A micro-grid electricity price-based electric vehicle scheduling method and system

Abstract

This invention relates to a method and system for electric vehicle scheduling based on microgrid electricity pricing. The method includes: acquiring microgrid and electric vehicle fleet parameters and initializing charging and discharging price sequences; constructing a two-layer optimization model, where the upper-layer model is a microgrid pricing model aiming to minimize total operating costs, and the lower-layer model is a Logit discrete choice model reflecting the action responses of electric vehicle users; the lower-layer model calculates the action utility and willingness probability of each vehicle based on the current electricity price, and obtains and aggregates the actual charging and discharging power by combining physical and travel demand constraints; the upper-layer model solves for candidate electricity price sequences based on the aggregated power trajectory; a damped update strategy is used to smoothly update the next round of electricity prices; the above process is iterated repeatedly until the rate of change of operating costs meets a preset convergence condition, and the optimal electricity price and scheduling strategy are output. This invention can accurately characterize the probabilistic price response of users, achieving peak shaving and valley filling of microgrids and reducing operating costs while ensuring travel demand.

Description

Technical Field

[0001] This invention relates to the field of microgrid energy management and vehicle-grid interaction technology, and in particular to a method and system for dispatching electric vehicles based on microgrid electricity prices. Background Technology

[0002] With the rapid growth of electric vehicle (EV) ownership, the unregulated charging of large-scale EVs has brought enormous challenges to the load balancing and peak-valley regulation of microgrids. Through vehicle-to-grid (V2G) technology, EVs can serve as mobile energy storage resources, providing the grid with flexible regulation capabilities such as peak shaving and valley filling, and ancillary services.

[0003] Existing technologies for addressing electric vehicle (EV) grid-connected scheduling often employ system-level centralized optimization or pre-set fixed participation rates. However, these methods typically rely on assumptions of "perfect user rationality" or "fixed preferences," making it difficult to accurately characterize the dynamic, heterogeneous, and probabilistic response behavior of EV users in the real world. Furthermore, existing research often lacks quantitative characterization of user participation elasticity across different electricity price periods, resulting in pricing signals that fail to effectively guide user participation. Simultaneously, when solving the strongly coupled bi-level optimization problem between microgrid operators (pricing parties) and EV users (responders), traditional algorithms are prone to severe oscillations in price and response, failing to converge to a stable scheduling point. Therefore, there is an urgent need for an EV scheduling method based on microgrid electricity prices that can accurately reflect users' price response intentions and efficiently resolve upstream and downstream conflicting interests. Summary of the Invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the problem that in the prior art, when dealing with the grid-connected scheduling problem of electric vehicles, the scheduling strategy of system-level centralized optimization or preset fixed participation rate is mostly adopted, which makes it difficult for the established electricity price signal to effectively guide users to actively participate.

[0005] To address the aforementioned technical problems, this invention provides a method for dispatching electric vehicles based on microgrid electricity prices, comprising:

[0006] Step S1: Obtain the microgrid system parameters and electric vehicle fleet parameters, and initialize the charging and discharging price sequence issued by the microgrid to the electric vehicle fleet;

[0007] Step S2: Construct a two-layer optimization model based on master-slave game theory, wherein the upper-layer model is a pricing model with the goal of minimizing the total operating cost of the microgrid, and the lower-layer model is a Logit discrete choice model that reflects the action response of electric vehicle users.

[0008] Step S3: Solve the lower-level model based on the charging and discharging price sequence of the current iteration: calculate the utility value of each electric vehicle under different action commands, input the utility value into the lower-level model to obtain the willingness probability of each action, and filter it in combination with the physical constraints and travel demand constraints of the electric vehicles to obtain the actual charging and discharging power of each electric vehicle, and then aggregate it into the total charging and discharging power trajectory of the fleet.

