Interaction adjustment method, device and equipment for electric vehicle and power grid and storage medium

Abstract

The invention discloses an interaction adjustment method, device and equipment for an electric vehicle and a power grid and a storage medium, is applied to a three-level framework comprising a user execution layer, a power grid control layer and an aggregation scheduling layer, and relates to the technical field of power grids, and the method comprises the steps that the user execution layer collects user and vehicle information of the electric vehicle to obtain an electric vehicle state parameter set; the power grid control layer collects power grid state and demand data and generates a power grid state set and a constraint matrix; the data and the power grid adjustment demand vector are uploaded to an aggregation scheduling layer through a communication unit, the aggregation scheduling layer generates a station-level resource set based on charging station information, and aggregates each vehicle power adjustable interval to form a capacity resource pool; using a mixed integer programming algorithm, the demand vector and the constraint matrix to solve a charging and discharging power track of each electric vehicle in a future time period; and issuing a charging and discharging instruction to the charging facility for execution, and collecting power and charge state feedback information to realize closed-loop adjustment so as to improve the efficiency of interactive adjustment between the electric vehicle and the power grid.

Description

Technical Field

[0001] This invention relates to the field of power grid technology, and in particular to a method, apparatus, device, and storage medium for the interactive regulation of electric vehicles and power grids. Background Technology

[0002] Currently, with the rapid growth of electric vehicle (EV) ownership, the need for interaction between EVs and the power grid is becoming increasingly prominent. On the one hand, large-scale disorderly charging of EVs may lead to problems such as widening the peak-valley difference in grid load and line overload, affecting the safe and stable operation of the power grid. On the other hand, as a distributed energy storage resource, EVs have the potential to participate in grid regulation and can assist the power grid in achieving goals such as peak shaving and valley filling, and absorbing renewable energy.

[0003] Existing vehicle-to-grid (V2G) scheduling methods mostly employ static optimization strategies, formulating scheduling plans solely based on initial moment-of-flight forecast data. This neglects the impact of real-time data updates on scheduling results, leading to low scheduling accuracy and poor adaptability. Furthermore, most scheduling methods focus solely on economic objectives, ignoring key indicators such as power smoothness and battery SOC (state of charge) deviation, making it difficult to balance grid security, user demand, and economic benefits. In addition, existing scheduling architectures are mostly two-tiered (user-side and grid-side), lacking an intermediate aggregation scheduling link. This hinders the efficient integration of dispersed electric vehicle resources, resulting in low scheduling command execution efficiency and delayed response times.

[0004] As can be seen from the above, improving the efficiency of interaction and regulation between electric vehicles and the power grid is an urgent problem to be solved. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a method, apparatus, device, and storage medium for the interactive regulation of electric vehicles and the power grid, which can improve the efficiency of interactive regulation between electric vehicles and the power grid during the interactive regulation process. The specific solution is as follows:

[0006] Firstly, this application provides a method for the interactive regulation of electric vehicles and the power grid, applied to a three-tier architecture including a user execution layer, a power grid control layer, and an aggregation scheduling layer, comprising:

[0007] The user execution layer collects user information and vehicle information corresponding to each electric vehicle, and generates a set of electric vehicle state parameters based on the user information and vehicle information.

[0008] The power grid control layer collects status data and demand data during power grid operation, generates a power grid status set based on the status data and demand data, and generates a constraint matrix based on the power grid status set and preset line power.

[0009] A data interaction link is established using a preset communication unit so that the three-level architecture can upload the electric vehicle state parameter set, the power grid state set, the demand vector, and the constraint matrix to the aggregation scheduling layer through the data interaction link; the demand vector is a vector generated based on the power grid's adjustment demand for the electric vehicle;

[0010] The aggregation scheduling layer is invoked and a station-level resource set is generated based on the charging station information. The adjustable power range of each electric vehicle is aggregated to obtain a capacity resource pool.

[0011] The data processing unit in the aggregation scheduling layer processes the electric vehicle state parameter set, the power grid state set, the station-level resource set, and the capacity resource pool to obtain a dataset to be processed. Then, a mixed integer programming algorithm is used to determine the charging and discharging power trajectory of the dataset to be processed within a preset rolling prediction window based on preset decision variables, objective function, demand vector, and constraint matrix.

[0012] The execution unit in the aggregation scheduling layer sends the charging and discharging power commands in the charging and discharging power trajectory to the corresponding charging facilities for execution, and collects feedback information, including power values ​​and vehicle charge status, during the execution process, so that the data processing unit can make adjustments to vehicle-to-grid interaction based on the feedback information.

[0013] Optionally, the step of collecting user information and vehicle information corresponding to each electric vehicle through the user execution layer, and generating an electric vehicle state parameter set based on the user information and the vehicle information, includes:

[0014] The user execution layer collects user information and vehicle information corresponding to each electric vehicle. The user information includes user identification, the expected parking time corresponding to the charging facility that the vehicle plans to access, charging and discharging energy requirements, charging and discharging price sensitivity threshold, and charging and discharging power range. The vehicle information includes vehicle identification, charging and discharging type of vehicle power battery, real-time state of charge of power battery, and total capacity of power battery.

[0015] A first time availability mask and a second time availability mask corresponding to the vehicle information are determined. The first time availability mask and the second time availability mask in the electric vehicle state parameter set are parsed to obtain the parsing result. Based on the parsing result, the set of schedulable times for the electric vehicle to receive charge and discharge control commands in all future scheduling periods is determined. The first time availability mask is used to identify whether charging is allowed in each time granularity; the second time availability mask is used to identify whether discharging is allowed in each time granularity.

[0016] Based on the user information, the vehicle information, and the set of schedulable times, a set of electric vehicle state parameters is constructed to reflect the schedulable potential of electric vehicles; the electric vehicle state parameter set includes real-time state of charge, total battery capacity, maximum charging power, minimum charging power, maximum discharging power, and minimum discharging power.

[0017] Optionally, the step of collecting power grid operation status data and demand data through the power grid control layer, generating a power grid status set based on the status data and demand data, and generating a constraint matrix based on the power grid status set and a preset line power, includes:

[0018] The power grid control layer collects real-time status data characterizing the power grid's operational status. It then acquires demand data, including time-of-use charging prices published by the electricity market and on-grid prices for power fed back to the grid by electric vehicles. Based on this status data and demand data, a power grid status set reflecting the real-time operational status of the power grid and market prices is generated. The status data includes bus voltage, line current, transmission line active power, current-carrying capacity limits for each line, transformer current-carrying capacity limits, and power quality indicators, including harmonic distortion rate.

[0019] The renewable energy output from wind power and photovoltaic power generation in a specific future period is predicted to obtain prediction data. Based on the grid topology, a grid security transmission limit is determined. Based on the prediction data and the grid security transmission limit, a scheduling demand instruction describing peak shaving and valley filling and reserve capacity service is generated. The scheduling demand instruction is then converted into the expected upper limit and expected lower limit of the interactive power at the connection point; the connection point is the connection point between the power grid and the electric vehicle.

[0020] The power grid control layer processes the power grid state set, the predicted data, the expected upper limit value, and the expected lower limit value to obtain a constraint matrix; the constraint matrix is ​​expressed as a system of linear inequalities.

[0021] Optionally, the step of establishing a data interaction link using a preset communication unit, so that the three-tier architecture can upload the electric vehicle state parameter set, the power grid state set, the demand vector, and the constraint matrix to the aggregation scheduling layer through the data interaction link, includes:

[0022] A first communication link is established between the user terminal of the electric vehicle and the cloud server of the dispatch center using a preset communication unit, and a second communication link is established between the electric vehicle and the charging pile using the preset communication unit. Then, a third communication link is established between the charging pile and the cloud server of the dispatch center using the preset communication unit, so as to construct a data interaction link based on the first communication link, the second communication link and the third communication link.

[0023] The three-tier architecture is invoked, and the electric vehicle state parameter set, the power grid state set, the demand vector, and the constraint matrix are uploaded to the aggregation scheduling layer through the data interaction link; wherein, the demand vector includes power demand, time parameters, and adjustment coefficients.

[0024] Optionally, the step of invoking the aggregation scheduling layer and generating a station-level resource set based on charging station information, and aggregating the power adjustable ranges of each electric vehicle to obtain a capacity resource pool, includes:

[0025] The aggregation scheduling layer is invoked to obtain the corresponding charging station information from the charging piles, and a station-level resource set is generated based on the charging station information. Then, the power adjustable range of each electric vehicle is aggregated based on the station-level resource set and the electric vehicle status parameter set to obtain a capacity resource pool. The capacity resource pool is used to characterize the overall schedulable power range. The station-level resource set includes the resource distribution and availability status of each charging station.

