Charging load guide scheduling method and system for shared electric vehicle

Abstract

The invention discloses a charging load guide scheduling method and system for shared electric vehicles. The method comprises the following steps: establishing a Stackelberg-Nash game-based charging load guide scheduling game model of an upper-layer electric traffic coupling system operator and a lower-layer shared electric vehicle fleet; target functions and constraint conditions of the charging load guide scheduling game model are determined, and the target functions comprise a first target function of the minimum cost of a shared electric vehicle fleet considering charging and discharging excitation and rebalance excitation and a second target function of the minimum cost of an electric traffic coupling system operator considering excitation response; and solving the charging load guide scheduling game model in a distributed manner by using a super-gradient descent algorithm to obtain a charging load guide scheduling scheme. In this way, an upper-layer system operator is guided to formulate an optimal traffic and power grid service excitation strategy through the scheduling model, guiding of a lower-layer motorcade and scheduling of charging loads are achieved, and collaborative optimization of energy supply and demand and traffic flow distribution is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a charging load guidance and dispatching method and system for shared electric vehicles. Background Art

[0002] With the widespread adoption of shared electric vehicles (SEVs) in intelligent transportation systems and smart grids, the coordinated optimization of vehicle-grid interaction and rebalancing scheduling has become an important means of improving system efficiency. SEVs not only feed back energy through V2G (vehicle-to-grid) interactions, providing flexible power support to the grid, but also optimize vehicle spatial distribution through appropriate rebalancing scheduling, alleviating urban traffic congestion.

[0003] However, in actual operation, when making charging and discharging decisions and rebalancing choices, SEV fleets are not only affected by electricity prices, but also need to comprehensively consider factors such as travel costs, route preferences, charging facility capacity limitations, and rebalancing incentives.

[0004] Therefore, how to design a reasonable incentive mechanism to guide SEV fleets to optimize their own behavior and achieve collaborative interaction between the power-transport coupling system operator (CSO) and the SEV fleet is an urgent problem that needs to be solved. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a charging load guidance and scheduling method and system for shared electric vehicles, which fully integrates the two dimensions of charging and discharging incentives and rebalancing incentives. It aims to guide the fleet to make reasonable path selection and rebalancing decisions on the basis of ensuring the stable operation of the power system, thereby achieving coordinated optimization of energy supply and demand and traffic flow distribution.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for guiding and scheduling charging load of a shared electric vehicle comprises the following steps:

[0008] Acquisition of electric transport coupling system operators and shared electric vehicle fleets;

[0009] Establish a charging load guidance scheduling game model based on the Stackelberg-Nash game between the upper-level power-transportation coupling system operator and the lower-level shared electric vehicle fleet;

[0010] Determining an objective function and constraints of the charging load guided scheduling game model, the objective function including a first objective function for minimizing the cost of the shared electric vehicle fleet considering charging and discharging incentives and rebalancing incentives, and a second objective function for minimizing the cost of the electric-transportation coupling system operator considering incentive response, the constraints including a first constraint corresponding to the first objective function and a second constraint corresponding to the second objective function;

[0011] The super gradient descent algorithm is used to distributely solve the charging load guidance scheduling game model to obtain a charging load guidance scheduling plan.

[0012] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0013] A charging load guidance and scheduling system for a shared electric vehicle includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the above-mentioned charging load guidance and scheduling method for a shared electric vehicle is implemented.

[0014] The present invention has the following beneficial effects: establishing a charging load guidance and scheduling game model based on the Stackelberg-Nash game between the upper-level power-transportation coupling system operator and the lower-level shared electric vehicle fleet; determining the objective function and constraints of the charging load guidance and scheduling game model, including a first objective function that minimizes the cost of the shared electric vehicle fleet, taking into account charging and discharging incentives and rebalancing incentives, and a second objective function that minimizes the cost of the power-transportation coupling system operator, taking into account incentive response; and using a super gradient descent algorithm to distribute the charging load guidance and scheduling game model to obtain a charging load guidance and scheduling plan. In this way, the scheduling model guides the upper-level system operator to formulate the optimal transportation and grid service incentive strategy, realizes the guidance of the lower-level fleet and the scheduling of charging load, and achieves the coordinated optimization of energy supply and demand and traffic flow distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a method for guiding and scheduling charging load of a shared electric vehicle according to an embodiment of the present invention;

[0016] Figure 2 This is a schematic diagram of a charging load guidance and scheduling system for shared electric vehicles according to an embodiment of the present invention;

[0017] Figure 3 This is a diagram of a collaborative scheduling framework for shared electric vehicles under power-traffic coupling according to an embodiment of the present invention;

[0018] Figure 4 This is a diagram of the Stackelberg-Nash game information interaction framework according to an embodiment of the present invention;

[0019] Figure 5 A system diagram of a calculation example of an embodiment of the present invention;

[0020] Figure 6 This is an iterative trend diagram of the revenue of the operator of the electric-transport coupling system according to an embodiment of the present invention;

[0021] Figure 7 This is a traffic congestion level diagram considering only vehicle-grid interaction incentives according to an embodiment of the present invention;

[0022] Figure 8 A traffic congestion diagram that takes into account both vehicle-grid interaction incentives and rebalancing incentives according to an embodiment of the present invention;

[0023] Figure 9 A comparison chart of the revenue of a shared electric vehicle fleet under different budgets according to an embodiment of the present invention;

[0024] Figure 10 A comparison chart of the revenue of a shared electric vehicle fleet at different scales according to an embodiment of the present invention;

[0025] Figure 11 This is a convergence trend diagram of the incentive for fleet No. 1 according to an embodiment of the present invention;

[0026] Figure 12 This is a graph showing the convergence results of the sensitivity of fleet 1 according to an embodiment of the present invention;

[0027] Figure 13 This is a convergence trend diagram of the decision variables of fleet No. 1 according to an embodiment of the present invention.

[0028] Description of labels:

[0029] 1. A charging load guidance and dispatching system for shared electric vehicles; 2. Memory; 3. Processor. DETAILED DESCRIPTION

[0030] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0031] In existing technologies, there are problems in that the incentive mechanism design in electric vehicle scheduling does not fully combine the dual incentives of transportation and power grid, and lacks personalized incentive design.

