Charging demand regulation and guidance method and device for autonomous on-demand mobile vehicle fleet

Abstract

The application provides a charging demand regulation and guidance method and device for autonomous on-demand mobile vehicle fleet, and belongs to the field of electric vehicle charging regulation and guidance. The method comprises the following steps: a network flow optimization scheduling model of the autonomous on-demand mobile vehicle fleet and an optimal pricing model of a charging station operator are respectively constructed; based on master-slave game, the optimal pricing model of the charging station operator is taken as an upper model, and the network flow optimization scheduling model of the autonomous on-demand mobile vehicle fleet is taken as a lower model; the lower model is embedded into the upper model as a constraint condition, an updated optimal pricing model of the charging station operator is obtained and solved, so that a pricing scheme for regulating the charging demand of the autonomous on-demand mobile vehicle fleet is obtained. In the decision-making process of charging pricing, the time and space behavior mode and the price response characteristics of the autonomous on-demand mobile vehicle fleet are fully considered, and a targeted price signal is formulated according to the price response characteristics, so that the regulation efficiency of the autonomous on-demand mobile vehicle fleet is improved.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle charging regulation and guidance, specifically relating to a method and device for regulating and guiding charging demand for autonomous on-demand mobile fleets. Background Technology

[0002] Building upon shared mobility, technological breakthroughs in areas such as vehicle-to-everything (V2X) and autonomous driving have further fueled the transformation of transportation modes. Based on this, autonomous on-demand mobility has emerged as a new transportation mode. Compared to private cars and traditional taxis where drivers make decentralized decisions, autonomous on-demand mobility fleets, centrally dispatched by platforms, can more effectively coordinate route planning and charging schedules, exhibiting greater decision-making control. Therefore, autonomous on-demand mobility holds significant potential in reducing human risk, alleviating road congestion, improving resource efficiency, and promoting transportation emissions reduction.

[0003] Considering the promising future of autonomous on-demand mobility (ATM) models, charging plans for ATM fleets will become a crucial link connecting the power and transportation systems, requiring guidance and regulation through reasonable charging demand management strategies. Utilizing price signals set by charging operators to guide and regulate charging load is a typical example of such a strategy. While significant progress has been made in pricing charging for general electric vehicles, in scenarios where ATM dominates urban transportation, pricing no longer considers the dispersed users of electric vehicles, but rather the centralized and rational dispatching model of ATM fleet managers. The development of pricing schemes for ATM fleets, particularly the interaction between charging station operators and the fleets themselves and its resulting effects, has not yet been fully considered and researched. The interaction between charging service pricing and ATM fleet dispatching will profoundly impact the operational dynamics of the power and transportation systems in both time and space, laying the foundation for fully exploring flexibility potential and supporting charging station operators' participation in demand response. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a method and device for regulating and guiding charging demand for autonomous on-demand mobile fleets. In the charging pricing decision-making process, this invention fully considers the spatiotemporal behavior patterns and price response characteristics of autonomous on-demand mobile fleets, and formulates targeted price signals based on these characteristics to improve the regulation efficiency of autonomous on-demand mobile fleets. This invention can capture the operational trends of the transportation system, including autonomous on-demand mobile vehicles and charging stations, assisting in achieving load guidance based on charging pricing, and further laying the foundation for tapping the potential of charging station spatiotemporal flexibility.

[0005] A first aspect of this invention proposes a method for guiding and regulating charging demand for autonomous on-demand mobile fleets, comprising:

[0006] We will construct a network flow optimization scheduling model for autonomous on-demand mobile fleets and an optimal pricing model for charging station operators, respectively.

[0007] Based on master-slave game theory, the optimal pricing model of the charging station operator is used as the upper-level model, and the network flow optimization scheduling model of the autonomous on-demand mobile fleet is used as the lower-level model. The lower-level model is then embedded into the upper-level model as a constraint to obtain the updated optimal pricing model of the charging station operator.

[0008] Solve the updated optimal pricing model for the charging station operator to obtain a pricing scheme for regulating the charging demand of the autonomous on-demand mobile fleet.

[0009] In one specific embodiment of the present invention, the method further includes:

[0010] The autonomous on-demand mobile fleet network flow optimization scheduling model describes traffic flow as a network flow in a time-space-energy network; the nodes in the time-space-energy network represent a spatial location in the traffic network, as well as a time period and a power level; the edges connecting the nodes in the time-space-energy network represent changes in spatial location, as well as changes in time and power in the traffic network.