[0026] Optionally, the step of processing the electric vehicle state parameter set, the power grid state set, the station-level resource set, and the capacity resource pool through the data processing unit in the aggregation scheduling layer to obtain a dataset to be processed, and then using a mixed integer programming algorithm based on preset decision variables, an objective function, the demand vector, and the constraint matrix to determine the charging and discharging power trajectory of the dataset to be processed within a preset rolling prediction window, includes:

[0027] In the aggregation scheduling layer, the data processing unit performs heterogeneous data cleaning and outlier removal on the electric vehicle status parameter set, the power grid status set, the station-level resource set, and the capacity resource pool to obtain data to be screened. The data to be screened is then filtered and information is extracted to obtain data to be aligned.

[0028] The time alignment operation is performed on the data to be aligned based on the preset timestamp reference to obtain the dataset to be processed. Then, decision variables are defined to characterize the mutual exclusion logic relationship between charging and discharging, and an objective function including economic indicators, power smoothness indicators and system operation safety indicators is constructed. The target constraint conditions are determined based on the constraint matrix and the preset physical operation conditions.

[0029] By using a mixed integer programming algorithm and iteratively solving the dataset to be processed within a preset rolling prediction time window based on the decision variables, the objective function, the objective constraints, and the demand vector, the charging and discharging power trajectories of each electric vehicle in the future window at each scheduling time are obtained.

[0030] Optionally, the construction includes an objective function comprising economic indicators, power smoothness indicators, and system operation safety indicators, and the determination of target constraints based on the constraint matrix and preset physical operating conditions, including:

[0031] The deviation between the actual terminal state of charge of electric vehicles and the expected value is determined to obtain the state of charge deviation term. The total electricity cost of all electric vehicles in the scheduling cycle is determined to obtain the economic cost term. Then, based on the preset smoothness auxiliary variable, the power smoothness term is determined to characterize the fluctuation of charging and discharging power in adjacent time periods. The line soft constraint relaxation penalty term is determined to punish the over-limit behavior of the system net power.

[0032] Determine the weights corresponding to the state of charge deviation term, the economic cost term, the power smoothness term, and the line soft constraint relaxation penalty term, respectively, and then determine the objective function based on the state of charge deviation term, the economic cost term, the power smoothness term, and the line soft constraint relaxation penalty term, and their respective weights.

[0033] The physical operating conditions corresponding to the electric vehicle and the charging pile are determined respectively, and the target constraint conditions are determined by using each physical operating condition and the constraint matrix; the physical operating conditions include charging and discharging mutual exclusion constraint conditions, charging and discharging power upper and lower limit constraint conditions, battery state of charge dynamic constraint conditions, and battery capacity constraint conditions.

[0034] Optionally, determining the target constraints using the physical operating conditions and the constraint matrix includes:

[0035] The state of charge dynamics relationship corresponding to the physical operating conditions is determined, and a state of charge dynamic equation is constructed based on the state of charge dynamics relationship to describe the change law of battery energy with the charging and discharging process. The state of charge dynamic equation is used to determine the upper and lower limits of the state of charge and the terminal state of charge corresponding to the battery. Then, the state of charge deviation variable is determined based on the upper and lower limits of the state of charge and the terminal state of charge.

[0036] Based on the state of charge deviation variable, a charge-discharge mutual exclusion relationship corresponding to the physical operating conditions is determined, and a charging state variable and a discharging state variable are determined based on the charge-discharge mutual exclusion relationship. This is used to determine the charge-discharge power information corresponding to the physical operating conditions, including the upper limit of charging power, the lower limit of charging power, the upper limit of discharging power, and the lower limit of discharging power. The charge-discharge power information is then bound to the charging state variable and the discharging state variable to obtain a binding result.

[0037] Based on the constraint matrix, the upper and lower limits of the soft constraints corresponding to the net power of the system are determined, and the net power of the system is determined based on the algebraic sum of the charging and discharging power corresponding to each electric vehicle. The target constraint conditions are determined based on the net power of the system, the upper and lower limits of the soft constraints, and the binding result.

[0038] Optionally, the step of issuing the charging and discharging power commands in the charging and discharging power trajectory to the corresponding charging facilities for execution through the execution unit in the aggregation scheduling layer, and collecting feedback information including power values ​​and vehicle state of charge during the execution process, so that the data processing unit can adjust the vehicle-to-grid interaction based on the feedback information, includes:

[0039] The execution unit in the aggregation scheduling layer sends the charging and discharging power command corresponding to the current scheduling time in the charging and discharging power trajectory to the corresponding charging facility, so that the charging facility can perform charging or discharging operations on the electric vehicle based on the charging and discharging power command, and collect feedback information from the charging facility and the electric vehicle during the execution process; the feedback information includes the actual measured charging and discharging power value and the vehicle's state of charge at the next moment;

[0040] The execution unit is invoked to send the actual power value and the feedback information back to the data processing unit, so that the data processing unit can use a preset state update function and update and correct the electric vehicle state parameter set of the electric vehicle based on the actual power value and the feedback information to obtain the corrected result, so that the data processing unit can make vehicle-to-grid interaction adjustments based on the corrected result.

[0041] Secondly, this application provides an interactive regulation device for electric vehicles and the power grid, applied to a three-tier architecture including a user execution layer, a power grid control layer, and an aggregation and scheduling layer, comprising:

[0042] The state parameter set generation module is used to collect user information and vehicle information corresponding to each electric vehicle through the user execution layer, so as to generate an electric vehicle state parameter set based on the user information and vehicle information.

[0043] The constraint matrix generation module is used to collect state data and demand data of the power grid operation through the power grid control layer, generate a power grid state set based on the state data and the demand data, and generate a constraint matrix based on the power grid state set and the preset line power.

[0044] An interactive link establishment module is used to establish a data interactive link using a preset communication unit, so that the three-level architecture can upload the electric vehicle state parameter set, the power grid state set, the demand vector, and the constraint matrix to the aggregation scheduling layer through the data interactive link; the demand vector is a vector generated based on the power grid's adjustment demand for the electric vehicle;

[0045] The capacity resource pool determination module is used to call the aggregation scheduling layer and generate a station-level resource set based on the charging station information, and aggregate the power adjustable range of each electric vehicle to obtain the capacity resource pool.

[0046] The dataset determination module is used to process the electric vehicle state parameter set, the power grid state set, the station-level resource set and the capacity resource pool through the data processing unit in the aggregation scheduling layer to obtain the dataset to be processed. Then, it uses a mixed integer programming algorithm and based on preset decision variables, objective function, demand vector and constraint matrix to determine the charging and discharging power trajectory of the dataset to be processed within a preset rolling prediction window.

[0047] The feedback information generation module is used to send the charging and discharging power commands in the charging and discharging power trajectory to the corresponding charging facilities for execution through the execution unit in the aggregation scheduling layer, and to collect feedback information, including power values ​​and vehicle charge status, corresponding to the execution process, so that the data processing unit can make vehicle-to-grid interaction adjustments based on the feedback information.

[0048] Thirdly, this application provides an electronic device, comprising:

[0049] Memory, used to store computer programs;

[0050] A processor is used to execute the computer program to implement the aforementioned method for the interactive regulation of electric vehicles and the power grid.

[0051] Fourthly, this application provides a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned method for the interactive regulation of electric vehicles and the power grid.

[0052] As can be seen from the above, before performing interactive regulation between electric vehicles and the power grid, this application needs to collect user information and vehicle information corresponding to each electric vehicle through the user execution layer, so as to generate an electric vehicle state parameter set based on the user information and vehicle information; collect the state data and demand data of the power grid operation through the power grid control layer, so as to generate a power grid state set based on the state data and demand data, and generate a constraint matrix based on the power grid state set and the preset line power; establish a data interaction link using a preset communication unit, so that the three-level architecture can upload the electric vehicle state parameter set, the power grid state set, the demand vector and the constraint matrix to the aggregation scheduling layer through the data interaction link; the demand vector is a vector generated based on the power grid's regulation demand for electric vehicles; and call the aggregation scheduling layer and based on the charging station The system generates a station-level resource set and aggregates the adjustable power ranges of each electric vehicle to obtain a capacity resource pool. The data processing unit in the aggregation scheduling layer processes the electric vehicle state parameter set, the power grid state set, the station-level resource set, and the capacity resource pool to obtain a dataset to be processed. Then, a mixed-integer programming algorithm is used, based on preset decision variables, objective functions, demand vectors, and constraint matrices, to determine the charging and discharging power trajectory of the dataset within a preset rolling prediction window. The execution unit in the aggregation scheduling layer sends the charging and discharging power commands from the trajectory to the corresponding charging facilities for execution, and collects feedback information, including power values ​​and vehicle state of charge, during the execution process. This feedback information allows the data processing unit to adjust vehicle-grid interaction accordingly.