[0032] In order to solve at least the above technical problems, the present invention provides a method for guiding and scheduling charging load of a shared electric vehicle, comprising the steps of:

[0033] Acquisition of electric transport coupling system operators and shared electric vehicle fleets;

[0034] Establish a charging load guidance scheduling game model based on the Stackelberg-Nash game between the upper-level power-transportation coupling system operator and the lower-level shared electric vehicle fleet;

[0035] Determining an objective function and constraints of the charging load guided scheduling game model, the objective function including a first objective function for minimizing the cost of the shared electric vehicle fleet considering charging and discharging incentives and rebalancing incentives, and a second objective function for minimizing the cost of the electric-transportation coupling system operator considering incentive response, the constraints including a first constraint corresponding to the first objective function and a second constraint corresponding to the second objective function;

[0036] The super gradient descent algorithm is used to distributely solve the charging load guidance scheduling game model to obtain a charging load guidance scheduling plan.

[0037] In this way, according to the embodiments of the present invention, it is possible to guide the upper-level system operators to formulate optimal traffic and grid service incentive strategies through the scheduling model, realize the guidance of the lower-level fleet and the scheduling of charging loads, and achieve the coordinated optimization of energy supply and demand and traffic flow distribution.

[0038] Hereinafter, the technical solutions according to the present disclosure will be described with reference to specific embodiments and in conjunction with the accompanying drawings.

[0039] Please refer to Figure 1 and Figure 3 , embodiment 1 of the present invention is:

[0040] Figure 1 1 is a flow chart showing a method for guiding and scheduling charging load of a shared electric vehicle according to an embodiment of the present disclosure. Figure 1 , method 1 includes the following steps 101 to 104.

[0041] In step 101 , an electric-transport coupling system operator and a shared electric vehicle fleet are obtained.

[0042] In step 102 , a charging load guidance scheduling game model based on the Stackelberg-Nash game is established between the upper-level electric-transportation coupling system operator and the lower-level shared electric vehicle fleet.

[0043] In some embodiments, the charging load guided scheduling game model is:

[0044]

[0045] Where, f i represents the first objective function, Ω i represents the first constraint, represents the first objective function, Ω CSOrepresents the first constraint, N represents the set of shared electric vehicle fleets, and CSO represents the electric-transportation coupling system operator.

[0046] In this way, the equilibrium solution of the charging load-guided dispatch game model can balance the behaviors of the power-transportation coupling system operators and the shared electric vehicle fleet, aiming to achieve a win-win situation rather than just promoting individual interests.

[0047] In some embodiments, the charging load-guided scheduling game model includes the following steps: P1: The power-transportation coupling system operator broadcasts an incentive to all shared electric vehicle fleets; P2: The shared electric vehicle fleets determine the optimal response strategy for the shared electric vehicle fleet based on the incentive broadcast by the power-transportation coupling system operator, in combination with a first objective function and a first constraint; P3: The power-transportation coupling system operator determines the optimal incentive based on the optimal response strategies of all shared electric vehicle fleets, in combination with a second objective function and a second constraint; P1 to P3 are repeated until the determined optimal incentive remains unchanged. In this way, the interaction between the power-transportation coupling system operator and multiple shared electric vehicle fleets during the scheduling process is captured.

[0048] In step 103, the objective function and constraints of the charging load guided scheduling game model are determined. The objective function includes a first objective function for minimizing the cost of the shared electric vehicle fleet considering charging and discharging incentives and rebalancing incentives, and a second objective function for minimizing the cost of the power-transportation coupling system operator considering incentive response. The constraints include a first constraint corresponding to the first objective function and a second constraint corresponding to the second objective function.

[0049] In some embodiments, a first benefit value of providing electricity by a shared electric vehicle fleet is calculated taking into account charging and discharging incentives; a second benefit value of providing rebalancing by a shared electric vehicle fleet is calculated taking into account rebalancing incentives; the deviation cost of changing routes or destinations by the shared electric vehicle fleet is calculated; the travel time cost of the shared electric vehicle fleet is calculated; the aggregate cost of the shared electric vehicle fleet is obtained by subtracting the sum of the first benefit value and the second benefit value from the sum of the deviation cost and the travel time cost; and the first objective function is obtained by minimizing the aggregate cost of the shared electric vehicle fleet. The first constraint condition includes a traffic flow conservation constraint, upper and lower limit constraints on the decision vector of the first objective function, and a coupled resource constraint. In this way, the cost function of each fleet takes into account four key factors: the benefits of providing energy or rebalancing services, travel time, and travel preferences. Each fleet aims to minimize its individual cost by selecting the probability of selecting a road segment and the probability of selecting a destination.

[0050] In some embodiments, the calculation formula of the first benefit value is:

[0051]

[0052] Where, represents the first benefit value, Indicates a charging station site. represents the incentive of the electric-transport coupling system operator to discharge the fleet i at destination d, Indicates the decay loss of the battery during discharge. represents the probability that the i-th team chooses the d-th destination, m i represents the number of shared electric vehicles owned by fleet i; in this way, it is possible to guide SEVs to perform energy services or be reallocated to specific locations, and design incentives at specific charging stations or rebalancing areas.

[0053] The calculation formula for the second benefit value is:

[0054]

[0055] Where, represents the second benefit value, Represents a rebalancing site, represents the rebalancing incentive provided by the electric-transport coupling system operator to fleet i at destination d, Indicates the battery degradation loss during the rebalancing process.

[0056] In some embodiments, the total travel time cost of all shared electric vehicle fleets is calculated; the discharge benefit value of the power-transport coupling system operator is calculated; the rebalancing benefit value of the power-transport coupling system operator is calculated; the sum of the discharge benefit value and the rebalancing benefit value is subtracted from the total travel time cost to obtain the power-transport coupling system operator cost; and the second objective function is obtained by minimizing the power-transport coupling system operator cost. The second constraint conditions include upper and lower bounds on the decision vector of the second objective function and a constraint on the total amount of incentive resources. In this way, the CSO cost function includes total travel time, total discharge benefit, and total rebalancing benefit, minimizing energy supply costs while avoiding exacerbating traffic congestion.

