Electric vehicle charging and discharging scheduling method and device

Abstract

The invention discloses an electric vehicle charging and discharging scheduling method and device, and belongs to the technical field of power system scheduling management, and the method comprises the steps: carrying out the day-ahead master-slave game of a signed electric vehicle and a non-signed electric vehicle based on a power grid scheduling parameter at a day-ahead stage, and obtaining a day-ahead scheduling plan; in the intra-day stage, according to the real-time load data and the day-ahead scheduling plan, whether an extra power response demand exists at present is judged; when the signed electric vehicle does not exist, performing intra-day master-slave game on the signed electric vehicle, and performing charging and discharging scheduling on the signed electric vehicle based on the obtained intra-day signed power strategy; and if yes, performing intra-day master-slave game on the signed electric vehicle and the non-signed electric vehicle, performing charging and discharging scheduling on the signed electric vehicle based on the obtained intra-day signed power strategy, and performing charging and discharging scheduling on the non-signed electric vehicle based on the obtained intra-day non-signed excitation strategy. Therefore, the problem that the charging and discharging scheduling effect of the electric vehicle is not ideal can be solved.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching and management technology, and in particular to a method and apparatus for dispatching the charging and discharging of electric vehicles. Background Technology

[0002] With the rapid growth in the number of new energy vehicles, the charging and discharging load of electric vehicles (EVs) is constantly increasing, and their disorderly charging poses new challenges to power grid regulation. However, if EVs can be fully scheduled, power grid quality can be effectively improved.

[0003] Most current studies assume that perfectly rational electric vehicle users sign agreements with the grid and strictly adhere to dispatch signals. In actual dispatching, this over-reliance on day-ahead forecast data makes the effectiveness of real-time dispatching highly dependent on the accuracy of the forecasts. Furthermore, dispatching strategies that directly control EV charging and discharging often overlook the dispatching potential of EVs that have not signed agreements, making it difficult to fully utilize them in practical applications. Summary of the Invention

[0004] This invention provides a method and apparatus for scheduling the charging and discharging of electric vehicles, which can solve the problem of unsatisfactory scheduling effect of electric vehicle charging and discharging in the prior art.

[0005] To address the aforementioned technical problems, this invention provides a method for scheduling the charging and discharging of electric vehicles, comprising: In the day-ahead phase, a day-ahead master-slave game is conducted between contracted and uncontracted electric vehicles based on grid dispatch parameters to derive the day-ahead dispatch plan. During the daytime phase, real-time load data is acquired; wherein, the real-time load data includes the real-time load demand of contracted electric vehicles, the real-time load demand of non-contracted electric vehicles, and the real-time number of contracted electric vehicles on the network. Based on the real-time load data and the day-ahead scheduling plan, determine whether there is any additional power response demand. When it is determined that there is no additional power response demand, an intraday master-slave game is performed on the contracted electric vehicles to obtain the intraday contracted power strategy, and the charging and discharging scheduling of the contracted electric vehicles is performed based on the intraday contracted power strategy. When it is determined that there is an additional power response demand, an intraday master-slave game is performed on the contracted electric vehicles and the non-contracted electric vehicles to obtain the intraday contracted power strategy and the intraday non-contracted incentive strategy. Based on the intraday contracted power strategy, the charging and discharging of the contracted electric vehicles is scheduled, and based on the intraday non-contracted incentive strategy, the charging and discharging of the non-contracted electric vehicles is scheduled.

[0006] As a preferred embodiment, the step of performing a day-ahead master-slave game on contracted and uncontracted electric vehicles based on grid dispatch parameters to derive a day-ahead dispatch plan includes: Obtain power grid dispatch parameters; wherein, the power grid dispatch parameters include total power response demand, electricity purchase cost, demand response subsidy price, and incentive response power of non-contracted electric vehicles; Based on the grid dispatch parameters, with the goal of maximizing the operating profit of aggregators, the day-ahead upper-level aggregator optimization model is solved to generate the day-ahead optimal electricity price strategy and the day-ahead optimal real-time incentive. Based on the day-ahead optimal electricity price strategy and the day-ahead optimal real-time incentive, with the goal of minimizing electric vehicle charging costs, the day-ahead lower-level electric vehicle optimization model is solved to obtain the day-ahead scheduling plan; wherein, the day-ahead scheduling plan includes the day-ahead contracted charging and discharging power and the day-ahead uncontracted load power.

[0007] As a preferred embodiment, the objective function of the current-day upper-level aggregator optimization model is: In the formula, This represents the current operating profit of the aggregator. The charging electricity price set for aggregators; The discharge electricity price set for aggregators; Let be the electricity purchase cost / price at time t; Incentive electricity prices set for aggregators; The number of preset scheduling time periods; This represents the number of electric vehicles that have already been contracted. The preset scheduling time interval; The demand response subsidy price at time t; The total power response at time t; The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; Let be the excitation response power of the unsigned electric vehicle at time t; The constraints of the day-ahead upper-level aggregator optimization model include day-ahead charging and discharging electricity price and incentive price constraints, day-ahead response quantity constraints, and day-ahead grid power constraints. The day-ahead charge / discharge tariff and incentive tariff constraints are as follows: In the formula, Minimum electricity price for aggregators; The charging electricity price set for aggregators; The maximum electricity price for aggregators; This represents the minimum incentive price for aggregators. Incentive electricity prices set for aggregators; This represents the maximum incentive price for aggregators. The discharge electricity price set for aggregators; The day-ahead response quantity constraint is: In the formula, The total power response at time t; Let be the excitation response power of the unsigned electric vehicle at time t; The total power response requirement at time t; This represents the total number of electric vehicles that have been contracted and those that have not been contracted. This refers to the number of non-contracted electric vehicles; The current-day grid power constraint is: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the total average power of all non-contracted electric vehicles at time t; This is the maximum power limit for the power grid; This represents the number of electric vehicles that have already been contracted. The objective function of the current-level electric vehicle optimization model is: In the formula, The total cost of charging an electric vehicle up to date; The charging electricity price set for aggregators; The discharge electricity price set for aggregators; Incentive electricity prices set for aggregators; The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; This represents the number of electric vehicles that have already been contracted. The preset scheduling time interval; The number of preset scheduling time periods; Let be the total average power of all non-contracted electric vehicles at time t; Real-time charging electricity price; The constraints of the daytime lower-level electric vehicle optimization model include daytime total charging power consumption constraints, daytime charging and discharging power constraints, and daytime electric vehicle state of charge constraints. The total daily charging power consumption constraint includes: In the formula, Predicted charging power before adjustment; The predicted discharge power before regulation; The adjusted charging power; The adjusted discharge power; The number of preset scheduling time periods; The daytime charge / discharge power constraint includes: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; Maximum charging power limit; Maximum discharge power limit; The current state of charge constraint for electric vehicles is: In the formula, State of charge of the electric vehicle; This represents the minimum state of charge for an electric vehicle. This represents the maximum state of charge for an electric vehicle.

[0008] As a preferred embodiment, when it is determined that there is no additional power response demand, the process of performing intraday master-slave game theory on the contracted electric vehicles to derive an intraday contracted power strategy, and then scheduling the charging and discharging of the contracted electric vehicles based on the intraday contracted power strategy, includes: When it is determined that there is no additional power response demand, the optimization model of non-urgent upper-level aggregators within the day is solved with the goal of maximizing the operating profit of aggregators, and the optimal electricity price strategy within the day is generated. Based on the intraday optimal electricity price strategy, with the goal of minimizing electric vehicle charging costs, the intraday non-emergency lower-level electric vehicle optimization model is solved to obtain the intraday contracted charging and discharging power. The charging and discharging schedule for the contracted electric vehicles is based on the contracted charging and discharging power within the day.