[0053] Therefore, this application first needs to collect user information and vehicle information corresponding to each electric vehicle through the user execution layer, so as to generate an electric vehicle state parameter set based on the user information and vehicle information; secondly, it needs to collect the state data and demand data of the power grid operation through the power grid control layer, so as to generate a power grid state set based on the state data and demand data, and generate a constraint matrix based on the power grid state set and the preset line power; then, it needs to establish a data interaction link using a preset communication unit, so that the three-level architecture can upload the electric vehicle state parameter set, the power grid state set, the demand vector and the constraint matrix to the aggregation scheduling layer through the data interaction link; subsequently, it calls the aggregation scheduling layer and generates a station-level resource set based on the charging station information, and performs resource allocation for each electric vehicle. The adjustable power ranges are aggregated to obtain a capacity resource pool. The data processing unit in the aggregation scheduling layer processes the electric vehicle state parameter set, the power grid state set, the station-level resource set, and the capacity resource pool to obtain a dataset to be processed. Then, a mixed-integer programming algorithm is used, based on preset decision variables, objective function, demand vector, and constraint matrix, to determine the charging and discharging power trajectory of the dataset within a preset rolling prediction window. Finally, the execution unit in the aggregation scheduling layer sends the charging and discharging power commands from the charging and discharging power trajectory to the corresponding charging facilities for execution, and collects feedback information, including power values ​​and vehicle state of charge, during the execution process. This allows the data processing unit to adjust vehicle-grid interaction based on the feedback information. In this way, the efficiency of interaction adjustment between electric vehicles and the power grid is improved, thereby enhancing the safety of the production process. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0055] Figure 1 This is a flowchart of a method for the interactive regulation of an electric vehicle and a power grid disclosed in this application;

[0056] Figure 2 This is a schematic diagram of a specific system hierarchy architecture for the interactive regulation of electric vehicles and the power grid disclosed in this application;

[0057] Figure 3 This is a schematic diagram of a specific interactive regulation process between an electric vehicle and the power grid disclosed in this application;

[0058] Figure 4 This application discloses a specific schematic diagram of the system net power curve and line constraints during scheduling-free optimization.

[0059] Figure 5 This application discloses a specific schematic diagram of the system net power curve and line constraints with and without scheduling optimization.

[0060] Figure 6 This is a schematic diagram of a specific EV47 power command without scheduling optimization disclosed in this application;

[0061] Figure 7 This application discloses a specific graph showing the EV47 soc variation during unscheduled optimization.

[0062] Figure 8 This application discloses a specific EV47 power command diagram with scheduling optimization.

[0063] Figure 9 This is a schematic diagram of the EV47 SoC variation under specific scheduling optimization disclosed in this application;

[0064] Figure 10 This is a schematic diagram of the structure of an electric vehicle and power grid interaction regulation device disclosed in this application;

[0065] Figure 11 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] Currently, with the rapid growth of electric vehicle ownership, the need for interaction between electric vehicles and the power grid is becoming increasingly prominent. On the one hand, large-scale disorderly charging of electric vehicles may lead to problems such as widening the peak-valley load difference and line overload, affecting the safe and stable operation of the power grid. On the other hand, as a distributed energy storage resource, electric vehicles have the potential to participate in grid regulation, assisting the power grid in achieving goals such as peak shaving and valley filling, and absorbing renewable energy. Therefore, this application provides a method for the interactive regulation of electric vehicles and the power grid, which can improve the efficiency of interactive regulation between electric vehicles and the power grid.

[0068] See Figure 1 As shown, this embodiment of the invention discloses a method for the interactive regulation of electric vehicles and the power grid, applied to a three-tier architecture including a user execution layer, a power grid control layer, and an aggregation scheduling layer, comprising:

[0069] Step S11: Collect user information and vehicle information corresponding to each electric vehicle through the user execution layer, and generate an electric vehicle state parameter set based on the user information and the vehicle information.

[0070] In this embodiment, the system hierarchy architecture for the interactive regulation of electric vehicles and the power grid is shown in the diagram below. Figure 2 As shown, and the schematic diagram of the interaction and regulation process between electric vehicles and the power grid is as follows. Figure 3 As shown. First, this application embodiment needs to obtain user and electric vehicle (EV) data to construct the EV user execution layer. Specifically, this application embodiment needs to process data for each electric vehicle... Collect user ID, vehicle parking time window, charging and discharging demand, charging and discharging price threshold and power range, and collect vehicle state of charge. Battery capacity Charge / discharge support types, and based on available time masks. , Determine the set of schedulable times This maps discrete user-side demands to a set of schedulable capabilities in the optimization model, thereby generating the EV state parameter set:

[0071] ;

[0072] Specifically, the user execution layer collects user information and vehicle information corresponding to each electric vehicle to generate an electric vehicle state parameter set. This can include: collecting user information and vehicle information corresponding to each electric vehicle through the user execution layer; user information includes user identification, the expected parking time corresponding to the charging facility the vehicle plans to access, charging and discharging energy demand, charging and discharging price sensitivity threshold, and charging and discharging power range; vehicle information includes vehicle identification, the charging and discharging type of the vehicle's power battery, the real-time state of charge of the power battery, and the total capacity of the power battery; determining a first time availability mask and a second time availability mask corresponding to the vehicle information, and applying these parameters to the electric vehicle state parameters. The first and second time availability masks in the data set are parsed to obtain the parsing results, which are used to determine the set of schedulable times for the electric vehicle to receive charge and discharge control commands in all future scheduling periods. The first time availability mask is used to identify whether charging is allowed in each time granularity; the second time availability mask is used to identify whether discharging is allowed in each time granularity. Based on user information, vehicle information, and the set of schedulable times, a set of electric vehicle state parameters reflecting the schedulable potential of the electric vehicle is constructed. The electric vehicle state parameter set includes real-time state of charge, total battery capacity, maximum charging power, minimum charging power, maximum discharging power, and minimum discharging power.

[0073] Step S12: Collect the status data and demand data of the power grid operation through the power grid control layer, generate a power grid status set based on the status data and the demand data, and generate a constraint matrix based on the power grid status set and the preset line power.

[0074] In this embodiment, the present application requires the acquisition of power grid data to construct a power grid control layer. Specifically, this embodiment can collect real-time data on voltage, current, line power, line capacity, power quality indicators, and time-of-use charging prices. Grid connection price Grid-side operating parameters are used to form a grid state set:

[0075] ;

[0076] Subsequently, the embodiments of this application need to be combined with renewable energy output forecasting. Generate line power inequality constraint matrix with grid constraints This allows the power grid security constraints to be explicitly represented mathematically in the scheduling optimization, thus enabling the power grid security constraints, price information, and regulation demand to be directly injected into the optimization solution process in mathematical form.

[0077] Specifically, the power grid control layer collects state and demand data during power grid operation. Based on this state and demand data, a power grid state set is generated. Then, a constraint matrix is ​​generated based on this power grid state set and a preset line power rating. This process can include: real-time collection of state data characterizing the power grid's operational status by the power grid control layer; acquisition of demand data including time-of-use charging prices published by the electricity market and on-grid prices for power fed back to the grid by electric vehicles; and generation of a power grid state set reflecting the real-time operating status of the power grid and market prices based on the state and demand data. The state data includes bus voltage, line current, transmission line active power, current carrying capacity limits for each line, and transformer current carrying capacity limits. The system includes power quality indicators, such as harmonic distortion rate; it forecasts the renewable energy output from wind and solar power generation in a specific future period, obtains forecast data, and determines grid security transmission limits based on the grid topology. Based on the forecast data and grid security transmission limits, it generates dispatch demand instructions describing peak shaving and reserve capacity services, and transforms these instructions into expected upper and lower limits of interactive power at connection points. Connection points are the points between the grid and electric vehicles. The grid control layer processes the grid state set, forecast data, and expected upper and lower limits to obtain a constraint matrix. The constraint matrix is ​​expressed as a system of linear inequalities.

[0078] Step S13: Establish a data interaction link using a preset communication unit so that the three-level architecture can upload the electric vehicle state parameter set, the power grid state set, the demand vector and the constraint matrix to the aggregation scheduling layer through the data interaction link; the demand vector is a vector generated based on the power grid's adjustment demand for the electric vehicle.

[0079] In this embodiment, the present application requires the construction of a communication unit to enable data interaction between different layers, and to establish an uplink between the EV user terminal and the dispatch center cloud to... The data is uploaded in real time to the dispatching layer, and then transmitted through the communication link between the power grid monitoring system and the dispatching center. and multiple adjustment demand vectors The optimized power command, obtained through optimization, is uploaded to the scheduling layer and transmitted via the communication link between the scheduling center and each charging pile. The data is then sent to the corresponding charging pile, thereby achieving two-way closed-loop data interaction at the information flow level, from the EV user execution layer to the scheduling layer and the power grid control layer.

[0080] In this embodiment, the communication unit is used to establish a communication link between the EV user terminal and the dispatch center cloud; a local communication link between the vehicle and the charging pile; and a remote communication link between the charging pile and the dispatch center cloud server. Therefore, this embodiment requires the communication unit to establish the following data streams:

[0081] EV User Execution Layer → Scheduling Layer: Upload ;

[0082] Power grid control layer → dispatch layer: Upload Line constraints, price, and multiple adjustable demand vectors ;

[0083] Scheduling layer → EV execution layer: Issue optimal power command ;

[0084] This forms a complete information flow closed loop, providing a continuously updated data foundation for rolling time-domain control.

