V2V charging platform path planning and area pricing strategy optimization method and system

Abstract

The invention relates to a V2V charging platform path planning and area pricing strategy optimization method and system. The method comprises the steps that charging and discharging vehicles are divided into a charging vehicle node set and a discharging vehicle node set in areas; sorting all the charging vehicles according to the electricity price critical value, inserting the charging vehicles which are not served into the discharging vehicle path in sequence, generating a matching and path scheme of initial charging and discharging vehicles, and determining an initial path distribution result; according to an initial path distribution result, exploring a neighborhood possibly containing a better solution through a perturbation program to obtain an alternative charging and discharging path optimization scheme; and adjusting the charging and discharging vehicles in a single route and the charging and discharging vehicles crossing multiple routes based on the neighborhood structures of the discharging vehicles and the charging vehicles, and calculating to obtain an optimal optimization scheme. The vehicle-vehicle mutual charging efficiency of a V2V platform can be improved, the mileage loss of a user and the platform operation cost are reduced, and a solution is provided for a new energy vehicle distributed charging network.

Description

Technical Field

[0001] The present invention belongs to the fields of intelligent transportation and new energy vehicles, and particularly relates to a method and system for optimizing the path planning and regional pricing strategy of a V2V charging platform. Background Art

[0002] It is clearly pointed out in the "New Energy Vehicle Industry Development Plan (2021 - 2035)" that "by 2025, the sales volume of new energy vehicles will reach about 20% of the total sales volume of new cars, and by 2035, pure electric vehicles will become the mainstream of newly sold vehicles, and all vehicles in the public domain will be fully electrified". Obviously, electrification has become the dominant trend driving the transformation and upgrading of the new energy vehicle industry. Although the penetration rate of new energy vehicles is increasing year by year, the charging problem of electric vehicles is the key restricting its further development. The traditional layout of fixed charging stations is difficult to meet the rapidly expanding market demand. The existing layout of charging infrastructure usually does not match the distribution of charging demand, and it is difficult to cope with emergency charging needs. According to statistics, the number of charging piles in China exceeded 5 million in 2024, but the utilization rate of charging piles only reached 10%. As a mobile charging solution, V2V charging service can flexibly allocate charging resources according to the location and real-time needs of vehicles, solve problems such as uneven distribution of charging piles and difficulty for users to find charging piles, and can effectively improve the utilization efficiency of charging resources, having practical application value.

[0003] However, the existing research focuses on the feasibility of V2V charging service, and further in-depth research is needed on its deployment and detailed operation, especially the willingness of users to choose and the pricing strategy of the platform. As an important extension of the new energy vehicle charging network, V2V charging service must design a scientific operation mode to solve the limitations of the existing charging network, attract new energy vehicles to participate, and stimulate market development. Summary of the Invention

[0004] To make up for the deficiencies of the existing technology, the present invention provides a method and system for optimizing the path planning and regional pricing strategy of a V2V charging platform.

[0005] To solve the above technical problems, the technical solutions adopted by the present invention are as follows:

[0006] In the first aspect, a method for optimizing the path planning and regional pricing strategy of a V2V charging platform is provided, including:

[0007] Obtain the location information of all charging and discharging vehicles on the V2V platform, and divide the charging and discharging vehicles into a charging vehicle node set and a discharging vehicle node set by region according to the location information;

[0008] Sort all charging vehicles according to the electricity price critical value of the charging vehicles in each area, and insert the unserved charging vehicles into the discharging vehicle path in sequence to generate the matching and path scheme of the initial charging and discharging vehicles, and determine the initial path allocation result;

[0009] According to the initial path allocation result, explore the neighborhood that may contain better solutions through a perturbation program to obtain an alternative optimized charging and discharging path scheme;

[0010] According to the alternative optimized charging and discharging path scheme, based on the neighborhood structures of the discharging vehicles and the charging vehicles, adjust the charging and discharging vehicles within a single route and the charging and discharging vehicles across multiple routes, and calculate to obtain the best optimized scheme.