[0057] In some embodiments, the total amount of incentive resources constraint includes:

[0058]

[0059] Where C b represents the total incentive amount, C represents the upper limit of the total incentive amount, p represents the incentive, and φ(w) represents the traffic flow vector at the destination; Indicates a charging station site. represents the incentive of the electric-transport coupling system operator to discharge the fleet i at destination d, Indicates the battery's decay loss during discharge; Represents a rebalancing site, represents the rebalancing incentive provided by the electric-transport coupling system operator to fleet i at destination d, Indicates the battery degradation loss during the rebalancing process; represents the fleet set, m i represents the number of shared electric vehicles owned by fleet i. In this way, the limited incentive resources are effectively considered, φ i It represents the total flow of fleet i arriving at each charging station.

[0060] In step 104, a distributed solution of the charging load guidance scheduling game model is performed using a super gradient descent algorithm to obtain a charging load guidance scheduling solution. This distributed solution based on super gradient descent protects fleet privacy and improves computational efficiency.

[0061] It can be seen that this embodiment proposes a shared electric vehicle scheduling method that considers personalized incentive design. The method is based on the Stackelberg-Nash game model and describes the hierarchical interactive relationship between the power-transportation coupling system operator and the SEV fleet: at the upper level, the system operator formulates transportation and grid service incentives under the constraint of a limited incentive budget to guide the rebalancing and V2G decision-making behavior of the lower-level SEV fleet; at the lower level, the Nash game is used to describe the non-cooperative game relationship between multiple SEV fleets.

[0062] This model primarily involves two types of game processes: the Stackelberg game: The CSO, as the leader of the Stackelberg game, guides the SEV fleet's charging and discharging behaviors and travel decisions by jointly setting V2G and rebalancing incentives, thereby optimizing the allocation of power and transportation resources from a system-wide perspective. The Nash game: SEV fleet managers, as followers of the Stackelberg game, make route selection and charging and discharging decisions based on the principle of maximizing their own benefits under the V2G and rebalancing incentives set by the CSO, and compete for the limited V2G equipment and road resources in the system. Within this game framework, the CSO optimizes system operating costs through a dual incentive mechanism (V2G and rebalancing incentives), improving scheduling efficiency and resource utilization while ensuring service quality and user experience.

[0063] System framework such as Figure 3 As shown in the figure, the power-transportation coupling system operator sends incentive information to the shared electric vehicle fleet, and the shared electric vehicle fleet returns travel selection information and incentive response sensitivity to the power-transportation coupling system operator. The shared electric vehicle fleet can also send traffic flow and energy flow to the coupling resources (including charging station resources and road resources), and the shared electric vehicle fleet obtains congestion information from the coupling resources.

[0064] Due to the large number of SEV fleets and the highly decentralized nature of individual decision-making, traditional centralized optimization methods face high computational costs and privacy risks in this scenario. To address this challenge, this embodiment employs a distributed solution method based on the super gradient descent algorithm, enabling the CSO and SEV fleet to interactively optimize in a distributed environment, ultimately converging to a SNG equilibrium solution. Compared to centralized solutions, this method not only significantly reduces computational complexity and protects fleet privacy, but also exhibits excellent scalability, making it suitable for large-scale SEV scheduling scenarios and enhancing the system's feasibility and practical deployment value.

[0065] Please refer to Figure 4 , the second embodiment of the present invention is:

[0066] Based on the first embodiment, this embodiment provides a specific implementation step of a method for guiding and scheduling charging load of a shared electric vehicle, including steps 201 to 204:

[0067] Step 201: A shared electric vehicle scheduling model considering dual incentives of charging, discharging, and rebalancing.

[0068] Specifically, consider a CSO that manages multiple fleets of SEVs through a well-designed incentive mechanism. Each fleet manager is responsible for dispatching a group of SEVs, which can choose to provide travel services based on actual demand or provide V2G energy support to the grid in the event of a power shortage.

[0069] In this embodiment, the transportation network is modeled as a connected graph Where G is the transportation network topology, represents the set of traffic nodes, and ε represents the set of road segments connecting these nodes. Charging stations and rebalancing locations correspond to subsets of the node set, respectively, and are denoted as and It should be pointed out that in order to avoid functional overlap, That is, each node can only be a charging station or a rebalancing location. The destination set is defined as It contains elements.

[0070] Step 2011: Establish a travel cost objective function for the shared electric vehicle fleet.

[0071] Specifically, each fleet i aims to select a road segment with probability v i and the destination selection probability w i To minimize their individual costs and maximize their individual benefits. The cost function of each fleet i considers four key factors: the benefits of providing energy or rebalancing services, travel time and travel preferences:

[0072] First, to guide SEVs to perform energy services or redistribute to specific locations, incentives are designed at specific charging stations or rebalancing areas. It can be described by the following function:

[0073]

[0074] Where, Indicates a charging station site. represents the probability that the i-th team chooses the d-th destination, represents the incentive of the electric-transport coupling system operator to discharge the fleet i at destination d, Indicates the decay loss of the battery during discharge, m i represents the number of shared electric vehicles owned by fleet i.

[0075] Similarly, the payoff for fleet i arriving at a certain rebalancing area is It is expressed as follows:

[0076]

[0077] Where, Represents a rebalancing site, represents the rebalancing incentive provided by the electric-transport coupling system operator to fleet i at destination d, Indicates the battery degradation loss during the rebalancing process.

[0078] Then, the SEV fleet usually has a planned route and destination during daily operation. When the CSO issues an incentive, the SEV manager is attracted to change the operating route or destination, which will generate a deviation cost. as follows:

[0079]

[0080] Where, α 1,i ,α 2,i represents the fleet-specific preference parameter, α 1,i A larger value means the fleet will pay more attention to the destination, 2,i A larger one means the team will pay more attention to the route; Represents the routes and destinations preferred by the fleet manager, defined as follows:

[0081]

[0082] Where, represents the proportion of basic vehicles allocated to the e-th road segment by the i-th fleet, and E represents the number of road segments; represents the basic probability that the i-th fleet chooses the d-th destination, and D represents the number of destinations.

