Method and device for scheduling charging and discharging tasks for rebalancing of shared electric vehicles

Abstract

The application discloses a shared electric vehicle rebalancing charging and discharging task scheduling method and device, relates to the technical field of electric power systems, and mainly aims to solve the problem of poor accuracy of existing shared electric vehicle rebalancing charging and discharging task scheduling. Including: obtaining basic information of a shared electric vehicle, the basic information including site information, vehicle information, peak and valley value information of electricity price, and driving information; determining main decision variables and auxiliary decision variables, and based on the basic information, the main decision variables and the auxiliary decision variables, forming the nonlinear programming model by constructing constraint conditions, the nonlinear programming model being used for describing a benefit function relationship of a charging and discharging scheduling task in shared electric vehicle rebalancing; linearizing the nonlinear programming model to obtain a linear programming model, and solving the linear programming model based on a constraint set and an effective inequality to obtain a charging and discharging task scheduling result of shared electric vehicle rebalancing.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method and apparatus for scheduling charging and discharging tasks for rebalancing shared electric vehicles. Background Technology

[0002] Against the backdrop of the coordinated development of smart grids and new energy vehicles, Vehicle-to-Grid (V2G) enables bidirectional energy flow management between shared electric vehicles and the power grid by constructing a vehicle-to-grid energy interaction system. Specifically, this technology allows the shared electric vehicle energy storage system to discharge to the grid as a distributed power source during peak grid load periods, effectively alleviating power supply pressure through a demand-side response mechanism. Conversely, during off-peak load periods, charging operations are implemented using off-peak electricity pricing mechanisms, reducing the peak-to-valley difference in grid usage while improving energy efficiency. In particular, with the rapid development of low-carbon and environmentally friendly shared vehicles, shared electric vehicles not only facilitate public travel but also significantly reduce the environmental pollution caused by traditional fuel vehicles.

[0003] Currently, existing shared electric vehicle systems face supply-demand matching challenges due to the heterogeneity of vehicle distribution in time and space: Travel demand fluctuates significantly over time, with peak demand during morning and evening rush hours contrasting sharply with low demand at midday and late at night; regional demand varies significantly in space, with strong demand in high-density areas such as city centers, commercial districts, and transportation hubs, while demand is relatively weak in low-density areas such as suburbs and industrial parks. Therefore, shared electric vehicle operators urgently need to build a vehicle rebalancing strategy system between stations to optimize resource allocation and achieve a synergistic improvement in operational revenue and service efficiency. However, most shared electric vehicle rebalancing scheduling strategies focus on the efficiency optimization of vehicle space reallocation, lacking time-series control over the status of parking spaces at stations. This leads to resource conflicts at fully loaded stations, where "vehicles have been scheduled to be moved in even though they have not been moved out." The dual value of shared electric vehicles as flexible loads and distributed energy storage resources has not been fully explored. In particular, in the rebalancing scheduling of shared electric vehicles, there is a lack of coordinated solutions that take into account both balance and systemic aspects between rebalancing scheduling and charging / discharging task scheduling. Therefore, there is an urgent need for a charging / discharging task scheduling method for shared electric vehicle rebalancing to solve the above problems. Summary of the Invention

[0004] In view of this, this application provides a method and apparatus for scheduling charging and discharging tasks for rebalancing shared electric vehicles. The main purpose is to solve the problems in the operation of existing shared electric vehicle systems, such as the supply and demand matching problem caused by the heterogeneity of vehicle spatiotemporal distribution, the insufficient exploitation of the dual value of vehicles as flexible loads and distributed energy storage, and the lack of systematic means for rebalancing scheduling and charging and discharging task scheduling.

[0005] According to one aspect of this application, a method for scheduling charging and discharging tasks for rebalancing shared electric vehicles is provided, comprising:

[0006] Obtain basic information about shared electric vehicles, including site information, vehicle information, peak and off-peak electricity prices, and driving information;

[0007] The main decision variables and auxiliary decision variables are determined, and based on the basic information, the main decision variables and the auxiliary decision variables, the nonlinear programming model is formed by constructing constraints. The nonlinear programming model is used to describe the revenue function relationship of the charging and discharging scheduling task in the rebalancing of shared electric vehicles.

[0008] The nonlinear programming model is linearized to obtain a linear programming model. The linear programming model is then solved based on the constraint set and effective inequalities to obtain the scheduling results of the charging and discharging tasks for the rebalancing of the shared electric vehicles. The effective inequalities are used to eliminate path symmetry in the scheduling process of the charging and discharging tasks for the rebalancing of the shared electric vehicles.

[0009] Furthermore, the process of forming the nonlinear programming model by constructing constraints based on the basic information, the main decision variables, and the auxiliary decision variables includes:

[0010] Based on the number of charging and discharging piles, the status of stored vehicles, and the parking status in the site information, site status identification is performed to obtain a site status identification classification set.

[0011] Based on the storage location and battery status in the vehicle information, vehicle status identification is performed to obtain a vehicle status identification classification set.

[0012] Based on the site status identification classification set and the vehicle status identification classification set, the main decision variables are determined and the auxiliary decision variables are configured. The main decision variables include path transition variables and time variables, and the auxiliary decision variables include peak and valley identification variables, product auxiliary variables, and absolute value auxiliary variables.

[0013] Using the electricity revenue set as the objective function component, and combining the main decision variables and the auxiliary decision variables, the nonlinear programming model is formed by constructing constraints.

[0014] Furthermore, the set of electricity revenue includes unscheduled discharge revenue, dispatched discharge revenue, dispatching costs, and imbalance penalty costs. The method also includes:

[0015] Based on the main decision variables and the auxiliary decision variables, a constraint set is constructed to constrain the nonlinear programming model. The constraint set includes multiple constraint relationships such as constraint benefits, constraint time, and constraint charging and discharging behavior.

[0016] Furthermore, the linearization process of the nonlinear programming model to obtain a linear programming model includes:

[0017] The nonlinear component in the nonlinear programming model is identified, and the nonlinear component is linearized based on the auxiliary decision variables to obtain a linear programming model.

[0018] The nonlinear part includes the nonlinear part of the multiplication of decision variables and the nonlinear part of the absolute value function.

[0019] Furthermore, before solving the linear programming model based on the constraint set and effective inequalities to obtain the charging and discharging task scheduling result for the shared electric vehicle rebalancing, the method further includes:

[0020] Based on the order constraint of parking space nodes, an ordered sequence transformation of symmetrical parking space nodes is performed to construct the first effective inequality;

[0021] Based on the allocation order between distance length and employee number, a second effective inequality is constructed.

[0022] Furthermore, after obtaining the charging and discharging task scheduling result of the shared electric vehicle rebalancing, the method further includes:

[0023] Based on the charging and discharging task scheduling results of the shared electric vehicle rebalancing, the target station and target driver of the shared electric vehicle to be charged and discharged are determined, and the target station is sent to the target driver so that the target driver can control the shared electric vehicle to drive to the target station.

