V2G-based multi-tenant virtual power plant scheduling method and server

Abstract

The invention relates to the technical field of power dispatching control, in particular to a V2G-based multi-tenant virtual power plant dispatching method and a server, and the method comprises the steps: obtaining the information of all target vehicles connected to a power grid within a preset duration, taking a target vehicle cluster as a virtual power plant, and enabling each virtual power plant to form a micro-grid; acquiring target information including off-grid time, target electric quantity and initial electric quantity uploaded by each target vehicle, and performing charging and discharging scheduling control on the target vehicle through a model prediction control algorithm based on the target information; aiming at the output power of the target vehicle in the micro-grid, preferentially meeting the target vehicle with the power requirement under the same micro-grid; new energy vehicles in a certain area are gathered together to form a virtual power plant, each virtual power plant forms a micro-grid, and the established power grid transmission and feed of the vehicles in the area are uniformly allocated according to the requirements of the power grid through a model prediction control algorithm, so that ordered charging and discharging are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching and control, and in particular to a V2G-based multi-tenant virtual power plant dispatching method and a server. Background Art

[0002] V2G stands for Vehicle-to-Grid, which utilizes the unique energy storage function of electric vehicles to interact with the power grid in a two-way manner. V2G technology not only helps alleviate the peak in electricity demand caused by the large-scale popularization of electric vehicles, but also can regard electric vehicles as energy storage devices to adjust power loads, thereby improving the efficiency and reliability of power grid operation and reducing the huge investment in power grid energy storage facilities.

[0003] With the increase in the number of electric vehicles, large-scale centralized or distributed access to the grid for charging, the unguided charging load of new energy vehicles may generate new load peaks, bringing challenges to the operation of the power grid. The disorderly growth of load can instantly lower the voltage and frequency of the grid and reduce active power output. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a V2G-based multi-tenant virtual power plant scheduling method and server, which can schedule energy vehicles to charge and discharge in an orderly manner.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: A V2G-based multi-tenant virtual power plant scheduling method comprises the following steps: S1. Acquire information of all target vehicles connected to the power grid within a preset time period, use the target vehicle cluster as a virtual power plant, and each virtual power plant forms a microgrid; S2. Obtain target information uploaded by each target vehicle, including off-grid time, target power, and initial power, and perform charging and discharging scheduling control on the target vehicle through a model predictive control algorithm based on the target information; The output power of the target vehicles in the microgrid is prioritized to meet the power needs of the target vehicles in the same microgrid; In step S2, based on the target information, the target vehicle is scheduled to be discharged and charged by a model predictive control algorithm, including: S21. Obtain battery data, power grid data, and user behavior data through a prediction model based on information of the target vehicle; S22, constructing a multi-objective optimization model with economic benefit maximization and grid stability as objectives and battery charge and discharge restrictions, maximum power restrictions of charging infrastructure and target power as constraints based on the target information, the battery data, the grid data and the user behavior data; The objective function for maximizing economic benefits is constructed as follows: ; Where T represents the prediction time window, t represents time, is the electricity purchase price, is the electricity selling price, is the charging and discharging power of the electric vehicle; The grid stability is ensured by minimizing the peak-to-valley difference, and the objective function is constructed as: ; in, represents the average power of the grid, Indicates the total power of the power grid; S23, solving the multi-objective optimization model by a rolling optimization algorithm, and performing charge and discharge scheduling control according to the solution results.