[0083] Finally, due to the limitation of road capacity, if too many teams choose the same route, it will cause congestion and generate negative congestion time cost. The definition is as follows:

[0084]

[0085] Where η i represents the unit time cost of fleet i, t e Indicates the travel time of calculating road section e, σ e Indicates the calculation of total traffic flow, v e represents the vector consisting of the proportion of vehicles in each fleet that choose the e-th road section, It represents the proportion of vehicles assigned to the e-th road segment by the i-th fleet.

[0086] t e (σ e (v e ))=a e +b e σ e (v e )

[0087]

[0088] Where a e ,b e The positive coefficient representing the congestion level of road section e, ξ e Represents the total number of other vehicles traveling on road segment e.

[0089] Therefore, the profit function of SEV fleet i includes four parts: discharge profit Rebalancing benefits Costs associated with deviations from desired route and destination and travel time benefits Among them, travel time benefits It is influenced not only by the choices of a single fleet but also by the decisions of other fleets. The remaining three components are influenced by their own decisions and the incentives provided by the CSO.

[0090] The aggregate cost function for SEV fleet i is as follows:

[0091]

[0092] Where v represents the matrix composed of the road segment selection of each fleet, w represents the matrix composed of the destination selection of each fleet, and σ(v) represents the vector composed of the traffic flow of each road.

[0093] Step 2012: Establish constraints, including flow conservation constraints, upper and lower limit constraints of decision variables, and coupling resource constraints.

[0094] Traffic flow conservation constraint: To ensure the conservation of traffic flow in the traffic network, that is, for each road segment e, the inflow of traffic must be equal to the outflow, the following constraint (1.1) is defined:

[0095]

[0096] In the formula, e:(j,k) represents the road segment from node j to node k, e:(k,m) represents the road segment from node k to node m, d represents the destination number, represents the probability that the i-th team chooses the d-th destination, o i represents the starting point of the i-th team.

[0097] Upper and lower bounds on decision variables: Fleet i’s choice of route and destination is modeled using probability distributions, so the following constraints (1.2) must be satisfied:

[0098]

[0099] Coupling resource constraints: To ensure that the traffic volume reaching each destination does not exceed the upper limit, the following constraints (1.3) are introduced:

[0100]

[0101] Where, δ d represents the maximum number of vehicles that the destination d can accommodate, δ e Indicates the maximum number of vehicles that road segment e can accommodate.

[0102] Define Ω i represents the decision variable x for fleet i i :=[v i ,w i ]’s feasible region:

[0103] Ω i :={x i ∣(1.1),(1.2),(1.3)}

[0104] Step 202 : Considering the incentive response cost model for the electric-transportation coupling system operator.

[0105] Specifically, the CSO is responsible for designing customized incentives to encourage SEV fleets to perform V2G charging or rebalancing at charging station nodes and designated rebalancing areas within the transportation network. Specifically, the CSO formulates a different incentive strategy for each fleet i at each destination d. The goal of this strategy is to maximize energy supply benefits while avoiding exacerbating traffic congestion. Therefore, the CSO's payoff function includes total travel time, total discharge benefits, and total rebalancing benefits.

[0106] Step 2021 , a multi-objective cost function considering the limited incentive resources.

[0107] The cost function of CSO is as follows:

[0108]

[0109] Where, φ t represents the total travel time cost, φ e represents the discharge benefit, φ r represents the rebalancing gain.

[0110] The total travel time cost of a fleet on all road segments is calculated by adding up the travel time of each fleet:

[0111]

[0112] Where, μ represents the CSO benefit per unit time. Total travel time is a key indicator for assessing traffic congestion and the overall efficiency of a transportation network. The negative sign indicates that longer total travel time in the system generates greater negative benefits.

[0113] CSOs can benefit from the electricity provided by SEVs and rely on the SEV fleet to provide power to key load nodes. The discharge benefits are in the following forms:

[0114]

[0115] Where, represents the traffic flow vector of destination d;

[0116]

[0117] φ d represents the sum of the traffic flows arriving at charging station d for each fleet, and β1 and β2 represent the V2G benefit parameters of the CSO. This quadratic form shows that the marginal benefit of discharge benefits should not increase with the increase in the number of vehicles.

[0118] The SEV rebalancing process reduces traffic congestion and helps stabilize the efficiency of the transportation system. From a system perspective, the rebalancing benefit can be expressed as a linear function:

[0119]

[0120] Where β3 represents the rebalancing benefit parameter of CSO.

[0121] Step 2022: Establish constraint conditions, including decision variable boundary constraints and incentive resource total amount constraints.

[0122] Specifically, the incentive value set by the CSO for SEV fleet i at destination d is intended to guide fleet behavior and must be positive, but not exceed the maximum incentive value that the CSO can provide. Therefore, the following constraint (2.1) is introduced:

[0123]

[0124] Where, represents the maximum value of the discharge incentive provided by CSO at destination d, represents the maximum rebalancing incentive provided by the CSO at destination d.

[0125] CSO issues guidance incentives to SEV fleets based on a preset budget. Therefore, the following incentive cap constraints must be met:

[0126]

[0127] Where C b represents the total amount of incentives, C represents the upper limit of the total amount of incentives, and p represents the incentive;

[0128]

[0129] φ i It represents the total flow of fleet i arriving at each charging station.

[0130] The incentive cap constraint is reformulated as a soft constraint and incorporated into the cost function of the CSO for ease of calculation, resulting in:

[0131]

[0132] Accordingly, the payoff function of the merged CSO is as follows:

[0133]

[0134] Where α represents the penalty coefficient to ensure compliance with the incentive ceiling constraint.

[0135]

[0136] Where: represents the personalized incentive vector, Represents a set of real matrices of dimension D×N.

[0137] Step 203: Analyze and solve the scheduling model based on the Stackelberg-Nash game.

[0138] Specifically, a Stackelberg-Nash game model is constructed to capture the interactions between various participants in the scheduling process. At the top level, the CSO serves as the leader, responsible for making incentive decisions. At the bottom level, N fleets of SEVs are considered followers, responding to the CSO's incentives. The CSO broadcasts personalized incentive information to each fleet, which then responds by reallocating to a rebalancing area or implementing V2G services. This response process can be determined by solving a set of interdependent optimization problems that form the Nash game at the bottom level.