[0009] As a preferred embodiment, the objective function of the intraday non-urgent upper-level aggregator optimization model is: In the formula, The aggregator's operating profit at time t under non-emergency conditions within the day; This represents the current operating profit of the aggregator. The charging electricity price set for aggregators; The discharge electricity price set for aggregators; Optimize the charging price at time t; The optimal discharge price at time t; The first penalty coefficient is set for deviations from the previous day's forecast; The second penalty coefficient is a preset value for deviations from the previous day's forecast; The constraints of the intraday non-emergency upper-level aggregator optimization model include intraday non-emergency charging and discharging electricity price and incentive electricity price constraints, intraday non-emergency response quantity constraints, and intraday non-emergency grid power constraints. The intraday non-emergency charging and discharging electricity price and incentive electricity price constraints are as follows: In the formula, Minimum electricity price for aggregators; The charging electricity price set for aggregators; The maximum electricity price for aggregators; Incentive electricity prices set for aggregators; The discharge electricity price set for aggregators; The daily non-emergency response volume constraint is as follows: In the formula, The total power response at time t; The total power response requirement at time t; The intraday non-emergency grid power constraint is as follows: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the total average power of all non-contracted electric vehicles at time t; This is the maximum power limit for the power grid; This represents the number of electric vehicles that have already been contracted. The objective function of the intraday non-emergency lower-level electric vehicle optimization model is: In the formula, The total cost of charging an electric vehicle at time t under non-emergency conditions during the day; The charging electricity price set for aggregators; The discharge electricity price set for aggregators; The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; This represents the number of electric vehicles that have already been contracted. The preset scheduling time interval; The constraints of the intraday non-emergency lower-level electric vehicle optimization model include intraday non-emergency total charging power consumption constraints, intraday non-emergency charging and discharging power constraints, and intraday non-emergency electric vehicle state of charge constraints. The daily non-emergency total charging power consumption constraint includes: In the formula, Predicted charging power before adjustment; The predicted discharge power before regulation; The adjusted charging power; The adjusted discharge power; The number of preset scheduling time periods; The intraday non-emergency charging and discharging power constraints include: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; Maximum charging power limit; Maximum discharge power limit; The intraday non-emergency electric vehicle state-of-charge constraint is as follows: In the formula, State of charge of the electric vehicle; This represents the minimum state of charge for an electric vehicle. This represents the maximum state of charge for an electric vehicle.

[0010] As a preferred embodiment, when it is determined that there is an additional power response demand, an intraday master-slave game is performed on the contracted electric vehicles and the non-contracted electric vehicles to derive an intraday contracted power strategy and an intraday non-contracted incentive strategy. Based on the intraday contracted power strategy, charging and discharging scheduling is performed on the contracted electric vehicles, and based on the intraday non-contracted incentive strategy, charging and discharging scheduling is performed on the non-contracted electric vehicles. This includes: When it is determined that there is an additional power response demand, the optimization model of the upper-level aggregator in the intraday emergency is solved with the goal of maximizing the operating profit of the aggregator, and the intraday optimal electricity price strategy and intraday optimal real-time incentive are generated. Based on the intraday optimal electricity price strategy and the intraday optimal real-time incentive, with the goal of minimizing electric vehicle charging costs, the intraday emergency lower-level electric vehicle optimization model is solved to obtain the intraday scheduling plan; wherein, the intraday scheduling plan includes the intraday contracted charging and discharging power and the intraday non-contracted load power; Based on the intraday optimal real-time stimulus and the intraday non-contracted load power, model-free adaptive control is performed on the non-contracted electric vehicles to obtain the intraday non-contracted demand response. The charging and discharging schedule for the contracted electric vehicles is based on the contracted charging and discharging power within the day, and the charging and discharging schedule for the non-contracted electric vehicles is based on the non-contracted demand response volume within the day.

[0011] As a preferred embodiment, the objective function of the intraday emergency upper-level aggregator optimization model is: In the formula, This refers to the aggregator's operating profit at time t during an emergency situation within the day. This represents the current operating profit of the aggregator. The charging electricity price set for aggregators; The discharge electricity price set for aggregators; Incentive electricity prices set for aggregators; Optimize the charging price at time t; The optimal discharge price at time t; Let be the optimal incentive electricity price at time t; The first penalty coefficient is set for deviations from the previous day's forecast; The second penalty coefficient is a preset value for deviations from the previous day's forecast; The third penalty coefficient is set to prevent deviation from the previous day's forecast. The constraints of the intraday emergency upper-level aggregator optimization model include intraday emergency charging and discharging electricity price and incentive electricity price constraints, intraday emergency response quantity constraints, and intraday emergency grid power constraints. The constraints on the intraday emergency charging and discharging electricity price and the incentive electricity price are as follows: In the formula, Minimum electricity price for aggregators; The charging electricity price set for aggregators; The maximum electricity price for aggregators; This represents the minimum incentive price for aggregators. Incentive electricity prices set for aggregators; This represents the maximum incentive price for aggregators. The discharge electricity price set for aggregators; The daily emergency response volume constraint is as follows: In the formula, The total power response at time t; Let be the excitation response power of the unsigned electric vehicle at time t; The total power response requirement at time t; This represents the total number of electric vehicles that have been contracted and those that have not been contracted. This refers to the number of non-contracted electric vehicles; The intraday emergency power grid constraint is as follows: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the total average power of all non-contracted electric vehicles at time t; This is the maximum power limit for the power grid; This represents the number of electric vehicles that have already been contracted. The objective function of the intraday emergency lower-level electric vehicle optimization model is: In the formula, The total cost of charging an electric vehicle at time t during an emergency within the day; The charging electricity price set for aggregators; The discharge electricity price set for aggregators; Incentive electricity prices set for aggregators; The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; This represents the number of electric vehicles that have already been contracted. The preset scheduling time interval; Let be the total average power of all non-contracted electric vehicles at time t; Real-time charging electricity price; The constraints of the intraday emergency lower-level electric vehicle optimization model include intraday emergency total charging power consumption constraints, intraday emergency charging and discharging power constraints, and intraday emergency electric vehicle state of charge constraints. The daily emergency total charging power consumption constraint includes: In the formula, Predicted charging power before adjustment; The predicted discharge power before regulation; The adjusted charging power; The adjusted discharge power; The number of preset scheduling time periods; The intraday emergency charge / discharge power constraints include: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; Maximum charging power limit; Maximum discharge power limit; The intraday emergency electric vehicle state-of-charge constraint is as follows: In the formula, State of charge of the electric vehicle; This represents the minimum state of charge for an electric vehicle. This represents the maximum state of charge for an electric vehicle.

[0012] As a preferred embodiment, the process of performing model-free adaptive control on non-contracted electric vehicles based on the intraday optimal real-time stimulus and the intraday non-contracted load power to derive the intraday non-contracted demand response includes: Based on the intraday optimal real-time incentive and the intraday non-contracted load power, a real-time dynamic demand response model is constructed, and the predicted load response is obtained by solving the real-time dynamic demand response model. The demand response model is subjected to dynamic linearization to obtain a dynamic linearization result, wherein the dynamic linearization result is represented by a pseudo-derivative. By setting a limit on the rate of change of the pseudo-derivative, a pseudo-derivative estimation criterion function is constructed, and the optimal estimate of the pseudo-derivative is obtained by solving the pseudo-derivative estimation criterion function. By setting a limit on the rate of change of incentives, a real-time incentive decision criterion function is constructed for real-time incentives. Combined with a preset real-time incentive output range constraint, the real-time incentive decision criterion function is solved to obtain the optimal real-time incentive output. Based on the optimal estimate of the pseudo-derivative and the optimal output of the real-time stimulus, the intraday non-contractual demand response volume is obtained.

[0013] As a preferred embodiment, the step of constructing a real-time dynamic demand response model based on the intraday optimal real-time stimulus and the intraday non-contracted load power, and obtaining the predicted load response by solving the real-time dynamic demand response model, includes: Based on the historical charging data of each of the aforementioned non-contracted electric vehicles, the aforementioned non-contracted electric vehicles are classified into peak-shaving clusters, valley-filling clusters, and non-peak-shaving and valley-filling clusters. Based on the preset impact coefficient and the intraday optimal real-time incentive, a dynamic expected reference electricity price is set. The value function of the demand response model is constructed based on the aforementioned dynamic expected reference electricity price; The charging load of the peak-shaving cluster, the valley-filling cluster, and the non-peak-shaving and valley-filling cluster is derived based on the intraday non-contractual demand response volume. By combining the charging loads of the peak-shaving cluster, the valley-filling cluster, and the non-peak-shaving and valley-filling cluster with the value function, the predicted load response is obtained.

[0014] Accordingly, the present invention provides an electric vehicle charging and discharging scheduling device, comprising: a day-ahead scheduling module, a data acquisition module, an additional demand judgment module, an intraday non-emergency scheduling module, and an intraday emergency scheduling module; The day-ahead scheduling module is used to perform a day-ahead master-slave game between contracted electric vehicles and non-contracted electric vehicles based on grid scheduling parameters during the day-ahead phase, and to derive the day-ahead scheduling plan. The data acquisition module is used to acquire real-time load data during the day; wherein, the real-time load data includes the real-time load demand of contracted electric vehicles, the real-time load demand of non-contracted electric vehicles, and the real-time number of contracted electric vehicles on the network. The additional demand determination module is used to determine whether there is an additional power response demand based on the real-time load data and the day-ahead scheduling plan. The intraday non-emergency scheduling module is used to perform intraday master-slave game on the contracted electric vehicles when it is determined that there is no additional power response demand, to obtain the intraday contracted power strategy, and to perform charging and discharging scheduling on the contracted electric vehicles based on the intraday contracted power strategy. The intraday emergency scheduling module is used to perform intraday master-slave game between contracted electric vehicles and non-contracted electric vehicles when it is determined that there is an additional power response demand, to obtain intraday contracted power strategy and intraday non-contracted incentive strategy, and to perform charging and discharging scheduling for contracted electric vehicles based on the intraday contracted power strategy, and to perform charging and discharging scheduling for non-contracted electric vehicles based on the intraday non-contracted incentive strategy.