[0009] Step S4: Input the total charging and discharging power trajectory of the fleet as a known quantity into the upper-level model, solve the upper-level model, and obtain the candidate charging and discharging price sequence of the microgrid;

[0010] Step S5: Using a damped update strategy, calculate the charge-discharge price sequence for the next iteration based on the charge-discharge price sequence of the current iteration and the candidate charge-discharge price sequence;

[0011] Step S6: Combine the charging and discharging price sequence of the current iteration round with the charging and discharging price sequence of the next iteration round to determine whether the rate of change of the total operating cost of the upper-level microgrid in the current iteration round meets the preset convergence condition; if it meets the condition, stop the iteration and output the optimal charging and discharging price sequence and electric vehicle scheduling strategy; if it does not meet the condition, return to step S3 until the preset convergence condition is met.

[0012] In one embodiment of the present invention, the upper-level model in step S2 is a pricing model aimed at minimizing the total operating cost of the microgrid, wherein minimizing the total operating cost of the microgrid is expressed as:

[0013] ;

[0014] In the formula, The total operating cost of the microgrid. Main grid interconnection electricity purchase and sale costs, For the operating and start-up / shutdown costs of diesel generators, The charging and discharging settlement cost of the fleet is calculated based on the microgrid charging and discharging price series and the total charging and discharging power trajectory of the fleet. This is a penalty term for composite load regulation.

[0015] In one embodiment of the present invention, the composite load adjustment penalty term is expressed as:

[0016] ;

[0017] In the formula, This is a load smoothing penalty term for adjacent time periods. This is a global load variance penalty term. This is a penalty for suppressing the highest load peak.

[0018] In one embodiment of the present invention, step S3 calculates the utility value of each electric vehicle under different action commands using a utility function, wherein the utility function is expressed as:

[0019] ;

[0020] In the formula, For electric vehicles In time Select Action utility function For action The marginal economic return, and when the action When discharging, the marginal economic return includes the peak-hour discharge incentive subsidy set by the microgrid; The electric vehicle's current battery level; Battery capacity; Costs related to battery degradation; These are the weighting coefficients for economic returns, available electricity, and degradation costs, respectively. The action This includes both discharge actions and idle actions.

[0021] In one embodiment of the present invention, in step S3, the utility value is input into the Logit behavior selection model to obtain the probability of willingness for each action, expressed as:

[0022] ;

[0023] In the formula, For electric vehicles In time Select Action Probability of willingness , Indicates a discharge action. Indicates an idle action. For electric vehicles In time Select Action Utility function; These are the various selectable actions in the action space, namely, the discharge action or the idle action.

[0024] In one embodiment of the present invention, the physical constraints and travel demand constraints of the electric vehicle in step S3 include:

[0025] The constraints include upper and lower limits of battery state of charge, extreme values ​​of charging and discharging power, mutual exclusion of charging and discharging, and target state of charge requirement constraints when the vehicle leaves the microgrid system.

[0026] In one embodiment of the present invention, step S5 employs a damped update strategy, calculating the charge-discharge price sequence for the next iteration based on the charge-discharge price sequence of the current iteration and the candidate charge-discharge price sequence, expressed as:

[0027] ;

[0028] ;

[0029] In the formula, and The first The charging and discharging prices for the next iteration. and The first The charging and discharging prices for the next iteration; and These are the candidate charging prices and candidate discharging prices obtained from the solution of the upper-level model, respectively. For the set damping step size and .

[0030] In one embodiment of the present invention, the charge-discharge price sequence is subject to a minimum price difference constraint to prevent arbitrage, and satisfies the following formula:

[0031] ;

[0032] In the formula, for The price of charging at any time, for The price of discharge at any given moment This is the minimum price difference that is greater than zero.

[0033] In one embodiment of the present invention, step S6 combines the charge-discharge price sequence of the current iteration round with the charge-discharge price sequence of the next iteration round to determine whether the rate of change of the total operating cost of the upper-level microgrid in the current iteration round meets a preset convergence condition, expressed as:

[0034] ;

[0035] In the formula, and They represent the first time. Second and third In the next iteration, the charging and discharging price sequence output from this iteration and the corresponding total charging and discharging power trajectory of the fleet are used as known variables to update the calculation of the total operating cost of the microgrid. The total actual operating cost of the upper-mode microgrid is obtained by summing up the various sub-costs in the calculation. The preset convergence tolerance, It is the absolute value symbol. To obtain the maximum value.