[0006] In order to solve the above technical problems, another technical solution adopted by the present invention is: A V2G-based multi-tenant virtual power plant scheduling service terminal includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: S1. Acquire information of all target vehicles connected to the power grid within a preset time period, use the target vehicle cluster as a virtual power plant, and each virtual power plant forms a microgrid; S2. Obtain target information uploaded by each target vehicle, including off-grid time, target power, and initial power, and perform charging and discharging scheduling control on the target vehicle through a model predictive control algorithm based on the target information; The output power of the target vehicles in the microgrid is prioritized to meet the power needs of the target vehicles in the same microgrid; In step S2, based on the target information, the target vehicle is scheduled to be discharged and charged by a model predictive control algorithm, including: S21. Obtain battery data, power grid data, and user behavior data through a prediction model based on information of the target vehicle; S22, constructing a multi-objective optimization model with economic benefit maximization and grid stability as objectives and battery charge and discharge restrictions, maximum power restrictions of charging infrastructure and target power as constraints based on the target information, the battery data, the grid data and the user behavior data; The objective function for maximizing economic benefits is constructed as follows: ; Where T represents the prediction time window, t represents time, is the electricity purchase price, is the electricity selling price, is the charging and discharging power of the electric vehicle; The grid stability is ensured by minimizing the peak-to-valley difference, and the objective function is constructed as: ; in, represents the average power of the grid, Indicates the total power of the power grid; S23, solving the multi-objective optimization model by a rolling optimization algorithm, and performing charge and discharge scheduling control according to the solution results.

[0007] The beneficial effects of the present invention are as follows: a V2G-based multi-tenant virtual power plant scheduling method and service end of the present invention gathers new energy vehicles in a certain area together to form a virtual power plant, and each of the virtual power plants forms a microgrid. Through the model predictive control algorithm, the established grid transmission and feed of the vehicles in this area are uniformly allocated according to the needs of the grid, thereby realizing orderly charging and discharging. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of the architecture of a V2G-based multi-tenant virtual power plant scheduling method according to an embodiment of the present invention; Figure 2 This is a flow chart of a V2G-based multi-tenant virtual power plant scheduling method according to an embodiment of the present invention; Figure 3 This is a structural diagram of a V2G-based multi-tenant virtual power plant scheduling service end according to an embodiment of the present invention; Description of labels: 1. A V2G-based multi-tenant virtual power plant dispatching server; 2. Processor; 3. Memory. DETAILED DESCRIPTION

[0009] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0010] Please refer to Figure 1 as well as Figure 2 , a V2G-based multi-tenant virtual power plant scheduling method, comprising the steps of: S1. Acquire information of all target vehicles connected to the power grid within a preset time period, use the target vehicle cluster as a virtual power plant, and each virtual power plant forms a microgrid; S2. Obtain target information uploaded by each target vehicle, including off-grid time, target power, and initial power, and perform charging and discharging scheduling control on the target vehicle through a model predictive control algorithm based on the target information; The output power of the target vehicles in the microgrid is prioritized to meet the power needs of the target vehicles in the same microgrid.

[0011] From the above description, it can be seen that the beneficial effects of the present invention are: a V2G-based multi-tenant virtual power plant scheduling method and service end of the present invention gathers new energy vehicles in a certain area together to form a virtual power plant, and each of the virtual power plants forms a microgrid. Through the model predictive control algorithm, the established grid transmission and feed of the vehicles in this area are uniformly allocated according to the needs of the grid, thereby realizing orderly charging and discharging.

[0012] Furthermore, in step S2, based on the target information, the discharge and charge scheduling of the target vehicle is performed by the model predictive control algorithm, including: S21. Obtain battery data, power grid data, and user behavior data through a prediction model based on information of the target vehicle; S22, constructing a multi-objective optimization model with economic benefit maximization and grid stability as objectives and battery charging and discharging restrictions, maximum power restrictions of charging infrastructure, expected charging time windows of users, and target power as constraints based on the target information, the battery data, the grid data, and the user behavior data; S23, solving the multi-objective optimization model by a rolling optimization algorithm, and performing charge and discharge scheduling control according to the solution results.

[0013] From the above description, it can be seen that building a multi-objective optimization model with the goals of cost minimization, grid stability and user satisfaction and solving it through a rolling optimization algorithm, and then performing charging and discharging scheduling control, can achieve better and balanced optimization scheduling.

[0014] Further, the prediction model includes a vehicle battery model, a power grid model, and a user behavior model; The vehicle battery model describes the relationship between the battery state of charge and the charging and discharging power over time and is used to predict battery data; The power grid model is used to predict power grid data, wherein the power grid data includes power grid load, renewable energy power generation and power grid electricity price; The user behavior model is used to predict the user behavior data of the vehicle user, including charging habits and travel plans, based on the historical travel data of the target vehicle.