[0011] The edges in the time-space-energy network are divided into order service edges, empty vehicle movement edges, charging edges, and stationary edges. An order service edge indicates that the vehicle is carrying passengers, its spatial location changes, and its battery level decreases over time. An empty vehicle movement edge indicates that the vehicle is not carrying passengers, its spatial location changes, and its battery level decreases over time. A charging edge indicates that the vehicle's spatial location remains unchanged, and its battery level increases over time. A stationary edge indicates that the vehicle's spatial location remains unchanged, and its battery level remains constant over time. The flow on an edge in the time-space-energy network represents the number of vehicles involved in the corresponding decision.

[0012] In a specific embodiment of the present invention, the objective function of the network flow optimization scheduling model for the autonomous on-demand mobile vehicle fleet is:

[0013]

[0014] Among them, I ser ,I re Let F represent the set of order service edges and the set of empty vehicle movement edges in the time-space-energy network, respectively; F is the set of charging stations, and T is the set of time periods. This represents the traffic flow on the i-th empty vehicle moving edge. This represents the traffic flow along the service edge of the i-th order; This represents the charging power at charging station f during time period t, where Δt is the length of the time period. λ represents the charging price at charging station f during time period t; dist r represents the unit distance cost of the vehicle. in d is the revenue per unit distance for serving passenger orders. i This represents the spatial distance corresponding to edge i in the time-space-energy network;

[0015] The constraints of the network flow optimization scheduling model for the autonomous on-demand mobile vehicle fleet include:

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022] Where, n time ,n soc These represent the time and energy corresponding to node n in the time-space-energy network, respectively; i ori i des These represent the start and end points of edge i in the time-space-energy network, respectively; i ori,～ i des,～ Let loc represent the starting and ending attributes of edge i in the time-space-energy network, respectively, where loc represents spatial location, time represents time, and soc represents energy; N is the set of nodes in the time-space-energy network; I ser ,I re ,I cha ,I stop These represent the sets of order service edges, empty vehicle movement edges, charging edges, and stationary edges in the time-space-energy network, respectively; the summation subscript type in equation (3) indicates the summation of I. ser ,I re ,I cha ,I stop Traverse the edges in the set; G is the set of spatial locations; This represents the traffic flow on the i-th charging edge; This represents the traffic flow on the i-th stationary edge; This represents the volume of traffic abandoned during time period t from the origin (ori) to the destination (des); n This represents the traffic flow from node n to the outside of the network; This represents the charging power at charging station f during time period t; ori,des,t For time period t, request the number of passenger orders moving from ori to des; n This represents the traffic flow injected into node n from outside the network, where:

[0023]

[0024] Among them, V g This represents the initial number of vehicles stationed at each spatial location g, with each vehicle initially having an energy level of c. 0 ;n loc This represents the spatial location of node n in the time-space-energy network;

[0025] P represents the maximum charging capacity of charging station f; cha This indicates the charging power of each vehicle or each charging station; t max This refers to the last time period within the scheduling scope.

[0026] In a specific embodiment of the present invention, the objective function of the optimal pricing model of the charging station operator is:

[0027]

[0028] Where, α f,t This represents the nodal electricity price at which charging station f connects to the distribution network during time period t. This represents the charging power at charging station f during time period t.

[0029] The constraints of the optimal pricing model for the charging station operator include:

[0030]

[0031]

[0032]

[0033] in, θ represents the time period in T during which pricing decisions need to be made; f,t It is an integer variable representing the charging price tier of charging station f within time period t; Δπ represents the difference between the price tiers; t price This describes the time scale of pricing, where prices change over a continuous period of time (t). price It remains unchanged within Δt.

[0034] In one specific embodiment of the present invention, the master-slave game includes:

[0035] Players in a master-slave game: The charging station operator is the leader, and the autonomous on-demand mobile vehicle fleet is the follower;

[0036] The strategy set of the master-slave game: the strategy set of the charging station operator is the feasible domain of charging pricing determined by equations (10)-(12), and the strategy set of the autonomous on-demand mobile fleet is the feasible domain of fleet scheduling determined by equations (2)-(7).

[0037] The payment functions of the master-slave game: the payment function of the charging station operator is the maximization formula (9), and the payment function of the autonomous on-demand mobile fleet is the maximization formula (1).

[0038] In a specific embodiment of the present invention, the step of embedding the lower-level model as a constraint into the upper-level model to obtain the updated optimal pricing model for the charging station operator includes:

[0039] The network flow optimization scheduling model for the autonomous on-demand mobile vehicle fleet is represented in the following matrix form:

[0040]

[0041] Where x is the decision variable e n , The vectors formed are: v is the dual variable vector corresponding to the equality constraint A1x=b1, w is the dual variable vector corresponding to the inequality constraint A2x=b2; c is the objective function coefficient vector; A1, A2, b1, b2 are all constant matrices.