[0015] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention provides a method for scheduling electric vehicle charging and discharging. In the day-ahead phase, a day-ahead master-slave game is performed on contracted and uncontracted electric vehicles based on grid dispatch parameters to derive a day-ahead dispatch plan. In the intraday phase, real-time load data is acquired. Based on the real-time load data and the day-ahead dispatch plan, it is determined whether there is an additional power response demand. When there is no additional power response demand, only contracted electric vehicles are dispatched, eliminating the need for charging and discharging dispatching of uncontracted electric vehicles, thus avoiding increased dispatching costs. When there is an additional power response demand, charging and discharging dispatching is performed on both contracted and uncontracted electric vehicles, leveraging the dispatching potential of uncontracted electric vehicles to effectively improve the charging and discharging dispatching efficiency of electric vehicles. Attached Figure Description

[0016] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating an embodiment of the electric vehicle charging and discharging scheduling method provided by the present invention; Figure 2 A flowchart illustrating an electric vehicle charging and discharging scheduling method that combines multi-timescale master-slave game theory with real-time incentives provided by the present invention. Figure 3 This is a schematic diagram of one embodiment of the electric vehicle charging and discharging scheduling device provided by the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0020] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0023] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0024] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0025] This invention employs a multi-timescale master-slave game theory approach combined with real-time incentives for scheduling. The scheduling of electric vehicles in this invention includes master-slave game theory at the day-ahead and intraday stages. Considering the master-slave game between electric vehicle aggregators and electric vehicle users, it is assumed that electric vehicle aggregators obtain peak-shaving and valley-filling demand through electricity market bidding. Electric vehicle users are divided into contracted and uncontracted users. Electric vehicle aggregators can control the charging and discharging power and time of contracted electric vehicles that meet the dispatchability prerequisites, while uncontracted users participate in demand response through basic time-of-use pricing and real-time incentives during peak and valley periods.

[0026] See Figure 2This is a flowchart illustrating an electric vehicle (EV) charging and discharging scheduling method combining multi-timescale master-slave game theory and real-time incentives, provided by this invention. In the day-ahead phase, EV aggregators report their scheduling capacity to the electricity trading market based on their EV day-ahead charging plans. The electricity trading market then transmits bidding demands to the EV aggregators based on the scheduling capacity. Based on these bidding demands, the EV aggregators publish day-ahead contracted EV electricity price curves, non-contracted EV incentive curves, and contracted EV scheduling plans to EV users. EV users then formulate their day-ahead charging plans based on these proposals. In the intraday phase, the power grid adjusts its demand response based on fluctuations in renewable energy output and base load, and publishes additional power response demands and subsidy settlements in the real-time phase. Electric vehicle aggregators will also adjust electricity prices and dispatch plans based on the allocated demand and the actual dispatchable load of contracted users. For peak and off-peak periods or additional emergency demands, they will guide the participation of non-contracted users through real-time incentives to make up for some of the response deficiencies. They will also transmit the dispatch plan of contracted users to smart charging piles so that the smart charging piles can charge each electric vehicle and feed back the charging and discharging load to the electric vehicle aggregator, as well as transmit power to the grid bus so that the grid bus can feed back real-time load data to the electricity trading market.

[0027] See Figure 1 To address the problem of unsatisfactory charging and discharging scheduling effects in existing technologies for electric vehicles, an embodiment of the present invention provides a charging and discharging scheduling method for electric vehicles. This method includes steps 101 to 105, each step of which is detailed below: Step 101: In the day-ahead phase, a day-ahead master-slave game is conducted on contracted electric vehicles and non-contracted electric vehicles based on the grid dispatch parameters to derive the day-ahead dispatch plan.

[0028] As a preferred embodiment, a day-ahead master-slave game is performed on contracted and uncontracted electric vehicles based on grid dispatch parameters to derive a day-ahead dispatch plan, including: Obtain power grid dispatch parameters; wherein, the power grid dispatch parameters include total power response demand, electricity purchase cost, demand response subsidy price, and incentive response power of non-contracted electric vehicles; Based on the grid dispatch parameters, with the goal of maximizing the operating profit of aggregators, the day-ahead upper-level aggregator optimization model is solved to generate the day-ahead optimal electricity price strategy and the day-ahead optimal real-time incentive. Based on the day-ahead optimal electricity price strategy and the day-ahead optimal real-time incentive, with the goal of minimizing electric vehicle charging costs, the day-ahead lower-level electric vehicle optimization model is solved to obtain the day-ahead scheduling plan; wherein, the day-ahead scheduling plan includes the day-ahead contracted charging and discharging power and the day-ahead uncontracted load power.

[0029] Master-slave game theory is a special type of non-cooperative game theory, characterized by its dynamic decision-making process. One party, as the initiator, formulates a strategy, and the other party responds accordingly. In this embodiment of the invention, the electric vehicle aggregator is the upper-level leader, whose objective function is to maximize its own interests; the electric vehicle user is the lower-level decision-maker, whose objective function is to minimize the overall charging cost.

[0030] Indicatively, during the day-ahead phase, the total power response demand predicted for the day-ahead period is first initialized. The electricity purchase price from the grid by the aggregator Demand response subsidy electricity price Excitation response power of non-contracted electric vehicles and load baseline value The process involves acquiring the day-ahead charging plans for contracted electric vehicles and the day-ahead load data for non-contracted electric vehicles within the initial electric vehicle charging plan. The day-ahead charging plans for contracted electric vehicles include charging and discharging power data to determine the charging and discharging power at different times, charging and discharging time data to determine the specific times when charging begins and ends, potential discharge time points, and battery state of charge data, covering the battery's state of charge before, during, and after charging and discharging. The day-ahead load data for non-contracted electric vehicles includes historical charging time data, historical charging power data, and historical charging duration data. The day-ahead scheduling plan is derived by iteratively solving the day-ahead upper-level aggregator optimization model and the day-ahead lower-level electric vehicle optimization model.

[0031] Specifically, the process of iteratively solving the current-day upper-level aggregator optimization model and the current-day lower-level electric vehicle optimization model is as follows: For the day-ahead upper-level aggregator optimization model, based on grid dispatch parameters and with the objective of maximizing aggregator operating profits, the day-ahead upper-level aggregator optimization model is solved to generate the day-ahead optimal electricity price strategy and the day-ahead optimal real-time incentive. The day-ahead optimal electricity price strategy includes the day-ahead optimal charging price and the day-ahead optimal discharging price.

[0032] The objective function of the current upper-level aggregator optimization model is: In the formula, This represents the current operating profit of the aggregator. The charging electricity price set for aggregators; The discharge electricity price set for aggregators; Let be the electricity purchase cost / price at time t; Incentive electricity prices set for aggregators; The number of preset scheduling time periods; This represents the number of electric vehicles that have already been contracted. The preset scheduling time interval; The demand response subsidy price at time t; The total power response at time t; The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; Let be the excitation response power of the unsigned electric vehicle at time t.

[0033] The objective function of the current upper-level aggregator optimization model is to minimize the aggregator's operating profit, which includes charging revenue, demand response revenue, discharge subsidy costs, electricity purchase costs, and real-time incentive costs. In the aforementioned objective function, Represents charging revenue. Indicates demand response revenue. This indicates the cost of the discharge subsidy. Indicates the cost of purchasing electricity. This represents the real-time incentive cost. During a day's scheduling process, assuming the preset scheduling time interval is set to 1 hour, the corresponding number of preset scheduling time periods is 24. During optimization, a discharge electricity price can be set to facilitate optimization. and charging electricity price The relationship between them, for example, k can be 1.5. The excitation response power of the unsigned electric vehicle at time t can be expressed by the formula... Find, where, Let be the baseline load of the unsigned electric vehicle at time t; Let t be the actual load of the uncontracted electric vehicle. The demand response subsidy electricity price at time t can be expressed by the formula... Find, where, This represents the baseline total load power.

[0034] The constraints of the day-ahead upper aggregator optimization model include day-ahead charging and discharging tariff and incentive tariff constraints, day-ahead response quantity constraints, and day-ahead grid power constraints. The day-ahead charge / discharge tariff and incentive tariff constraints are as follows: In the formula, Minimum electricity price for aggregators; The charging electricity price set for aggregators; The maximum electricity price for aggregators; This represents the minimum incentive price for aggregators. Incentive electricity prices set for aggregators; This represents the maximum incentive price for aggregators. The discharge electricity price set for aggregators.

[0035] The day-ahead response quantity constraint is: In the formula, The total power response at time t; Let be the excitation response power of the unsigned electric vehicle at time t; The total power response requirement at time t; This represents the total number of electric vehicles that have been contracted and those that have not been contracted. This represents the number of non-contracted electric vehicles. Setting response quantity constraints can ensure the aggregator's response completion rate while maintaining the incentive experience for non-contracted users during the real-time incentive phase.