[0036] To address the aforementioned technical problems, this invention provides an electric vehicle dispatching system based on microgrid electricity pricing, comprising:

[0037] Parameter initialization module: acquires microgrid system parameters and electric vehicle fleet parameters, and initializes the charging and discharging price sequence issued by the microgrid to the electric vehicle fleet;

[0038] Two-layer model construction module: used to construct a two-layer optimization model based on master-slave game, wherein the upper-layer model is a pricing model with the goal of minimizing the total operating cost of the microgrid, and the lower-layer model is a Logit discrete choice model that reflects the action response of electric vehicle users;

[0039] The lower-level solution and response aggregation module is used to solve the lower-level model based on the charging and discharging price sequence of the current iteration round: calculate the utility value of each electric vehicle under different action commands, input the utility value into the lower-level model to obtain the willingness probability of each action, and filter it in combination with the physical constraints and travel demand constraints of electric vehicles to obtain the actual charging and discharging power of each electric vehicle, and then aggregate it into the total charging and discharging power trajectory of the fleet.

[0040] Upper-level pricing optimization module: This module is used to input the total charging and discharging power trajectory of the fleet as a known quantity into the upper-level model, solve the upper-level model, and obtain the candidate charging and discharging price sequence of the microgrid.

[0041] Damped price update module: used to calculate the charge-discharge price sequence for the next iteration based on the charge-discharge price sequence of the current iteration and the candidate charge-discharge price sequence using a damped update strategy;

[0042] Iterative convergence determination module: This module combines the charge / discharge price sequence of the current iteration round with the charge / discharge price sequence of the next iteration round to determine whether the rate of change of the total operating cost of the upper-level microgrid in the current iteration round meets the preset convergence condition. If it does, the iteration stops, and the optimal charge / discharge price sequence and electric vehicle scheduling strategy are output. If it does not, the module returns to the operation of the lower-level solution and response aggregation module until the preset convergence condition is met.

[0043] Compared with the prior art, the above-described technical solution of the present invention has the following advantages:

[0044] The electric vehicle dispatching method based on microgrid electricity price described in this invention introduces the Logit discrete choice model to construct the lower-level user response model. Compared with the traditional assumption of perfect rationality, it can more realistically characterize the probabilistic and nonlinear response behavior of electric vehicle users to dynamic electricity prices.

[0045] This invention combines an alternating optimization algorithm with damped updates, which effectively breaks the "price-response" oscillation problem caused by strong coupling in the two-level game model, and ensures the stable convergence of the method under multiple hard physical constraints.

[0046] This invention can proactively guide electric vehicles to participate in "off-peak charging and peak discharge" while ensuring users' travel needs, significantly reducing the peak load of the microgrid and improving the flexibility and economy of microgrid operation. Attached Figure Description

[0047] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0048] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0049] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0050] Example 1

[0051] Reference Figure 1 As shown, this invention relates to a method for dispatching electric vehicles based on microgrid electricity prices, comprising:

[0052] Step S1: Obtain the microgrid system parameters and electric vehicle fleet parameters, and initialize the charging and discharging price sequence issued by the microgrid to the electric vehicle fleet;

[0053] Step S2: Construct a two-layer optimization model based on master-slave game theory, wherein the upper-layer model is a pricing model with the goal of minimizing the total operating cost of the microgrid, and the lower-layer model is a Logit discrete choice model that reflects the action response of electric vehicle users.

[0054] Step S3: Solve the lower-level model based on the charging and discharging price sequence of the current iteration: calculate the utility value of each electric vehicle under different action commands, input the utility value into the lower-level model to obtain the willingness probability of each action, and filter it in combination with the physical constraints and travel demand constraints of the electric vehicles to obtain the actual charging and discharging power of each electric vehicle, and then aggregate it into the total charging and discharging power trajectory of the fleet.

