to the model; in the second part, after the passenger gets on the car, the model will update the status of the order according to the simulation operation cycle (in this embodiment, the operation cycle is set to five minutes), where the order status includes waiting, whether shared, whether completed, and the current location; the main information that the order needs to include includes the estimated boarding time, the estimated arrival time, the longitude and latitude of the boarding point, the longitude and latitude of the destination, etc.

[0051] A23. Charging station modeling:

[0052] Five charging pile models are established in the simulation, and each charging pile is set to charge only one SAV vehicle at a time. The charging station simulates the charging process of the vehicle by receiving information such as the current power level and current time of the vehicle, and the time to complete charging; if all five charging piles are full, the new SAV will queue up for charging on a first-come-first-served basis. The model simulates the charging process by determining the time when the vehicle enters the charging station, the time when charging starts, and the time when charging is completed. After charging starts, the charging status of the vehicle is re-determined every cycle.

[0053] A24. Determine the relevant constraints of each model entity:

[0054] The relevant constraints are determined according to the modeling constraints of steps A21-A23, and the relevant constraints include power constraints, charging requirement constraints, quantity constraints, order time constraints and carpooling constraints, where:

[0055] A241. The power constraint is that the current power of the vehicle must be sufficient to complete the current order or to complete the journey to the target charging area; the charging requirement constraint is that the vehicle is not allowed to charge when the current power is higher than the upper limit of charging, and must charge when the current power is lower than the lower limit of charging. When the current power of the vehicle is between the upper limit and the lower limit of charging, whether to charge is determined according to the power constraint, and the charging power of the vehicle cannot be higher than the upper limit of power supply. The detailed formula is:

[0056]

[0057] P sav (t)≤ε·G(t),

[0058] Among them, SOC i represents the current power of vehicle i, η SOC Indicates the number of kilometers that the vehicle can run per grid of power, y ik represents the decision variable for vehicle charging, d ik represents the distance between vehicle i and charging pile k, Represents a collection of charging piles. represents a collection of vehicles, represents the upper limit of charging, z ik It represents the upper decision variable of the vehicle charging decision variable, M represents an integer with no practical meaning, represents the lower bound of charging, w ik represents the lower decision variable of the vehicle charging decision variable, P sav (t) represents the charging power of the vehicle at time t, ε represents the power supply parameter of the power grid, and G(t) represents the load number of the power grid.

[0059] A242. Quantity constraints include that a vehicle can only receive one order at a time, an order can only be received by one vehicle, and a vehicle can only go to one charging station at a time. The relevant expressions are:

[0060]

[0061] Among them, x ij represents the decision variable for vehicle i to pick up order j, Represents the set of travel requests participating in the dispatch.

[0062] A243. The order time constraint is the time constraint that the actual pick-up time and the actual delivery time should meet the passenger's time window. Its expression is:

[0063]

[0064] in, Indicates the estimated pickup time of the order. represents the actual time it takes for an order to be picked up, Δ represents the maximum tolerance time for passengers, Indicates the actual drop-off time of the order.

[0065] A244. Carpooling constraints include time carpooling constraints and space carpooling constraints, and their expressions are:

[0066]

[0067] X AB =n AB ×m AB ,

[0068] Among them, m AB The decision variable indicating whether order A and order B can be assembled in time, m1, m2 and m3 are all m AB Auxiliary variables, Indicates the estimated pickup time of order A. Indicates the actual pickup time of order A. Indicates the actual time of order A and order B. It indicates the shortest journey time after order A and order B are shared. and Both are n AB Auxiliary variables, n1 and n2 both represent Auxiliary variables.

[0069] Based on the above simulation model, the flow chart is as follows Figure 1 The method shown is used to study vehicle charging scheduling, and the detailed steps are as follows:

[0070] S1. Divide the simulation model scenario into multiple sub-areas, and obtain relevant parameters and vehicle dispatch constraints for each sub-area in the simulation operation; wherein the relevant parameters include regional power grid load data, regional photovoltaic power generation data, online car-hailing specifications, order data and regional charging pile data. For vehicle constraints, see steps A241-A244.