[0042] The linear programming problem shown in equation (13) is equivalent to the following constraint conditions:

[0043]

[0044] The constraints shown in equation (14) are embedded into the optimal pricing model of the charging station operator:

[0045] The objective function for the charging station operator is: Equation (9)

[0046] S charging pricing constraints: Equations (10)-(12)(15)

[0047] Equivalent constraints of the autonomous on-demand mobile fleet scheduling model: Equation (14)

[0048] Equation (15) is the updated optimal pricing model for charging station operators.

[0049] In one specific embodiment of the present invention, the method further includes:

[0050] Solving the model shown in equation (15), we obtain... θ f,t The optimal solution is the pricing scheme for charging station operators; and we also obtain... e n , The optimal solution is the autonomous, on-demand mobile fleet's action mode guided by price signals.

[0051] A second aspect of the present invention provides a charging demand regulation and guidance device for autonomous on-demand mobile fleets, comprising:

[0052] The model building module is used to build a network flow optimization scheduling model for autonomous on-demand mobile fleets and an optimal pricing model for charging station operators, respectively.

[0053] The master-slave game module is used to take the optimal pricing model of the charging station operator as the upper-level model and the network flow optimization scheduling model of the autonomous on-demand mobile fleet as the lower-level model based on the master-slave game; and to embed the lower-level model as a constraint condition into the upper-level model to obtain the updated optimal pricing model of the charging station operator.

[0054] The regulation and optimization module is used to solve the updated optimal pricing model of the charging station operator to obtain a pricing scheme for regulating the charging demand of the autonomous on-demand mobile fleet.

[0055] A third aspect of the present invention provides an electronic device comprising:

[0056] At least one processor; and a memory communicatively connected to said at least one processor;

[0057] The memory stores instructions that can be executed by the at least one processor, the instructions being configured to execute the aforementioned method for regulating and guiding charging demand for autonomous on-demand mobile fleets.

[0058] A fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to execute the above-described method for regulating and guiding charging demand for autonomous on-demand mobile fleets.

[0059] The features and beneficial effects of this invention are as follows:

[0060] 1. This invention establishes a network flow optimization scheduling model for autonomous on-demand mobile fleets, describing the action patterns and price response characteristics of autonomous on-demand mobile fleets at an appropriate spatiotemporal scale, providing support for modeling and evaluating the process by which charging station operators use price signals to regulate and guide charging load.

[0061] 2. This invention utilizes a master-slave game framework to establish a decision-making model for charging station operators regarding charging pricing in the face of autonomous on-demand mobile fleets. With the charging station operator as the leader and the autonomous on-demand mobile fleets as followers, it captures the operational trends of the transportation system, including autonomous on-demand mobile vehicles and charging stations, and uses price signals to guide and regulate vehicle charging load to an optimal state. Furthermore, considering the characteristics of master-slave games, this invention provides a solution method that utilizes strong duality to eliminate lower-level problems and transform the game problem into a single-level mixed integer programming problem.

[0062] 3. This invention utilizes charging price signals for charging load guidance and regulation, taking into account not only the temporal flexibility of vehicle charging but also the spatial mobility of charging locations within a vehicle fleet. This facilitates fully tapping the flexibility potential of the transportation system and lays a solid foundation for charging station operators to participate in applications such as grid demand-side response, ancillary services, and the aggregation of virtual power plants. Attached Figure Description

[0063] Figure 1 This is an overall flowchart of a charging demand regulation and guidance method for autonomous on-demand mobile fleets according to an embodiment of the present invention. Detailed Implementation

[0064] The present invention proposes a method and device for regulating and guiding charging demand for autonomous on-demand mobile fleets, which will be further described below with reference to the accompanying drawings and specific embodiments.

[0065] A method for regulating and guiding charging demand for autonomous on-demand mobile fleets, as proposed in the first aspect of this invention, includes:

[0066] We will construct a network flow optimization scheduling model for autonomous on-demand mobile fleets and an optimal pricing model for charging station operators, respectively.

[0067] Based on master-slave game theory, the optimal pricing model of the charging station operator is used as the upper-level model, and the network flow optimization scheduling model of the autonomous on-demand mobile fleet is used as the lower-level model. The lower-level model is then embedded into the upper-level model as a constraint to obtain the updated optimal pricing model of the charging station operator.

[0068] Solve the updated optimal pricing model for the charging station operator to obtain a pricing scheme for regulating the charging demand of the autonomous on-demand mobile fleet.