[0055] Step S4: Input the total charging and discharging power trajectory of the fleet as a known quantity into the upper-level model, solve the upper-level model, and obtain the candidate charging and discharging price sequence of the microgrid;

[0056] Step S5: Using a damped update strategy, calculate the charge-discharge price sequence for the next iteration based on the charge-discharge price sequence of the current iteration and the candidate charge-discharge price sequence;

[0057] Step S6: Combine the charging and discharging price sequence of the current iteration round with the charging and discharging price sequence of the next iteration round to determine whether the rate of change of the total operating cost of the upper-level microgrid in the current iteration round meets the preset convergence condition; if it meets the condition, stop the iteration and output the optimal charging and discharging price sequence and electric vehicle scheduling strategy; if it does not meet the condition, return to step S3 until the preset convergence condition is met.

[0058] The following is a detailed description of this embodiment:

[0059] Step S1: Obtain relevant parameters.

[0060] Obtain microgrid system parameters (load forecast values) Photovoltaic power output forecast Wind power output forecast ), and the size parameters of the electric vehicle fleet (total number of vehicles) Battery capacity (Target state of charge (SOC) limits, vehicle arrival / departure times, etc.). Microgrid settings are then distributed to users. Charging price at any time and Price of discharge at any time The upper and lower limits are defined. To prevent arbitrage in extreme cases, this embodiment sets a minimum price difference constraint. ,in, For the set minimum price difference greater than zero and Set the initial number of iterations. Initialize an initial price sequence that meets the boundary constraints. .

[0061] Step S2: Construct the Stackelberg two-layer game model.

[0062] The microgrid operator is positioned as the game leader (upper-level model), guiding system load direction by setting dynamic electricity prices; electric vehicle users are positioned as game followers (lower-level model), formulating individual charging and discharging strategies based on the received electricity prices. A closed-loop interaction is formed between the upper and lower levels through price signals and response power.

[0063] Step S3: Solving the lower-level model and evaluating the aggregated user response.

[0064] The microgrid broadcasts the current iteration round to users. The electricity price sequence. Lower-level electric vehicle users operate on the principle of maximizing their personal economic interests. Specifically, in action... Introduce a peak-period virtual incentive coefficient into the marginal economic return during discharge. (This excitation coefficient is the core excitation parameter for achieving "peak discharge," and it is actually implicitly included in the "marginal economic return" of the utility function formula.) "Inside, that is, when the action is to discharge," Includes base electricity price return and Subsidies are provided to encourage users to discharge their systems during peak load periods. The utility of performing a "discharge" or "idle" action at each time step is evaluated using a Logit discrete choice model.

[0065] Step S3 calculates the utility value of each electric vehicle under different action commands using a utility function, wherein the utility function is expressed as:

[0066]

[0067] In the formula, For electric vehicles In time Select Action utility function For action The marginal economic return, and when the action When discharging, the marginal economic return includes the peak-hour discharge incentive subsidy set by the microgrid; The electric vehicle's current battery level; Battery capacity; Costs related to battery degradation; These are the weighting coefficients for economic returns, available electricity, and degradation costs, respectively. The action This includes both discharge actions and idle actions.

[0068] Subsequently, the probability of the intention to perform the action is calculated using an exponential mapping. However, even with a high probability of discharging, the individual's physical constraints have the highest execution priority. The system must screen constraints including the upper and lower limits of battery state of charge (SOC), extreme charging and discharging power constraints, charging and discharging mutual exclusion constraints, and the target travel energy demand (which must be met at the departure time). ), For electric vehicles At the moment of departure The state of charge, The system includes hard constraints, such as the target trip charge level set by the user (or the minimum state of charge required upon departure). If any probabilistic action violates any of the above conditions, the discharge power is forcibly reduced or reset to zero. The actual charging and discharging power of each vehicle after constraint filtering is summed to obtain the fleet's charging and discharging power trajectory.

[0069] The probability of intention for each action in step S3 is calculated using the following formula:

[0070]

[0071] In the formula, For electric vehicles In time Select Action Probability of willingness , Indicates a discharge action. Indicates an idle action. For electric vehicles In time Select Action Utility function; These are the various selectable actions in the action space, namely, the discharge action or the idle action.

[0072] Step S4: Solving the upper-level model and optimizing microgrid pricing.