[0071] S2. Calculate the travel benefits of the sharing platform:

[0072] The travel benefits of the sharing platform are calculated based on order data, regional photovoltaic power generation data and vehicle scheduling constraints, and the expression is:

[0073]

[0074] Among them, F1 represents the travel benefits of the sharing platform. represents the set of online ride-hailing vehicles participating in the dispatch, represents the set of travel requests involved in scheduling, Indicates the benefits generated by vehicle pick-up and drop-off orders, Represents the cost of vehicle pick-up and drop-off orders, represents the regional photovoltaic power generation subsidy coefficient, represents the cost of the vehicle going to charge, represents the set of charging stations participating in the scheduling, k represents the charging station number, x ij represents the decision variable for vehicle i to pick up order j, y ik represents the decision variable for vehicle i to go to charging station k, P pv (t) represents photovoltaic power generation, P max (t) represents the vehicle charging power.

[0075] The benefits generated by vehicle pick-up and drop-off orders are composed of two parts: the commission of the sum of the starting price, mileage and driving time, and the labor fee of the remaining amount. The calculation formula is as follows:

[0076]

[0077] (p sta +p km ×s j +p min ×t j )·(1-c pla )·θ pla ,

[0078] Among them, p sta Indicates the starting price of the order, p km Indicates the cost per kilometer of the order, s j represents the order distance, p min represents the service fee per minute for an order, t j Indicates the order time, c pla represents the commission of the total amount of the order, θ pla Indicates the labor fee commission of the remaining amount of the order.

[0079] The cost of vehicle pick-up orders is mainly determined by the distance between the vehicle's current location and the order pick-up point. What needs to be explained is the loss of the platform when the vehicle runs idly for 1 km. km " is calculated based on the hourly wages of local drivers, platform order benefits and other information, and the formula is as follows:

[0080]

[0081] And d ij Indicates the distance between the vehicle and the order.

[0082] The regional photovoltaic power generation subsidy coefficient is determined by the local photovoltaic power generation company, because the photovoltaic power generation company hopes that vehicles can be charged during the peak period of photovoltaic power generation to increase the utilization rate of photovoltaics.

[0083] The cost of a vehicle going to a charging station is mainly determined by the current electricity price, the distance between the vehicle and the charging station, and the estimated waiting time at the charging station. It is expressed in three parts, representing the distance cost from the current location of the vehicle to the charging station, the estimated waiting cost at the charging station, and the charging cost, as shown below:

[0084]

[0085] in, Indicates the estimated waiting time for the vehicle to charge at the charging station, L min It represents the loss caused to the platform for every idle minute. Indicates the estimated time for charging the vehicle, e price Indicates the electricity price.

[0086] S3. Calculate the regional fairness benefit of each sub-region:

[0087] S31. The order data also includes the order quantity. The order quantity within half an hour in each sub-area is obtained, and the order quantity in the future preset time period is predicted based on the order quantity within half an hour.

[0088] S32. Calculate the regional fairness benefit of each sub-region based on the number of orders in the future preset time period and the vehicle scheduling constraints, and the expression is:

[0089]

[0090] Among them, p represents a sub-region, P represents a sub-region set, SAVp p represents the number of vehicles in area p after scheduling, TOTAL SAV Represents the total number of vehicles involved in the dispatch, RE p Indicates the predicted number of requests from region p to this sub-region, TOTAL RE represents the total number of requests; the number of vehicles in area p after scheduling includes the original number of vehicles in area p, the number of vehicles going to area p due to orders, and the number of vehicles going to area p due to charging.