[0139] Therefore, the SNG model of a leader and N followers is expressed as follows:

[0140]

[0141] The components of the model are as follows:

[0142] Participants: CSO and collective The SEV fleet in the strategy set is the incentive p set by the CSO and the travel choices of all fleet agents. Cost function: For SEV fleet The travel cost function is defined in step 2011 , and the total system cost function of the CSO is defined in step 2021 .

[0143] The game model of this embodiment assumes that all participants are rational and self-interested. This means they understand the current situation, identify all possible scenarios, and maximize their expected payoffs. By gradually eliminating strictly dominant strategies, these rational participants will eventually reach a Nash equilibrium. Therefore, the equilibrium solution of the proposed SNG model balances the actions of all participants, aiming for a win-win situation, not just for individual gains.

[0144] Step 2031, Stackelberg-Nash game process.

[0145] Please refer to Figure 4 , in the standardized Stackelberg-Nash game with a single leader and N followers, the process is as follows:

[0146] P1. The leader selects a strategy from its strategy set and broadcasts it to all followers. Given the leader’s decision priority, the game begins when the leader broadcasts its strategy, i.e., incentive p, to all followers.

[0147] P2. Followers determine their best response strategy x based on the leader’s strategy *Specifically, after receiving the leader’s strategy, the followers play a non-cooperative Nash game to reach a Nash equilibrium (NE), which is obtained as follows:

[0148]

[0149] Where x -i represents the decisions of all other teams in the lower layer except team i.

[0150] P3, the leader determines its optimal strategy based on the best response strategies of all followers. Use the best response strategy x calculated in step 2 * , the leader further selects the best strategy p from its feasible strategy set * , calculated as follows:

[0151]

[0152] P4. Repeat steps P1 to P3 to find the best strategy for the leader and followers until all participants no longer change their strategies, indicating that the optimal strategy has been found.

[0153] Step 2032: Stackelberg-Nash game equilibrium analysis.

[0154] In game theory, the solution to a Nash game is usually defined as a Nash equilibrium (NE), in which no participant can unilaterally deviate from NE to increase their payoff. In the model of this embodiment, NE is used to describe the equilibrium of the lower level. Similarly, the solution to a Stackelberg game is usually defined as a Stackelberg equilibrium (SE), in which no participant can unilaterally change their strategy, and SE is used to describe the balance between the upper and lower levels. Based on these definitions of equilibrium, the concept of the solution to a Stackelberg-Nash game is represented by the Stackelberg-Nash equilibrium (SNE), as shown in FIG. Figure 4 shown.

[0155] According to the SNG model defined above, if and only if When a set of strategies (p * ,x * ) constitutes an SNE, satisfying the following conditions:

[0156]

[0157] Where, represents the best response of the lower fleet i, represents the best response of all other followers to the leader's strategy; the first inequality in the formula indicates that the leader cannot * The second inequality indicates that when the leader chooses the optimal strategy p * After that, no followers can unilaterally change their strategy To increase their income. In SNE, the benefits of both CSO and SEV fleets are maximized.

[0158] To address this trade-off, existing literature typically reformulates the entire problem as a mixed integer programming problem and uses existing solver software to find the global optimal solution. However, as the problem size increases, the computational complexity rises dramatically, making it unsuitable for large-scale problems. Therefore, step 2032 employs a super gradient algorithm, focusing on efficiently finding the local optimal solution.

[0159] Step 2032: Distributed solution method based on super gradient descent algorithm.

[0160] This example employs a solution based on the super gradient descent algorithm, designed to scale to larger-scale two-tier games. This algorithm, based on weak assumptions about the upper-tier objective function, employs a general design and optimizes using a simple and easily implemented iterative update rule. Furthermore, this method maintains the distributed structure of the game problem, allowing the lower tier to accommodate a large number of participants, thus offering good scalability.

[0161] Specifically, define a single-value mapping from p to NE:

[0162]

[0163] And use this mapping to construct the CSO optimization problem as follows:

[0164]

[0165] subjectto p∈Ωcso

[0166] The objective function of CSO is φ CSO Depends on x * (·), which indicates that during the optimization process, CSO needs to predict the rational response of the SEV fleet to p. The derivative form with respect to p is expressed as:

[0167]

[0168] The core goal of this algorithm is to use projected gradient descent to find the local optimal solution in the optimization problem. This process can be characterized by the following chain rule:

[0169]

[0170] The flow of the outer loop SNE solution algorithm of this algorithm is shown in the following pseudo code:

[0171] Parameter: Step size Threshold

[0172] Initialization: k←0,

[0173] Iterate until convergence:

[0174] For CSO: Calculate pseudo gradient:

[0175]

[0176] Update incentives:

[0177]

[0178] will p k+1 Broadcast to SEV fleet managers;

[0179] For CSO+SEV fleet managers: Estimating segment selection and sensitivity:

[0180] (x k+1 ,s k+1 )=InnerLoop(p k+1 ,x k ,s k ,σ k )

[0181] Each iteration k←k+1.

[0182] In order to calculate the upper gradient in the pseudocode above, the CSO needs to obtain x(p) and its Jacobian matrix, denoted as Jx(p). This Jacobian matrix is ​​used to describe the sensitivity of the lower SEV fleet and can intuitively reflect the response of the lower SEV fleet to the incentive p. From a mathematical point of view, the gradient descent algorithm calculates a quantity called "super gradient", denoted as It is a generalized form for non-smooth and non-convex functions.

[0183] The two-layer game model uses a double-loop iteration method to achieve hierarchical decision-making. In each outer loop iteration k, the CSO broadcasts the current personalized incentive to the SEV fleet i. Subsequently, in each inner loop iteration, the SEV fleet manager uses the aggregated variables broadcast by the CSO to and Choose its driving route and destination Estimate and update their response to stimulus, i.e. sensitivity

[0184] The process of the inner loop NE solution algorithm of this algorithm is shown in the following pseudo code:

[0185] Parameters: step size γ;

[0186] Input: p, x, s, σ;

[0187] definition:

[0188] Initialization: l←0, ζ=0

[0189]

[0190]

[0191] Iterate until convergence:

[0192] For SEV fleet managers

[0193] Update path selection:

[0194] When ζ=1, update S 1,i , S 2,i and S 3,i :

[0195]

[0196]

[0197]

[0198] When ζ≠1, S is not updated. 1,i , S 2,i and S 3,i .