[0015] From the above description, it can be seen that through the three prediction models of vehicle battery model, power grid model and user behavior model, relevant data are predicted to support the construction and solution of multi-objective optimization model.

[0016] Furthermore, step S2 further includes the steps of: S24, acquiring actual values ​​of the battery data and the grid data in real time, and performing feedback update on the prediction model.

[0017] It can be seen from the above description that the prediction accuracy of the prediction model is improved by obtaining the actual values ​​of the battery data and the power grid data in real time and then performing feedback update on the prediction model.

[0018] Further, the charge and discharge scheduling control includes at least one of a price-based discharge strategy, a frequency and voltage regulation service, a peak shaving and valley filling strategy, and battery health and life management; Price-based discharge strategy: adjust the discharge timing according to the grid electricity price or demand response signal, and discharge to the grid when the electricity price is high or during peak electricity consumption; Frequency and voltage regulation services: Automatically adjust the discharge rate according to the frequency or voltage changes of the power grid; Peak shaving and valley filling strategy: During the peak load period of the power grid, each target vehicle is controlled to discharge in a centralized manner, and charged during the low load period; Battery health and life management: Control the battery discharge depth, frequency and temperature based on the battery SOC and SOH.

[0019] From the above description, it can be seen that through charging and discharging scheduling control, a price-based discharge strategy can be implemented, the benefits of discharge can be improved, frequency and voltage regulation services can be realized, the grid can be stabilized, the peak shaving and valley filling strategy can be implemented, which helps to smooth the grid load curve, improve the overall efficiency of the system, realize battery health and life management, and extend the battery life.

[0020] Please refer to Figure 3 A V2G-based multi-tenant virtual power plant scheduling server includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned V2G-based multi-tenant virtual power plant scheduling method are implemented.

[0021] From the above description, it can be seen that the beneficial effects of the present invention are: a V2G-based multi-tenant virtual power plant scheduling method and service end of the present invention gathers new energy vehicles in a certain area together to form a virtual power plant, and each of the virtual power plants forms a microgrid. Through the model predictive control algorithm, the established grid transmission and feed of the vehicles in this area are uniformly allocated according to the needs of the grid, thereby realizing orderly charging and discharging.

[0022] Furthermore, in step S2, based on the target information, the discharge and charge scheduling of the target vehicle is performed by the model predictive control algorithm, including: S21. Obtain battery data, power grid data, and user behavior data through a prediction model based on information of the target vehicle; S22, constructing a multi-objective optimization model with economic benefit maximization and grid stability as objectives and battery charging and discharging restrictions, maximum power restrictions of charging infrastructure, expected charging time windows of users, and target power as constraints based on the target information, the battery data, the grid data, and the user behavior data; S23, solving the multi-objective optimization model by a rolling optimization algorithm, and performing charge and discharge scheduling control according to the solution results.

[0023] From the above description, it can be seen that building a multi-objective optimization model with the goals of cost minimization, grid stability and user satisfaction and solving it through a rolling optimization algorithm, and then performing charging and discharging scheduling control, can achieve better and balanced optimization scheduling.

[0024] Further, the prediction model includes a vehicle battery model, a power grid model, and a user behavior model; The vehicle battery model describes the relationship between the battery state of charge and the charging and discharging power over time and is used to predict battery data; The power grid model is used to predict power grid data, wherein the power grid data includes power grid load, renewable energy power generation and power grid electricity price; The user behavior model is used to predict the user behavior data of the vehicle user, including charging habits and travel plans, based on the historical travel data of the target vehicle.

[0025] From the above description, it can be seen that through the three prediction models of vehicle battery model, power grid model and user behavior model, relevant data are predicted to support the construction and solution of multi-objective optimization model.

[0026] Furthermore, step S2 further includes the steps of: S24, acquiring actual values ​​of the battery data and the grid data in real time, and performing feedback update on the prediction model.

[0027] It can be seen from the above description that the prediction accuracy of the prediction model is improved by obtaining the actual values ​​of the battery data and the power grid data in real time and then performing feedback update on the prediction model.