[0036] The current-day grid power constraint is: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the total average power of all non-contracted electric vehicles at time t; This is the maximum power limit for the power grid; This represents the number of electric vehicles that have been contracted.

[0037] For the day-ahead lower-level electric vehicle optimization model, based on the day-ahead optimal electricity price strategy and the day-ahead optimal real-time incentive, the day-ahead scheduling plan is obtained by solving the lower-level electric vehicle optimization model with the goal of minimizing the electric vehicle charging cost. The day-ahead scheduling plan includes the day-ahead contracted charging power, the day-ahead contracted discharging power, and the day-ahead uncontracted load power.

[0038] The objective function of the current lower-level electric vehicle optimization model is: In the formula, The total cost of charging an electric vehicle up to date; The charging electricity price set for aggregators; The discharge electricity price set for aggregators; Incentive electricity prices set for aggregators; The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; This represents the number of electric vehicles that have already been contracted. The preset scheduling time interval; The number of preset scheduling time periods; Let be the total average power of all non-contracted electric vehicles at time t; Real-time charging electricity price; The objective function of the current lower-level electric vehicle optimization model is to minimize the total charging cost of electric vehicles. This total charging cost includes the cost of contracted electric vehicles and the cost of non-contracted electric vehicles. The cost of contracted electric vehicles includes both charging costs and discharge subsidies. In the aforementioned objective function, Indicates charging cost, This indicates the cost of the discharge subsidy. This indicates the cost of non-contracted electric vehicles.

[0039] The constraints of the current day lower-level electric vehicle optimization model include current day total charging power consumption constraints, current day charging and discharging power constraints, and current day electric vehicle state of charge constraints. The total daily charging power consumption constraint includes: In the formula, Predicted charging power before adjustment; The predicted discharge power before regulation; The adjusted charging power; The adjusted discharge power; This sets the number of preset scheduling time periods. By setting a total charging power consumption constraint, it can be ensured that the energy of the regulated electric vehicles is consistent with the originally planned power consumption.

[0040] The daytime charge / discharge power constraint includes: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; Maximum charging power limit; This limits the maximum discharge power. By setting charging and discharging power constraints, the safety of electric vehicle charging and discharging can be ensured, while also ensuring that charging and discharging can only be performed in one mode.

[0041] The current state of charge constraint for electric vehicles is: In the formula, State of charge of the electric vehicle; This represents the minimum state of charge for an electric vehicle. This represents the maximum state of charge (SOC) for electric vehicles. Each contracted electric vehicle has a set or default maximum and minimum SOC to ensure that the SOC remains within a reasonable range when adjusting the vehicle's charge level.

[0042] After obtaining the current day's contracted charging power, current day's contracted discharging power, and current day's uncontracted load power, determine the partial derivatives of the profit function with respect to the charging price, discharging price, and incentive price: Based on the partial derivatives of the profit function with respect to the charging price, discharging price, and incentive price, the electric vehicle aggregator updates the charging price, discharging price, and incentive price along the gradient direction: In the formula, The learning rate can be initially set to 0.01 and then gradually decreased to 0.001.

[0043] The day-ahead upper-level aggregator optimization model and the day-ahead lower-level electric vehicle optimization model are iteratively solved. When the preset convergence condition is met, the iteration terminates and the current day-ahead scheduling plan is output as reference data for intraday stage scheduling.

[0044] The iteration terminates when one of the following preset convergence conditions is met: The parameter change simultaneously satisfies , , , 0.001 is acceptable; The rate of change of profit satisfies , 1% is acceptable; The maximum number of iterations has been reached. , 50 is acceptable.

[0045] Step 102: During the daytime phase, acquire real-time load data; wherein, the real-time load data includes the real-time load demand of contracted electric vehicles, the real-time load demand of non-contracted electric vehicles, and the real-time number of contracted electric vehicles on the network.

[0046] In this embodiment of the invention, during the intraday phase, the real-time demand, real-time load, and real-time number of contracted electric vehicles on the grid are obtained. By comparing with the day-ahead dispatch plan, it can be determined whether there are additional power response demands such as emergency demand response or peak-valley time shortage demand response, so that subsequent dispatch can be carried out in combination with additional power response demands.

[0047] Step 103: Based on the real-time load data and the day-ahead scheduling plan, determine whether there is an additional power response requirement.

[0048] In this embodiment of the invention, the real-time total power response demand can be calculated based on the real-time load demand of contracted electric vehicles and the real-time load demand of non-contracted electric vehicles. The estimated total power response demand can be calculated based on the real-time number of contracted electric vehicles and the daily scheduling plan. By comparing the real-time total power response demand and the estimated total power response demand, if the real-time total power response demand is greater than the estimated total power response demand, it is determined that there is an additional power response demand. The daily scheduling plan can be set to be updated once per hour. Therefore, when calculating the estimated charging and discharging demand, the estimated charging and discharging demand for the current hour can be calculated. By determining whether there is an additional power response demand, a reference basis can be provided for the scheduling plan within one hour of the daily period.

[0049] Step 104: When it is determined that there is no additional power response demand, an intraday master-slave game is performed on the contracted electric vehicles to obtain the intraday contracted power strategy, and the charging and discharging scheduling of the contracted electric vehicles is performed based on the intraday contracted power strategy.

[0050] As a preferred embodiment, when it is determined that there is no additional power response demand, an intraday master-slave game is performed on the contracted electric vehicles to obtain an intraday contracted power strategy, and charging and discharging scheduling is performed on the contracted electric vehicles based on the intraday contracted power strategy, including: When it is determined that there is no additional power response demand, the optimization model of non-urgent upper-level aggregators within the day is solved with the goal of maximizing the operating profit of aggregators, and the optimal electricity price strategy within the day is generated. Based on the intraday optimal electricity price strategy, with the goal of minimizing electric vehicle charging costs, the intraday non-emergency lower-level electric vehicle optimization model is solved to obtain the intraday contracted charging and discharging power. The charging and discharging schedule for the contracted electric vehicles is based on the contracted charging and discharging power within the day.

[0051] In this embodiment of the invention, when it is determined that there is no additional power response demand based on the real-time total power response demand and the estimated total power response demand, charging and discharging scheduling only for contracted electric vehicles can meet the real-time total grid demand. There is no need to schedule non-contracted electric vehicles, thus avoiding increased scheduling costs. Based on the real-time data of contracted electric vehicles, the intraday non-urgent upper-layer aggregator optimization model is solved to obtain an intraday optimal electricity price strategy, including the intraday optimal charging price and the intraday optimal discharging price. Then, based on the intraday optimal electricity price strategy, the intraday non-urgent lower-layer electric vehicle optimization model is solved to obtain the intraday contracted charging power and the intraday contracted discharging power, thereby enabling charging and discharging scheduling for contracted electric vehicles.

[0052] Taking an hourly update to the daily scheduling plan as an example, this embodiment of the invention will employ a rolling optimization model for intraday scheduling optimization. The objective function of the intraday non-urgent upper-level aggregator optimization model is: In the formula, The aggregator's operating profit at time t under non-emergency conditions within the day; This represents the current operating profit of the aggregator. The charging electricity price set for aggregators; The discharge electricity price set for aggregators; Optimize the charging price at time t; The optimal discharge price at time t; The first penalty coefficient is set for deviations from the previous day's forecast; This is a preset second penalty coefficient for deviations from the previous day's forecast.

[0053] The solution steps for the master-slave game in the intraday phase are the same as those in the day-ahead phase. The difference lies in the objective functions and constraints of the upper-level aggregator optimization model and the lower-level electric vehicle optimization model. Also, each optimization in the intraday phase only takes the final optimized electricity price and incentives for the current hour, and the intraday rolling optimization is performed once per hour.

[0054] Specifically, the constraints of the intraday non-emergency upper-level aggregator optimization model include intraday non-emergency charging and discharging electricity price and incentive electricity price constraints, intraday non-emergency response quantity constraints, and intraday non-emergency grid power constraints. The intraday non-emergency charging and discharging electricity price and incentive electricity price constraints are as follows: In the formula, Minimum electricity price for aggregators; The charging electricity price set for aggregators; The maximum electricity price for aggregators; Incentive electricity prices set for aggregators; The discharge electricity price set for aggregators; The daily non-emergency response volume constraint is as follows: In the formula, The total power response at time t; The total power response requirement at time t; The intraday non-emergency grid power constraint is as follows: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the total average power of all non-contracted electric vehicles at time t; This is the maximum power limit for the power grid; This represents the number of electric vehicles that have been contracted.