[0069] In one specific embodiment of the present invention, the overall process of the charging demand regulation and guidance method for autonomous on-demand mobile fleets is as follows: Figure 1 As shown, it includes the following steps:

[0070] 1) Establish a network flow optimization scheduling model for autonomous on-demand mobile vehicle fleets. This model consists of an objective function and constraints. The specific steps are as follows:

[0071] 1-1) Determine the objective function R of the network flow optimization scheduling model for the autonomous on-demand mobile vehicle fleet. AMoD :

[0072]

[0073] In this embodiment, the network flow optimization scheduling model for the autonomous on-demand mobile fleet describes traffic flow as a network flow within a time-space-energy network. This time-space-energy network is an extension of the original traffic network according to the time and energy dimensions after discretizing time and energy consumption. Nodes in the time-space-energy network represent a spatial location, a time period, and an energy level within the traffic network. Edges connecting nodes in the time-space-energy network represent changes in spatial location, time, and energy consumption within the traffic network.

[0074] The edges in a time-space-energy network can be categorized into order service edges, empty vehicle movement edges, charging edges, and stationary edges. These represent different types of vehicle decisions and are associated with corresponding changes in spatial location, time, and battery level. Specifically, an order service edge indicates that the vehicle is carrying passengers, its spatial location changes, and its battery level decreases over time; an empty vehicle movement edge indicates that the vehicle is not carrying passengers, its spatial location changes, and its battery level decreases over time; a charging edge indicates that the vehicle's spatial location remains unchanged, and its battery level increases over time; and a stationary edge indicates that the vehicle's spatial location remains unchanged, and its battery level remains constant over time. The flow on an edge in the time-space-energy network represents the number of vehicles involved in the corresponding decision.

[0075] A similar modeling approach can be found in the reference "Sheng Y, Lin Y, Zeng H, et al. Emission-concerned coordinated dispatching of electrified autonomous mobility-on-demand system and power system incorporating heterogeneous spatiotemporal scales[J].Sustainable Cities and Society,2023,98:104755."

[0076] Among them, I ser ,I re Let F represent the set of order service edges and the set of empty vehicle movement edges in the time-space-energy network, respectively. Let T represent the set of charging stations and the set of time periods. This represents the traffic flow on the i-th empty vehicle moving edge. This represents the traffic flow on the side of the service for the i-th order. This represents the charging power at charging station f during time period t. Δt is the length of the time period. λ represents the charging price at charging station f during time period t. dist This represents the unit distance cost of the vehicle. in Revenue for passenger orders served per unit distance. i This represents the spatial distance corresponding to edge i in the time-space-energy network.

[0077] In this embodiment, the objective function (1) represents the maximization of the net operating revenue of the autonomous on-demand mobile fleet. (Item 1) The second item represents the charging cost of an autonomous, on-demand mobile fleet. The third item represents the distance cost incurred by fleet order services. The fourth item represents the distance cost incurred by moving an empty vehicle. This indicates the revenue from orders received by the fleet.

[0078] 1-2) Determine the constraints of the network flow optimization scheduling model for the autonomous on-demand mobile vehicle fleet, including:

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] Where, n time ,n soc Let i represent the time and energy corresponding to node n in the time-space-energy network, where both time and energy are discrete segments. ori i des Let i represent the start and end points of edge i in the time-space-energy network, respectively. ori,～ i des,～Let represent the attributes corresponding to the start and end points of edge i in the time-space-energy network, respectively, where "~" can be loc (spatial location), time (time), or soc (electrical quantity). N is the set of nodes in the time-space-energy network.

[0086] I ser ,I re ,I cha ,I stop Let G represent the sets of order service edges, empty vehicle movement edges, charging edges, and stationary edges in the time-space-energy network, respectively. G is the set of spatial locations. This represents the traffic flow on the i-th charging edge. This represents the traffic flow on the i-th stationary edge. This represents the volume of traffic that was abandoned during the passenger demand period t from origin (ori) to destination (des). n This represents the traffic flow from node n to the outside of the network. This represents the charging power at charging station f during time period t. ori,des,t Request the number of passenger orders moving from ori to des during time period t. n This represents the traffic flow injected into node n from outside the network, which gives the initial conditions of the system at t=0.

[0087]

[0088] Among them, V g This represents the initial number of vehicles stationed at each spatial location g, with each vehicle initially having an energy level of c. 0 ;n loc This represents the spatial location of node n in the time-space-energy network;

[0089] P represents the maximum charging capacity of charging station f. cha This represents the charging power of each vehicle or charging station; in this embodiment, it is assumed to be a fixed value. max This refers to the last time period within the scheduling scope.