[0024] Furthermore, the method also includes:

[0025] In response to the basic information update instruction, the updated basic information is obtained, the steps of determining the main decision variables and auxiliary decision variables are re-executed, and the nonlinear programming model is formed by constructing constraints based on the basic information, the main decision variables, and the auxiliary decision variables, so as to obtain the charging and discharging task scheduling results of the shared electric vehicle rebalancing again.

[0026] According to another aspect of this application, a charging and discharging task scheduling device for rebalancing shared electric vehicles is provided, comprising:

[0027] The acquisition module is used to acquire basic information about shared electric vehicles, including site information, vehicle information, peak and off-peak electricity prices, and driving information.

[0028] The determination module is used to determine the main decision variables and auxiliary decision variables, and based on the basic information, the main decision variables and the auxiliary decision variables, to form the nonlinear programming model by constructing constraints. The nonlinear programming model is used to describe the revenue function relationship of the charging and discharging scheduling task in the rebalancing of shared electric vehicles.

[0029] The solution module is used to linearize the nonlinear programming model to obtain a linear programming model, and solve the linear programming model based on the constraint set and effective inequalities to obtain the charging and discharging task scheduling result of the shared electric vehicle rebalancing. The effective inequalities are used to eliminate path symmetry in the charging and discharging task scheduling process of the shared electric vehicle rebalancing.

[0030] Furthermore,

[0031] The determining module is specifically used to identify the station status based on the number of charging and discharging piles, the status of stored vehicles, and the parking status in the station information, and obtain a station status identification classification set; to identify the vehicle status based on the storage station and the power status in the vehicle information, and obtain a vehicle status identification classification set; to determine the main decision variables based on the station status identification classification set and the vehicle status identification classification set, and to configure the auxiliary decision variables, wherein the main decision variables include path transition variables and time variables, and the auxiliary decision variables include peak and valley identification variables, product auxiliary variables, and absolute value auxiliary variables; and to form the nonlinear programming model by constructing constraints, using the power revenue set as the objective function component, and combining the main decision variables and the auxiliary decision variables.

[0032] Furthermore, the power revenue set includes unscheduled discharge revenue, dispatched discharge revenue, dispatching costs, and imbalance penalty costs; the device also includes:

[0033] The construction module is used to construct a constraint set for constraining the nonlinear programming model based on the main decision variables and the auxiliary decision variables. The constraint set includes multiple constraint relationships such as constraint benefits, constraint time, and constraint charging and discharging behavior.

[0034] Furthermore,

[0035] The solution module is specifically used to determine the nonlinear part in the nonlinear programming model and linearize the nonlinear part based on the auxiliary decision variables to obtain a linear programming model; wherein, the nonlinear part includes the nonlinear part of the multiplication of decision variables and the nonlinear part of the absolute value function.

[0036] Furthermore,

[0037] The construction module is also used to perform ordered sequence transformation on symmetrical parking space nodes based on parking space node order constraints to construct a first effective inequality; and to construct a second effective inequality based on the allocation order between distance length and employee number.

[0038] Furthermore, the determining module is also used to determine the target station and target driver of the shared electric vehicle to be charged and discharged based on the charging and discharging task scheduling result of the shared electric vehicle rebalancing, and send the target station to the target driver so that the target driver can control the shared electric vehicle to drive to the target station.

[0039] Furthermore, the device also includes:

[0040] The acquisition module is further configured to respond to the basic information update instruction, acquire and update the basic information, re-execute the steps of determining the main decision variables and auxiliary decision variables, and form the nonlinear programming model by constructing constraints based on the basic information, the main decision variables and the auxiliary decision variables, so as to obtain the charging and discharging task scheduling results of the shared electric vehicle rebalancing.

[0041] According to another aspect of this application, a storage medium is provided that stores at least one executable instruction, which causes a processor to perform operations corresponding to the above-described shared electric vehicle rebalancing charging and discharging task scheduling method.

[0042] According to another aspect of this application, a terminal is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0043] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described shared electric vehicle rebalancing charging and discharging task scheduling method.

[0044] By employing the above technical solutions, the technical solutions provided in the embodiments of this application have at least the following advantages:

[0045] This application provides a method and apparatus for scheduling charging and discharging tasks in the rebalancing of shared electric vehicles. Compared with the prior art, the embodiments of this application obtain basic information of shared electric vehicles, including site information, vehicle information, peak and off-peak electricity prices, and driving information; determine the main decision variables and auxiliary decision variables; and based on the basic information, the main decision variables, and the auxiliary decision variables, construct constraints to form a nonlinear programming model. The nonlinear programming model is used to describe the revenue function relationship of charging and discharging scheduling tasks in the rebalancing of shared electric vehicles; the nonlinear programming model is linearized to obtain a linear programming model, and the linear programming model is solved based on the constraint set and effective inequalities to obtain the scheduling result of charging and discharging tasks in the rebalancing of shared electric vehicles. This achieves the time-series constraint of "releasing parking spaces first and then receiving vehicles at fully loaded sites," breaking through the limitation of traditional models that only focus on spatial dimension resource allocation and improving the turnover efficiency of fully loaded sites. In addition, the nonlinear programming model and linearization solution method constructed in this application fully explore the dual value of shared electric vehicles as flexible loads and distributed energy storage resources, greatly reducing the rebalancing scheduling cost of shared electric vehicle operators, ensuring that vehicles located at stations without charging and discharging piles meet the power requirements after scheduling, reducing the solution time for scheduling a large number of vehicle tasks, and thus improving the redistribution efficiency of charging and discharging task scheduling for shared electric vehicle rebalancing.

[0046] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0047] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0048] Figure 1 This paper presents a flowchart of a charging and discharging task scheduling method for rebalancing shared electric vehicles according to an embodiment of this application.

[0049] Figure 2 This paper illustrates a flowchart of a site status identification and classification process provided in an embodiment of this application.

[0050] Figure 3 This application provides a flowchart of a vehicle state recognition and classification process according to an embodiment.

[0051] Figure 4This illustration shows a process diagram of charging and discharging task scheduling for rebalancing of shared electric vehicles according to an embodiment of this application;

[0052] Figure 5 This illustration shows a block diagram of a charging and discharging task scheduling device for rebalancing shared electric vehicles, as provided in an embodiment of this application.

[0053] Figure 6 A schematic diagram of the structure of a terminal provided in an embodiment of this application is shown. Detailed Implementation

[0054] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0055] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0056] This application provides a method for scheduling charging and discharging tasks for rebalancing shared electric vehicles, such as... Figure 1 As shown, the method includes:

[0057] 101. Obtain basic information about shared electric vehicles.