[0085] Specifically, a data interaction link is established using a pre-defined communication unit so that the three-tier architecture can upload the electric vehicle state parameter set, power grid state set, demand vector, and constraint matrix to the aggregation scheduling layer via the data interaction link. This can include: establishing a first communication link between the electric vehicle user terminal and the dispatch center cloud server using a pre-defined communication unit; establishing a second communication link between the electric vehicle and the charging pile using a pre-defined communication unit; and then establishing a third communication link between the charging pile and the dispatch center cloud server using a pre-defined communication unit, thereby constructing a data interaction link based on the first, second, and third communication links; calling the three-tier architecture and uploading the electric vehicle state parameter set, power grid state set, demand vector, and constraint matrix to the aggregation scheduling layer via the data interaction link; wherein, the demand vector includes power demand, time parameters, and adjustment coefficients.

[0086] Step S14: Invoke the aggregation scheduling layer and generate a station-level resource set based on the charging station information, and aggregate the adjustable power range of each electric vehicle to obtain a capacity resource pool.

[0087] In this embodiment, the present application requires obtaining charging pile data of electric vehicle charging stations to construct charging pile units of the aggregation scheduling layer. Then, for each charging pile in the station, its maximum / minimum charging / discharging power, the upper limit of the number of connected EVs, and the connection topology are collected to generate a station-level resource set.

[0088] ;

[0089] In this embodiment, the power ranges from multiple EVs need to be considered. , Aggregation is performed at the station level to form an equivalent adjustable power capacity facing the grid side.

[0090] Specifically, the process involves calling the aggregation scheduling layer to generate a station-level resource set based on charging station information, and then aggregating the adjustable power ranges of each electric vehicle to obtain a capacity resource pool. This can include: calling the aggregation scheduling layer to obtain the corresponding charging station information from the charging piles, generating a station-level resource set based on the charging station information, and then aggregating the adjustable power ranges of each electric vehicle based on the station-level resource set and the electric vehicle status parameter set to obtain a capacity resource pool. The capacity resource pool is used to represent the overall schedulable power range. The station-level resource set includes the resource distribution and availability status of each charging station.

[0091] Step S15: The data processing unit in the aggregation scheduling layer processes the electric vehicle state parameter set, the power grid state set, the station-level resource set, and the capacity resource pool to obtain a dataset to be processed. Then, a mixed integer programming algorithm is used to determine the charging and discharging power trajectory of the dataset to be processed within a preset rolling prediction window based on preset decision variables, objective function, demand vector, and constraint matrix.

[0092] In this embodiment, the present application embodiment processes and records data between layers by constructing data units, so as to transfer the data obtained through the communication unit. , and Data cleaning, filtering, and time alignment are performed to create a unified dataset for optimization solutions. The original data and optimization results are stored in a rolling manner over time, serving as a historical sample library for the prediction and optimization solution modules in rolling time-domain control. Subsequently, an optimization solution unit is constructed based on a mixed-integer programming algorithm, using weights, objective functions, and constraints as inputs to obtain the optimal power trajectory within the rolling prediction window. In each rolling window... The above shows the charging and discharging power of each EV at each time step. , and their corresponding binary state variables , For the decision variables, construct a multi-objective weighted objective function:

[0093] ;

[0094] Subsequently, SOC dynamic constraints, SOC boundary and terminal target constraints, charge-discharge mutual exclusion constraints, time mask constraints, power and switch binding constraints, power smoothness constraints, and system net power soft constraints were applied. The constrained set The solution is then obtained to satisfy multiple adjustment requirements. The optimal power trajectory of the EV cluster; where the system net power curve and line constraint diagram without scheduling optimization are shown in the figure. Figure 4 As shown, the system net power curve and line constraint diagram with and without scheduling optimization are as follows: Figure 5 As shown.

[0095] Specifically, the data processing unit in the aggregation scheduling layer processes the electric vehicle state parameter set, the power grid state set, the station-level resource set, and the capacity resource pool to obtain the dataset to be processed. Then, a mixed-integer programming algorithm is used, based on preset decision variables, objective function, demand vector, and constraint matrix, to determine the charging and discharging power trajectory of the dataset to be processed within a preset rolling prediction window. This may include: using the data processing unit in the aggregation scheduling layer to perform heterogeneous data cleaning and outlier removal on the electric vehicle state parameter set, power grid state set, station-level resource set, and capacity resource pool to obtain the data to be filtered, and then filtering the data to be filtered. Information is extracted to obtain data to be aligned; time alignment is performed on the data to be aligned based on a preset timestamp benchmark to obtain the dataset to be processed; then, decision variables are defined to characterize the mutually exclusive logical relationship between charging and discharging, and an objective function including economic indicators, power smoothness indicators, and system operation safety indicators is constructed, and the target constraints are determined based on the constraint matrix and preset physical operating conditions; the mixed integer programming algorithm is used to iteratively solve the dataset to be processed within a preset rolling prediction time window based on the decision variables, objective function, target constraints, and demand vector to obtain the charging and discharging power trajectory of each electric vehicle in the future window at each scheduling time.

[0096] In this embodiment, a binary variable is defined to constrain the mutual exclusion relationship between charging and discharging; subsequently, a multi-objective weighted objective function is constructed to integrate economic, smooth, and safety objectives; and constraints are set to ensure the physical feasibility of the scheduling results and the safety of system operation.

[0097] In one specific implementation, a binary variable is defined, including: a charging / discharging state binary variable: , ,in, Used to represent time steps electric vehicles The charging state is a binary variable. Used to represent time steps electric vehicles The discharge state is a binary variable.

[0098] Furthermore, this application embodiment requires the construction of a multi-objective weighted objective function, which consists of the following four functions:

[0099] 1) SOC deviation item: ,in, The weight of the SOC deviation term. For electric vehicles For electric vehicles The absolute value of the terminal SOC deviation;

[0100] 2) Economic cost item:

[0101] ;

[0102] in, As the weight of the economic cost item, For the set of time steps, For time steps electric vehicles The charging power, For time step, for Time step Electricity price for charging For time steps electric vehicles The discharge power, For time steps hourly discharge electricity price For electric vehicles The depreciation cost of discharged batteries;

[0103] 3) Power smoothness term: ,in, For the power smoothness term weight, For time steps electric vehicles Smoothing auxiliary variables

[0104] 4) Penalty for slack line constraints: ,in, The weight of the soft constraint relaxation penalty term for the line. For time steps Positive relaxation variables of the system's net power soft constraint. For time steps Negative relaxation variables of the system's net power soft constraint;

[0105] It is worth mentioning that the above multi-objective weighted objective function is obtained by weighted summation of the above four functions, and is used to comprehensively balance the objectives of economy, smoothness and safety.

[0106] Specifically, an objective function is constructed that includes economic indicators, power smoothness indicators, and system operation safety indicators. Based on a constraint matrix and preset physical operating conditions, the target constraints are determined. These constraints may include: determining a penalty term for the deviation between the actual terminal state of charge (SBC) of electric vehicles and the expected value, resulting in a SBC deviation term; determining the total electricity cost of all electric vehicles within the scheduling cycle, resulting in an economic cost term; determining a power smoothness term to characterize the fluctuation of charging and discharging power in adjacent time periods based on preset smoothness auxiliary variables; and determining a line soft constraint relaxation penalty term to penalize the system's net power exceeding limits. Weights corresponding to the SBC deviation term, economic cost term, power smoothness term, and line soft constraint relaxation penalty term are determined to establish the objective function. Physical operating conditions corresponding to electric vehicles and charging piles are determined to establish the target constraints using these physical operating conditions and the constraint matrix. These physical operating conditions include charging and discharging mutual exclusion constraints, charging and discharging power upper and lower limit constraints, battery SBC dynamic constraints, and battery capacity constraints.

[0107] The constraints consist of the following seven items:

[0108] 1) SOC dynamic constraints:

[0109] ;

[0110] in, For time steps electric vehicles The state of charge, For time steps electric vehicles The state of charge, For electric vehicles Charging efficiency, For electric vehicles The discharge efficiency, For electric vehicles Battery capacity;

[0111] 2) SOC boundary and terminal target constraints: , , , , ,in, For electric vehicles SOC lower limit, For electric vehicles SOC upper limit, The maximum time step of the rolling prediction window. For electric vehicles The target OC;

[0112] 3) Charge / discharge mutual exclusion constraint: ;

[0113] 4) Time mask constraint: , ,in, For time steps electric vehicles Charging time mask, For time steps electric vehicles The discharge time mask;

[0114] 5) Power and switch binding constraints: , , , ,in, For electric vehicles The lower limit of charging power, For electric vehicles The upper limit of charging power, For electric vehicles The lower limit of discharge power, For electric vehicles The upper limit of discharge power;

[0115] 6) Power smoothness constraint: , ,in, For the electric vehicle at time step "t" in the previous optimization Power plan;

[0116] 7) System net power soft constraint: , , ,in, For time steps Net power of the system at that time For time steps The lower limit of the soft constraint on the net power of the system. For time steps The upper limit of the soft constraint on the system's net power;

[0117] In this embodiment, the above seven constraints are used to jointly ensure the physical feasibility of the scheduling results and the security of system operation. Furthermore, the soft constraint on system net power includes: multiple adjustment requirements, that is, through abstract modeling of three-dimensional parameters—power level, duration, and response speed—to uniformly describe the power behavior in peak shaving and valley filling, renewable energy consumption, and reserve capacity scenarios.