[0028] Further, the charge and discharge scheduling control includes at least one of a price-based discharge strategy, a frequency and voltage regulation service, a peak shaving and valley filling strategy, and battery health and life management; Price-based discharge strategy: adjust the discharge timing according to the grid electricity price or demand response signal, and discharge to the grid when the electricity price is high or during peak electricity consumption; Frequency and voltage regulation services: Automatically adjust the discharge rate according to the frequency or voltage changes of the power grid; Peak shaving and valley filling strategy: During the peak load period of the power grid, each target vehicle is controlled to discharge in a centralized manner, and charged during the low load period; Battery health and life management: Control the battery discharge depth, frequency and temperature based on the battery SOC and SOH.

[0029] From the above description, it can be seen that through charging and discharging scheduling control, a price-based discharge strategy can be implemented, the benefits of discharge can be improved, frequency and voltage regulation services can be realized, the grid can be stabilized, the peak shaving and valley filling strategy can be implemented, which helps to smooth the grid load curve, improve the overall efficiency of the system, realize battery health and life management, and extend the battery life.

[0030] The present invention provides a V2G-based multi-tenant virtual power plant scheduling method and server, which are suitable for V2G-based energy scheduling of electric vehicles.

[0031] Please refer to Figure 1 and Figure 2 , Embodiment 1 of the present invention is: A V2G-based multi-tenant virtual power plant scheduling method comprises the following steps: S1. Obtain information of all target vehicles connected to the power grid within a preset time period, use the target vehicle cluster as a virtual power plant, and each virtual power plant forms a microgrid.

[0032] The main application of V2G is peak load regulation and frequency regulation. Peak load regulation means that when the grid load is low, the grid charges electric vehicles to store energy; when the load is peak, EVs use the stored energy to feed back to the grid to smooth the load curve of the grid. Usually, the electricity price is adjusted according to the load situation as an incentive mechanism to encourage users to adjust their electricity demand, such as time-of-use pricing, tiered pricing, and real-time pricing. For the grid, this can shift the peak to fill the valley and reduce the grid load rate; for users, buying electricity at a low price can save money, and supplying electricity to the grid can also get electricity price compensation. The so-called frequency regulation can be said to be an auxiliary mechanism, which refers to fine-tuning the frequency of the grid in a short period of time, such as using the time-of-use charging price of electric vehicles to adjust it at the minute level.

[0033] In this embodiment, the electric vehicle group is used as a virtual power plant, and the connected vehicles within a certain period of time are regarded as a group and subject to unified scheduling. Under the premise of ensuring that the charging task is completed on time, the V2G function of the electric vehicle is fully utilized to reduce the charging cost and the load of the power grid.

[0034] S2. Obtain target information uploaded by each target vehicle, including off-grid time, target power, and initial power, and perform charging and discharging scheduling control on the target vehicle through a model predictive control algorithm based on the target information; The output power of the target vehicles in the microgrid is prioritized to meet the power needs of the target vehicles in the same microgrid.

[0035] In this embodiment, the dispatch center is an information center responsible for realizing the cooperation between electric vehicles and power grids in the region, and has the ability to collect, publish information and calculate. After the electric vehicle is connected to the grid, it needs to receive centralized dispatch from the dispatch center. After the user connects the electric vehicle to the grid, the user needs to set the off-grid time and target power for the vehicle, and upload this information and the initial power of the electric vehicle to the dispatch center. During the time of connecting to the grid, the electric vehicle is in a dispatchable state, and its charging and discharging behavior is controlled by the dispatch center. The dispatch center formulates a charging and discharging dispatch strategy on the premise of ensuring that all vehicle charging tasks are completed on time, and disconnects the electric vehicle from the grid when it reaches the off-grid time.

[0036] In this embodiment, a microgrid formed by each virtual power plant is connected to the dispatch center to exchange information. The dispatch center receives the load and power information that can be provided from each microgrid, and performs dispatch through the corresponding dispatch algorithm (model predictive control algorithm) to control the charging and discharging behavior of electric vehicles in the microgrid. Each microgrid gives priority to internal calls to reduce the demand load on the power grid.