[0058] In this embodiment, the current execution terminal, acting as the main body for scheduling the charging and discharging tasks of rebalancing shared electric vehicles, can be a terminal server or a cloud server, such as a dispatch center server, to obtain basic information about the shared electric vehicles. This basic information includes site information, vehicle information, peak and off-peak electricity price information, and driving information. Site information includes, but is not limited to, site location, site ID, distance between sites, and the number of vehicles parked at each site. Vehicle information includes, but is not limited to, vehicle ID, vehicle location, number of vehicles, vehicle battery rated capacity, and driving range. Peak and off-peak electricity price information includes, but is not limited to, the electricity price revenue during peak and off-peak periods. Driving information includes, but is not limited to, driver ID, number of drivers, and the speed and duration of driving. This embodiment does not impose specific limitations on these details.

[0059] It should be noted that the shared electric vehicles in this application embodiment are shareable motor electric vehicles, which can provide vehicles for different shared drivers, such as small electric cars, electric SUVs, etc. This application embodiment does not make specific limitations.

[0060] 102. Determine the main decision variables and auxiliary decision variables, and based on the basic information, the main decision variables and the auxiliary decision variables, form the nonlinear programming model by constructing constraints.

[0061] In this embodiment, after obtaining basic information, the current execution end determines the main decision variables and auxiliary decision variables. The main decision variables characterize the primary variables affecting the charging and discharging task scheduling of shared electric vehicles, including but not limited to path transition variables and time variables for drivers arriving at nodes (nodes may include dispatch centers, sets of vehicles, and sets of parking spaces). The auxiliary decision variables characterize the secondary variables affecting the charging and discharging task scheduling of shared electric vehicles, including but not limited to the time it takes for employees to dispatch vehicles to designated parking spaces, the time it takes for vehicles to complete discharging operations at parking spaces with charging and discharging piles, peak and valley identifier variables for their respective peak and valley periods, and variables used for model linearization. This embodiment does not impose specific limitations on these variables. Furthermore, based on the basic information, main decision variables, and auxiliary decision variables, a nonlinear programming model is formed by constructing constraints. This nonlinear programming model describes the revenue function relationship of the charging and discharging scheduling tasks included in the rebalancing of shared electric vehicles. Specifically, the objective function of the nonlinear programming model can be expressed as (Formula 1):

[0062] (Formula 1);

[0063] in, The charging revenue of vehicles parked in parking spaces with charging / discharging stations but not scheduled for operation. The revenue earned by drivers from discharging their vehicles into the power grid after the vehicles have arrived at parking spaces with charging and discharging stations. The dispatch costs for drivers cycling and driving vehicles. This refers to the penalty cost incurred due to uneven vehicle distribution after the dispatching process ends.

[0064] 103. Linearize the nonlinear programming model to obtain a linear programming model, and solve the linear programming model based on the constraint set and effective inequalities to obtain the charging and discharging task scheduling results of the shared electric vehicle rebalancing.

[0065] In this embodiment, to improve the effectiveness of scheduling charging and discharging tasks for shared electric vehicle rebalancing and avoid problems such as insufficient solution accuracy and excessive computational complexity caused by the nonlinear relationship of the model, the current execution end first linearizes the nonlinear programming model to obtain a linear programming model. The linear programming model is then solved based on the constraint set and effective inequalities to obtain the scheduling result of the charging and discharging tasks for shared electric vehicle rebalancing. The constraint set characterizes the conditions for charging and discharging tasks required when solving the linear programming model. The constraint set includes multiple constraint relationships regarding constraint benefits, constraint time, and constraint charging and discharging behavior, which are not specifically limited in this embodiment. Furthermore, by using the time-series constraint of "fully loaded stations release parking spaces first, then receive vehicles," the limitation of traditional models focusing only on spatial resource allocation is overcome, improving the turnover efficiency of fully loaded stations. Effective inequalities are used to eliminate path symmetry in the scheduling process of charging and discharging tasks for shared electric vehicle rebalancing, thereby improving the solution efficiency when solving the linear programming model.

[0066] It should be noted that in some implementation scenarios, shared electric vehicle dispatching companies provide sharing services within a certain area, with one dispatch center (0) and a group of parking stations (denoted as the set). P ) and a group of parking spaces (denoted as set) S ), for shared electric vehicles (denoted as a collection) C Parking is available at any location. The service operates on a one-way, station-based model, allowing users to borrow / return vehicles at any stop. During off-peak hours (e.g., at night), the platform dispatches staff to relocate vehicles. To ensure vehicles are available for operation the following day, the maximum relocation time is no more than [time period missing]. The driver rides a folding bicycle that can be carried with the car to the station where the vehicle needs to be dispatched, drives a vehicle that needs to be dispatched to a station where a vehicle needs to be dispatched, and repeats this process until the employee completes the task assigned by the dispatcher. The driver then rides the bicycle back to the dispatch center 0. At this point, the task assigned above is the result of the rebalancing dispatch of the charging and discharging task determined in steps 101-103.

[0067] In another embodiment of the invention, for further definition and explanation, the step of forming the nonlinear programming model by constructing constraints based on the basic information, the main decision variables, and the auxiliary decision variables includes:

[0068] Based on the number of charging and discharging piles, the status of stored vehicles, and the parking status in the site information, site status identification is performed to obtain a site status identification classification set.

[0069] Based on the storage location and battery status in the vehicle information, vehicle status identification is performed to obtain a vehicle status identification classification set.

[0070] The main decision variables are determined based on the site status identification classification set and the vehicle status identification classification set, and the auxiliary decision variables are configured.

[0071] Using the electricity revenue set as the objective function component, and combining the main decision variables and the auxiliary decision variables, the nonlinear programming model is formed by constructing constraints.

[0072] To achieve coordinated scheduling of vehicle rebalancing and charging / discharging tasks, the current execution end, when constructing the nonlinear programming model for coordinated scheduling, first performs site status identification and vehicle status identification. The basic information includes site information, vehicle information, peak and off-peak electricity prices, and driving information. At this stage, site information includes the site (or dispatch center). and Distance between Site The difference between the actual number of parked vehicles and the ideal number of vehicles. Site Number of parking spaces Number of vehicles stored ,remember For the site p Internal parking spaces collection, parking spaces Charge / discharge configuration status (if If the parking space is equipped with a charging station, then it is called a parking space with a charging station. If the parking space is not a charging station, then it is called a parking space without a charging station, resulting in an unbalanced penalty cost. Vehicle information includes the rated capacity of the vehicle battery. q (>0), the range on a full charge is e (>0), vehicles at the start of the planning period Remaining battery charge (in decimal form) Vehicle minimum battery level threshold The threshold for whether the vehicle has sufficient battery power. The vehicle's rated discharge power Electricity price peak / valley information includes peak time periods. The net profit per unit discharge is During the trough period The net benefit per unit of discharge is negative (only charging and not discharging during off-peak hours). Driving information includes identifying the set of employees. K The speed at which the driver rides the bicycle Unit distance cycling cost Speed ​​of driving vehicles Unit distance driving cost Maximum scheduling time for employees .