[0118] Specifically, determining the target constraints using various physical operating conditions and constraint matrices can include: determining the state-of-charge (POC) dynamics relationship corresponding to the physical operating conditions, and constructing a POC dynamic equation based on the POC dynamics relationship to describe the change of battery energy with the charging and discharging process. This POC dynamic equation is used to determine the upper and lower limits of the POC and the final POC corresponding to the battery. Then, based on the upper and lower limits of the POC and the final POC, the POC deviation variable is determined. Based on the POC deviation variable, the charging and discharging mutual exclusion relationship corresponding to the physical operating conditions is determined, and based on the charging and discharging mutual exclusion relationship, the charging state variable and discharging state variable are determined. This determines the charging and discharging power information corresponding to the physical operating conditions, including the upper limit of charging power, the lower limit of charging power, the upper limit of discharging power, and the lower limit of discharging power. The charging and discharging power information is then bound to the charging and discharging state variables to obtain the binding result. Based on the constraint matrix, the upper and lower limits of the soft constraints corresponding to the system net power are determined, and the system net power is determined based on the algebraic sum of the charging and discharging power corresponding to each electric vehicle. Finally, the target constraints are determined based on the system net power, the upper and lower limits of the soft constraints, and the binding result.

[0119] Step S16: The execution unit in the aggregation scheduling layer sends the charging and discharging power command in the charging and discharging power trajectory to the corresponding charging facility for execution, and collects feedback information, including power value and vehicle charge state, corresponding to the execution process, so that the data processing unit can make vehicle-to-grid interaction adjustments based on the feedback information.

[0120] In this embodiment, the present application requires the construction of an execution unit, which is used to send the power command obtained from the optimization solution to the charging pile for execution, that is, to... The active power reference value for a single charging pile is issued according to the EV-charging pile mapping relationship, and the actual power after execution is collected. Feedback from SOC This serves as the data update input for the next rolling cycle, thereby achieving closed-loop feedback of the execution effect.

[0121] Subsequently, a control center is constructed, consisting of data units, optimization and solution units, and execution units. The control center completes the process of retrieving data from a unified dataset within each rolling cycle. To the optimal power trajectory Then to execution feedback Mapping, implementation The centralized solution process creates a regulation service interface within the scheduling layer that can be directly invoked by the power grid side. Based on the control center, charging pile unit, communication unit, and data unit, a scheduling layer is constructed, enabling it to handle multiple regulation demand vectors from the power grid control layer. With line constraints , Mapped to EV cluster net power demand .

[0122] On the other hand, the embodiments of this application need to utilize the distributed adjustability of the EV user execution layer. Aggregates resources into a station-level schedulable resource pool to achieve unified matching between multi-objective demand on the power grid side and multi-entity supply on the EV side. Subsequently, a scheduling module is constructed based on the minimum time unit and rolling forecast window. The complete process cycle of "collecting data from the EV user execution layer and the power grid control layer once—transmitting data through the communication unit—the control center calling the optimization solution unit to solve the problem—issuing the power trajectory to each vehicle's power plan table—updating and executing the power command" is set as the minimum scheduling time unit. (In minute-level or 5-15 minute-level step sizes), and formed by aggregating several smallest time units to create a rolling prediction window of length H, at each time step From the window Inner solution However, only the current step is executed. When power commands are received, a "prediction-optimization-execution-re-optimization" scheduling framework based on Receding Horizon Control (RHC) is implemented. Through the collaboration of the EV user execution layer, scheduling layer, and grid control layer, combined with the RHC mechanism, the overall structure of the vehicle-grid interaction system is constructed and multi-adjustment optimization scheduling is performed, so that the system can meet the EV terminal SOC target, grid line safety constraints, and multi-adjustment requirements. Under the premise of this, bidirectional power exchange of the EV cluster:

[0123] ;

[0124] In this embodiment, the system is controlled within the soft constraint range of the line. This allows for a multi-objective balance between economy, power smoothness, and system safety within a unified mathematical optimization framework.

[0125] Subsequently, a control execution module is generated to issue optimal power commands to each charging pile using the scheduling control unit in the control execution module, and the feedback correction unit updates the vehicle and system status based on the execution feedback; the status recording unit is used to generate a rolling time-domain operation log for subsequent iterations.

[0126] It is worth mentioning that the execution unit can The data is sent to the charging stations to collect actual execution feedback, and the feedback information obtained is as follows: Subsequently, it will be updated to [the following] in the next cycle. This forms a rolling time-domain closed-loop mechanism of "prediction → optimization → execution → re-optimization".

[0127] Specifically, the execution unit in the aggregation scheduling layer sends the charging and discharging power commands from the charging and discharging power trajectory to the corresponding charging facilities for execution, and collects feedback information, including power values ​​and vehicle state of charge, during the execution process. This allows the data processing unit to adjust vehicle-to-grid interaction based on the feedback information. This can include: sending the charging and discharging power commands corresponding to the current scheduling time from the charging and discharging power trajectory to the corresponding charging facilities, enabling the charging facilities to perform charging or discharging operations on the electric vehicle based on the charging and discharging power commands, and collecting feedback information from the charging facilities and the electric vehicle during execution. The feedback information includes the actual measured charging and discharging power values ​​and the vehicle's state of charge at the next moment; calling the execution unit to send the actual power values ​​and feedback information back to the data processing unit, so that the data processing unit can update and correct the electric vehicle state parameter set of the electric vehicle using a preset state update function based on the actual power values ​​and feedback information, obtaining a corrected result, which the data processing unit can then use to adjust vehicle-to-grid interaction. Figure 6 Power command diagram for EV47 without scheduling optimization; Figure 7 The graph shows the change in the state of charge (SOC) of the EV47soc (a 47V electric vehicle battery) without scheduling optimization. Figure 8 The power command diagram for EV47 with scheduling optimization; Figure 9 The graph shows the changes in EV47 SoC when scheduling optimization is implemented.

[0128] In this embodiment, a control center needs to be constructed. The control center consists of a data unit, an optimization solution unit, and an execution unit, including: a communication module integrated into the data unit, used to summarize information from the EV user execution layer and the power grid control layer; an optimization solution module, the core module of the optimization solution unit, used to generate the optimal power trajectory for each EV; a control execution module, the core module of the execution unit, used to send power commands to the charging piles and collect execution feedback, and update the SOC and system status; a scheduling layer consists of the communication unit, the charging pile unit, and the control center; based on the charging and discharging grid-connected electricity price and service incentives, a 24-hour time-of-use electricity price table is established, and combined with the optimization solution module, the optimal power trajectory at the station level for future time periods is generated.

[0129] Subsequently, in this embodiment, a scheduling unit can be constructed based on the minimum time unit and the rolling prediction window. That is, the complete process cycle of "collecting data from the EV user execution layer and the power grid control layer once—transmitting data through the communication unit—the control center calling the optimization solution unit to solve the problem—issuing the power trajectory to each vehicle's power plan table—updating and executing the power command" is set as the minimum scheduling time unit. In one specific implementation, the minimum time unit is a minute-level or 15-minute-level step; the minimum scheduling unit is formed by aggregating several minimum time units to form a rolling prediction window; within each rolling cycle, prediction, optimization, command issuance, and status update operations are executed sequentially.

[0130] It is worth mentioning that the embodiments of this application utilize a scheduling unit and form a scheduling framework based on rolling time domain control (RHC) based on a data acquisition module, a prediction module, an optimization solution module, and an execution module, thereby completing a closed loop of 'prediction-optimization-execution-re-optimization'. This includes: using the prediction module to generate short-term prediction data and transmitting it to the optimization solution module; the optimization solution module combining the prediction data and the basic data processed by the data unit to solve for the optimal power trajectory; the execution module converting the optimal power trajectory into a power command, issuing and executing it; and the execution module collecting execution feedback and transmitting it to the data unit to update the system state, entering the next rolling cycle, thereby completing the closed loop.

[0131] It is worth mentioning that the prediction module includes: a time series prediction unit, used for short-term prediction based on historical power data and new energy output data; an external feature fusion unit, used to embed unstructured data such as weather and holidays into the prediction model; and an adaptive adjustment unit, used to dynamically adjust the length of the rolling prediction window according to the prediction error.