[0199] Update sensitivity:

[0200] For CSOs:

[0201] polymerization:

[0202] like Then ζ=1, otherwise, ζ=0;

[0203] broadcast ζ;

[0204] Each iteration l←l+1 until

[0205] Final output

[0206] For the above pseudo code, when the estimated result reaches a sufficient accuracy, the inner loop terminates. * (p) and Jx * (p) is solved exactly, but CSO still collects the approximate NE and its sensitivity, i.e., x k+1 and s k+1 , and use this information to update the incentive p through projected gradient descent. Represents the calculated inexact gradient, also known as super gradient.

[0207] In the inner loop NE solution algorithm, in order to update the traffic flow, the SEV fleet manager’s decision needs to be projected onto the polyhedron set Ω i This is equivalent to repeatedly solving a parameterized quadratic programming problem, which can be solved efficiently by a suitable solver. On the other hand, the feasible solution set of CSO is a closed set, so the process of projecting onto this set can be calculated analytically. The inner loop NE solution algorithm updates the sensitivity and requires each fleet manager i to calculate its corresponding auxiliary matrix S 1,i 、S 2,i and S 3,i , these matrices record the mapping About x i , s i , the Jacobian matrix of σ(x).

[0208] Please refer to Figures 5 to 13 , the third embodiment of the present invention is:

[0209] This embodiment performs a calculation analysis based on the first or second embodiment:

[0210] This embodiment is analyzed on the IEEE 33-node system and the improved Nguyen Dupuis traffic system (31 road segments and 22 nodes). Figure 5 As shown in Figure 2, in this electric-transport coupled system, two charging stations and two rebalancing locations are set up. The charging stations are located at the nodes where the electric and transport networks are coupled. The SEV fleet departs from node 1 and travels to destinations at nodes 13, 20, 22, and 24.

[0211] In a competitive model based on cost minimization, the CSO broadcasts the electricity purchase price and rebalancing incentive p to the SEV fleet manager, and the SEV fleet provides feedback to the CSO based on its route selection x and response s to the price and incentive. Each SEV fleet has a preference for destination and operating path, with some fleets prioritizing destinations while others focus on path selection. Therefore, this embodiment considers dispatching 10 SEV fleets, each with different preference parameters α1 and α2. The available electric energy of each SEV fleet follows a uniform distribution: P~N(25,2), and the number of dispatchable vehicles ranges from 10 to 15. In addition, the unit time profit constants of the CSO and SEV fleet are set to 0.01 and 1, respectively.

[0212] To further verify the effectiveness and feasibility of the proposed two-tier game model and solution algorithm in actual operation, this example conducts numerical verification from multiple perspectives. First, by analyzing the convergence of the CSO's total revenue, we evaluate its ability to optimize the overall system revenue during the game scheduling process.

[0213] (1) CSO total revenue iteration trend verification

[0214] In the two-tier game structure, CSO, as the upper-level leader, has the core goal of guiding the SEV fleet to reasonably participate in V2G discharge and rebalancing by designing personalized electricity price incentives, thereby improving the overall benefits of the system. The achievement of this goal was verified by tracking and analyzing the changing trend of CSO's total benefits during the algorithm iteration process. Figure 6 As shown in the figure, the components of the CSO profit function have essentially converged by the 21st iteration and stabilized in subsequent iterations. The total profit fluctuates throughout the iterations, ultimately stabilizing at a positive level, indicating that the profits of the CSO and the SEV fleet gradually reach equilibrium during the optimization process.

[0215] This positive return is primarily due to the guiding role of the incentive mechanism. CSO effectively mobilizes the SEV fleet through incentives to participate in V2G discharging and rebalancing, which together constitute the core source of total revenue. In the 8th and 20th iterations, the penalty cost term reached non-zero values, indicating that the incentive expenditure exceeded the set budget cap. At this point, the algorithm automatically adjusts its strategy and continues to iterate to ensure that the incentive cap constraint is met.

[0216] Ultimately, the algorithm converges to a set of optimal incentive schemes that meet budget constraints, which not only ensures sufficient V2G energy supply benefits in the system, but also achieves effective rebalancing of traffic flow through incentive guidance, thereby achieving the comprehensive goals of alleviating power outage losses, improving energy utilization efficiency and maintaining stable operation of the transportation system.

[0217] (2) Verification of the effect of rebalancing incentives on alleviating traffic congestion

[0218] To further verify the role of rebalancing incentives in traffic guidance, this example will compare and analyze the differences in system operation effects when rebalancing incentives are introduced or not, focusing on the degree of congestion in the traffic network. Figure 7 and Figure 8 As shown. In the scheme without rebalancing incentives, CSO only considers guiding the SEV fleet to participate in V2G discharge through electricity price incentives, without regulating the vehicle's path distribution and spatial rebalancing behavior. Simulation results show that in this case, significant traffic aggregation has formed near some nodes, and the load of some paths is close to the capacity limit, resulting in obvious congestion risks. Due to the lack of balanced guidance, the SEV fleet tends to concentrate on the same high-efficiency destination, resulting in an imbalance in traffic load distribution, which in turn affects the overall operating efficiency of the system. In contrast, after the introduction of rebalancing incentives, CSO simultaneously considers the discharge benefits and traffic distribution effects when formulating incentive strategies, guiding some SEV fleets to the rebalancing area and achieving orderly adjustment of vehicle spatial distribution. From the heat map and path load statistics, it can be seen that the path flow in the traffic network is more balanced, the number of high-load paths has decreased, and the overall congestion level has been alleviated.

[0219] This comparative verification demonstrates that rebalancing incentives, as a key control measure in the game model, can effectively guide fleets to avoid high-congestion areas, thereby improving the resilience and stability of the transportation system while indirectly increasing the regulation space of the power system. Therefore, in scenarios targeting large-scale SEV dispatch, an incentive mechanism that integrates rebalancing incentives is more practical.