[0073] In some embodiments, for site status identification, the number of charging / discharging piles, the status of stored vehicles, and the parking status in the site information can be used to identify the site status, ultimately obtaining a site status identification classification set. Specifically, based on the site... The presence or absence of charging / discharging stations categorizes parking lots into those with and without charging / discharging stations. All parking spaces within the same station have the same charging / discharging station configuration. Its parking space of The values ​​are all the same. (Based on the site) The difference between the actual number of parked vehicles and the ideal number of vehicles. And assume The stations are divided into stations where vehicles need to be dispatched and stations where vehicles need to be dispatched. If the vehicle needs to be dispatched, the station is called a station where the vehicle needs to be dispatched; otherwise, the station is called a station where the vehicle needs to be dispatched. (Based on the station...) The relationship between the number of vehicles and the number of parking spaces divides the stations into full-load stations and non-full-load stations. If the number of parking spaces is less than 1, the station is considered a partially full station; otherwise, it is considered a fully full station. (Parking space set) S It can be further divided into two disjoint subsets: the set of parking spaces located at fully occupied stations. A collection of parking spaces located at stations that are not at full capacity. And there are In this embodiment of the application, the site status identification and classification process is as follows: Figure 2 As shown.

[0074] In some embodiments, vehicle status recognition can be performed based on the storage location and battery status in the vehicle information, ultimately resulting in a vehicle status classification set. Specifically, vehicles are grouped according to the full load status of their parking locations. C It is divided into two disjoint subsets: the set of vehicles at fully loaded stations. A collection of vehicles at stations that are not fully booked. And there are Vehicles are categorized into those with sufficient battery power and those with insufficient battery power based on whether their remaining battery level reaches the sufficient battery threshold. Then it is called a vehicle. i The vehicle is considered to have a fully charged battery; otherwise, it is considered a vehicle. i For vehicles with low battery power, among which, Given parameters, in this embodiment of the application, the vehicle state recognition and classification process is as follows: Figure 3 As shown.

[0075] Furthermore, based on the site status recognition classification set and the vehicle status recognition classification set, the main decision variables are determined, and auxiliary decision variables are configured. At this point, the main decision variables include path transition variables and time variables. Among them, the path transition variables... Used to identify drivers (i.e., employees of shared electric vehicle companies). k From node i Transfer to node j Time variable Used to characterize employees k Reaching the node j Time, path transition variables Represented as:

[0076] ;

[0077] Symbolic functions are represented as:

[0078] Here, "nodes" include a dispatch center, a set of vehicles, and a set of parking spaces. A symbolic function is used to determine whether a vehicle has sufficient battery power and the status of its parking location. Based on the location status identification classification set and the vehicle status identification classification set, the main decision variables are constructed. Simultaneously, the configured auxiliary decision variables may include peak and valley identification variables, i.e., including... , , , The following are the representations:

[0079] ;

[0080] ;

[0081] ;

[0082] ;

[0083] In addition, auxiliary decision variables can also include product auxiliary variables and absolute value auxiliary variables. Product auxiliary variables include... , , Absolute value auxiliary variables include A nonlinear programming model with Equation 1 as the objective function is constructed based on the set of electricity revenue, the main decision variables, and the auxiliary decision variables. The set of electricity revenue includes unscheduled discharge revenue, dispatched discharge revenue, dispatch cost, and imbalance penalty cost. Unscheduled discharge revenue refers to the discharge revenue generated when a vehicle is parked in a charging / discharging station parking space and has not been dispatched. Discharge revenue after dispatch refers to the discharge revenue generated when a vehicle arrives at a parking space with a charging / discharging station after being dispatched by an employee.

[0084] In another embodiment of the invention, for further definition and explanation, the steps further include:

[0085] Based on the main decision variables and the auxiliary decision variables, a set of constraints is constructed to constrain the nonlinear programming model.

[0086] To improve the adaptability of charging and discharging task scheduling for shared electric vehicles by improving the rebalancing of charging and discharging tasks based on constraint relationships, the current execution end constructs a constraint set based on the main decision variables and auxiliary decision variables. This constraint set includes multiple constraint benefits, constraint time, and constraint relationships on charging and discharging behavior. Furthermore, since the electricity revenue set includes unscheduled discharge revenue, scheduled discharge revenue, scheduling costs, and imbalance penalty costs, the constructed constraint set includes the following:

[0087] (Formula 2);

[0088] (Formula 3);

[0089] (Formula 4);

[0090] (Formula 5);

[0091] (Formula 6);

[0092] (Formula 7);

[0093] (Formula 8);

[0094] (Formula 9);

[0095] (Formula 10);

[0096] (Formula 11);

[0097] (Formula 12);

[0098] (Formula 13);

[0099] (Formula 14);

[0100] (Formula 15);

[0101] (Formula 16);

[0102] (Formula 17);

[0103] (Formula 18);

[0104] (Formula 19);

[0105] (Formula 20);

[0106] (Formula 21);

[0107] (Formula 22);

[0108] (Formula 23);

[0109] (Equation 24).