[0090] Constraint (2) describes the behavior of fulfilling passenger orders. Passenger demand originating from ori and ending at des in time period t corresponds to an edge in the time-space-energy network. The flow of autonomous on-demand mobile fleets on this edge is less than or equal to the number of passenger orders; the difference between the two represents the number of passenger orders that are abandoned. Constraint (3) describes the flow conservation of nodes in the time-space-energy network. The summation subscript type in equation (3) indicates that for I... ser ,I re ,I cha ,I stopThe edges in the set are traversed. Constraint (4) calculates the charging power of the charging station based on the charging current and restricts it from exceeding the upper limit of the charging station's capacity. Constraint (5) specifies that the time in the time-space-energy network has not reached the last time period t. max And the battery is insufficient. 0 The flow of a node to the outside of the network is 0. Constraint (6) ensures that the sum of all flows to the outside of the network equals the sum of the initial total number of vehicles. Constraints (5) and (6) together actually require that all flows in the last time period t are equal. max It must be from a quantity of at least c 0 The nodes flowing out of the network require that all vehicles maintain a battery level of no less than c when the scheduling ends. 0 Constraint (7) stipulates that all decision variables are non-negative.

[0091] 2) Establish an optimal pricing model for charging station operators. This model consists of an objective function and constraints. The specific steps are as follows:

[0092] 2-1) Determine the objective function R of the optimal pricing model for charging station operators. CSO :

[0093]

[0094] in, Let α be the charging price at charging station f during time period t. f,t This represents the nodal electricity price at which charging station f connects to the distribution network during time period t. This represents the charging power at charging station f during the t-th time period.

[0095] The objective function (9) represents the maximization of revenue for the charging station operator.

[0096] 2-2) Determine the constraints of the optimal pricing model for charging station operators, including:

[0097]

[0098]

[0099]

[0100] in, This represents the time period in time T during which pricing decisions need to be made; prices remain unchanged during other time periods. θ f,t `f` is an integer variable representing the charging price tier at charging station `f` within time period `t`. `Δπ` represents the price difference between the tiers. price The pricing timescale is described; in this embodiment, the price is over a continuous period of time t. price It remains unchanged within Δt.

[0101] Constraint (10) calculates the charging price, indicating that the charging station operator makes pricing decisions based on the distribution network electricity price. The portion that can be freely adjusted is the service fee and other additional costs set by the charging station operator itself. Constraint (11) specifies the time scale for pricing, where the price is continuously t... price The constraint (12) does not change within each Δt period. The charging price in each time period is discretized into three levels: high, medium, and low.

[0102] 3) Construct a master-slave game problem with charging station operators as the upper layer and autonomous on-demand mobile fleets as the lower layer. The upper-layer model in the master-slave game is the optimal pricing model of the charging station operators, and the lower-layer model is the network flow optimization scheduling model of the autonomous on-demand mobile fleets.

[0103] In this game of master and follower, the charging station operators are the leaders (upper level), while the autonomous, on-demand mobile vehicle fleets are the followers (lower level).

[0104] The strategy sets of the master-slave game: the strategy set of the charging station operator is the feasible region of charging pricing determined by the constraints (10)-(12), and the strategy set of the autonomous on-demand mobile fleet is the feasible region of fleet scheduling determined by the constraints (2)-(7). The strategy sets of the two parties are not coupled.

[0105] The payment functions in the master-slave game are as follows: the payment function of the charging station operator is to maximize its revenue objective (9), while the payment function of the autonomous on-demand mobile fleet is to maximize its net revenue (1). The payment functions of the two parties influence each other and are coupled.

[0106] 4) Apply the strong duality theory of linear programming to transform the lower-level model into a set of equivalent constraints and embed them into the upper-level model to obtain the updated optimal pricing model for the charging station operator.

[0107] In this embodiment, the lower-level model in the master-slave game is a network flow optimization scheduling model for autonomous on-demand mobile vehicle fleets, which is mathematically a linear programming problem. Therefore, the strong duality of linear programming can be used to eliminate the lower-level optimization problem, thereby transforming the master-slave game problem into a single-level optimization problem that can be directly solved.

[0108] Specifically, the network flow optimization scheduling model of autonomous on-demand mobile fleets (1)-(7) can be simplified into the following matrix form:

[0109]

[0110] Where x represents all decision variables. e n , The vectors formed are: v is the dual variable vector corresponding to the equality constraint A1x = b1, and w is the dual variable vector corresponding to the inequality constraint A2x = b2. The objective function coefficient vector c contains the influence of charging pricing. A1, A2, b1, and b2 are all constant matrices.

[0111] Based on the strong duality of linear programming, the linear programming problem shown in equation (13) can be replaced by an equivalent set of constraints:

[0112]

[0113] By embedding the constraints shown in equation (14) into the upper-level model, the master-slave game problem is transformed into a single-layer optimization problem that can be directly solved:

[0114] The objective function for the charging station operator is: Equation (9)

[0115] S charging pricing constraints: Equations (10)-(12)(15)

[0116] Equivalent constraints of the autonomous on-demand mobile fleet scheduling model: Equation (14)

[0117] Equation (15) is the updated optimal pricing model for charging station operators.