[0055] The objective function of the intraday non-emergency lower-level electric vehicle optimization model is: In the formula, The total cost of charging an electric vehicle at time t under non-emergency conditions during the day; The charging electricity price set for aggregators; The discharge electricity price set for aggregators; The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; This represents the number of electric vehicles that have already been contracted. The preset scheduling time interval; The constraints of the intraday non-emergency lower-level electric vehicle optimization model include intraday non-emergency total charging power consumption constraints, intraday non-emergency charging and discharging power constraints, and intraday non-emergency electric vehicle state of charge constraints. The daily non-emergency total charging power consumption constraint includes: In the formula, Predicted charging power before adjustment; The predicted discharge power before regulation; The adjusted charging power; The adjusted discharge power; The number of preset scheduling time periods; The intraday non-emergency charging and discharging power constraints include: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; Maximum charging power limit; Maximum discharge power limit; The intraday non-emergency electric vehicle state-of-charge constraint is as follows: In the formula, State of charge of the electric vehicle; This represents the minimum state of charge for an electric vehicle. This represents the maximum state of charge for an electric vehicle.

[0056] Step 105: When it is determined that there is an additional power response demand, an intraday master-slave game is performed on the contracted electric vehicles and the non-contracted electric vehicles to obtain the intraday contracted power strategy and the intraday non-contracted incentive strategy. Based on the intraday contracted power strategy, the charging and discharging of the contracted electric vehicles is scheduled, and based on the intraday non-contracted incentive strategy, the charging and discharging of the non-contracted electric vehicles is scheduled.

[0057] As a preferred embodiment, when it is determined that there is an additional power response demand, an intraday master-slave game is performed between the contracted electric vehicles and the non-contracted electric vehicles to derive an intraday contracted power strategy and an intraday non-contracted incentive strategy. Based on the intraday contracted power strategy, charging and discharging scheduling is performed on the contracted electric vehicles, and based on the intraday non-contracted incentive strategy, charging and discharging scheduling is performed on the non-contracted electric vehicles, including: When it is determined that there is an additional power response demand, the optimization model of the upper-level aggregator in the intraday emergency is solved with the goal of maximizing the operating profit of the aggregator, and the intraday optimal electricity price strategy and intraday optimal real-time incentive are generated. Based on the intraday optimal electricity price strategy and the intraday optimal real-time incentive, with the goal of minimizing electric vehicle charging costs, the intraday emergency lower-level electric vehicle optimization model is solved to obtain the intraday scheduling plan; wherein, the intraday scheduling plan includes the intraday contracted charging and discharging power and the intraday non-contracted load power; Based on the intraday optimal real-time stimulus and the intraday non-contracted load power, model-free adaptive control is performed on the non-contracted electric vehicles to obtain the intraday non-contracted demand response. The charging and discharging schedule for the contracted electric vehicles is based on the contracted charging and discharging power within the day, and the charging and discharging schedule for the non-contracted electric vehicles is based on the non-contracted demand response volume within the day.

[0058] In this embodiment of the invention, when it is determined that there is an additional power response demand based on the real-time total power response demand and the estimated total power response demand, charging and discharging scheduling is performed on both contracted and uncontracted electric vehicles. Based on an intraday master-slave game theory approach combining the intraday emergency upper-layer aggregator optimization model and the intraday emergency lower-layer electric vehicle optimization model, the intraday contracted charging and discharging power and the intraday uncontracted load power are obtained. The intraday contracted charging and discharging power is used to schedule the charging and discharging of contracted electric vehicles, while the obtained intraday uncontracted load power requires further model-free adaptive control processing on the uncontracted electric vehicles to obtain the intraday uncontracted demand response amount. Therefore, charging and discharging scheduling is performed on the uncontracted electric vehicles based on the intraday uncontracted demand response amount.

[0059] The steps of the master-slave game between contracted and non-contracted electric vehicles are the same as those when there is no additional power response requirement. The difference lies in the objective function and constraints of the upper-level aggregator optimization model and the lower-level electric vehicle optimization model.

[0060] Specifically, the objective function of the intraday emergency upper-level aggregator optimization model is: In the formula, This refers to the aggregator's operating profit at time t during an emergency situation within the day. This represents the current operating profit of the aggregator. The charging electricity price set for aggregators; The discharge electricity price set for aggregators; Incentive electricity prices set for aggregators; Optimize the charging price at time t; The optimal discharge price at time t; Let be the optimal incentive electricity price at time t; The first penalty coefficient is set for deviations from the previous day's forecast; The second penalty coefficient is a preset value for deviations from the previous day's forecast; The third penalty coefficient is set to prevent deviation from the previous day's forecast. The constraints of the intraday emergency upper-level aggregator optimization model include intraday emergency charging and discharging electricity price and incentive electricity price constraints, intraday emergency response quantity constraints, and intraday emergency grid power constraints. The constraints on the intraday emergency charging and discharging electricity price and the incentive electricity price are as follows: In the formula, Minimum electricity price for aggregators; The charging electricity price set for aggregators; The maximum electricity price for aggregators; This represents the minimum incentive price for aggregators. Incentive electricity prices set for aggregators; This represents the maximum incentive price for aggregators. The discharge electricity price set for aggregators; The daily emergency response volume constraint is as follows: In the formula, The total power response at time t; Let be the excitation response power of the unsigned electric vehicle at time t; The total power response requirement at time t; This represents the total number of electric vehicles that have been contracted and those that have not been contracted. This refers to the number of non-contracted electric vehicles; The intraday emergency power grid constraint is as follows: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the total average power of all non-contracted electric vehicles at time t; This is the maximum power limit for the power grid; This represents the number of electric vehicles that have been contracted.

[0061] The objective function of the intraday emergency lower-level electric vehicle optimization model is: In the formula, The total cost of charging an electric vehicle at time t during an emergency within the day; The charging electricity price set for aggregators; The discharge electricity price set for aggregators; Incentive electricity prices set for aggregators; The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; This represents the number of electric vehicles that have already been contracted. The preset scheduling time interval; Let be the total average power of all non-contracted electric vehicles at time t; Real-time charging electricity price; The constraints of the intraday emergency lower-level electric vehicle optimization model include intraday emergency total charging power consumption constraints, intraday emergency charging and discharging power constraints, and intraday emergency electric vehicle state of charge constraints. The daily emergency total charging power consumption constraint includes: In the formula, Predicted charging power before adjustment; The predicted discharge power before regulation; The adjusted charging power; The adjusted discharge power; The number of preset scheduling time periods; The intraday emergency charge / discharge power constraints include: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; Maximum charging power limit; Maximum discharge power limit; The intraday emergency electric vehicle state-of-charge constraint is as follows: In the formula, State of charge of the electric vehicle; This represents the minimum state of charge for an electric vehicle. This represents the maximum state of charge for an electric vehicle.

[0062] As a preferred embodiment, based on the intraday optimal real-time stimulus and the intraday non-contractual load power, model-free adaptive control is performed on the non-contractual electric vehicles to obtain the intraday non-contractual demand response, including: Based on the intraday optimal real-time incentive and the intraday non-contracted load power, a real-time dynamic demand response model is constructed, and the predicted load response is obtained by solving the real-time dynamic demand response model. The demand response model is subjected to dynamic linearization to obtain a dynamic linearization result, wherein the dynamic linearization result is represented by a pseudo-derivative. By setting a limit on the rate of change of the pseudo-derivative, a pseudo-derivative estimation criterion function is constructed, and the optimal estimate of the pseudo-derivative is obtained by solving the pseudo-derivative estimation criterion function. By setting a limit on the rate of change of incentives, a real-time incentive decision criterion function is constructed for real-time incentives. Combined with a preset real-time incentive output range constraint, the real-time incentive decision criterion function is solved to obtain the optimal real-time incentive output. Based on the optimal estimate of the pseudo-derivative and the optimal output of the real-time stimulus, the intraday non-contractual demand response volume is obtained.

[0063] As a preferred embodiment, a real-time dynamic demand response model is constructed based on the intraday optimal real-time stimulus and the intraday non-contracted load power, and the predicted load response is obtained by solving the real-time dynamic demand response model, including: Based on the historical charging data of each of the aforementioned non-contracted electric vehicles, the aforementioned non-contracted electric vehicles are classified into peak-shaving clusters, valley-filling clusters, and non-peak-shaving and valley-filling clusters. Based on the preset impact coefficient and the intraday optimal real-time incentive, a dynamic expected reference electricity price is set. The value function of the demand response model is constructed based on the aforementioned dynamic expected reference electricity price; The charging load of the peak-shaving cluster, the valley-filling cluster, and the non-peak-shaving and valley-filling cluster is derived based on the intraday non-contractual demand response volume. By combining the charging loads of the peak-shaving cluster, the valley-filling cluster, and the non-peak-shaving and valley-filling cluster with the value function, the predicted load response is obtained.