[0011] Furthermore, obtain the location information of all charging and discharging vehicles on the V2V platform, and divide the charging and discharging vehicles into a charging vehicle node set and a discharging vehicle node set by region according to the location information, including:

[0012] Obtain the location information of all charging and discharging vehicles on the V2V platform;

[0013] Divide the charging vehicles into a charging vehicle node set C by region according to the location information,

[0014] C = {1,..., m 1 +...+m J-1 +m J ,...} = C 1 ∪...∪C J ∪...

[0015] C J = {m 1 +...+m J-1 +1,..., m 1 +...+m J-1 +m J} represents the charging vehicle node set in region J; the discharging vehicle node set F = {1,..., n}, where n corresponds to the total number of discharging vehicles.

[0016] Furthermore, the method further includes:

[0017] Define the electricity price critical value of the charging vehicle when the cost of the charging vehicle i choosing to charge on the V2V platform is the same as that of choosing to charge at a charging pile

[0018] The expression for the same cost of charging on the V2V platform and at the charging pile is:

[0019]

[0020] Among them, t iThe consumption time to find an available fixed charging pile for charging vehicle i in area j; e i The charging demand of charging vehicle i; W pile The charging power of the charging pile The time value cost for the charging vehicle to charge using the charging pile The time value cost for the charging vehicle to choose to charge on the V2V platform The expected start time of the charging service for charging vehicle i The expected end time of the charging service for charging vehicle i; P pile The electricity price of the charging pile; P v2v The electricity price of the V2V platform

[0021] Furthermore, the electricity price threshold Indicates the acceptance degree of the user of charging vehicle i for the electricity price P of the V2V platform v2v of; When the electricity price threshold exceeds the electricity price P of the V2V platform v2v , choose to charge on the V2V platform; When the electricity price threshold does not exceed the electricity price P of the V2V platform v2v , choose to charge with the charging pile

[0022] Furthermore, after determining the initial path allocation result, it also includes:

[0023] Set the evaluation function h(y * ) to eliminate the infeasible solutions of the initial path allocation result y * ;

[0024] The expression of the evaluation function h(y * ) is:

[0025] h(y * ) = f(y * ) + γ 1 N cd (y * ) + γ 2 N cdt ( * ) + γ 3 N fdt (y * ) + γ 4 N fdw (y * );

[0026] Among them, f(y * ) represents the total revenue of the V2V platform; N cd (y * ) represents the violation selected by the charging vehicle; N cdt (y *) indicates a violation of the charging vehicle time window; N fdt (y * ) represents the violation of the discharge vehicle time window, N fdw (y * ) represents the violation of the remaining power of the discharge vehicle; γ 1 , γ 2 , γ 3 and γ 4 These are all preset penalty coefficients.

[0027] Further, the disturbance program includes disturbance program S1, disturbance program S2, disturbance program S3 and disturbance program S4.

[0028] The perturbation procedure S1 randomly selects a critical value of electricity price of unserviced charging vehicles, and the electricity price threshold is greater than The charging vehicles are inserted into the discharge vehicle routes;

[0029] The perturbation procedure S2 randomly selects a first charging vehicle that has been served, selects two second charging vehicles that are similar to the first charging vehicle and removes them, temporarily stores the second charging vehicles in the set R1, and then calculates the distance from each charging vehicle to each discharging vehicle in the set R1 in turn, and selects the path of the discharging vehicle with the closest distance for insertion;

[0030] The perturbation procedure S3 randomly removes two third charging vehicles that have been served, temporarily stores the third charging vehicles in the set R2, traverses all the insertable positions of the discharge vehicle routes, and calculates the cost change after insertion. The maximum cost reduction generated by inserting different positions is defined as the regret value. In descending order of regret value, each charging vehicle in the set R2 is inserted into the optimal position of the discharge vehicle route in turn;

[0031] The disturbance program S4 randomly selects a first discharge vehicle and calculates the current remaining power. If the current remaining power is less than 20%, the fourth charging vehicle on the corresponding discharge vehicle route is removed according to the power demand of the first discharge vehicle, and the fourth charging vehicle is temporarily stored in the set R3. The remaining power of other discharge vehicles is analyzed, and a second discharge vehicle with sufficient remaining power and no power risk to its subsequent service after charging and insertion is selected. Each charging vehicle in the set R3 is inserted into the discharge vehicle path corresponding to the second discharge vehicle in turn.