[0110] It should be noted that in the above constraint set (Formulas 2-24), Formula 1 is the objective function of the nonlinear programming model, which is to maximize the operator's rebalancing scheduling revenue. Formula 2 in the constraint set is used to constrain the discharge revenue of vehicles parked in parking spaces with charging / discharging stations but not yet scheduled. Formula 3 in the constraint set is used to constrain the revenue of employees discharging vehicles into the grid after they arrive at parking spaces with charging / discharging stations. Formula 4 in the constraint set is used to constrain the scheduling costs of employees riding bicycles and driving vehicles. Formula 5 in the constraint set is used to constrain the total number of unbalanced vehicle distributions between stations after scheduling. Formula 6 in the constraint set is used to constrain the time constraint for employees to ride bicycles from the dispatch center to the station where vehicles need to be dispatched. Formula 7 in the constraint set is used to constrain the time constraint for employees to cycle from the parking space node to the vehicle node or dispatch center, where, Formula 8 in the constraint set is used to constrain the time constraint for employees driving vehicles from the vehicle node to the target parking space node, where, Formulas 9-11 in the constraint set are used to constrain that the time when a vehicle arrives at the target parking space node falls within either the peak or trough period. Formula 9 in the constraint set is used to constrain the time when the vehicle arrives at the target parking space node during a peak period, where... Formula 10 in the constraint set is used to constrain the time when the vehicle arrives at the target parking space node when it falls within a valley value. Formula 11 in the constraint set is used to constrain the time during which employees transport vehicles to the target parking space node to fall within either the peak or valley time period. Formulas 12-14 in the constraint set are used to constrain the time when vehicle discharge is completed to fall within either the peak or valley time period, where... , , Formula 14 in the constraint set is used to constrain the vehicle discharge completion time to fall within at most one of the peak or trough time periods. Formula 15 in the constraint set is the mathematical expression of the timing constraint "full-load stations release parking spaces first, then receive vehicles," used to ensure that the time when employees remove vehicles from parking spaces at full-load stations is earlier than the time when vehicles are moved into parking spaces at full-load stations. Formula 16 in the constraint set ensures that vehicles with insufficient battery power in parking spaces without charging / discharging stations must be relocated to parking spaces with charging / discharging stations. Formula 17 in the constraint set is an infeasible transformation constraint, in which the five parts correspond to the transformations between vehicle nodes, between parking space nodes, from vehicle node to dispatch center node, from dispatch center to parking space node, and from vehicle node to the parking space node where it is parked. Formula 18 in the constraint set ensures that each employee can be dispatched at most once. Formula 19 in the constraint set ensures that each vehicle node can be dispatched at most once (accessed). Formula 20 in the constraint set ensures that if an employee leaves the dispatch center, they must return to the dispatch center. Formula 21 in the constraint set ensures that the in-degree and out-degree of employees at vehicle nodes and parking space nodes are balanced, that is, when an employee enters a node, they must leave that node. Formula 22 in the constraint set constrains the maximum dispatch time for employees. Formula 23 in the constraint set constrains the vehicle's driving range. Formula 24 in the constraint set constrains the type and value range of decision variables.

[0111] In another embodiment of the invention, for further definition and explanation, the step of linearizing the nonlinear programming model to obtain a linear programming model includes:

[0112] The nonlinear part of the nonlinear programming model is identified, and the nonlinear part is linearized based on the auxiliary decision variables to obtain a linear programming model.

[0113] To avoid solution errors caused by nonlinearity and thus reduce the error in the scheduling results of charging and discharging tasks for shared electric vehicles, the current execution end linearizes the nonlinear programming model to obtain a linear programming model. The nonlinear part includes the nonlinearity of the multiplication of decision variables and the nonlinearity of the absolute value function. Specifically, Equation 3 in the constraint set includes the nonlinearity caused by the multiplication of multiple decision variables, i.e., the nonlinearity of the multiplication of decision variables. as well as At this point, during linearization, the auxiliary variable is multiplied. , , Formula 3 in the constraint set can be linearized as follows:

[0114] (Formula 25);

[0115] (Formula 26);

[0116] (Formula 27);

[0117] (Formula 28);

[0118] (Formula 29);

[0119] (Formula 30);

[0120] (Formula 31);

[0121] (Formula 32);

[0122] (Formula 33);

[0123] (Formula 34);

[0124] (Formula 35);

[0125] (Formula 36);

[0126] (Formula 37);

[0127] (Formula 38);

[0128] (Formula 39);

[0129] Among them, the mathematical relationships in formulas 26-30 are used to... Perform linearization, using to replace ,in, , Using formulas 31-34 to... Perform linearization, using to replace ,in, Using formulas 35-39 to... Perform linearization, using to replace Formula 5 in the constraint set contains an absolute value function that is nonlinear, i.e., a nonlinear part of the absolute value function. This is addressed by introducing a nonnegative absolute value auxiliary variable. Formula 5 in the constraint set can be linearized as follows:

[0130] (Formula 40);

[0131] in,

[0132] (Formula 41);

[0133] (Formula 42).

[0134] In another embodiment of the invention, to further define and illustrate the method, before solving the linear programming model based on the constraint set and valid inequalities to obtain the charging and discharging task scheduling result of the shared electric vehicle rebalancing, the method further includes:

[0135] Based on the order constraint of parking space nodes, an ordered sequence transformation of symmetrical parking space nodes is performed to construct the first effective inequality;

[0136] Based on the allocation order between distance length and employee number, a second effective inequality is constructed.

[0137] To improve the solution efficiency of the linear programming model, an effective inequality is introduced at the current execution stage to eliminate the impact of path symmetry on the solution in the charging and discharging task scheduling of shared electric vehicles. Here, path symmetry mainly refers to the symmetry of parking spaces at the site and the symmetry of employee transportation paths. Parking space symmetry means that different parking spaces at the same site are equivalent in terms of charging / discharging pile configuration (e.g., pile type, power level) and spatial location; that is, exchanging the usage order of any two parking spaces at the same site does not affect the target value. Employee transportation path symmetry means that the cost of employees driving a vehicle per unit distance is the same, and the cost of different employees transporting the same vehicle from one location to another is the same. Furthermore, based on the parking space node order constraint, an ordered sequence transformation is performed on the symmetrical parking space nodes to construct the first effective inequality. Specifically, vehicles are allocated to the site according to the parking space node order. When employees drive their vehicles to the receiving station, by introducing parking space node order constraints, the symmetrical parking space nodes are transformed into an ordered sequence. This allows the "parking space allocation logic" of the linear programming model to follow either ascending or descending order of this sequence. In this application example, ascending order is used, thereby eliminating the symmetry in the linear programming model and effectively improving the solution speed. Specifically, when a vehicle is transported to the station... Different parking spaces The first valid inequality is constructed as follows:

[0138] (Formula 43).

[0139] Meanwhile, the current execution end allocates employees involved in the dispatch based on the total mileage of vehicles along the route. Since there is no difference between employees, shorter distance tasks can be assigned to employees with smaller IDs, and longer distance tasks can be assigned to employees with larger IDs. Based on the allocation order between distance and employee ID, a second valid inequality is constructed as follows:

[0140] (Equation 44).

[0141] In another embodiment of the invention, to further define and illustrate, after obtaining the charging and discharging task scheduling result of the shared electric vehicle rebalancing, the method further includes:

[0142] Based on the charging and discharging task scheduling results of the shared electric vehicle rebalancing, the target station and target driver of the shared electric vehicle to be charged and discharged are determined, and the target station is sent to the target driver so that the target driver can control the shared electric vehicle to drive to the target station.

[0143] To achieve coordinated scheduling of shared electric vehicle rebalancing and charging / discharging tasks, vehicle-to-grid (V2G) technology can be employed. This involves bidirectional energy flow between shared electric vehicles and the power grid through two-way charging and discharging technology. In this scenario, the shared electric vehicle battery can act as a mobile energy storage unit, charging and storing energy during off-peak grid periods and supplying power back to the grid during peak periods to balance grid pressure. Therefore, in this embodiment, after obtaining the rebalancing scheduling results for charging and discharging tasks, the target stations for charging and discharging of the shared electric vehicles and the target drivers are determined, and the target stations are sent to the target drivers so that they can control the shared electric vehicles to travel to the target stations. This embodiment does not impose specific limitations.