[0073] The upper-level microgrid operator receives the aggregated response trajectory from step S3, uses it as the fixed parameters for this round, and solves the problem with the objective of minimizing the total operating cost of the microgrid system:

[0074]

[0075] In the formula, The total operating cost of the microgrid. Main grid interconnection electricity purchase and sale costs, For the operating and start-up / shutdown costs of diesel generators, The charging and discharging settlement cost of the fleet is calculated based on the microgrid charging and discharging price series and the total charging and discharging power trajectory of the fleet. This is a penalty term for composite load regulation; in this embodiment, the total charging and discharging power trajectory of the fleet is a component of the total load of the microgrid system, jointly determining... , as well as The specific value. To effectively smooth the load, a composite load adjustment penalty term is defined. , This is a load smoothing penalty term for adjacent time periods. This is a global load variance penalty term. This is a peak load suppression penalty term. In this embodiment, the equivalent load curve of the microgrid is penalized from three dimensions: abrupt changes in ramp-up speed between adjacent time periods, the global variance deviating from the daily average load, and the absolute peak value of the peak load. Based on satisfying the microgrid bus power balance constraints, the candidate electricity price sequence for this round of upper-level model is calculated through an optimization solver. .

[0076] Step S5: Damping update of electricity price signal.

[0077] To overcome the extreme price-response oscillation phenomenon commonly seen in solving strongly coupled two-level games, this embodiment employs a damped step size. ( The mechanism employs a smooth convex combination update mechanism. Specifically, based on the charge / discharge price sequence of the current iteration and the candidate charge / discharge price sequences, the charge / discharge price sequence for the next iteration is calculated.

[0078]

[0079]

[0080] In the formula, and The first The charging and discharging prices for the next iteration. and The first The charging and discharging prices for the next iteration; and These are the candidate charging prices and candidate discharging prices obtained from the solution of the upper-level model, respectively. For the set damping step size and .

[0081] Step S6: Iterative convergence determination.

[0082] By combining the charge / discharge price sequence of the current iteration round with the charge / discharge price sequence of the next iteration round, it is determined whether the rate of change of the total operating cost of the upper-level microgrid in the current iteration round meets the preset convergence condition, expressed as:

[0083]

[0084] In the formula, and They represent the first time. Second and third In the next iteration, the charging and discharging price sequence output from this iteration and the corresponding total charging and discharging power trajectory of the fleet are used as known variables to update the calculation of the total operating cost of the microgrid. The various sub-costs in the (i.e.) After that, the specific value of the actual total operating cost of the upper-mode microgrid is obtained by summing them up; The preset convergence tolerance, It is the absolute value symbol. To obtain the maximum value.

[0085] If the rate of change is less than the preset convergence tolerance Or the number of iterations reaches the set maximum value. If the system reaches a stable equilibrium, it is considered to have broken out of the cycle and outputs the final optimal dynamic electricity price curve and the fleet charging and discharging scheduling plan; otherwise, it is considered to have reached a stable equilibrium. Then return to step S3 and continue the alternating optimization process until the preset convergence condition is met.

[0086] Example 2

[0087] This embodiment provides an electric vehicle dispatching system based on microgrid electricity pricing, including:

[0088] Parameter initialization module: acquires microgrid system parameters and electric vehicle fleet parameters, and initializes the charging and discharging price sequence issued by the microgrid to the electric vehicle fleet;

[0089] Two-layer model construction module: used to construct a two-layer optimization model based on master-slave game, wherein the upper-layer model is a pricing model with the goal of minimizing the total operating cost of the microgrid, and the lower-layer model is a Logit discrete choice model that reflects the action response of electric vehicle users;

[0090] The lower-level solution and response aggregation module is used to solve the lower-level model based on the charging and discharging price sequence of the current iteration round: calculate the utility value of each electric vehicle under different action commands, input the utility value into the lower-level model to obtain the willingness probability of each action, and filter it in combination with the physical constraints and travel demand constraints of electric vehicles to obtain the actual charging and discharging power of each electric vehicle, and then aggregate it into the total charging and discharging power trajectory of the fleet.

[0091] Upper-level pricing optimization module: This module is used to input the total charging and discharging power trajectory of the fleet as a known quantity into the upper-level model, solve the upper-level model, and obtain the candidate charging and discharging price sequence of the microgrid.