[0032] Furthermore, according to the alternative charging and discharging route optimization scheme, based on the neighborhood structure of the discharging vehicles and the charging vehicles, the charging and discharging vehicles within a single route and the charging and discharging vehicles across multiple routes are adjusted, and the best optimization scheme is calculated, including:

[0033] According to the alternative charging and discharging path optimization scheme y′, based on the neighborhood structures of the discharging vehicles and the charging vehicles, adjust the charging and discharging vehicles within a single route and the charging and discharging vehicles across multiple routes, and calculate the path allocation result y″ at the k-th time, where k is a positive integer greater than 0;

[0034] Judge whether the total revenue f(y″) of the V2V platform of the path allocation result y″ at the k-th time is greater than the total revenue f(y * of the V2V platform of * );

[0035] If f(y″) < f(y * ), then readjust to obtain the path allocation result at the (k + 1)-th time;

[0036] If f(y″) > f(y * ), then take the path allocation result y″ at the k-th time as the new path allocation result;

[0037] Judge whether k reaches the preset iteration times threshold K;

[0038] If k < K, then readjust to obtain the path allocation result at the (k + 1)-th time;

[0039] If k = K, then determine the path allocation result y″ at the k-th time as the optimal optimization scheme.

[0040] In a second aspect, a V2V charging platform path planning and regional pricing strategy optimization system is provided, including:

[0041] A node set acquisition module, configured to acquire the position information of all charging and discharging vehicles on the V2V platform, and divide the charging and discharging vehicles into a charging vehicle node set and a discharging vehicle node set by region according to the position information;

[0042] An initial path planning module, configured to sort all charging vehicles according to the electricity price critical value of the charging vehicles in each region, and insert the unserved charging vehicles into the discharging vehicle path in sequence to generate a matching and path scheme for the initial charging and discharging vehicles, and determine the initial path allocation result;

[0043] A variable neighborhood perturbation module, configured to explore the neighborhood that may contain a better solution through a perturbation program according to the initial path allocation result to obtain an alternative charging and discharging path optimization scheme;

[0044] An optimal optimization scheme calculation module, configured to adjust the charging and discharging vehicles within a single route and the charging and discharging vehicles across multiple routes according to the alternative charging and discharging path optimization scheme, based on the neighborhood structures of the discharging vehicles and the charging vehicles, and calculate the optimal optimization scheme.

[0045] Beneficial effects achieved by the present invention:

[0046] Obtain the location information of all charging and discharging vehicles on the V2V platform, and divide the charging and discharging vehicles into a charging vehicle node set and a discharging vehicle node set according to the location information; sort all charging vehicles according to the electricity price threshold of the charging vehicles in each area, and insert the unserved charging vehicles into the discharging vehicle path in sequence to generate a matching and path plan for the initial charging and discharging vehicles, and determine the initial path allocation result; according to the initial path allocation result, explore the neighborhood that may contain better solutions through a perturbation program to obtain an alternative charging and discharging path optimization plan; according to the alternative charging and discharging path optimization plan, based on the neighborhood structure of the discharging vehicles and the charging vehicles, adjust the charging and discharging vehicles within a single route and the charging and discharging vehicles across multiple routes, and calculate to obtain the best optimization plan. It can improve the vehicle-to-vehicle charging efficiency of the V2V platform, reduce the mileage loss of users and the operating cost of the platform, and provide a solution for the distributed charging network of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flowchart of the method for optimizing the path planning and regional pricing strategy of the V2V charging platform of the present invention;

[0048] Figure 2 It is a schematic diagram of a small network based on the Sioux Falls network of the present invention;

[0049] Figure 3 It is a structural diagram of the system for optimizing the path planning and regional pricing strategy of the V2V charging platform of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0050] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and should not be used to limit the protection scope of the present invention.