[0064] In this embodiment of the invention, model-free adaptive control is performed on non-contracted electric vehicles. First, a real-time dynamic demand response model is constructed. The predicted load response is obtained by solving the model. Then, the model is dynamically linearized to obtain the dynamic linearization result. By constructing the pseudo-derivative estimation criterion function and the real-time incentive decision criterion function, the optimal estimate of the pseudo-derivative and the optimal output of the real-time incentive are obtained by solving the two functions respectively. Finally, the intraday non-contracted demand response is calculated based on the optimal estimate of the pseudo-derivative and the optimal output of the real-time incentive.

[0065] In this embodiment of the invention, the construction of a real-time dynamic demand response model first requires classifying non-contracted electric vehicles. Considering the demand response in the real-time stage, the main response time period is the peak and valley period. Therefore, historical charging data of non-contracted electric vehicles is obtained. Combining historical charging data and the division of peak, flat and valley periods, non-contracted electric vehicles are divided into three categories: peak-shaving clusters, valley-filling clusters, and non-peak-shaving and valley-filling clusters.

[0066] The specific classification criteria are as follows: Electric vehicles that spend more than 30% of their total charging time during peak hours and have more than 20% of their idle time on the grid during flat or valley hours are considered to have relatively strong peak-shaving capabilities and are classified as peak-shaving clusters; Excluding peak-shaving clusters, electric vehicles that spend more than 30% of their total charging time during flat hours and have more than 20% of their idle time on the grid during valley hours are considered to have strong valley-filling capabilities and are classified as valley-filling clusters; All electric vehicles excluding the above two clusters of peak-shaving and valley-filling are assumed to have weak peak-shaving and valley-filling capabilities and are classified as non-peak-shaving and valley-filling clusters.

[0067] In this embodiment of the invention, the historical charging data used as the classification basis can be data collected over a continuous week. Different response parameters will be provided for different clusters in subsequent modeling.

[0068] Because the response of electric vehicles is influenced by various subjective and objective factors, it is difficult to directly model and analyze. Traditional methods based on price elasticity moments have relatively fixed parameters, making it difficult to handle models that change in real time. Therefore, third-generation prospect theory can be introduced as the theoretical basis for modeling. Prospect theory considers the asymmetry of user response revenue loss, diminishing marginal returns, and reference point effects. Third-generation prospect theory adds dynamic reference points to the original model, transforming it from a static model to a dynamically changing model, which better reflects the actual response. At the same time, the design of dynamic reference points also allows some objective factors to be directly incorporated into the demand response model.

[0069] This model primarily considers the relationship between incentive signals and response quantities. The input is the aggregator's incentive electricity price, and the output is the actual response quantity of non-contracted users. Using risk aversion from third-generation prospect theory and a dynamic reference point model, the incentive signal is transformed into a user's value function, reflecting the user's willingness to have their load transferred. This willingness is then converted into the actual load quantity of electric vehicles.

[0070] The specific modeling steps for the dynamic demand response model are as follows: First, set the dynamic expected reference electricity price. : In the formula, This is a dynamic expected reference electricity price; This is the reference electricity price for the previous moment; The base electricity price at the previous moment; The electricity price is based on the social influencing factors of the previous moment; The first influence coefficient is preset; This is a preset second influence coefficient.

[0071] Constructing a value function for non-contracted electric vehicles under incentive conditions based on a dynamic expected reference electricity price: In the formula, Let be the willingness of the electric vehicle to respond at time t; This is a dynamic expected reference electricity price; This refers to the actual electricity price; The first risk factor; The second risk factor; Let be the loss aversion coefficient. When , it means that electric vehicles are more averse to losses than to gains.

[0072] Calculate the demand response based on the value function: Assume the user receives an excitation signal and controls the charging power via a smart app, responding by reducing charging power or delaying charging. Therefore, the demand response satisfies the function: In the formula, This represents the demand response volume; this represents the current charging load of the cluster. Let be the load transfer probability, where It is related to the overall basic response probability of the cluster; the higher the probability, the higher the local response enthusiasm.

[0073] Demand response is divided into peak shaving response and valley filling response. Due to the different peak shaving and valley filling capabilities of the three major clusters, different response coefficients are given for each cluster. Therefore, the overall response function is as follows: In the formula, This represents the total demand response. This represents the demand response capacity of a peak-shaving cluster. This represents the demand response capacity of a valley-fillable cluster. For the demand response of non-peak-shaving and valley-filling clusters; This refers to the proportion of transferable load in a peak-shaving cluster to the total cluster load. This refers to the proportion of transferable load in the valley-fillable cluster to the total cluster load. This refers to the proportion of transferable load in the total cluster load within a non-peak-shaving / valley-filling cluster; and it addresses peak-shaving and valley-filling responses. , and The coefficients take different values, among which, during peak shaving... Too high, filling the valley Too high.

[0074] The analysis of the dynamic demand response model above reveals that it is a complex system with strong nonlinearity, dynamically changing model parameters, and a certain delay. Furthermore, its effectiveness in representing the actual response remains to be verified. Therefore, this embodiment of the invention will combine a model-free adaptive real-time data-driven control method for effective control.

[0075] Since compact-format model-free control (MFAC) is designed for single-input single-output systems, it iteratively updates the pseudo-derivatives within the controller by adapting to changes in the control object through real-time output and input changes. Analysis of the demand-side resource cluster excitation response characteristics reveals that the aforementioned dynamic demand response model satisfies the following conditions: except at finite time points, the derivative of the overall cluster power with respect to real-time excitation is continuous, and except at finite time points, it satisfies the generalized Lipschitz condition.

[0076] Therefore, the dynamic demand response model can be dynamically linearized. To better describe it, a pseudo-derivative is introduced. The result of dynamic linearization can be expressed as: In the formula, This represents the power variation of the uncontracted electric vehicle cluster. This indicates the change in real-time stimulus.

[0077] At each action point, the electric vehicle cluster stimulus-response model is linearized. It is a slowly time-varying parameter. Due to the nonlinear and time-varying characteristics of the system, The dynamic characteristics are difficult to describe by mathematical expressions, but their values ​​can be estimated by using the real-time input and output values ​​of the electric vehicle cluster, namely the real-time excitation and the real-time power of the cluster.

[0078] right The optimal parameter estimation can be designed as an optimization problem. The optimization objective is to minimize the deviation between the model's estimated output and the actual output. This takes into account the possibility that some sampled data may be inaccurate, leading to... The estimation results fluctuate too much, affecting the stability of the system. Therefore, a constraint on the rate of change of the pseudo-derivative can be added to construct a pseudo-derivative estimation criterion function: In the formula, Here, μ is the pseudo-derivative parameter estimation criterion function; μ is the pseudo-derivative weighting factor, which determines the... The degree of restriction on change; This is an estimate of the pseudo-derivative from the previous sampling time.

[0079] Through the Optimization solution, pseudo-derivative The optimal estimate can be expressed as: In the formula, The introduced pseudo-derivative step size factor makes the algorithm more flexible; This is the optimal estimate of the pseudo-derivative.

[0080] Based on dynamic linearization, the optimal control output of real-time excitation can be designed. At each action point, the decision objective of real-time excitation is to minimize the deviation of the output power of the electric vehicle cluster from the expected value at the next moment. Similarly, to avoid data transmission errors... If the value is too large, set a limit on the rate of change of incentive to construct a real-time incentive decision criterion function for real-time incentives: In the formula, Optimize the criterion function for real-time incentives; As a real-time incentive weighting factor, it determines the... The degree of restriction; This represents the expected output for the next step in the electric vehicle cluster.

[0081] By optimizing the real-time incentive decision criterion function, the optimal output of the real-time incentive can be obtained: In the formula, The introduced real-time excitation step size factor makes the algorithm more flexible; To provide the optimal output in real time.

[0082] Based on the relationship between real-time stimulus and demand response as shown in the dynamic linearization results, and according to the optimal estimate of the pseudo-derivative and the optimal output of the real-time stimulus, the intraday non-contracted demand response can be obtained. Based on the intraday non-contracted demand response, charging and discharging scheduling of non-contracted electric vehicles can be performed.

[0083] Implementing the above embodiments has the following effects: This invention provides a method for scheduling electric vehicle charging and discharging. In the day-ahead phase, a day-ahead master-slave game is performed on contracted and uncontracted electric vehicles based on grid dispatch parameters to derive a day-ahead dispatch plan. In the intraday phase, real-time load data is acquired. Based on the real-time load data and the day-ahead dispatch plan, it is determined whether there is an additional power response demand. When there is no additional power response demand, only contracted electric vehicles are dispatched, eliminating the need for charging and discharging dispatching of uncontracted electric vehicles, thus avoiding increased dispatching costs. When there is an additional power response demand, charging and discharging dispatching is performed on both contracted and uncontracted electric vehicles, leveraging the dispatching potential of uncontracted electric vehicles to effectively improve the charging and discharging dispatching efficiency of electric vehicles.