[0092] Damped price update module: used to calculate the charge-discharge price sequence for the next iteration based on the charge-discharge price sequence of the current iteration and the candidate charge-discharge price sequence using a damped update strategy;

[0093] Iterative convergence determination module: This module combines the charge / discharge price sequence of the current iteration round with the charge / discharge price sequence of the next iteration round to determine whether the rate of change of the total operating cost of the upper-level microgrid in the current iteration round meets the preset convergence condition. If it does, the iteration stops, and the optimal charge / discharge price sequence and electric vehicle scheduling strategy are output. If it does not, the module returns to the operation of the lower-level solution and response aggregation module until the preset convergence condition is met.

[0094] Example 3

[0095] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the electric vehicle scheduling method based on microgrid electricity price described in Embodiment 1.

[0096] Example 4

[0097] This embodiment provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of the electric vehicle scheduling method based on microgrid electricity price described in Embodiment 1.

[0098] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0099] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for dispatching electric vehicles based on microgrid electricity prices, characterized in that, include: Step S1: Obtain the microgrid system parameters and electric vehicle fleet parameters, and initialize the charging and discharging price sequence issued by the microgrid to the electric vehicle fleet; Step S2: Construct a two-layer optimization model based on master-slave game theory, wherein the upper-layer model is a pricing model with the goal of minimizing the total operating cost of the microgrid, and the lower-layer model is a Logit discrete choice model that reflects the action response of electric vehicle users. Step S3: Solve the lower-level model based on the charging and discharging price sequence of the current iteration: calculate the utility value of each electric vehicle under different action commands, input the utility value into the lower-level model to obtain the willingness probability of each action, and filter it in combination with the physical constraints and travel demand constraints of the electric vehicles to obtain the actual charging and discharging power of each electric vehicle, and then aggregate it into the total charging and discharging power trajectory of the fleet. Step S4: Input the total charging and discharging power trajectory of the fleet as a known quantity into the upper-level model, solve the upper-level model, and obtain the candidate charging and discharging price sequence of the microgrid; Step S5: Using a damped update strategy, calculate the charge-discharge price sequence for the next iteration based on the charge-discharge price sequence of the current iteration and the candidate charge-discharge price sequence; Step S6: Combine the charging and discharging price sequence of the current iteration round with the charging and discharging price sequence of the next iteration round to determine whether the rate of change of the total operating cost of the upper-level microgrid in the current iteration round meets the preset convergence condition; if it meets the condition, stop the iteration and output the optimal charging and discharging price sequence and electric vehicle scheduling strategy; if it does not meet the condition, return to step S3 until the preset convergence condition is met.

2. The electric vehicle dispatching method based on microgrid electricity pricing according to claim 1, characterized in that: In step S2, the upper-level model is a pricing model aimed at minimizing the total operating cost of the microgrid, wherein minimizing the total operating cost of the microgrid is expressed as: ； In the formula, The total operating cost of the microgrid. Main grid interconnection electricity purchase and sale costs, For the operating and start-up / shutdown costs of diesel generators, The charging and discharging settlement cost of the fleet is calculated based on the microgrid charging and discharging price series and the total charging and discharging power trajectory of the fleet. This is a penalty term for composite load regulation.

3. The electric vehicle dispatching method based on microgrid electricity prices according to claim 2, characterized in that: The composite load adjustment penalty term is expressed as follows: ； In the formula, This is a load smoothing penalty term for adjacent time periods. This is a global load variance penalty term. This is a penalty for suppressing the highest load peak.

4. The electric vehicle dispatching method based on microgrid electricity pricing according to claim 1, characterized in that: Step S3 calculates the utility value of each electric vehicle under different action commands using a utility function, wherein the utility function is expressed as: ； In the formula, For electric vehicles In time Select Action utility function For action The marginal economic return, and when the action When discharging, the marginal economic return includes the peak-hour discharge incentive subsidy set by the microgrid; The electric vehicle's current battery level; Battery capacity; Costs related to battery degradation; These are the weighting coefficients for economic returns, available electricity, and degradation costs, respectively. The action This includes both discharge actions and idle actions.