[0051] As Figure 1 shown, an embodiment of the present invention provides a method for optimizing the path planning and regional pricing strategy of a V2V charging platform, including:

[0052] 101. Obtain the location information of all charging and discharging vehicles on the V2V platform, and divide the charging and discharging vehicles into a charging vehicle node set and a discharging vehicle node set according to the location information;

[0053] In this embodiment, obtain the location information of all charging and discharging vehicles on the V2V platform;

[0054] According to the location information, divide the charging vehicles into a charging vehicle node set C by region; the expression of C is:

[0055] C = {1,..., m 1+…+m J-1 +m J ,…}=C 1 ∪…∪C J ∪…,

[0056] C J ={m 1 +…+m J-1 +1,…,m 1 +…+m J-1 +m J} represents the set of charging vehicle nodes in area J; the set of discharging vehicle nodes F = {1, …, n}, where n corresponds to the total number of discharging vehicles;

[0057] The charging vehicles and discharging vehicles are represented in the form of a small network based on the Sioux Falls network. As Figure 2 shown in the schematic diagram of the small network based on the Sioux Falls network, where the red dots represent the set of charging vehicle nodes, the green dots represent the set of discharging vehicle nodes, and the white dots are the original nodes, representing vehicles that do not need to charge or discharge; in area 1, there are charging vehicle nodes 7 and 8 and discharging vehicle node 3; in area 2, there are charging vehicle nodes 4, 5, and 6 and discharging vehicle nodes 1 and 2.

[0058] 102. Sort all charging vehicles according to the electricity price critical value of the charging vehicles in each area, and insert the unserved charging vehicles into the discharging vehicle path in sequence to generate the matching and path scheme of the initial charging and discharging vehicles, and determine the initial path allocation result;

[0059] In this embodiment, the electricity price critical value of the charging vehicle when the cost of charging vehicle i choosing to charge on the V2V platform is the same as that of choosing a charging pile is defined

[0060] The expression for the same cost of charging on the V2V platform and the charging pile is:

[0061]

[0062] where t i is the consumption time for charging vehicle i to find an available fixed charging pile in area j; e i is the charging demand of charging vehicle i; W pile is the charging power of the charging pile; is the time value cost of charging vehicle using the charging pile. The unit time value cost of the charging vehicle waiting for the charging pile service is randomly generated between 50 yuan / hour, 60 yuan / hour, 70 yuan / hour, 80 yuan / hour, and 90 yuan / hour; The time - value cost of selecting V2V platform charging for a charging vehicle. The time - value cost per unit hour for the charging vehicle waiting for V2V charging service is randomly generated between 10 yuan / hour, 20 yuan / hour, 30 yuan / hour, and 40 yuan / hour; is the expected start time of the charging service for charging vehicle i; is the expected end time of the charging service for charging vehicle i. The time - window size of the charging vehicle is randomly generated between 1 hour, 1.5 hours, and 2 hours, while the time - window size of the discharging vehicle is randomly generated between 2 hours, 3 hours, and 4 hours; P pile is the charging - pile electricity price; P v2v is the V2V - platform electricity price;

[0063] Electricity - price critical value represents the acceptance degree of the user of charging vehicle i for the V2V - platform electricity price P v2v ; when the electricity - price critical value exceeds the V2V - platform electricity price P v2v , select V2V - platform charging; when the electricity - price critical value does not exceed the V2V - platform electricity price P v2v , select charging - pile charging.