[0132] It is understood that the embodiments of this application construct the overall structure of the vehicle-to-grid interaction system through the collaboration of the EV user execution layer, the scheduling layer, and the grid control layer, combined with the RHC mechanism. This includes: using the EV user execution layer to send user information and EV information to the control center of the scheduling layer via a communication unit; the grid control layer sending grid information and regulation requirements to the control center of the scheduling layer via a communication unit; the control center of the scheduling layer forming a schedulable resource pool based on the received information from the EV user execution layer and the grid control layer, and performing group-based optimized scheduling; the grid control layer providing electricity price data, line power constraint data, and multiple regulation requirement data; the RHC mechanism real-time rolling monitoring of information flow and energy flow through the scheduling framework: the energy flow is the bidirectional power exchange between the grid and EVs, and the information flow covers EV-side status data, electricity price data, ancillary service requirement data, line capacity data, and forecast data; the scheduling layer, as the interface, transforms the multi-objective requirements of the grid side into executable power trajectories at the station domain level.

[0133] As can be seen from the above, the embodiments of this application first need to collect user information and vehicle information corresponding to each electric vehicle through the user execution layer, so as to generate an electric vehicle state parameter set based on the user information and vehicle information; secondly, the power grid control layer collects the state data and demand data of the power grid operation, so as to generate a power grid state set based on the state data and demand data, and generate a constraint matrix based on the power grid state set and the preset line power; then, a data interaction link is established using a preset communication unit, so that the three-level architecture can upload the electric vehicle state parameter set, the power grid state set, the demand vector and the constraint matrix to the aggregation scheduling layer through the data interaction link; subsequently, the aggregation scheduling layer is called and a station-level resource set is generated based on the charging station information, and the electric vehicle state parameter set, the power grid state set, the demand vector and the constraint matrix are generated based on the preset line power; then, the aggregation scheduling layer is called and a station-level resource set is generated based on the charging station information, and the electric vehicle state parameter set, the power grid state ... The adjustable power range of vehicles is aggregated to obtain a capacity resource pool. The data processing unit in the aggregation scheduling layer processes the electric vehicle state parameter set, the power grid state set, the station-level resource set, and the capacity resource pool to obtain a dataset to be processed. Then, a mixed-integer programming algorithm is used, based on preset decision variables, objective function, demand vector, and constraint matrix, to determine the charging and discharging power trajectory of the dataset within a preset rolling prediction window. Finally, the execution unit in the aggregation scheduling layer issues charging and discharging power commands from the charging and discharging power trajectory to the corresponding charging facilities for execution, and collects feedback information, including power values ​​and vehicle state of charge, during the execution process. This allows the data processing unit to adjust vehicle-grid interaction based on the feedback information. In this way, the efficiency of interaction and regulation between electric vehicles and the power grid is improved, thereby enhancing the safety of the production process.

[0134] Accordingly, see Figure 10 As shown, this application also provides an interactive regulation device for electric vehicles and the power grid, applied to a three-tier architecture including a user execution layer, a power grid control layer, and an aggregation scheduling layer, comprising:

[0135] The state parameter set generation module 11 is used to collect user information and vehicle information corresponding to each electric vehicle through the user execution layer, so as to generate an electric vehicle state parameter set based on the user information and vehicle information.

[0136] The constraint matrix generation module 12 is used to collect state data and demand data of the power grid operation through the power grid control layer, generate a power grid state set based on the state data and the demand data, and generate a constraint matrix based on the power grid state set and the preset line power.

[0137] The interaction link establishment module 13 is used to establish a data interaction link using a preset communication unit, so that the three-level architecture can upload the electric vehicle state parameter set, the power grid state set, the demand vector and the constraint matrix to the aggregation scheduling layer through the data interaction link; the demand vector is a vector generated based on the power grid's adjustment demand for the electric vehicle;

[0138] The capacity resource pool determination module 14 is used to call the aggregation scheduling layer and generate a station-level resource set based on the charging station information, and aggregate the power adjustable range of each electric vehicle to obtain the capacity resource pool.

[0139] The dataset determination module 15 is used to process the electric vehicle state parameter set, the power grid state set, the station-level resource set and the capacity resource pool through the data processing unit in the aggregation scheduling layer to obtain the dataset to be processed. Then, it uses a mixed integer programming algorithm and based on preset decision variables, objective function, demand vector and constraint matrix to determine the charging and discharging power trajectory of the dataset to be processed within a preset rolling prediction window.

[0140] The feedback information generation module 16 is used to send the charging and discharging power command in the charging and discharging power trajectory to the corresponding charging facility for execution through the execution unit in the aggregation scheduling layer, and to collect feedback information including power value and vehicle charge state corresponding to the execution process, so that the data processing unit can make vehicle-to-grid interaction adjustments based on the feedback information.

[0141] In some specific embodiments, the state parameter set generation module 11 may specifically include:

[0142] The user information acquisition unit is used to collect user information and vehicle information corresponding to each electric vehicle through the user execution layer. The user information includes user identity identifier, expected parking time corresponding to the charging facility to which the vehicle is planned to access, charging and discharging energy demand, charging and discharging price sensitivity threshold, and charging and discharging power range. The vehicle information includes vehicle identity identifier, charging and discharging type of vehicle power battery, real-time state of charge of power battery, and total capacity of power battery.

[0143] The parsing result generation unit is used to determine a first time availability mask and a second time availability mask corresponding to the vehicle information, parse the first time availability mask and the second time availability mask in the electric vehicle state parameter set to obtain a parsing result, and determine the set of schedulable times for the electric vehicle to receive charging and discharging control commands in all future scheduling periods based on the parsing result; wherein, the first time availability mask is a mask used to identify whether charging is allowed in each time granularity; and the second time availability mask is a mask used to identify whether discharging is allowed in each time granularity.

[0144] The state parameter set generation subunit is used to construct an electric vehicle state parameter set reflecting the schedulable potential of electric vehicles based on the user information, the vehicle information and the schedulable time set; the electric vehicle state parameter set includes real-time state of charge, total battery capacity, maximum charging power, minimum charging power, maximum discharging power and minimum discharging power.

[0145] In some specific embodiments, the constraint matrix generation module 12 may specifically include:

[0146] The status data acquisition unit is used to collect status data characterizing the grid operation status in real time through the grid control layer, and then acquire demand data including time-of-use charging prices published by the electricity market and on-grid prices for power fed back to the grid by electric vehicles. Based on the status data and the demand data, a grid status set reflecting the real-time operation status of the grid and market prices is generated. The status data includes bus voltage, line current, transmission line active power, current carrying capacity limits for each line, transformer current carrying capacity limits, and power quality indicators, including harmonic distortion rate.

[0147] The prediction data generation unit is used to predict the renewable energy output of wind power and photovoltaic power generation in a specific future period, obtain prediction data, and determine the grid security transmission limit based on the grid topology. Based on the prediction data and the grid security transmission limit, it generates a scheduling demand instruction describing peak shaving and valley filling and reserve capacity service, and converts the scheduling demand instruction into the expected upper limit and expected lower limit of the interactive power at the connection point; the connection point is the connection point between the power grid and the electric vehicle.

[0148] The constraint matrix generation sub-unit is used to process the power grid state set, the predicted data, the expected upper limit value and the expected lower limit value through the power grid control layer to obtain the constraint matrix; the constraint matrix is ​​expressed in the form of a system of linear inequalities.

[0149] In some specific embodiments, the interaction link establishment module 13 may specifically include:

[0150] A communication link establishment unit is used to establish a first communication link between the user terminal of the electric vehicle and the cloud server of the dispatch center using a preset communication unit, and to establish a second communication link between the electric vehicle and the charging pile using the preset communication unit, and then to establish a third communication link between the charging pile and the cloud server of the dispatch center using the preset communication unit, so as to construct a data interaction link based on the first communication link, the second communication link and the third communication link;

[0151] The constraint matrix uploading unit is used to call the three-level architecture and upload the electric vehicle state parameter set, the power grid state set, the demand vector, and the constraint matrix to the aggregation scheduling layer through the data interaction link; wherein, the demand vector includes power demand, time parameters, and adjustment coefficients.

[0152] In some specific embodiments, the capacity resource pool determination module 14 may specifically include:

[0153] The station-level resource set generation unit is used to call the aggregation scheduling layer to obtain the corresponding charging station information from the charging piles, generate a station-level resource set based on the charging station information, and then perform an aggregation operation on the power adjustable range of each electric vehicle based on the station-level resource set and the electric vehicle status parameter set to obtain a capacity resource pool; the capacity resource pool is used to characterize the overall schedulable power range; the station-level resource set includes the resource distribution and availability status of each charging station.

[0154] In some specific embodiments, the dataset determination module 15 may specifically include:

[0155] The data to be filtered unit is used in the aggregation scheduling layer to use the data processing unit to perform heterogeneous data cleaning and outlier removal on the electric vehicle status parameter set, the power grid status set, the station-level resource set and the capacity resource pool to obtain the data to be filtered, and to filter and extract information from the data to be filtered to obtain the data to be aligned.