[0220] (3) Verification of revenue equilibrium under different budget and fleet size conditions

[0221] To further verify the balanced distribution of fleet revenue under different operating conditions using the incentive mechanism, this example analyzes two dimensions: first, adjusting the budget for a fixed fleet size, and second, proportionally expanding the budget while the fleet size increases. The revenue distribution is analyzed along four dimensions: time cost, V2G revenue, rebalancing revenue, and travel preference cost. A radar chart displays the revenue levels of different fleets across these dimensions.

[0222] Figure 9 This is a comparison chart of the benefits of shared electric vehicle fleets under different budgets. The first quadrant is the travel preference deviation cost, the second quadrant is the total time cost, the third quadrant is the V2G benefit, and the fourth quadrant is the rebalancing benefit. Figure 9The figure shows the distribution of benefits for each SEV fleet when the budget is set to 20, 25, and 30, respectively, with the number of fleets fixed at 10. It can be observed that as the budget increases from 20 to 30, the fleets experience an overall improvement in V2G benefits and rebalancing benefits, and the columnar distribution becomes fuller. At the same time, the gap between fleets in these two benefits gradually narrows, indicating that when incentive resources are more sufficient, the system is more likely to guide fleets to participate in services in a balanced manner, the distribution of benefits is fairer, and the system reaches a more stable game equilibrium. Furthermore, the time cost and travel preference cost remain largely consistent, indicating that these costs are primarily determined by the network structure and task allocation, and are directly related to the budget, thus better reflecting the stability of the fleet strategy itself.

[0223] Figure 10 This is a comparison chart of the benefits of shared electric vehicle fleets at different scales. The first quadrant is the cost of travel preference deviation, the second quadrant is the total time cost, the third quadrant is the V2G benefit, and the fourth quadrant is the rebalancing benefit. Figure 10 The model shows the revenue distribution for fleets of 10, 15, and 20, with corresponding budgets set at 20, 30, and 40, maintaining a relatively consistent per-fleet budget. As fleet size increases, the distribution structure of revenue across all dimensions remains stable, with no unusual fluctuations in time costs and travel preference costs, indicating a relatively balanced distribution of tasks across fleets. Despite the scale increase, the distribution of V2G and rebalancing revenue remains symmetrical, with no significant loss of revenue for marginal fleets, demonstrating the system's ability to adjust resources as it scales. The combined structure of the four revenue categories across fleets converges, further confirming the model's well-balanced and scalable capabilities.

[0224] In summary, the distribution of SEV fleets in multiple revenue dimensions maintains good consistency and balance, regardless of different budget levels or in fleet expansion scenarios. This shows that the proposed game model and incentive strategy can achieve balanced revenue distribution under resource constraints and changes in participation scale, and has good scalability.

[0225] To further verify the convergence of the proposed algorithm, we analyze key variables at both the upper and lower levels. Due to the large number of variables in the system, this example selects Fleet 1 as a representative and analyzes its convergence performance during the iterative process for ease of demonstration and explanation. Specifically, this includes the personalized incentive price designed by the CSO, Fleet 1's sensitivity response, and its final dispatch decision variables.

[0226] (1) Convergence of CSO Incentive Prices

[0227] In each outer iteration, CSO will update the personalized incentive price it sends to each team based on the information fed back by the current SEV team. For Team 1, the incentive given is pd,1 As the iteration proceeds, CSO adjusts p according to the gradient direction. d,1 Adjustments are made to guide the fleet to participate in V2G discharge and rebalancing operations more effectively. The simulation results show that the incentive variable pd,1 reaches stability within a finite number of iterations, indicating that the upper-level optimization objective of the system has good convergence characteristics, such as Figure 11 shown.

[0228] (2) Convergence of the sensitivity of fleet 1

[0229] Sensitivity s reflects the intensity of the SEV fleet's response to the incentive price issued by the CSO, indicating the changing trend of the SEV fleet's path selection under incentive disturbances. This embodiment takes the sensitivity s1 distribution of Fleet No. 1 as an example, and uses a heat map to visualize it. The results show that the sensitivity values ​​of most locations are close to zero, and only two locations have weak non-zero values, and the values ​​are small. This shows that in the converged state, Fleet No. 1 has no significant response to the incentive adjustments of most charging stations and rebalancing areas, and its behavioral decisions tend to be stable, such as Figure 12 This highly sparse sensitivity structure indicates that after reaching equilibrium, the fleet retains only a weak response possibility to a few strategic directions, reflecting the stability and local convergence characteristics of the game solution.

[0230] (3) Convergence of the travel decision of Fleet 1

[0231] In each iteration, Fleet 1 selects the optimal charging station, route, and whether to participate in the rebalancing task based on the current incentive price and the principle of maximizing its own benefits. Its route decision is represented by the variable x1, as follows: Figure 13 The numerical results show that the iterative process shows a trend of gradual convergence and eventually stabilizes on a set of optimal strategies, indicating that the SEV fleet can quickly respond to incentives and make stable decisions under the game framework.

[0232] Please refer to Figure 2 , the fourth embodiment of the present invention is:

[0233] A charging load guidance and scheduling system 1 for a shared electric vehicle includes a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, each step of a charging load guidance and scheduling method for a shared electric vehicle according to any one of embodiments 1 to 3 is implemented.

[0234] In summary, the present invention provides a method and system for guiding and dispatching charging loads for shared electric vehicles. This method addresses the incentive design issues for SEV dispatching in a power-transportation coupled system. It establishes a dispatching model based on the Stackelberg-Nash game, which deeply depicts the game relationship between the system operator (CSO) and the SEV fleet in terms of incentive guidance and strategic response. The model design fully integrates the two dimensions of power incentives and rebalancing incentives, aiming to guide the fleet in making reasonable path selection and rebalancing decisions while ensuring stable power system operation, thereby achieving coordinated optimization of energy supply and demand and traffic flow distribution.

[0235] To address the non-convexity and non-smoothness of the model, a distributed solution method based on super-gradient descent was proposed. The existence and uniqueness of the Stackelberg-Nash equilibrium were theoretically analyzed to ensure the stability of the model. Subsequently, simulation experiments systematically verified the model's actual performance.