[0064] It should be noted that in the Variable Neighborhood Search (VNS) algorithm, four situations that can lead to infeasible solutions can be observed;

[0065] After determining the initial path - allocation result in step 102, it is also necessary to set an evaluation function h(y * ) to eliminate the infeasible solutions of the initial path - allocation result y * ;

[0066] The expression of the evaluation function h(y * ) is:

[0067] h(y * ) = f(y * ) + γ 1 N cd (y * ) + γ 2 N cdt (y * ) + γ 3 N fdt (y * ) + γ 4 N fdw (y * );

[0068] Among them, f(y * ) represents the total revenue of the V2V platform; N cd(y * ) indicates a violation of the charging vehicle selection, that is, the charging vehicle did not select the V2V platform, but the V2V platform serves it; N cdt (y * ) indicates a violation of the charging vehicle time window, that is, the charging vehicle service end time exceeds the time window limit; N fdt (y * ) indicates the violation of the time window of the discharge vehicle, that is, the time for the discharge vehicle to return to the starting node exceeds the time window limit; N fdw (y * ) represents the violation of the remaining power of the discharging vehicle, that is, when the discharging vehicle completes the discharging service, the remaining power is insufficient to return to the starting node; γ 1 , γ 2 , γ 3 and γ 4 These are all preset penalty coefficients.

[0069] 103, based on the initial path allocation result, the neighborhood that may contain better solutions is explored through a perturbation program to obtain an alternative charging and discharging path optimization solution;

[0070] In this embodiment, the disturbance program includes disturbance program S1, disturbance program S2, disturbance program S3 and disturbance program S4.

[0071] The perturbation procedure S1 randomly selects a critical value of electricity price of unserviced charging vehicles, and the electricity price threshold is greater than The charging vehicles are inserted into the discharge vehicle routes;

[0072] The perturbation procedure S2 randomly selects a first charging vehicle that has been served, selects two second charging vehicles that are similar to the first charging vehicle and removes them, temporarily stores the second charging vehicles in the set R1, and then calculates the distance from each charging vehicle to each discharging vehicle in the set R1 in turn, and selects the path of the discharging vehicle with the closest distance for insertion;

[0073] The perturbation procedure S3 randomly removes two third charging vehicles that have been served, temporarily stores the third charging vehicles in the set R2, traverses all the insertable positions of the discharge vehicle routes, and calculates the cost change after insertion. The maximum cost reduction generated by inserting different positions is defined as the regret value. In descending order of regret value, each charging vehicle in the set R2 is inserted into the optimal position of the discharge vehicle route in turn;

[0074] The perturbation program S4 randomly selects a first discharging vehicle, calculates the current remaining power. If the current remaining power is lower than 20%, then according to the power demand of the first discharging vehicle, the fourth charging vehicle on the corresponding discharging vehicle route is removed and temporarily stored in the set R3. Analyze the remaining power of other discharging vehicles, select a second discharging vehicle with sufficient remaining power and no power risk to its subsequent services after charging insertion, and sequentially insert each charging vehicle in the set R3 into the discharging vehicle path corresponding to the second discharging vehicle.

[0075] 104. According to the alternative charging and discharging path optimization scheme, based on the neighborhood structures of the discharging vehicles and the charging vehicles, adjust the charging and discharging vehicles within a single route and the charging and discharging vehicles across multiple routes, and calculate to obtain the optimal optimization scheme.

[0076] In this embodiment, according to the alternative charging and discharging path optimization scheme y′, based on the neighborhood structures of the discharging vehicles and the charging vehicles, adjust the charging and discharging vehicles within a single route and the charging and discharging vehicles across multiple routes, and calculate to obtain the path allocation result y″ of the kth time, where k is a positive integer greater than 0;

[0077] Judge whether the total revenue f(y″) of the V2V platform of the path allocation result y″ of the kth time is greater than the total revenue f(y * of the V2V platform of the initial path allocation result y * )

[0078] If f(y″) < f(y * ), then readjust to obtain the path allocation result of the (k + 1)th time;

[0079] If f(y″) > f(y * ), then take the path allocation result y″ of the kth time as the new path allocation result;

[0080] Judge whether k reaches the preset iteration times threshold K;

[0081] If k < K, then readjust to obtain the path allocation result of the (k + 1)th time;

[0082] If k = K, then determine the path allocation result y″ of the kth time as the optimal optimization scheme.