[0144] In another embodiment of the invention, for further definition and explanation, the steps further include:

[0145] In response to the basic information update instruction, the updated basic information is obtained, the steps of determining the main decision variables and auxiliary decision variables are re-executed, and the nonlinear programming model is formed by constructing constraints based on the basic information, the main decision variables, and the auxiliary decision variables, so as to obtain the charging and discharging task scheduling results of the shared electric vehicle rebalancing again.

[0146] In order to coordinate and schedule the rebalancing and charging / discharging tasks of shared electric vehicles, information updates can be requested at preset time intervals, such as once a day or once an hour. After receiving the basic information update instruction, the updated basic information can be obtained, and the contents of steps 101-103 can be re-executed to obtain the updated scheduling results of the rebalancing and charging / discharging tasks of shared electric vehicles. Furthermore, to achieve coordinated scheduling of shared electric vehicle rebalancing and vehicle charging / discharging tasks, the preset time interval for information updates can be precisely matched with the dispatch rhythm, taking into account the dispatch scenarios during nighttime demand slumps. For example, if updating once a day, the update time can be set 1-2 hours before nighttime dispatch (e.g., 9:00 PM to 10:00 PM daily, when user demand has decreased and vehicle distribution is close to the stable state of the day, avoiding reliance on outdated data for dispatch planning). If an update frequency of once an hour is set for the nighttime dispatch period (not 24 hours a day), then real-time vehicle data can be synchronized every hour during the nighttime dispatch period (e.g., 10:00 PM to 5:00 AM the next day), focusing on tracking key information such as location changes, power consumption, and malfunctions during the dispatch process. In this way, upon receiving basic information update instructions, it can provide accurate basis for nighttime dispatch planning and provide dynamic support for monitoring the dispatch execution process through high-frequency updates specific to the nighttime period, ensuring timely arrival of vehicles for operation the next day.

[0147] In a specific implementation scenario, the number of rented parking spaces in a single public parking lot is within the range [3,5]. Approximately five employees form a dispatch team responsible for the rebalancing and relocation of vehicles within a specific area. The employee driving speed... The cycling speed is 50 km / h. Cost of cycling per unit distance at 25 km / h The cost per unit distance is 0.1 yuan / km. The cost is 0.2 yuan / km, and the maximum scheduling time is... For 7 hours, the cost of unbalanced penalties The price is 60 yuan per vehicle. Maximum driving range of the vehicle. e For a range of 320 km, the minimum battery capacity is [not specified]. The threshold value is 0.2, indicating whether the battery is sufficiently charged. The rated capacity of the battery is 0.35. q The rated discharge power of the vehicle is 40 kWh. The initial power of the shared electric vehicle in the example is randomly generated within the range [0.3, 1]. Based on the actual time-of-use pricing policy, the net revenue from discharge during different time periods is shown in Table 1, with the unit net discharge revenue during peak periods being... The price is 1.5 yuan / kWh. Peak start time. The peak time is 22 hours. The time for the valley to end is 24 hours. It takes 5 hours.

[0148] Table 1. Charge / discharge prices and net discharge revenue at different times.

[0149]

[0150] In addition, each site in the small-scale and large-scale examples The difference between the number of vehicles stored inside and the number of vehicles under ideal conditions. Table 2 shows the information regarding the number of stations, vehicles, and the difference between the number of vehicles stored at each station and the ideal number of vehicles in the intervals [-2,2] and [-3,3]. The Gurobi solver was used with Python, and the results are shown in Tables 3 and 4. Model 1 includes formulas 1-42. Table 3 shows the target value and solution time obtained by solving Model 1. Furthermore, based on Model 1, one or all of the effective inequalities formulas 43-44 were added. The corresponding models are then denoted as Model 1-43, Model 1-44, and Model 1-ALL, respectively. The target value and solution time of the three models after adding the effective inequalities are based on the target value and solution time given in Formula 1, with relative deviations, and the solutions are recalculated. Table 3 shows that all three models after adding the effective inequalities obtained optimal solutions, and the target values ​​were completely consistent with the target values ​​given based on Model 1, demonstrating the correctness of these two effective inequalities. At this point, although the solution time of Model 1-ALL in solving Example 4 increased by 32.60% (reaching 5.41s) compared to the original model, in terms of the average solution time of multiple examples, the average solution time of Model 1-ALL in small-scale examples decreased by 54.05% compared to the original model, while the single inequality models (1-43, 1-44) decreased by 5.17% and 22.43% respectively compared to the original model.

[0151] Furthermore, in the large-scale examples, the obtained target values ​​and solution times are shown in Table 4. Compared to Model 1, the target difference obtained by all models with valid inequalities in the large-scale examples is 0%. Looking at the average solution time across multiple examples, the average solution time of Model 1-ALL in the large-scale examples is reduced by 63.59% compared to the original model, while the single inequality models (1-43, 1-44) are reduced by 45.15% and 19.86% respectively compared to the original model. In comparison, Model 1-ALL exhibits a more significant synergistic effect. Combining the solution results of small-scale and large-scale examples, it can be concluded that both proposed valid inequalities are effective, and future examples solving this problem will all use Model 1-ALL with two valid inequalities.

[0152] Table 2 Main Parameters

[0153]

[0154] Table 3 Small-scale examples

[0155]

[0156] Table 4 Large-scale examples

[0157]

[0158] In Example 1, such as Figure 4 As shown, in a shared vehicle network consisting of 4 parking stations and 8 vehicles, only the stations... and It is equipped with charging and discharging stations, except for vehicles. All other vehicles have sufficient battery power. The diagram is arranged chronologically (…). t 0- t 5) This section demonstrates the parking status of vehicles at various stations at different times, and the process of employees relocating shared electric vehicles by "driving / riding," showcasing the scheduling logic of the charging and discharging tasks for shared electric vehicle rebalancing. The initial vehicle distribution is known to be [1,1,4,2], and the difference between the number of vehicles stored at each station and the ideal number of vehicles is [-1,-1, 1,1]. The optimal scheduling path obtained from the solution for the charging and discharging task scheduling of shared electric vehicle rebalancing is: employees start from dispatch center 0 and ride a folding bicycle (which can be carried with the car) to the station where vehicles need to be relocated. Vehicles parked at stations without charging stations and with insufficient battery power will be... Transfer to sites with charging and discharging piles At this time, the employees are on the vehicle Perform the discharge operation, then ride a bicycle to the station. Driving vehicles that need to be dispatched To the station where vehicles need to be transferred After completing their dispatch tasks, employees cycled back to the dispatch center. Vehicles parked in parking spaces equipped with charging stations but not yet dispatched... The total revenue from the discharge was 108 yuan, and the dispatch vehicle... The total discharge revenue is 3.21 yuan, the employee scheduling cost is 12.79 yuan, the imbalance penalty cost is 0 yuan, and the target value is 98.42 yuan.