[0118] 5) Using an optimization solver that supports mixed-integer programming, solve the updated optimal pricing model for the charging station operator obtained in step 4), and obtain... θ f,t The optimal solution is the pricing scheme for the charging station operator. Simultaneously, we obtain... e n , The optimal solution is the autonomous, on-demand mobile fleet's action mode guided by price signals.

[0119] Based on the solution result, the charging station f During time period t The charging price within the facility is set as follows This price signal guides and regulates the charging load of charging station f within time period t.

[0120] To achieve the above embodiments, a second aspect of the present invention provides a charging demand regulation and guidance device for autonomous on-demand mobile fleets, comprising:

[0121] The model building module is used to build a network flow optimization scheduling model for autonomous on-demand mobile fleets and an optimal pricing model for charging station operators, respectively.

[0122] The master-slave game module is used to take the optimal pricing model of the charging station operator as the upper-level model and the network flow optimization scheduling model of the autonomous on-demand mobile fleet as the lower-level model based on the master-slave game; and to embed the lower-level model as a constraint condition into the upper-level model to obtain the updated optimal pricing model of the charging station operator.

[0123] The regulation and optimization module is used to solve the updated optimal pricing model of the charging station operator to obtain a pricing scheme for regulating the charging demand of the autonomous on-demand mobile fleet.

[0124] It should be noted that the aforementioned explanation of an embodiment of a charging demand regulation and guidance method for autonomous on-demand mobile fleets also applies to a charging demand regulation and guidance device for autonomous on-demand mobile fleets in this embodiment, and will not be repeated here. According to an embodiment of the present invention, a charging demand regulation and guidance device for autonomous on-demand mobile fleets constructs a network flow optimization scheduling model for the autonomous on-demand mobile fleet and an optimal pricing model for the charging station operator, respectively. Based on master-slave game theory, the optimal pricing model of the charging station operator is used as the upper-level model, and the network flow optimization scheduling model of the autonomous on-demand mobile fleet is used as the lower-level model. The lower-level model is embedded as a constraint condition into the upper-level model to obtain an updated optimal pricing model for the charging station operator. The updated optimal pricing model of the charging station operator is solved to obtain a pricing scheme for regulating the charging demand of the autonomous on-demand mobile fleet. This allows for full consideration of the spatiotemporal behavior patterns and price response characteristics of the autonomous on-demand mobile fleet during the charging pricing decision-making process, and the formulation of targeted price signals based on the price response characteristics to improve the regulation efficiency of the autonomous on-demand mobile fleet.

[0125] To implement the above embodiments, a third aspect of the present invention provides an electronic device, comprising:

[0126] At least one processor; and a memory communicatively connected to said at least one processor;

[0127] The memory stores instructions that can be executed by the at least one processor, the instructions being configured to execute the aforementioned method for regulating and guiding charging demand for autonomous on-demand mobile fleets.

[0128] To implement the above embodiments, a fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to execute the above-described method for regulating and guiding charging demand for autonomous on-demand mobile fleets.

[0129] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0130] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform a charging demand regulation and guidance method for an autonomous on-demand mobile fleet according to the above embodiments.