[0083] The beneficial effects of the embodiments of the present invention are:

[0084] Obtain the location information of all charging and discharging vehicles on the V2V platform, divide the charging and discharging vehicles into a charging vehicle node set and a discharging vehicle node set by region according to the location information; sort all charging vehicles according to the electricity price threshold of the charging vehicles in each region, and insert the unserved charging vehicles into the discharging vehicle path in sequence to generate a matching and path plan for the initial charging and discharging vehicles, and determine the initial path allocation result; according to the initial path allocation result, explore the neighborhood that may contain a better solution through a perturbation program to obtain an alternative optimized charging and discharging path plan; according to the alternative optimized charging and discharging path plan, based on the neighborhood structures of the discharging vehicles and the charging vehicles, adjust the charging and discharging vehicles within a single route and the charging and discharging vehicles across multiple routes, and calculate to obtain the best optimized plan. It can improve the vehicle-to-vehicle charging efficiency of the V2V platform, reduce the mileage loss of users and the operating cost of the platform, and provide a solution for the distributed charging network of new energy vehicles.

[0085] Combined with the V2V charging platform path planning and regional pricing strategy optimization method described in the above embodiments, the V2V charging platform path planning and regional pricing strategy optimization system will be described below through embodiments.

[0086] As Figure 3 shown, an embodiment of the present invention provides a V2V charging platform path planning and regional pricing strategy optimization system, including:

[0087] A node set acquisition module 301, configured to obtain the location information of all charging and discharging vehicles on the V2V platform, and divide the charging and discharging vehicles into a charging vehicle node set and a discharging vehicle node set by region according to the location information;

[0088] An initial path planning module 302, configured to sort all charging vehicles according to the electricity price threshold of the charging vehicles in each region, and insert the unserved charging vehicles into the discharging vehicle path in sequence to generate a matching and path plan for the initial charging and discharging vehicles, and determine the initial path allocation result;

[0089] A variable neighborhood perturbation module 303, configured to explore the neighborhood that may contain a better solution through a perturbation program according to the initial path allocation result to obtain an alternative optimized charging and discharging path plan;

[0090] A best optimization plan calculation module 304, configured to adjust the charging and discharging vehicles within a single route and the charging and discharging vehicles across multiple routes based on the neighborhood structures of the discharging vehicles and the charging vehicles according to the alternative optimized charging and discharging path plan, and calculate to obtain the best optimized plan.

[0091] The beneficial effects of the embodiments of the present invention are:

[0092] The node set acquisition module 301 acquires the location information of all charging and discharging vehicles on the V2V platform, and divides the charging and discharging vehicles into a charging vehicle node set and a discharging vehicle node set by region according to the location information; the initial path planning module 302 sorts all charging vehicles according to the electricity price threshold of the charging vehicles in each region, and inserts the unserved charging vehicles into the discharging vehicle path in sequence to generate a matching and path scheme for the initial charging and discharging vehicles, and determines the initial path allocation result; the variable neighborhood perturbation module 303 explores the neighborhood that may contain a better solution through a perturbation program according to the initial path allocation result to obtain an alternative optimized charging and discharging path scheme; the optimal optimization scheme calculation module 304 adjusts the charging and discharging vehicles within a single route and the charging and discharging vehicles across multiple routes based on the neighborhood structure of the discharging vehicles and the charging vehicles according to the alternative optimized charging and discharging path scheme, and calculates to obtain the optimal optimization scheme. It can improve the vehicle-to-vehicle charging efficiency of the V2V platform, reduce the mileage loss of users and the operating cost of the platform, and provide a solution for the distributed charging network of new energy vehicles.