[0084] like Figure 3 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; One embodiment of the present invention provides an electric vehicle charging and discharging scheduling device, including: a day-ahead scheduling module, a data acquisition module, an additional demand judgment module, an intraday non-emergency scheduling module, and an intraday emergency scheduling module; The day-ahead scheduling module is used to perform a day-ahead master-slave game between contracted electric vehicles and non-contracted electric vehicles based on grid scheduling parameters during the day-ahead phase, and to derive the day-ahead scheduling plan. The data acquisition module is used to acquire real-time load data during the day; wherein, the real-time load data includes the real-time load demand of contracted electric vehicles, the real-time load demand of non-contracted electric vehicles, and the real-time number of contracted electric vehicles on the network. The additional demand determination module is used to determine whether there is an additional power response demand based on the real-time load data and the day-ahead scheduling plan. The intraday non-emergency scheduling module is used to perform intraday master-slave game on the contracted electric vehicles when it is determined that there is no additional power response demand, to obtain the intraday contracted power strategy, and to perform charging and discharging scheduling on the contracted electric vehicles based on the intraday contracted power strategy. The intraday emergency scheduling module is used to perform intraday master-slave game between contracted electric vehicles and non-contracted electric vehicles when it is determined that there is an additional power response demand, to obtain intraday contracted power strategy and intraday non-contracted incentive strategy, and to perform charging and discharging scheduling for contracted electric vehicles based on the intraday contracted power strategy, and to perform charging and discharging scheduling for non-contracted electric vehicles based on the intraday non-contracted incentive strategy.

[0085] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the electric vehicle charging and discharging scheduling method provided by any of the above-described method embodiments of the present invention.

[0086] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0087] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for scheduling the charging and discharging of an electric vehicle, characterized in that, include: In the day-ahead phase, a day-ahead master-slave game is conducted between contracted and uncontracted electric vehicles based on grid dispatch parameters to derive the day-ahead dispatch plan. During the daytime phase, real-time load data is acquired; wherein, the real-time load data includes the real-time load demand of contracted electric vehicles, the real-time load demand of non-contracted electric vehicles, and the real-time number of contracted electric vehicles on the network. Based on the real-time load data and the day-ahead scheduling plan, determine whether there is any additional power response demand. When it is determined that there is no additional power response demand, an intraday master-slave game is performed on the contracted electric vehicles to obtain the intraday contracted power strategy, and the charging and discharging scheduling of the contracted electric vehicles is performed based on the intraday contracted power strategy. When it is determined that there is an additional power response demand, an intraday master-slave game is performed on the contracted electric vehicles and the non-contracted electric vehicles to obtain the intraday contracted power strategy and the intraday non-contracted incentive strategy. Based on the intraday contracted power strategy, the charging and discharging of the contracted electric vehicles is scheduled, and based on the intraday non-contracted incentive strategy, the charging and discharging of the non-contracted electric vehicles is scheduled.

2. The electric vehicle charging and discharging scheduling method according to claim 1, characterized in that, The method of performing a day-ahead master-slave game on contracted and uncontracted electric vehicles based on grid dispatch parameters to derive a day-ahead dispatch plan includes: Obtain power grid dispatch parameters; wherein, the power grid dispatch parameters include total power response demand, electricity purchase cost, demand response subsidy price, and incentive response power of non-contracted electric vehicles; Based on the grid dispatch parameters, with the goal of maximizing the operating profit of aggregators, the day-ahead upper-level aggregator optimization model is solved to generate the day-ahead optimal electricity price strategy and the day-ahead optimal real-time incentive. Based on the day-ahead optimal electricity price strategy and the day-ahead optimal real-time incentive, with the goal of minimizing electric vehicle charging costs, the day-ahead lower-level electric vehicle optimization model is solved to obtain the day-ahead scheduling plan; wherein, the day-ahead scheduling plan includes the day-ahead contracted charging and discharging power and the day-ahead uncontracted load power.

3. The electric vehicle charging and discharging scheduling method according to claim 2, characterized in that, The objective function of the current-day upper-level aggregator optimization model is: In the formula, This represents the current operating profit of the aggregator. The charging electricity price set for aggregators; The discharge electricity price set for aggregators; Let be the electricity purchase cost / price at time t; Incentive electricity prices set for aggregators; The number of preset scheduling time periods; This represents the number of electric vehicles that have already been contracted. The preset scheduling time interval; The demand response subsidy price at time t; The total power response at time t; The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; Let be the excitation response power of the unsigned electric vehicle at time t; The constraints of the day-ahead upper-level aggregator optimization model include day-ahead charging and discharging electricity price and incentive price constraints, day-ahead response quantity constraints, and day-ahead grid power constraints. The day-ahead charge / discharge tariff and incentive tariff constraints are as follows: In the formula, Minimum electricity price for aggregators; The charging electricity price set for aggregators; The maximum electricity price for aggregators; This represents the minimum incentive price for aggregators. Incentive electricity prices set for aggregators; This represents the maximum incentive price for aggregators. The discharge electricity price set for aggregators; The day-ahead response quantity constraint is: In the formula, The total power response at time t; Let be the excitation response power of the unsigned electric vehicle at time t; The total power response demand at time t; This represents the total number of electric vehicles that have been contracted and those that have not been contracted. This refers to the number of non-contracted electric vehicles; The current-day grid power constraint is: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the total average power of all non-contracted electric vehicles at time t; This is the maximum power limit for the power grid; This represents the number of electric vehicles that have already been contracted. The objective function of the current-level electric vehicle optimization model is: In the formula, The total cost of charging an electric vehicle up to date; The charging electricity price set for aggregators; The discharge electricity price set for aggregators; Incentive electricity prices set for aggregators; The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; This represents the number of electric vehicles that have already been contracted. The preset scheduling time interval; The number of preset scheduling time periods; Let be the total average power of all non-contracted electric vehicles at time t; Real-time charging electricity price; The constraints of the daytime lower-level electric vehicle optimization model include daytime total charging power consumption constraints, daytime charging and discharging power constraints, and daytime electric vehicle state of charge constraints. The total daily charging power consumption constraint includes: In the formula, Predicted charging power before adjustment; The predicted discharge power before regulation; The adjusted charging power; The adjusted discharge power; The number of preset scheduling time periods; The daytime charge / discharge power constraint includes: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; Maximum charging power limit; Maximum discharge power limit; The current state of charge constraint for electric vehicles is: In the formula, State of charge of the electric vehicle; This represents the minimum state of charge for an electric vehicle. This represents the maximum state of charge of an electric vehicle.

4. The electric vehicle charging and discharging scheduling method according to claim 3, characterized in that, When it is determined that there is no additional power response demand, an intraday master-slave game is performed on the contracted electric vehicles to obtain an intraday contracted power strategy, and charging and discharging scheduling is performed on the contracted electric vehicles based on the intraday contracted power strategy, including: When it is determined that there is no additional power response demand, the optimization model of non-urgent upper-level aggregators within the day is solved with the goal of maximizing the operating profit of aggregators, and the optimal electricity price strategy within the day is generated. Based on the intraday optimal electricity price strategy, with the goal of minimizing electric vehicle charging costs, the intraday non-emergency lower-level electric vehicle optimization model is solved to obtain the intraday contracted charging and discharging power. The charging and discharging schedule for the contracted electric vehicles is based on the contracted charging and discharging power within the day.

5. The electric vehicle charging and discharging scheduling method according to claim 4, characterized in that, The objective function of the intraday non-urgent upper-level aggregator optimization model is: In the formula, The aggregator's operating profit at time t under non-emergency conditions within the day; This represents the current operating profit of the aggregator. The charging electricity price set for aggregators; The discharge electricity price set for aggregators; Optimize the charging price at time t; The optimal discharge price at time t; The first penalty coefficient is set for deviations from the previous day's forecast; The second penalty coefficient is a preset value for deviations from the previous day's forecast; The constraints of the intraday non-emergency upper-level aggregator optimization model include intraday non-emergency charging and discharging electricity price and incentive electricity price constraints, intraday non-emergency response quantity constraints, and intraday non-emergency grid power constraints. The intraday non-emergency charging and discharging electricity price and incentive electricity price constraints are as follows: In the formula, Minimum electricity price for aggregators; The charging electricity price set for aggregators; The maximum electricity price for aggregators; Incentive electricity prices set for aggregators; The discharge electricity price set for aggregators; The daily non-emergency response volume constraint is as follows: In the formula, The total power response at time t; The total power response demand at time t; The intraday non-emergency grid power constraint is as follows: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the total average power of all non-contracted electric vehicles at time t; This is the maximum power limit for the power grid; This represents the number of electric vehicles that have already been contracted. The objective function of the intraday non-emergency lower-level electric vehicle optimization model is: In the formula, The total cost of charging an electric vehicle at time t under non-emergency conditions during the day; The charging electricity price set for aggregators; The discharge electricity price set for aggregators; The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; This represents the number of electric vehicles that have already been contracted. The preset scheduling time interval; The constraints of the intraday non-emergency lower-level electric vehicle optimization model include intraday non-emergency total charging power consumption constraints, intraday non-emergency charging and discharging power constraints, and intraday non-emergency electric vehicle state of charge constraints. The daily non-emergency total charging power consumption constraint includes: In the formula, Predicted charging power before adjustment; The predicted discharge power before regulation; The adjusted charging power; The adjusted discharge power; The number of preset scheduling time periods; The intraday non-emergency charging and discharging power constraints include: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; Maximum charging power limit; Maximum discharge power limit; The intraday non-emergency electric vehicle state-of-charge constraint is as follows: In the formula, State of charge of the electric vehicle; This represents the minimum state of charge for an electric vehicle. This represents the maximum state of charge of an electric vehicle.