[0037] Specifically, in this embodiment, the scheduling strategies implemented include but are not limited to: Price-based discharge strategy: Adjust the discharge timing according to the grid electricity price or demand response signal, and discharge to the grid when the electricity price is high or during peak electricity consumption. When the electricity price is high (usually during peak electricity consumption), electric vehicles discharge to the grid, and users can receive financial compensation from the grid.

[0038] Frequency and voltage regulation service: Automatically adjust the discharge rate according to the frequency or voltage changes of the power grid to provide auxiliary services. When the grid frequency drops or the voltage is unstable, it responds quickly to discharge and helps stabilize the grid.

[0039] Peak shaving and valley filling strategy: During the peak load period of the power grid, each target vehicle is controlled to discharge in a centralized manner, and charged during the low load period, using low-cost electricity. This helps to smooth the load curve of the power grid and improve the overall efficiency of the system.

[0040] Battery health and life management: According to the battery SOC and SOH, control the battery discharge depth, frequency and temperature to extend the battery life.

[0041] Economic incentive mechanism: Design a reasonable economic incentive model to encourage electric vehicle users to participate in V2G projects and increase user participation through revenue sharing, discounted electricity bills, reward points, etc.

[0042] Embodiment 2 of the present invention is: A V2G-based multi-tenant virtual power plant scheduling method is different from the first embodiment in that, in step S2, based on the target information, the target vehicle is scheduled to be discharged and charged through a model predictive control algorithm, including: S21. Obtain battery data, power grid data, and user behavior data through a prediction model based on information of the target vehicle; The prediction model includes a vehicle battery model, a power grid model, and a user behavior model; The vehicle battery model describes the relationship between the battery state of charge and the change of the charging and discharging power over time, and is used to predict battery data, including: State of Charge (SOC): Accurately predict the battery's SOC to ensure the battery operates within a safe range.

[0043] State of Health (SOH): Assess the health status of the battery and predict the battery life and performance degradation.

[0044] Battery parameters: Collect basic battery parameters, such as maximum capacity, internal resistance, polarization internal resistance, time constant, etc.

[0045] Charge and discharge data: record battery charge and discharge current, voltage, temperature and other data.

[0046] External data: Collect external data such as ambient temperature and charging equipment parameters.

[0047] Among them, the relationship between the battery's state of charge (SOC) and the change in charge and discharge power over time is described as: ; Where P represents the charge and discharge power, Q max represents the maximum capacity of the battery, and Δt represents the time step.

[0048] The power grid model is used to predict power grid data, which includes power grid load, renewable energy power generation and power grid electricity price, including: Load data: records the historical load data of the power grid, including data at different time scales such as hours, days, weeks, and months.

[0049] Electricity price data: record historical electricity price data, including time-of-use electricity prices, real-time electricity prices, etc.

[0050] Weather data: Record historical weather data, including temperature, humidity, wind speed, etc.

[0051] Among them, the grid demand and supply can be expressed as: ; in, Represents the total power of the grid, represents the load demand, Indicates the charging and discharging power of an electric vehicle.

[0052] The user behavior model is used to predict the user behavior data of the vehicle user, including charging habits and travel plans, based on the historical travel data of the target vehicle. It requires recording the user's charging and discharging behavior and travel plans and other data.

[0053] In this example, the model predictive control (MPC) algorithm is used to schedule the charging and discharging process of electric vehicles, aiming to optimize the interaction between electric vehicles and the power grid to improve energy efficiency, grid stability, and provide economic benefits to electric vehicle users.

[0054] The implementation of the model predictive control algorithm includes the establishment of a system model, which usually implements three models including the above vehicle battery model, power grid model and user behavior model.

[0055] S22. Based on the target information, the battery data, the grid data and the user behavior data, a multi-objective optimization model is constructed with the goals of maximizing economic benefits and grid stability, and with battery charging and discharging restrictions, the maximum power limit of the charging infrastructure, the user's expected charging time window and the target power as constraints.

[0056] In this embodiment, the model predictive control algorithm is used to schedule charging and discharging, and the following contents are also included: 1. Designing the objective function. The design of the objective function in this embodiment includes: (1) Maximizing economic benefits: Comprehensively consider the charging and discharging costs (based on real-time electricity prices) and the battery usage costs (taking into account battery degradation).