[0091] Among them, the number of vehicles in area p after scheduling is the original number of vehicles in the area, the number of vehicles going to the area due to orders, and the number of charging vehicles going to the area due to charging. The calculation formula for the number of vehicles going to the area due to orders is:

[0092]

[0093] The calculation formula for the number of vehicles going to the area for charging is:

[0094]

[0095] S4. Calculate the photovoltaic power generation utilization rate of each sub-area in each period:

[0096] The photovoltaic power generation utilization rate is calculated within the range of vehicle charging constraints, and its expression is:

[0097]

[0098] Among them, F3 represents the utilization rate of photovoltaic power generation, T represents the target period, P pv (t) represents photovoltaic power generation, P sav (t) represents the vehicle charging power.

[0099] S5. Generate vehicle charging scheduling strategy:

[0100] S51. Take the travel benefits of the sharing platform, regional fairness benefits and photovoltaic power generation utilization rate as objective functions.

[0101] S52. Calculate the evaluation value based on the objective function using the Pareto optimal solution rule:

[0102] S521, normalize each objective function, and its expression is:

[0103]

[0104] in, represents the normalized function of the ζth objective function in the kth Pareto solution; represents the ζth objective function in the kth Pareto solution; represents the minimum value of the ζth objective function; It represents the maximum value of the ζth objective function; ζ represents the objective function type, and when ζ=1, it represents the travel benefit of the sharing platform; when ζ=2, it represents the regional fairness benefit; and when ζ=3, it represents the utilization rate of photovoltaic power generation.

[0105] S522, weight allocation is performed according to the normalization processing result, and the evaluation value of the solution of each objective function is calculated, and its expression is:

[0106]

[0107] Among them, μ k represents the evaluation value of the kth Pareto solution, ω ζRepresents the weight value of the ζth objective function.

[0108] S53. Select a group of Pareto solutions with the largest evaluation value as the optimal solution for the scheduling strategy, and generate a vehicle charging scheduling plan based on the optimal solution.

[0109] This embodiment also provides a new energy vehicle dynamic charging-sharing coupling scheduling system for implementing the above method. Technical personnel in the relevant field can clearly understand that for the convenience and conciseness of description, the specific working process described can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0110] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A new energy vehicle dynamic charging-sharing coupling scheduling method, characterized in that: The method includes: Divide the target area into multiple sub-areas, and obtain relevant parameters and vehicle dispatch constraints for each sub-area; the relevant parameters include regional power grid load data, regional photovoltaic power generation data, online car-hailing specifications, order data, and regional charging pile data; The travel benefits of the sharing platform are calculated based on order data, regional photovoltaic power generation data and vehicle dispatch constraints; the order data includes the benefits generated by vehicle pick-up and drop-off orders, the costs generated by vehicle pick-up and drop-off orders, and the costs generated by vehicles going to charge; the regional photovoltaic power generation data includes the regional photovoltaic power generation subsidy coefficient and the regional photovoltaic power generation power; The order data also includes the order quantity, obtaining the order quantity of each sub-region in a preset time period, predicting the order quantity of a future preset time period based on the order quantity of the preset time period, and calculating the regional fairness benefit of each sub-region based on the order quantity of the future preset time period and the vehicle scheduling constraint; Calculate the photovoltaic power generation utilization rate in each sub-area and each time period based on vehicle scheduling constraints; The travel benefit of the sharing platform, the regional fairness benefit and the utilization rate of photovoltaic power generation are taken as objective functions, the evaluation value of each objective function is calculated using the Pareto optimal solution method, and a vehicle charging scheduling strategy is generated based on the evaluation value.

2. A new energy vehicle dynamic charging-sharing coupling scheduling method according to claim 1, characterized in that: The vehicle dispatch constraints include power constraints, charging requirement constraints, quantity constraints, order time constraints and carpooling constraints; The power constraint is that the current power of the vehicle must be sufficient to complete the current order or to complete the journey to the target charging area; the charging requirement constraint is that the vehicle is not allowed to charge when the current power is higher than the upper limit of charging, and must charge when the current power is lower than the lower limit of charging. When the current power of the vehicle is between the upper limit and the lower limit of charging, whether to charge is determined according to the power constraint, and the charging power of the vehicle cannot be higher than the upper limit of power supply; The quantity constraints include that a vehicle can only receive one order at a time, an order can only be received by one vehicle, and a vehicle can only go to one charging station at a time; the order time constraints are that the actual pick-up time and the actual delivery time should meet the passenger's time window; the carpooling constraints include time carpooling constraints and space carpooling constraints.