[0131] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0132] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0133] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0134] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0135] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0136] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0137] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0138] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0139] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for regulating and guiding charging demand for autonomous on-demand mobile fleets, characterized in that, include: We will construct a network flow optimization scheduling model for autonomous on-demand mobile fleets and an optimal pricing model for charging station operators, respectively. Based on master-slave game theory, the optimal pricing model of the charging station operator is used as the upper-level model, and the network flow optimization scheduling model of the autonomous on-demand mobile fleet is used as the lower-level model. The lower-level model is then embedded into the upper-level model as a constraint to obtain the updated optimal pricing model of the charging station operator. Solve the updated optimal pricing model for the charging station operator to obtain a pricing scheme for regulating the charging demand of the autonomous on-demand mobile fleet. The autonomous on-demand mobile fleet network flow optimization scheduling model describes traffic flow as a network flow in a time-space-energy network; the nodes in the time-space-energy network represent a spatial location in the traffic network, as well as a time period and a power level; the edges connecting the nodes in the time-space-energy network represent changes in spatial location, as well as changes in time and power levels in the traffic network. The edges in the time-space-energy network are divided into order service edges, empty vehicle movement edges, charging edges, and stationary edges. An order service edge indicates that the vehicle is carrying passengers, its spatial location changes, and its battery level decreases over time. An empty vehicle movement edge indicates that the vehicle is not carrying passengers, its spatial location changes, and its battery level decreases over time. A charging edge indicates that the vehicle's spatial location remains unchanged, and its battery level increases over time. A stationary edge indicates that the vehicle's spatial location remains unchanged, and its battery level remains constant over time. The flow on an edge in the time-space-energy network represents the number of vehicles involved in the corresponding decision. The objective function of the network flow optimization scheduling model for the autonomous on-demand mobile vehicle fleet is: （1） in, Let these represent the sets of order service edges and empty vehicle movement edges in the time-space-energy network, respectively. For charging station collection, For time periods; Indicates the first An empty car moves alongside the traffic flow. Indicates the first Traffic flow next to the order service; Indicates at the charging station No. Charging power during the period, The duration is the length of the time period. For charging stations During the period The price of charging within the area; The cost per unit distance for vehicles. Revenue per unit distance for serving passenger orders Representing edges in a time-space-energy network The corresponding spatial distance; The constraints of the network flow optimization scheduling model for the autonomous on-demand mobile vehicle fleet include: （2） （3） （4） （5） （6） （7） in, These represent nodes in the time-space-energy network. The corresponding time and battery level; These represent the edges in the time-space-energy network. The starting point and the ending point; These represent the edges in the time-space-energy network. The attributes corresponding to the start and end points are: loc represents spatial location, time represents time, and soc represents battery level. It is a set of nodes in a time-space-energy network; These represent the sets of order service edges, empty vehicle movement edges, charging edges, and stationary edges in the time-space-energy network, respectively; the summation subscript 'type' in equation (3) indicates the summation of the edges in the time-space-energy network. Traverse the edges in the set; A set of spatial locations; Indicates the first Traffic flow next to the charging station; Indicates the first Traffic flow on a stationary side; Indicates time period The traffic flow that is abandoned in the passenger demand from origin to destination; Represents a node Traffic flow to areas outside the network; Indicates at the charging station No. Charging power during the time period; For the time period Request the number of passenger orders moving from ori to des; Represents a node Traffic injected from outside the network, including: （8） in, Indicate each spatial location The initial number of vehicles stationed inside, and the initial battery level of each vehicle. ;n loc Representing nodes in a time-space-energy network Corresponding spatial location; Indicates charging station Maximum charging capacity; This indicates the charging power of each vehicle or each charging station; This refers to the last time period within the scheduling scope. The objective function of the optimal pricing model for the charging station operator is: （9） in, Indicates charging station During the period The nodal electricity price for connection to the distribution network. Indicates at the charging station No. Charging power during the time period; The constraints of the optimal pricing model for the charging station operator include: （10） （11） （12） in, express The period during which pricing decisions need to be made; It is an integer variable representing a charging station. During the period Charging price tiers within the product range; This indicates the price difference between different price tiers; It describes the time scale of pricing, where prices are in continuous time. indivual The internal structure remains unchanged; The master-slave game includes: Players in a master-slave game: The charging station operator is the leader, and the autonomous on-demand mobile vehicle fleet is the follower; The strategy set of the master-slave game: the strategy set of the charging station operator is the charging pricing feasible domain determined by equations (10)-(12), and the strategy set of the autonomous on-demand mobile fleet is the fleet scheduling feasible domain determined by equations (2)-(7). The payment functions of the master-slave game: the payment function of the charging station operator is the maximization formula (9), and the payment function of the autonomous on-demand mobile fleet is the maximization formula (1).

2. The method according to claim 1, characterized in that, The step of embedding the lower-level model as a constraint into the upper-level model to obtain the updated optimal pricing model for the charging station operator includes: The network flow optimization scheduling model for the autonomous on-demand mobile vehicle fleet is represented in the following matrix form: （13） in, Decision variables The vector formed It corresponds to equality constraints. The dual variable vector, It corresponds to inequality constraints. The dual variable vector; The coefficient vector of the objective function; All are constant matrices; The linear programming problem shown in equation (13) is equivalent to the following constraint conditions: （14） The constraints shown in equation (14) are embedded into the optimal pricing model of the charging station operator: （15） Equation (15) is the updated optimal pricing model for charging station operators.

3. The method according to claim 2, characterized in that, The method further includes: Solving the model shown in equation (15), we obtain... The optimal solution is the pricing scheme for charging station operators; and we also obtain... The optimal solution is the autonomous, on-demand mobile fleet's action mode guided by price signals.