[0159] This application provides a method for scheduling charging and discharging tasks in the rebalancing of shared electric vehicles. Compared with the prior art, this application obtains basic information about the shared electric vehicles, including site information, vehicle information, peak and off-peak electricity prices, and driving information; determines the main decision variables and auxiliary decision variables; and, based on the basic information, the main decision variables, and the auxiliary decision variables, constructs constraints to form a nonlinear programming model. This nonlinear programming model describes the revenue function relationship of charging and discharging scheduling tasks in the rebalancing of shared electric vehicles. The nonlinear programming model is then linearized to obtain a linear programming model, and based on the constraint set... The linear programming model is solved using effective inequalities to obtain the scheduling results of the charging and discharging tasks for the rebalancing of shared electric vehicles. The timing constraint of "full-loaded stations first release parking spaces and then receive vehicles" breaks through the limitation of traditional models that only focus on spatial resource allocation, improving the turnover efficiency of full-loaded stations. The effective inequalities are used to eliminate path symmetry in the scheduling process of the charging and discharging tasks for the rebalancing of shared electric vehicles, greatly reducing the rebalancing scheduling cost for shared electric vehicle operators, ensuring that vehicles at stations without charging and discharging piles meet the power requirements after scheduling, reducing the solution time for scheduling a large number of vehicle tasks, and thus improving the redistribution efficiency of the charging and discharging tasks for the rebalancing of shared electric vehicles.

[0160] Furthermore, as a response to the above Figure 1 The implementation of the method shown in this application provides a charging and discharging task scheduling device for rebalancing shared electric vehicles, such as... Figure 5 As shown, the device includes:

[0161] The acquisition module 21 is used to acquire basic information about shared electric vehicles, including site information, vehicle information, peak and off-peak electricity prices, and driving information.

[0162] The determination module 22 is used to determine the main decision variables and auxiliary decision variables, and based on the basic information, the main decision variables and the auxiliary decision variables, to form the nonlinear programming model by constructing constraints. The nonlinear programming model is used to describe the revenue function relationship of the charging and discharging scheduling task in the rebalancing of shared electric vehicles.

[0163] The solution module 23 is used to linearize the nonlinear programming model to obtain a linear programming model, and solve the linear programming model based on the constraint set and effective inequalities to obtain the charging and discharging task scheduling result of the shared electric vehicle rebalancing. The effective inequalities are used to eliminate path symmetry in the charging and discharging task scheduling process of the shared electric vehicle rebalancing.

[0164] Furthermore,

[0165] The determining module is specifically used to identify the station status based on the number of charging and discharging piles, the status of stored vehicles, and the parking status in the station information, and obtain a station status identification classification set; to identify the vehicle status based on the storage station and the power status in the vehicle information, and obtain a vehicle status identification classification set; to determine the main decision variables based on the station status identification classification set and the vehicle status identification classification set, and to configure the auxiliary decision variables, wherein the main decision variables include path transition variables and time variables, and the auxiliary decision variables include peak and valley identification variables, product auxiliary variables, and absolute value auxiliary variables; and to form the nonlinear programming model by constructing constraints, using the power revenue set as the objective function component, and combining the main decision variables and the auxiliary decision variables.

[0166] Furthermore, the power revenue set includes unscheduled discharge revenue, dispatched discharge revenue, dispatching costs, and imbalance penalty costs; the device also includes:

[0167] The construction module is used to construct a constraint set for constraining the nonlinear programming model based on the main decision variables and the auxiliary decision variables. The constraint set includes multiple constraint relationships such as constraint benefits, constraint time, and constraint charging and discharging behavior.

[0168] Furthermore,

[0169] The solution module is specifically used to determine the nonlinear part in the nonlinear programming model and linearize the nonlinear part based on the auxiliary decision variables to obtain a linear programming model; wherein, the nonlinear part includes the nonlinear part of the multiplication of decision variables and the nonlinear part of the absolute value function.

[0170] Furthermore,

[0171] The construction module is also used to perform ordered sequence transformation on symmetrical parking space nodes based on parking space node order constraints to construct a first effective inequality; and to construct a second effective inequality based on the allocation order between distance length and employee number.

[0172] Furthermore, the determining module is also used to determine the target station and target driver of the shared electric vehicle to be charged and discharged based on the charging and discharging task scheduling result of the shared electric vehicle rebalancing, and send the target station to the target driver so that the target driver can control the shared electric vehicle to drive to the target station.

[0173] Furthermore, the device also includes:

[0174] The acquisition module is further configured to respond to the basic information update instruction, acquire and update the basic information, re-execute the steps of determining the main decision variables and auxiliary decision variables, and form the nonlinear programming model by constructing constraints based on the basic information, the main decision variables and the auxiliary decision variables, so as to obtain the charging and discharging task scheduling results of the shared electric vehicle rebalancing.

[0175] This application provides a charging and discharging task scheduling device for the rebalancing of shared electric vehicles. Compared with the prior art, this application obtains basic information about the shared electric vehicles, including site information, vehicle information, peak and off-peak electricity prices, and driving information; determines the main decision variables and auxiliary decision variables; and, based on the basic information, the main decision variables, and the auxiliary decision variables, constructs a nonlinear programming model by establishing constraints. The nonlinear programming model describes the revenue function relationship of the charging and discharging scheduling tasks included in the rebalancing of shared electric vehicles. The nonlinear programming model is linearized to obtain a linear programming model, and the linear programming model is solved based on the constraint set and effective inequalities to obtain the charging and discharging task scheduling result for the rebalancing of shared electric vehicles. This achieves the time-series constraint of "releasing parking spaces first and then receiving vehicles at fully loaded sites," breaking through the limitation of traditional models that only focus on spatial dimension resource allocation and improving the turnover efficiency of fully loaded sites. In addition, the nonlinear programming model and linearization solution method constructed in this application fully explore the dual value of shared electric vehicles as flexible loads and distributed energy storage resources, greatly reducing the rebalancing scheduling cost of shared electric vehicle operators, ensuring that vehicles located at stations without charging and discharging piles meet the power requirements after scheduling, reducing the solution time for scheduling a large number of vehicle tasks, and thus improving the redistribution efficiency of charging and discharging task scheduling for shared electric vehicle rebalancing.

[0176] According to one embodiment of this application, a storage medium is provided, the storage medium storing at least one executable instruction that can execute the shared electric vehicle rebalancing charging and discharging task scheduling method in any of the above method embodiments.

[0177] Figure 6 The diagram shows a structural schematic of a terminal according to one embodiment of the present application. The specific embodiments of the present application do not limit the specific implementation of the terminal.

[0178] like Figure 6 As shown, the terminal may include: a processor 302, a communication interface 304, a memory 306, and a communication bus 308.