[0156] The decision variable generation unit is used to perform time alignment operation on the data to be aligned based on a preset timestamp benchmark to obtain the dataset to be processed. Then, it defines decision variables to characterize the mutual exclusion logic relationship between charging and discharging, and constructs an objective function including economic indicators, power smoothness indicators and system operation safety indicators. The unit also determines the target constraint conditions based on the constraint matrix and preset physical operating conditions.

[0157] The charging and discharging power trajectory generation unit is used to iteratively solve the dataset to be processed within a preset rolling prediction time window using a mixed integer programming algorithm based on the decision variables, the objective function, the objective constraints, and the demand vector, to obtain the charging and discharging power trajectory of each electric vehicle in the future window at each scheduling time.

[0158] In some specific embodiments, the dataset determination module 15 may specifically include:

[0159] The state of charge deviation term determination unit is used to determine the deviation between the actual terminal state of charge of electric vehicles and the expected value for punishment, thereby obtaining the state of charge deviation term. It also determines the total electricity cost of all electric vehicles within the scheduling cycle, thereby obtaining the economic cost term. Then, based on the preset smoothness auxiliary variable, it determines the power smoothness term used to characterize the fluctuation of charging and discharging power in adjacent time periods, and determines the line soft constraint relaxation penalty term used to punish the over-limit behavior of the system net power.

[0160] The objective function determination unit is used to determine the weights corresponding to the state of charge deviation term, the economic cost term, the power smoothness term, and the line soft constraint relaxation penalty term, respectively, so as to determine the objective function based on the state of charge deviation term, the economic cost term, the power smoothness term, and the line soft constraint relaxation penalty term and their corresponding weights.

[0161] The constraint determination unit is used to determine the physical operating conditions corresponding to the electric vehicle and the charging pile respectively, so as to determine the target constraint conditions by using each of the physical operating conditions and the constraint matrix; the physical operating conditions include charging and discharging mutual exclusion constraint conditions, charging and discharging power upper and lower limit constraint conditions, battery state of charge dynamic constraint conditions, and battery capacity constraint conditions.

[0162] In some specific embodiments, the dataset determination module 15 may specifically include:

[0163] The dynamic relationship determination unit is used to determine the state of charge dynamic relationship corresponding to the physical operating conditions, and to construct a state of charge dynamic equation based on the state of charge dynamic relationship to describe the change law of battery energy with the charging and discharging process. The state of charge dynamic equation is used to determine the upper and lower limits of the state of charge and the terminal state of charge corresponding to the battery. Then, the state of charge deviation variable is determined based on the upper and lower limits of the state of charge and the terminal state of charge.

[0164] The binding result determination unit is used to determine the charge-discharge mutual exclusion relationship corresponding to the physical operating conditions based on the state of charge deviation variable, and to determine the charging state variable and the discharging state variable based on the charge-discharge mutual exclusion relationship, so as to determine the charge-discharge power information including the upper limit of charging power, the lower limit of charging power, the upper limit of discharging power, and the lower limit of discharging power corresponding to the physical operating conditions, and to bind the charge-discharge power information with the charging state variable and the discharging state variable to obtain the binding result;

[0165] The system net power determination unit is used to determine the upper limit and lower limit of soft constraints corresponding to the system net power based on the constraint matrix, determine the system net power based on the algebraic sum of the charging and discharging power corresponding to each electric vehicle, and determine the target constraint conditions based on the system net power, the upper limit of soft constraints, the lower limit of soft constraints and the binding result.

[0166] In some specific embodiments, the feedback information generation module 16 may specifically include:

[0167] The feedback information generation unit is used to send the charging and discharging power command corresponding to the current scheduling time in the charging and discharging power trajectory to the corresponding charging facility through the execution unit in the aggregation scheduling layer, so that the charging facility can perform charging or discharging operations on the electric vehicle based on the charging and discharging power command, and collect feedback information from the charging facility and the electric vehicle during the execution process; the feedback information includes the actual measured charging and discharging power value and the vehicle's state of charge at the next moment;

[0168] The feedback information transmission unit is used to call the execution unit to transmit the actual power value and the feedback information back to the data processing unit, so that the data processing unit can use a preset state update function and update and correct the electric vehicle state parameter set of the electric vehicle based on the actual power value and the feedback information to obtain the corrected result, so that the data processing unit can make vehicle-to-grid interaction adjustments based on the corrected result.

[0169] Furthermore, embodiments of this application also disclose an electronic device, Figure 11 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the electric vehicle and power grid interaction regulation method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0170] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0171] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0172] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the electric vehicle and power grid interaction regulation method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0173] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for the interaction and regulation of electric vehicles and the power grid. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0174] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0175] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0176] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0177] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0178] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for interactive regulation between electric vehicles and the power grid, characterized in that, Applied to a three-tier architecture comprising the user execution layer, the power grid control layer, and the aggregation scheduling layer, including: The user execution layer collects user information and vehicle information corresponding to each electric vehicle, and generates a set of electric vehicle state parameters based on the user information and vehicle information. The power grid control layer collects status data and demand data during power grid operation, generates a power grid status set based on the status data and demand data, and generates a constraint matrix based on the power grid status set and preset line power. A data interaction link is established using a preset communication unit so that the three-level architecture can upload the electric vehicle state parameter set, the power grid state set, the demand vector, and the constraint matrix to the aggregation scheduling layer through the data interaction link; the demand vector is a vector generated based on the power grid's adjustment demand for the electric vehicle; The aggregation scheduling layer is invoked and a station-level resource set is generated based on the charging station information. The adjustable power range of each electric vehicle is aggregated to obtain a capacity resource pool. The data processing unit in the aggregation scheduling layer processes the electric vehicle state parameter set, the power grid state set, the station-level resource set, and the capacity resource pool to obtain a dataset to be processed. Then, a mixed integer programming algorithm is used to determine the charging and discharging power trajectory of the dataset to be processed within a preset rolling prediction window based on preset decision variables, objective function, demand vector, and constraint matrix. The execution unit in the aggregation scheduling layer sends the charging and discharging power commands in the charging and discharging power trajectory to the corresponding charging facilities for execution, and collects feedback information, including power values ​​and vehicle charge status, during the execution process, so that the data processing unit can make adjustments to vehicle-to-grid interaction based on the feedback information.

2. The method for interactive regulation of electric vehicles and power grids according to claim 1, characterized in that, The step of collecting user information and vehicle information corresponding to each electric vehicle through the user execution layer, and generating an electric vehicle state parameter set based on the user information and vehicle information, includes: The user execution layer collects user information and vehicle information corresponding to each electric vehicle. The user information includes user identification, the expected parking time corresponding to the charging facility that the vehicle plans to access, charging and discharging energy requirements, charging and discharging price sensitivity threshold, and charging and discharging power range. The vehicle information includes vehicle identification, charging and discharging type of vehicle power battery, real-time state of charge of power battery, and total capacity of power battery. A first time availability mask and a second time availability mask corresponding to the vehicle information are determined. The first time availability mask and the second time availability mask in the electric vehicle state parameter set are parsed to obtain the parsing result. Based on the parsing result, the set of schedulable times for the electric vehicle to receive charge and discharge control commands in all future scheduling periods is determined. The first time availability mask is used to identify whether charging is allowed in each time granularity; the second time availability mask is used to identify whether discharging is allowed in each time granularity. Based on the user information, the vehicle information, and the set of schedulable times, a set of electric vehicle state parameters is constructed to reflect the schedulable potential of electric vehicles; the electric vehicle state parameter set includes real-time state of charge, total battery capacity, maximum charging power, minimum charging power, maximum discharging power, and minimum discharging power.

3. The method for interactive regulation of electric vehicles and power grids according to claim 1, characterized in that, The step of collecting power grid operation status data and demand data through the power grid control layer, generating a power grid status set based on the status data and demand data, and generating a constraint matrix based on the power grid status set and a preset line power, includes: The power grid control layer collects real-time status data characterizing the power grid's operational status. It then acquires demand data, including time-of-use charging prices published by the electricity market and on-grid prices for power fed back to the grid by electric vehicles. Based on this status data and demand data, a power grid status set reflecting the real-time operational status of the power grid and market prices is generated. The status data includes bus voltage, line current, transmission line active power, current-carrying capacity limits for each line, transformer current-carrying capacity limits, and power quality indicators, including harmonic distortion rate. The renewable energy output from wind power and photovoltaic power generation in a specific future period is predicted to obtain prediction data. Based on the grid topology, a grid security transmission limit is determined. Based on the prediction data and the grid security transmission limit, a scheduling demand instruction describing peak shaving and valley filling and reserve capacity service is generated. The scheduling demand instruction is then converted into the expected upper limit and expected lower limit of the interactive power at the connection point; the connection point is the connection point between the power grid and the electric vehicle. The power grid control layer processes the power grid state set, the predicted data, the expected upper limit value, and the expected lower limit value to obtain a constraint matrix; the constraint matrix is ​​expressed as a system of linear inequalities.