4. A charging demand regulation and guidance device for autonomous on-demand mobile fleets, characterized in that, include: The model building module is used to build a network flow optimization scheduling model for autonomous on-demand mobile fleets and an optimal pricing model for charging station operators, respectively. The master-slave game module is used to take the optimal pricing model of the charging station operator as the upper-level model and the network flow optimization scheduling model of the autonomous on-demand mobile fleet as the lower-level model based on the master-slave game; and to embed the lower-level model as a constraint condition into the upper-level model to obtain the updated optimal pricing model of the charging station operator. The regulation and optimization module is used to solve the updated optimal pricing model of the charging station operator to obtain a pricing scheme for regulating the charging demand of the autonomous on-demand mobile fleet. The autonomous on-demand mobile fleet network flow optimization scheduling model describes traffic flow as a network flow in a time-space-energy network; the nodes in the time-space-energy network represent a spatial location in the traffic network, as well as a time period and a power level; the edges connecting the nodes in the time-space-energy network represent changes in spatial location, as well as changes in time and power levels in the traffic network. The edges in the time-space-energy network are divided into order service edges, empty vehicle movement edges, charging edges, and stationary edges. An order service edge indicates that the vehicle is carrying passengers, its spatial location changes, and its battery level decreases over time. An empty vehicle movement edge indicates that the vehicle is not carrying passengers, its spatial location changes, and its battery level decreases over time. A charging edge indicates that the vehicle's spatial location remains unchanged, and its battery level increases over time. A stationary edge indicates that the vehicle's spatial location remains unchanged, and its battery level remains constant over time. The flow on an edge in the time-space-energy network represents the number of vehicles involved in the corresponding decision. The objective function of the network flow optimization scheduling model for the autonomous on-demand mobile vehicle fleet is: （1） in, Let these represent the sets of order service edges and empty vehicle movement edges in the time-space-energy network, respectively. For charging station collection, For time periods; Indicates the first An empty car moves alongside the traffic flow. Indicates the first Traffic flow next to the order service; Indicates at the charging station No. Charging power during the period, The duration is the length of the time period. For charging stations During the period The price of charging within the area; The cost per unit distance for vehicles. Revenue per unit distance for serving passenger orders Representing edges in a time-space-energy network The corresponding spatial distance; The constraints of the network flow optimization scheduling model for the autonomous on-demand mobile vehicle fleet include: （2） （3） （4） （5） （6） （7） in, These represent nodes in the time-space-energy network. The corresponding time and battery level; These represent the edges in the time-space-energy network. The starting point and the ending point; These represent the edges in the time-space-energy network. The attributes corresponding to the start and end points are: loc represents spatial location, time represents time, and soc represents battery level. It is a set of nodes in a time-space-energy network; These represent the sets of order service edges, empty vehicle movement edges, charging edges, and stationary edges in the time-space-energy network, respectively; the summation subscript 'type' in equation (3) indicates the summation of the edges in the time-space-energy network. Traverse the edges in the set; A set of spatial locations; Indicates the first Traffic flow next to the charging station; Indicates the first Traffic flow on a stationary side; Indicates time period The traffic flow that is abandoned in the passenger demand from origin to destination; Represents a node Traffic flow to areas outside the network; Indicates at the charging station No. Charging power during the time period; For the time period Request the number of passenger orders moving from ori to des; Represents a node Traffic injected from outside the network, including: （8） in, Indicate each spatial location The initial number of vehicles stationed inside, and the initial battery level of each vehicle. ;n loc Representing nodes in a time-space-energy network Corresponding spatial location; Indicates charging station Maximum charging capacity; This indicates the charging power of each vehicle or each charging station; This refers to the last time period within the scheduling scope. The objective function of the optimal pricing model for the charging station operator is: （9） in, Indicates charging station During the period The nodal electricity price for connection to the distribution network. Indicates at the charging station No. Charging power during the time period; The constraints of the optimal pricing model for the charging station operator include: （10） （11） （12） in, express The period during which pricing decisions need to be made; It is an integer variable representing a charging station. During the period Charging price tiers within the product range; This indicates the price difference between different price tiers; It describes the time scale of pricing, where prices are in continuous time. indivual The internal structure remains unchanged; The master-slave game includes: Players in a master-slave game: The charging station operator is the leader, and the autonomous on-demand mobile vehicle fleet is the follower; The strategy set of the master-slave game: the strategy set of the charging station operator is the charging pricing feasible domain determined by equations (10)-(12), and the strategy set of the autonomous on-demand mobile fleet is the fleet scheduling feasible domain determined by equations (2)-(7). The payment functions of the master-slave game: the payment function of the charging station operator is the maximization formula (9), and the payment function of the autonomous on-demand mobile fleet is the maximization formula (1).

5. An electronic device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, the instructions being configured to perform the method described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method according to any one of claims 1-3.

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