[0179] The processor 302, communication interface 304, and memory 306 communicate with each other via communication bus 308.

[0180] Communication interface 304 is used to communicate with other network elements such as clients or other servers.

[0181] The processor 302 is used to execute program 310, specifically to execute the relevant steps in the above embodiment of the shared electric vehicle rebalancing charging and discharging task scheduling method.

[0182] Specifically, program 310 may include program code that includes computer operation instructions.

[0183] Processor 302 may be a central processing unit (CPU), a specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The terminal includes one or more processors, which may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.

[0184] Memory 306 is used to store program 310. Memory 306 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0185] Specifically, program 310 can be used to cause processor 302 to perform the following operations:

[0186] Obtain basic information about shared electric vehicles, including site information, vehicle information, peak and off-peak electricity prices, and driving information;

[0187] The main decision variables and auxiliary decision variables are determined, and based on the basic information, the main decision variables and the auxiliary decision variables, the nonlinear programming model is formed by constructing constraints. The nonlinear programming model is used to describe the revenue function relationship of the charging and discharging scheduling task in the rebalancing of shared electric vehicles.

[0188] The nonlinear programming model is linearized to obtain a linear programming model. The linear programming model is then solved based on the constraint set and effective inequalities to obtain the scheduling results of the charging and discharging tasks for the rebalancing of shared electric vehicles. The time-series constraint of "fully loaded stations release parking spaces first and then receive vehicles" breaks through the limitation of traditional models that only focus on spatial dimension resource allocation and improves the turnover efficiency of fully loaded stations. The effective inequalities are used to eliminate path symmetry in the scheduling process of the charging and discharging tasks for the rebalancing of shared electric vehicles.

[0189] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0190] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for scheduling charging and discharging tasks for rebalancing shared electric vehicles, characterized in that, include: Obtain basic information about shared electric vehicles, including site information, vehicle information, peak and off-peak electricity prices, and driving information; The main decision variables and auxiliary decision variables are determined, and based on the basic information, the main decision variables and the auxiliary decision variables, the nonlinear programming model is formed by constructing constraints. The nonlinear programming model is used to describe the revenue function relationship of the charging and discharging scheduling task in the rebalancing of shared electric vehicles. The nonlinear programming model is linearized to obtain a linear programming model. The linear programming model is then solved based on the constraint set and effective inequalities to obtain the scheduling results of the charging and discharging tasks for the rebalancing of the shared electric vehicles. The effective inequalities are used to eliminate path symmetry in the scheduling process of the charging and discharging tasks for the rebalancing of the shared electric vehicles. The process of forming the nonlinear programming model by constructing constraints based on the basic information, the main decision variables, and the auxiliary decision variables includes: Based on the number of charging and discharging piles, the status of stored vehicles, and the parking status in the site information, site status identification is performed to obtain a site status identification classification set. Based on the storage location and battery status in the vehicle information, vehicle status identification is performed to obtain a vehicle status identification classification set. Based on the site status identification classification set and the vehicle status identification classification set, the main decision variables are determined and the auxiliary decision variables are configured. The main decision variables include path transition variables and time variables, and the auxiliary decision variables include peak and valley identification variables, product auxiliary variables, and absolute value auxiliary variables. Using the electricity revenue set as the objective function component, and combining the main decision variables and the auxiliary decision variables, the nonlinear programming model is formed by constructing constraints.

2. The method according to claim 1, characterized in that, The power revenue set includes unscheduled discharge revenue, dispatched discharge revenue, dispatch costs, and imbalance penalty costs. The method further includes: Based on the main decision variables and the auxiliary decision variables, a constraint set is constructed to constrain the nonlinear programming model. The constraint set includes multiple constraint relationships such as constraint benefits, constraint time, and constraint charging and discharging behavior.

3. The method according to claim 1, characterized in that, The linearization process of the nonlinear programming model to obtain a linear programming model includes: The nonlinear component in the nonlinear programming model is identified, and the nonlinear component is linearized based on the auxiliary decision variables to obtain a linear programming model. The nonlinear part includes the nonlinear part of the multiplication of decision variables and the nonlinear part of the absolute value function.

4. The method according to claim 1, characterized in that, Before solving the linear programming model based on the constraint set and effective inequalities to obtain the charging and discharging task scheduling results for the shared electric vehicle rebalancing, the method further includes: Based on the order constraint of parking space nodes, an ordered sequence transformation of symmetrical parking space nodes is performed to construct the first effective inequality; Based on the allocation order between distance length and employee number, a second effective inequality is constructed.

5. The method according to claim 1, characterized in that, After obtaining the charging and discharging task scheduling result of the shared electric vehicle rebalancing, the method further includes: Based on the charging and discharging task scheduling results of the shared electric vehicle rebalancing, the target station and target driver of the shared electric vehicle to be charged and discharged are determined, and the target station is sent to the target driver so that the target driver can control the shared electric vehicle to drive to the target station.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: In response to the basic information update instruction, the updated basic information is obtained, the steps of determining the main decision variables and auxiliary decision variables are re-executed, and the nonlinear programming model is formed by constructing constraints based on the basic information, the main decision variables, and the auxiliary decision variables, so as to obtain the charging and discharging task scheduling results of the shared electric vehicle rebalancing again.

7. A charging and discharging task scheduling device for rebalancing shared electric vehicles, characterized in that, include: The acquisition module is used to acquire basic information about shared electric vehicles, including site information, vehicle information, peak and off-peak electricity prices, and driving information. The determination module is used to determine the main decision variables and auxiliary decision variables, and based on the basic information, the main decision variables and the auxiliary decision variables, to form the nonlinear programming model by constructing constraints. The nonlinear programming model is used to describe the revenue function relationship of the charging and discharging scheduling task in the rebalancing of shared electric vehicles. The solution module is used to linearize the nonlinear programming model to obtain a linear programming model, and solve the linear programming model based on the constraint set and effective inequalities to obtain the charging and discharging task scheduling result of the shared electric vehicle rebalancing. The effective inequalities are used to eliminate path symmetry in the charging and discharging task scheduling process of the shared electric vehicle rebalancing. The determining module is specifically used to identify the site status based on the number of charging and discharging piles, the status of stored vehicles, and the parking status in the site information, and to obtain a site status identification classification set. Based on the storage sites and battery status in the vehicle information, vehicle status identification is performed to obtain a vehicle status identification classification set; based on the site status identification classification set and the vehicle status identification classification set, the main decision variables are determined, and the auxiliary decision variables are configured. The main decision variables include path transition variables and time variables, and the auxiliary decision variables include peak-valley identification variables, product auxiliary variables, and absolute value auxiliary variables; using the battery revenue set as the objective function component, and combining the main decision variables and the auxiliary decision variables, the nonlinear programming model is formed by constructing constraints.

8. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method of claim 1.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method of claim 1.

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