[0236] In simulation experiments, convergence analysis showed that the main decision variables at both the upper and lower levels exhibited a favorable convergence trend during the iteration process, particularly with respect to key variables such as fleet sensitivity and CSO benefits, demonstrating good stability. In feasibility verification, a comparative analysis of traffic congestion and system benefits was conducted with and without the introduction of a rebalancing incentive strategy. Furthermore, the system's adaptability and resource allocation balance in multiple scenarios were explored, considering different budgets and fleet sizes. Finally, through perturbation experiments involving upper and lower level variables, the degree of deviation from the optimal solution was assessed, verifying that the proposed method can maintain a high level of benefits under various perturbation conditions, demonstrating strong optimality and robustness.

[0237] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for guiding and dispatching charging load of a shared electric vehicle, characterized in that: Including steps: Acquisition of electric transport coupling system operators and shared electric vehicle fleets; Establish a charging load guidance scheduling game model based on the Stackelberg-Nash game between the upper-level power-transportation coupling system operator and the lower-level shared electric vehicle fleet; Determining an objective function and constraints of the charging load guided scheduling game model, the objective function including a first objective function for minimizing the cost of the shared electric vehicle fleet considering charging and discharging incentives and rebalancing incentives, and a second objective function for minimizing the cost of the electric-transportation coupling system operator considering incentive response, the constraints including a first constraint corresponding to the first objective function and a second constraint corresponding to the second objective function; The super gradient descent algorithm is used to distributely solve the charging load guidance scheduling game model to obtain a charging load guidance scheduling plan.

2. The charging load guidance and scheduling method for a shared electric vehicle according to claim 1, characterized in that: The first objective function for minimizing the cost of a shared electric vehicle fleet considering charging, discharging, and rebalancing incentives includes: Calculate the first benefit value of the electricity provided by the shared electric vehicle fleet by considering the charging and discharging incentives; Considering the rebalancing incentive, the second benefit value of rebalancing provided by the shared electric vehicle fleet is calculated; Calculate the deviation costs of changing routes or destinations for a shared electric vehicle fleet; Calculate travel time costs for shared electric vehicle fleets; Subtract the sum of the first benefit value and the second benefit value from the sum of the deviation cost and the travel time cost to obtain the aggregated cost of the shared electric vehicle fleet; The first objective function is obtained by minimizing the aggregate cost of the shared electric vehicle fleet.

3. The charging load guidance and scheduling method for a shared electric vehicle according to claim 2, characterized in that: Considering the charging and discharging incentives, the first benefit value of the shared electric vehicle fleet providing electricity is calculated, including: Where, Indicates the first benefit value, v e Indicates a charging station site. represents the incentive of the electric-transport coupling system operator to discharge the fleet i at destination d, Indicates the decay loss of the battery during discharge. represents the probability that the i-th team chooses the d-th destination, m i represents the number of shared electric vehicles owned by fleet i; Considering the rebalancing incentive, the second benefit value of rebalancing provided by the shared electric vehicle fleet is calculated, including: Where, Represents the second benefit value, v r Represents a rebalancing site, represents the rebalancing incentive provided by the electric-transport coupling system operator to fleet i at destination d, Indicates the battery degradation loss during the rebalancing process.

4. The method for guiding and dispatching charging load of a shared electric vehicle according to claim 2, characterized in that: The first constraint condition includes a vehicle flow conservation constraint, upper and lower limit constraints of the decision vector of the first objective function, and a coupling resource constraint.

5. The charging load guidance and scheduling method for a shared electric vehicle according to claim 1, characterized in that: The second objective function of minimizing the cost of the electric-transport coupling system operator considering the incentive response includes: Calculate the total travel time cost for all shared electric vehicle fleets; Calculating the discharge revenue value of the operator of the electric-transport coupling system; Calculating a rebalancing benefit value for the operator of the electric-transport coupling system; Subtracting the sum of the discharge benefit value and the rebalancing benefit value from the total travel time cost to obtain the cost of the electric-transport coupling system operator; The second objective function is obtained by minimizing the operator cost of the electric-transport coupling system.

6. The method for guiding and dispatching charging load of a shared electric vehicle according to claim 5, characterized in that: The second constraint conditions include upper and lower limit constraints of the decision vector of the second objective function and a constraint on the total amount of incentive resources.

7. The method for guiding and dispatching charging load of a shared electric vehicle according to claim 6, characterized in that: The total amount of incentive resources constraints include: Where C b represents the total incentive amount, C represents the upper limit of the total incentive amount, p represents the incentive, φ(w) represents the traffic flow vector of the destination; V e Indicates a charging station site. represents the incentive of the electric-transport coupling system operator to discharge the fleet i at destination d, Indicates the decay loss of the battery during discharge; V r Represents a rebalancing site, represents the rebalancing incentive provided by the electric-transport coupling system operator to fleet i at destination d, Indicates the battery degradation loss during the rebalancing process; represents the fleet set, m i represents the number of shared electric vehicles owned by fleet i, φ i It represents the total flow of fleet i arriving at each charging station.

8. The method for guiding and dispatching charging load of a shared electric vehicle according to claim 1, characterized in that: The charging load guidance scheduling game model is: Where, f i represents the first objective function, Ω i represents the first constraint, represents the first objective function, Ω CSO represents the first constraint, represents the shared electric vehicle fleet, and CSO represents the electric-transportation coupling system operator.

9. The method for guiding and dispatching charging load of a shared electric vehicle according to claim 8, characterized in that: The charging load guided scheduling game model includes the following steps: P1. The electric-transport coupling system operator broadcasts incentives to all shared electric vehicle fleets; P2. The shared electric vehicle fleet determines the optimal response strategy of the shared electric vehicle fleet based on the incentive broadcast by the electric-transportation coupling system operator, in combination with a first objective function and a first constraint condition; P3. The electric-transport coupling system operator determines the optimal incentive based on the best response strategy of all shared electric vehicle fleets in combination with the second objective function and the second constraint condition; Repeat steps P1 to P3 until the determined optimal excitation remains unchanged.

10. A charging load guidance and scheduling system for shared electric vehicles, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step of the charging load guidance and scheduling method for a shared electric vehicle as described in any one of claims 1 to 9 is implemented.

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