3. A new energy vehicle dynamic charging-sharing coupling scheduling method according to claim 1, characterized in that: The method for calculating the travel benefits of the sharing platform is: Among them, F1 represents the travel benefits of the sharing platform. represents the set of online ride-hailing vehicles participating in the dispatch, represents the set of travel requests involved in scheduling, Indicates the benefits generated by vehicle pick-up and drop-off orders, Represents the cost of vehicle pick-up and drop-off orders, represents the regional photovoltaic power generation subsidy coefficient, represents the cost of the vehicle going to charge, represents the set of charging stations participating in the scheduling, k represents the charging station number, x ij represents the decision variable for vehicle i to pick up order j, y ik represents the decision variable for vehicle i to go to charging station k, P pv (t) represents photovoltaic power generation, P max (t) represents the vehicle charging power.

4. A new energy vehicle dynamic charging-sharing coupling scheduling method according to claim 3, characterized in that: The benefits generated by the vehicle pick-up and drop-off order are obtained by the weighted sum of the commission on the total order amount and the commission on labor costs of the remaining amount.

5. A new energy vehicle dynamic charging-sharing coupling scheduling method according to claim 3, characterized in that: The cost of the vehicle pick-up and drop-off order is obtained by multiplying the current position of the vehicle and the idle running loss, and the idle running loss is calculated based on the hourly wage of the driver in the target area and the platform order efficiency.

6. A new energy vehicle dynamic charging-sharing coupling scheduling method according to claim 3, characterized in that: The cost of the vehicle going to charge includes the empty running cost of the vehicle going to the target charging area, the waiting time cost and the vehicle charging cost.

7. A new energy vehicle dynamic charging-sharing coupling scheduling method according to claim 1, characterized in that: The method for calculating the said fairness benefit is: Among them, p represents a sub-region, P represents a sub-region set, SAV p represents the number of vehicles in area p after scheduling, TOTAL sAV Represents the total number of vehicles involved in the dispatch, RE p Indicates the predicted number of requests from region p to this sub-region, TOTAL RE represents the total number of requests; the number of vehicles in area p after scheduling includes the original number of vehicles in area p, the number of vehicles going to area p due to orders, and the number of vehicles going to area p due to charging.

8. A new energy vehicle dynamic charging-sharing coupling scheduling method according to claim 1, characterized in that: The method for calculating the photovoltaic power generation utilization rate in each time period of the target charging area is: Among them, F3 represents the utilization rate of photovoltaic power generation, T represents the target period, P pv (t) represents photovoltaic power generation, P sav (t) represents the vehicle charging power.

9. A new energy vehicle dynamic charging-sharing coupling scheduling method according to claim 1, characterized in that: The step of calculating the evaluation value of each objective function includes: The travel benefits of the shared platform, regional fairness benefits and photovoltaic power generation utilization rate are normalized, and the expression is: in, represents the normalized function of the ζth objective function in the kth Pareto solution, represents the ζth objective function in the kth Pareto solution, represents the minimum value of the ζth objective function, represents the maximum value of the ζth objective function; ζ represents the objective function type, and when ζ=1, it represents the travel benefit of the sharing platform; when ζ=2, it represents the regional fairness benefit; when ζ=3, it represents the photovoltaic power generation utilization rate; The evaluation value is calculated based on the normalized function of each objective function, and its expression is: Among them, μ k represents the evaluation value of the kth Pareto solution, ω ζ represents the weight value of the ζth objective function; A group of Pareto solutions with the largest evaluation value is selected as the optimal solution for the scheduling strategy.

10. A new energy vehicle dynamic charging-sharing coupling scheduling system, characterized in that: The system is used to implement the method described in any one of claims 1 to 9.

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