5. The electric vehicle dispatching method based on microgrid electricity pricing according to claim 4, characterized in that: In step S3, the utility value is input into the Logit behavior selection model to obtain the probability of willingness for each action, expressed as: ； In the formula, For electric vehicles In time Select Action Probability of willingness , Indicates a discharge action. Indicates an idle action. For electric vehicles In time Select Action Utility function; These are the various selectable actions in the action space, namely, the discharge action or the idle action.

6. The electric vehicle dispatching method based on microgrid electricity pricing according to claim 1, characterized in that: The physical constraints and travel demand constraints of the electric vehicle in step S3 include: The constraints include upper and lower limits of battery state of charge, extreme values ​​of charging and discharging power, mutual exclusion of charging and discharging, and target state of charge requirement constraints when the vehicle leaves the microgrid system.

7. The electric vehicle dispatching method based on microgrid electricity pricing according to claim 1, characterized in that: Step S5 employs a damped update strategy, calculating the charge-discharge price sequence for the next iteration based on the charge-discharge price sequence of the current iteration and the candidate charge-discharge price sequences, as follows: ； ； In the formula, and The first The charging and discharging prices for the next iteration. and The first The charging and discharging prices for the next iteration; and These are the candidate charging prices and candidate discharging prices obtained from the solution of the upper-level model, respectively. For the set damping step size and .

8. The electric vehicle dispatching method based on microgrid electricity pricing according to claim 1, characterized in that: The charge / discharge price sequence is constrained by the minimum price difference for arbitrage prevention and satisfies the following formula: ； In the formula, for The price of charging at any time, for The price of discharge at any given moment This is the minimum price difference that is greater than zero.

9. The electric vehicle dispatching method based on microgrid electricity pricing according to claim 1, characterized in that: Step S6 combines the charge / discharge price sequence of the current iteration round with the charge / discharge price sequence of the next iteration round to determine whether the rate of change of the total operating cost of the upper-level microgrid in the current iteration round meets the preset convergence condition, expressed as: ； In the formula, and They represent the first time. Second and third In the next iteration, the charging and discharging price sequence output from this iteration and the corresponding total charging and discharging power trajectory of the fleet are used as known variables to update the calculation of the total operating cost of the microgrid. The total actual operating cost of the upper-mode microgrid is obtained by summing up the various sub-costs in the calculation. The preset convergence tolerance, It is the absolute value symbol. To obtain the maximum value.

10. An electric vehicle dispatching system based on microgrid electricity pricing, characterized in that, include: Parameter initialization module: acquires microgrid system parameters and electric vehicle fleet parameters, and initializes the charging and discharging price sequence issued by the microgrid to the electric vehicle fleet; Two-layer model construction module: used to construct a two-layer optimization model based on master-slave game, wherein the upper-layer model is a pricing model with the goal of minimizing the total operating cost of the microgrid, and the lower-layer model is a Logit discrete choice model that reflects the action response of electric vehicle users; The lower-level solution and response aggregation module is used to solve the lower-level model based on the charging and discharging price sequence of the current iteration round: calculate the utility value of each electric vehicle under different action commands, input the utility value into the lower-level model to obtain the willingness probability of each action, and filter it in combination with the physical constraints and travel demand constraints of electric vehicles to obtain the actual charging and discharging power of each electric vehicle, and then aggregate it into the total charging and discharging power trajectory of the fleet. Upper-level pricing optimization module: This module is used to input the total charging and discharging power trajectory of the fleet as a known quantity into the upper-level model, solve the upper-level model, and obtain the candidate charging and discharging price sequence of the microgrid. Damped price update module: used to calculate the charge-discharge price sequence for the next iteration based on the charge-discharge price sequence of the current iteration and the candidate charge-discharge price sequence using a damped update strategy; Iterative convergence determination module: This module combines the charge / discharge price sequence of the current iteration round with the charge / discharge price sequence of the next iteration round to determine whether the rate of change of the total operating cost of the upper-level microgrid in the current iteration round meets the preset convergence condition. If it does, the iteration stops, and the optimal charge / discharge price sequence and electric vehicle scheduling strategy are output. If it does not, the module returns to the operation of the lower-level solution and response aggregation module until the preset convergence condition is met.

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