4. The method for interactive regulation of electric vehicles and power grids according to claim 1, characterized in that, The step of establishing a data interaction link using a preset communication unit, so that the three-tier architecture can upload the electric vehicle state parameter set, the power grid state set, the demand vector, and the constraint matrix to the aggregation scheduling layer through the data interaction link, includes: A first communication link is established between the user terminal of the electric vehicle and the cloud server of the dispatch center using a preset communication unit, and a second communication link is established between the electric vehicle and the charging pile using the preset communication unit. Then, a third communication link is established between the charging pile and the cloud server of the dispatch center using the preset communication unit, so as to construct a data interaction link based on the first communication link, the second communication link and the third communication link. The three-tier architecture is invoked, and the electric vehicle state parameter set, the power grid state set, the demand vector, and the constraint matrix are uploaded to the aggregation scheduling layer through the data interaction link; wherein, the demand vector includes power demand, time parameters, and adjustment coefficients.

5. The method for interactive regulation of electric vehicles and power grids according to claim 4, characterized in that, The process involves invoking the aggregation scheduling layer and generating a station-level resource set based on charging station information, then aggregating the adjustable power ranges of each electric vehicle to obtain a capacity resource pool, including: The aggregation scheduling layer is invoked to obtain the corresponding charging station information from the charging piles, and a station-level resource set is generated based on the charging station information. Then, the power adjustable range of each electric vehicle is aggregated based on the station-level resource set and the electric vehicle status parameter set to obtain a capacity resource pool. The capacity resource pool is used to characterize the overall schedulable power range. The station-level resource set includes the resource distribution and availability status of each charging station.

6. The method for interactive regulation of electric vehicles and power grids according to claim 1, characterized in that, The process involves processing the electric vehicle state parameter set, the power grid state set, the station-level resource set, and the capacity resource pool through the data processing unit in the aggregation scheduling layer to obtain a dataset to be processed. Then, a mixed-integer programming algorithm is used, based on preset decision variables, an objective function, the demand vector, and the constraint matrix, to determine the charging and discharging power trajectory of the dataset to be processed within a preset rolling prediction window. This includes: In the aggregation scheduling layer, the data processing unit performs heterogeneous data cleaning and outlier removal on the electric vehicle status parameter set, the power grid status set, the station-level resource set, and the capacity resource pool to obtain data to be screened. The data to be screened is then filtered and information is extracted to obtain data to be aligned. The time alignment operation is performed on the data to be aligned based on the preset timestamp reference to obtain the dataset to be processed. Then, decision variables are defined to characterize the mutual exclusion logic relationship between charging and discharging, and an objective function including economic indicators, power smoothness indicators and system operation safety indicators is constructed. The target constraint conditions are determined based on the constraint matrix and the preset physical operation conditions. By using a mixed integer programming algorithm and iteratively solving the dataset to be processed within a preset rolling prediction time window based on the decision variables, the objective function, the objective constraints, and the demand vector, the charging and discharging power trajectories of each electric vehicle in the future window at each scheduling time are obtained.

7. The method for interactive regulation of electric vehicles and power grids according to claim 6, characterized in that, The construction of the objective function includes economic indicators, power smoothness indicators, and system operation safety indicators, and the determination of the objective constraints based on the constraint matrix and preset physical operating conditions, including: The deviation between the actual terminal state of charge of electric vehicles and the expected value is determined to obtain the state of charge deviation term. The total electricity cost of all electric vehicles in the scheduling cycle is determined to obtain the economic cost term. Then, based on the preset smoothness auxiliary variable, the power smoothness term is determined to characterize the fluctuation of charging and discharging power in adjacent time periods. The line soft constraint relaxation penalty term is determined to punish the over-limit behavior of the system net power. Determine the weights corresponding to the state of charge deviation term, the economic cost term, the power smoothness term, and the line soft constraint relaxation penalty term, respectively, and then determine the objective function based on the state of charge deviation term, the economic cost term, the power smoothness term, and the line soft constraint relaxation penalty term, and their respective weights. The physical operating conditions corresponding to the electric vehicle and the charging pile are determined respectively, and the target constraint conditions are determined by using each physical operating condition and the constraint matrix; the physical operating conditions include charging and discharging mutual exclusion constraint conditions, charging and discharging power upper and lower limit constraint conditions, battery state of charge dynamic constraint conditions, and battery capacity constraint conditions.

8. The method for interactive regulation of electric vehicles and power grids according to claim 7, characterized in that, The step of determining the target constraint conditions using the physical operating conditions and the constraint matrix includes: The state of charge dynamics relationship corresponding to the physical operating conditions is determined, and a state of charge dynamic equation is constructed based on the state of charge dynamics relationship to describe the change law of battery energy with the charging and discharging process. The state of charge dynamic equation is used to determine the upper and lower limits of the state of charge and the terminal state of charge corresponding to the battery. Then, the state of charge deviation variable is determined based on the upper and lower limits of the state of charge and the terminal state of charge. Based on the state of charge deviation variable, a charge-discharge mutual exclusion relationship corresponding to the physical operating conditions is determined, and a charging state variable and a discharging state variable are determined based on the charge-discharge mutual exclusion relationship. This is used to determine the charge-discharge power information corresponding to the physical operating conditions, including the upper limit of charging power, the lower limit of charging power, the upper limit of discharging power, and the lower limit of discharging power. The charge-discharge power information is then bound to the charging state variable and the discharging state variable to obtain a binding result. Based on the constraint matrix, the upper and lower limits of the soft constraints corresponding to the net power of the system are determined, and the net power of the system is determined based on the algebraic sum of the charging and discharging power corresponding to each electric vehicle. The target constraint conditions are determined based on the net power of the system, the upper and lower limits of the soft constraints, and the binding result.

9. The method for interactive regulation of electric vehicles and power grids according to any one of claims 1 to 8, characterized in that, The execution unit in the aggregation scheduling layer sends the charging and discharging power commands from the charging and discharging power trajectory to the corresponding charging facilities for execution, and collects feedback information, including power values ​​and vehicle state of charge, during the execution process. This allows the data processing unit to adjust vehicle-to-grid interaction based on the feedback information, including: The execution unit in the aggregation scheduling layer sends the charging and discharging power command corresponding to the current scheduling time in the charging and discharging power trajectory to the corresponding charging facility, so that the charging facility can perform charging or discharging operations on the electric vehicle based on the charging and discharging power command, and collect feedback information from the charging facility and the electric vehicle during the execution process; the feedback information includes the actual measured charging and discharging power value and the vehicle's state of charge at the next moment; The execution unit is invoked to send the actual power value and the feedback information back to the data processing unit, so that the data processing unit can use a preset state update function and update and correct the electric vehicle state parameter set of the electric vehicle based on the actual power value and the feedback information to obtain the corrected result, so that the data processing unit can make vehicle-to-grid interaction adjustments based on the corrected result.

10. An interactive regulation device for electric vehicles and power grids, characterized in that, Applied to a three-tier architecture comprising the user execution layer, the power grid control layer, and the aggregation scheduling layer, including: The state parameter set generation module is used to collect user information and vehicle information corresponding to each electric vehicle through the user execution layer, so as to generate an electric vehicle state parameter set based on the user information and vehicle information. The constraint matrix generation module is used to collect state data and demand data of the power grid operation through the power grid control layer, generate a power grid state set based on the state data and the demand data, and generate a constraint matrix based on the power grid state set and the preset line power. An interactive link establishment module is used to establish a data interactive link using a preset communication unit, so that the three-level architecture can upload the electric vehicle state parameter set, the power grid state set, the demand vector, and the constraint matrix to the aggregation scheduling layer through the data interactive link; the demand vector is a vector generated based on the power grid's adjustment demand for the electric vehicle; The capacity resource pool determination module is used to call the aggregation scheduling layer and generate a station-level resource set based on the charging station information, and aggregate the power adjustable range of each electric vehicle to obtain the capacity resource pool. The dataset determination module is used to process the electric vehicle state parameter set, the power grid state set, the station-level resource set and the capacity resource pool through the data processing unit in the aggregation scheduling layer to obtain the dataset to be processed. Then, it uses a mixed integer programming algorithm and based on preset decision variables, objective function, demand vector and constraint matrix to determine the charging and discharging power trajectory of the dataset to be processed within a preset rolling prediction window. The feedback information generation module is used to send the charging and discharging power commands in the charging and discharging power trajectory to the corresponding charging facilities for execution through the execution unit in the aggregation scheduling layer, and to collect feedback information, including power values ​​and vehicle charge status, corresponding to the execution process, so that the data processing unit can make vehicle-to-grid interaction adjustments based on the feedback information.

11. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the method for interactive regulation of electric vehicles and the power grid as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements the method for interactive regulation of electric vehicles and the power grid as described in any one of claims 1 to 9.

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