[0093] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for realizing in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0095] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device realizes in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps of the function specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 in one block or a plurality of blocks.

[0097] The above are only embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included within the scope of the claims of the present invention pending approval of the application.

Claims

1. A V2V charging platform path planning and regional pricing strategy optimization method, characterized in that: include: Acquire the location information of all charging and discharging vehicles on the V2V platform, and divide the charging and discharging vehicles into charging vehicle node sets and discharging vehicle node sets according to the location information; All charging vehicles are sorted according to the critical value of the electricity price of charging vehicles in each area, and unserved charging vehicles are inserted into the discharge vehicle path in order to generate the matching and path plan of the initial charging and discharging vehicles and determine the initial path allocation result; According to the initial path allocation result, a neighborhood that may contain a better solution is explored through a perturbation program to obtain an alternative charging and discharging path optimization solution; According to the alternative charging and discharging route optimization scheme, based on the neighborhood structure of discharging vehicles and charging vehicles, the charging and discharging vehicles within a single route and the charging and discharging vehicles across multiple routes are adjusted to calculate the best optimization scheme.

2. The V2V charging platform path planning and regional pricing strategy optimization method according to claim 1 is characterized in that: The acquiring of the location information of all charging and discharging vehicles on the V2V platform, and dividing the charging and discharging vehicles into charging vehicle node sets and discharging vehicle node sets according to the location information, includes: Obtain the location information of all charging and discharging vehicles on the V2V platform; According to the location information, the charging vehicles are divided into charging vehicle node sets C by region, and the charging vehicle node set C is C={1,…,m1+…+m J-1 +m J , ...} = C1∪…∪C J ∪…, the C J ={{m1+…+m J-1 +1,…,m1+…+m J-1 +m J } represents the charging vehicle node set of area J; the discharging vehicle node set F = {1,…,n}, where n corresponds to the total number of discharging vehicles.

3. The V2V charging platform path planning and regional pricing strategy optimization method according to claim 2 is characterized in that: The method further comprises: Define the critical value of the electricity price of the charging vehicle when the cost of charging the charging vehicle i by choosing the V2V platform is the same as that of charging the charging pile The cost of charging the V2V platform and charging the charging pile is the same as that of charging the charging pile: Among them, the t i is the time it takes for the charging vehicle i to find an available fixed charging pile in area j; i is the charging demand of the charging vehicle i; pile The charging power of the charging pile; The time value cost of charging a charging vehicle using a charging pile; The time value cost of selecting the V2V platform for charging the charging vehicle; The expected charging service start time for charging vehicle i; is the expected end time of charging service for charging vehicle i; pile is the electricity price of the charging pile; v2v is the electricity price of the V2V platform.

4. The V2V charging platform path planning and regional pricing strategy optimization method according to claim 3 is characterized in that: The electricity price threshold represents the electricity price P of the V2V platform for the user of the charging vehicle i v2v acceptance level; when the electricity price threshold Exceeding the V2V platform electricity price P v2v When the electricity price is at a critical value, the V2V platform is selected for charging; Not exceeding the V2V platform electricity price P v2v When the charging station is selected, the charging station is selected for charging.

5. The V2V charging platform path planning and regional pricing strategy optimization method according to claim 1 is characterized in that: After determining the initial path allocation result, the method further includes: Set the evaluation function h(y * ) Eliminate the initial path allocation result y * Infeasible solution of ; The evaluation function h(y * ) is: h(y * )=f(y * )+γ1N cd (y * )+γ2N cdt (y * )+γ3N fdt (y * )+γ4N rdw (y * ); Wherein, the f(y * ) represents the total revenue of the V2V platform; cd (y * ) indicates a violation of the charging vehicle selection; N cdt (y * ) indicates a violation of the charging vehicle time window; the N fdt (y * ) indicates a violation of the discharge vehicle time window, the N fdw (y * ) represents the violation of discharging the remaining power of the vehicle; γ1, γ2, γ3 and γ4 are all preset penalty coefficients.