6. The electric vehicle charging and discharging scheduling method according to claim 3, characterized in that, When it is determined that there is an additional power response demand, an intraday master-slave game is performed between contracted and uncontracted electric vehicles to derive an intraday contracted power strategy and an intraday uncontracted incentive strategy. Based on the intraday contracted power strategy, charging and discharging scheduling is performed for the contracted electric vehicles, and based on the intraday uncontracted incentive strategy, charging and discharging scheduling is performed for the uncontracted electric vehicles. This includes: When it is determined that there is an additional power response demand, the optimization model of the upper-level aggregator in the intraday emergency is solved with the goal of maximizing the operating profit of the aggregator, and the intraday optimal electricity price strategy and intraday optimal real-time incentive are generated. Based on the intraday optimal electricity price strategy and the intraday optimal real-time incentive, with the goal of minimizing electric vehicle charging costs, the intraday emergency lower-level electric vehicle optimization model is solved to obtain the intraday scheduling plan; wherein, the intraday scheduling plan includes the intraday contracted charging and discharging power and the intraday non-contracted load power; Based on the intraday optimal real-time stimulus and the intraday non-contracted load power, model-free adaptive control is performed on the non-contracted electric vehicles to obtain the intraday non-contracted demand response. The charging and discharging schedule for the contracted electric vehicles is based on the contracted charging and discharging power within the day, and the charging and discharging schedule for the non-contracted electric vehicles is based on the non-contracted demand response volume within the day.

7. The electric vehicle charging and discharging scheduling method according to claim 6, characterized in that, The objective function of the intraday emergency upper-level aggregator optimization model is: In the formula, This refers to the aggregator's operating profit at time t during an emergency situation within the day. This represents the current operating profit of the aggregator. The charging electricity price set for aggregators; The discharge electricity price set for aggregators; Incentive electricity prices set for aggregators; Optimize the charging price at time t; The optimal discharge price at time t; Let be the optimal incentive electricity price at time t; The first penalty coefficient is set for deviations from the previous day's forecast; The second penalty coefficient is a preset value for deviations from the previous day's forecast; The third penalty coefficient is set to prevent deviation from the previous day's forecast. The constraints of the intraday emergency upper-level aggregator optimization model include intraday emergency charging and discharging electricity price and incentive electricity price constraints, intraday emergency response quantity constraints, and intraday emergency grid power constraints. The constraints on the intraday emergency charging and discharging electricity price and the incentive electricity price are as follows: In the formula, Minimum electricity price for aggregators; The charging electricity price set for aggregators; The maximum electricity price for aggregators; This represents the minimum incentive price for aggregators. Incentive electricity prices set for aggregators; This represents the maximum incentive price for aggregators. The discharge electricity price set for aggregators; The daily emergency response volume constraint is as follows: In the formula, The total power response at time t; Let be the excitation response power of the unsigned electric vehicle at time t; The total power response demand at time t; This represents the total number of electric vehicles that have been contracted and those that have not been contracted. This refers to the number of non-contracted electric vehicles; The intraday emergency power grid constraint is as follows: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the total average power of all non-contracted electric vehicles at time t; This is the maximum power limit for the power grid; This represents the number of electric vehicles that have already been contracted. The objective function of the intraday emergency lower-level electric vehicle optimization model is: In the formula, The total cost of charging an electric vehicle at time t under emergency conditions during the day; The charging electricity price set for aggregators; The discharge electricity price set for aggregators; Incentive electricity prices set for aggregators; The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; This represents the number of electric vehicles that have already been contracted. The preset scheduling time interval; Let be the total average power of all non-contracted electric vehicles at time t; Real-time charging electricity price; The constraints of the intraday emergency lower-level electric vehicle optimization model include intraday emergency total charging power consumption constraints, intraday emergency charging and discharging power constraints, and intraday emergency electric vehicle state of charge constraints. The daily emergency total charging power consumption constraint includes: In the formula, Predicted charging power before adjustment; The predicted discharge power before regulation; The adjusted charging power; The adjusted discharge power; The number of preset scheduling time periods; The intraday emergency charge / discharge power constraints include: In the formula, The charging power of the i-th contracted electric vehicle at time t; Let be the discharge power of the i-th contracted electric vehicle at time t; Maximum charging power limit; Maximum discharge power limit; The intraday emergency electric vehicle state-of-charge constraint is as follows: In the formula, State of charge of the electric vehicle; This represents the minimum state of charge for an electric vehicle. This represents the maximum state of charge of an electric vehicle.

8. The electric vehicle charging and discharging scheduling method according to claim 7, characterized in that, The process of performing model-free adaptive control on non-contracted electric vehicles based on the intraday optimal real-time stimulus and the intraday non-contracted load power yields the intraday non-contracted demand response, including: Based on the intraday optimal real-time incentive and the intraday non-contracted load power, a real-time dynamic demand response model is constructed, and the predicted load response is obtained by solving the real-time dynamic demand response model. The demand response model is subjected to dynamic linearization to obtain a dynamic linearization result, wherein the dynamic linearization result is represented by a pseudo-derivative. By setting a limit on the rate of change of the pseudo-derivative, a pseudo-derivative estimation criterion function is constructed, and the optimal estimate of the pseudo-derivative is obtained by solving the pseudo-derivative estimation criterion function. By setting a limit on the rate of change of incentives, a real-time incentive decision criterion function is constructed for real-time incentives. Combined with a preset real-time incentive output range constraint, the real-time incentive decision criterion function is solved to obtain the optimal real-time incentive output. Based on the optimal estimate of the pseudo-derivative and the optimal output of the real-time stimulus, the intraday non-contractual demand response volume is obtained.

9. The electric vehicle charging and discharging scheduling method according to claim 8, characterized in that, The process involves constructing a real-time dynamic demand response model based on the intraday optimal real-time stimulus and the intraday non-contracted load power, and obtaining the predicted load response by solving the real-time dynamic demand response model, including: Based on the historical charging data of each of the aforementioned non-contracted electric vehicles, the aforementioned non-contracted electric vehicles are classified into peak-shaving clusters, valley-filling clusters, and non-peak-shaving and valley-filling clusters. Based on the preset impact coefficient and the intraday optimal real-time incentive, a dynamic expected reference electricity price is set. The value function of the demand response model is constructed based on the dynamic expected reference electricity price; The charging load of the peak-shaving cluster, the valley-filling cluster, and the non-peak-shaving and valley-filling cluster is derived based on the intraday non-contractual demand response volume. By combining the charging loads of the peak-shaving cluster, the valley-filling cluster, and the non-peak-shaving and valley-filling cluster with the value function, the predicted load response is obtained.

10. A charging and discharging scheduling device for electric vehicles, characterized in that, include: The module includes a daytime scheduling module, a data acquisition module, an additional demand judgment module, an intraday non-urgent scheduling module, and an intraday emergency scheduling module. The day-ahead scheduling module is used to perform a day-ahead master-slave game between contracted electric vehicles and non-contracted electric vehicles based on grid scheduling parameters during the day-ahead phase, and to derive the day-ahead scheduling plan. The data acquisition module is used to acquire real-time load data during the day; wherein, the real-time load data includes the real-time load demand of contracted electric vehicles, the real-time load demand of non-contracted electric vehicles, and the real-time number of contracted electric vehicles on the network. The additional demand determination module is used to determine whether there is an additional power response demand based on the real-time load data and the day-ahead scheduling plan. The intraday non-emergency scheduling module is used to perform intraday master-slave game on the contracted electric vehicles when it is determined that there is no additional power response demand, to obtain the intraday contracted power strategy, and to perform charging and discharging scheduling on the contracted electric vehicles based on the intraday contracted power strategy. The intraday emergency scheduling module is used to perform intraday master-slave game between contracted electric vehicles and non-contracted electric vehicles when it is determined that there is an additional power response demand, to obtain intraday contracted power strategy and intraday non-contracted incentive strategy, and to perform charging and discharging scheduling for contracted electric vehicles based on the intraday contracted power strategy, and to perform charging and discharging scheduling for non-contracted electric vehicles based on the intraday non-contracted incentive strategy.

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