6. The V2V charging platform path planning and regional pricing strategy optimization method according to claim 5 is characterized in that: The disturbance program includes disturbance program S1, disturbance program S2, disturbance program S3 and disturbance program S4. The perturbation procedure S1 is to randomly select a critical value of electricity price of unserviced charging vehicles, and the electricity price threshold is greater than the The charging vehicles are inserted into the discharge vehicle routes; The perturbation procedure S2 randomly selects a first charging vehicle that has been served, selects two second charging vehicles that are similar to the first charging vehicle and removes them, temporarily stores the second charging vehicles in the set R1, and then calculates the distance from each charging vehicle to each discharging vehicle in the set R1 in turn, and selects the path of the discharging vehicle with the closest distance for insertion; The perturbation procedure S3 is to randomly remove two third charging vehicles that have been served, temporarily store the third charging vehicles in the set R2, traverse the insertable positions of all discharge vehicle routes, and calculate the cost change after insertion, define the maximum cost reduction generated by inserting different positions as the regret value, and insert each charging vehicle in the set R2 into the optimal position of the discharge vehicle route in descending order of the regret value; The disturbance program S4 randomly selects a first discharging vehicle and calculates the current remaining power. If the current remaining power is less than 20%, the fourth charging vehicle on the corresponding discharging vehicle route is removed according to the power demand of the first discharging vehicle, and the fourth charging vehicle is temporarily stored in the set R3. The remaining power of other discharging vehicles is analyzed, and a second discharging vehicle with sufficient remaining power and which will not cause power risks to its subsequent services after charging and insertion is selected, and each charging vehicle in the set R3 is inserted into the discharging vehicle path corresponding to the second discharging vehicle in turn.

7. The V2V charging platform path planning and regional pricing strategy optimization method according to claim 6 is characterized in that: The method of optimizing the alternative charging and discharging routes, adjusting the charging and discharging vehicles within a single route and the charging and discharging vehicles across multiple routes based on the neighborhood structure of the discharging vehicles and the charging vehicles, and calculating the best optimization solution includes: According to the alternative charging and discharging path optimization scheme y′, based on the neighborhood structure of the discharging vehicles and the charging vehicles, the charging and discharging vehicles within a single route and the charging and discharging vehicles across multiple routes are adjusted to calculate the kth path allocation result y″, where the value of k is a positive integer greater than 0; Determine whether the total revenue f(y″) of the V2V platform of the k-th path allocation result y″ is greater than the initial path allocation result y * The total revenue of the V2V platform f(y * ); If f(y″)<f(y * ), then readjust to obtain the k+1th path allocation result; If f(y″)>f(y * ), then the k-th path allocation result y″ is used as the new path allocation result; Determine whether k reaches a preset iteration number threshold K; If k<K, readjust to obtain the k+1th path allocation result; If k=K, then the k-th path allocation result y″ is determined to be the best optimization solution.

8. A V2V charging platform path planning and regional pricing strategy optimization system, characterized in that: include: A node set acquisition module is used to acquire the location information of all charging and discharging vehicles on the V2V platform, and divide the charging and discharging vehicles into charging vehicle node sets and discharging vehicle node sets according to the location information; The initial path planning module is used to sort all charging vehicles according to the electricity price threshold of charging vehicles in each area, and insert unserved charging vehicles into the discharge vehicle path in order, generate the matching and path plan of the initial charging and discharging vehicles, and determine the initial path allocation result; A variable neighborhood perturbation module is used to explore the neighborhood that may contain a better solution through a perturbation program according to the initial path allocation result, so as to obtain an alternative charging and discharging path optimization solution; The best optimization solution calculation module is used to adjust the charging and discharging vehicles within a single route and the charging and discharging vehicles across multiple routes according to the alternative charging and discharging path optimization solution and based on the neighborhood structure of the discharging vehicles and the charging vehicles, and calculate the best optimization solution.

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