[0057] In this embodiment, the objective function for maximizing economic benefits is constructed as follows: ; in, is the electricity purchase price, It is the price of electricity sold.

[0058] (2) Grid stability: ensuring grid frequency and voltage stability, such as responding to grid transient imbalances through control strategies.

[0059] In this embodiment, the peak-to-valley difference is minimized to ensure grid stability, and the objective function is constructed as follows: ; in, represents the average power of the grid, Indicates the total power of the power grid; (3) User satisfaction: Ensure that the charging needs of electric vehicle users are met without affecting their daily travel.

[0060] 2. Establishing Constraints (1) Battery charge and discharge limit: Ensure that the SOC is within a safe range and avoid over-charge and over-discharge: ; in, and Indicates the minimum and maximum SOC of the battery.

[0061] (2) Charging and discharging power limit: Consider the maximum power limit of the charging infrastructure.

[0062] ; in, and represents the minimum and maximum charge and discharge power of the electric vehicle, SOC(t) represents the battery SOC at time t, P EV (t) represents the charging and discharging power of the electric vehicle at time t.

[0063] (3) User needs: meet the user's expected charging time window and SOC requirements.

[0064] Therefore, in this embodiment, a multi-objective optimization model is constructed based on the above objective functions and constraints.

[0065] S23, solving the multi-objective optimization model by a rolling optimization algorithm, and performing charge and discharge scheduling control according to the solution results.

[0066] In this embodiment, the above multi-objective optimization model is solved based on the rolling optimization algorithm: (1) Prediction phase: Based on the current state, predict the system behavior in the future (e.g., several hours).

[0067] Initial state: Set the initial battery SOC and grid state.

[0068] Prediction time window: Set the prediction time window T and time step △t.

[0069] Prediction Model: Battery SOC prediction: ; Grid power forecast: .

[0070] (2) Optimization solution: Find the optimal charging and discharging strategy sequence (power level and time schedule) within the predicted time domain, optimize the objective function, and satisfy all constraints.

[0071] In this embodiment, the objective function of minimizing the peak-to-valley difference is taken as an example for description.

[0072] Construct the optimization problem: ; Solve the optimization problem: In this embodiment, an optimization algorithm (such as linear programming, quadratic programming, etc.) is used to solve the above optimization problem to obtain the optimal charging and discharging power .

[0073] Application Control Input: The optimal charging and discharging power applied to actual systems.

[0074] Status update: Update the battery SOC and grid status according to the actual system status.

[0075] Rolling optimization: Within each time step, roll the prediction time window forward by one time step and repeat the above steps.

[0076] Take an electric car as an example, assuming its initial SOC is 50%, the grid load demand and electricity price are shown in Table 1: Table 1

[0077] Use MPC algorithm for discharge and charge scheduling: initialization: Initial SOC: 50% Prediction time window: 8 hours Time step: 1 hour Battery SOC prediction: ; Grid power forecast: ; Optimization solution: ; Solve the optimization problem and get the optimal charging and discharging power .

[0078] Application control input: the optimal charge and discharge power applied to actual systems.

[0079] Status update: Update the battery SOC and grid status according to the actual system status.

[0080] Rolling optimization: Within each time step, roll the prediction time window forward by one time step and repeat the above steps.

[0081] S24, acquiring actual values ​​of the battery data and the grid data in real time, and performing feedback update on the prediction model.

[0082] Please refer to Figure 3 , Embodiment 3 of the present invention is: A V2G-based multi-tenant virtual power plant scheduling service end 1 comprises a processor 2, a memory 3, and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, the steps of a V2G-based multi-tenant virtual power plant scheduling method described in the above embodiment 1 or 2 are implemented.

[0083] That is, the dispatching center is realized through the server.

[0084] To sum up, the present invention provides a V2G-based multi-tenant virtual power plant scheduling method and service end, which gathers new energy vehicles in a certain area together to form a virtual power plant. Each of the virtual power plants forms a microgrid. Through the model predictive control algorithm, the established grid transmission and feed of the vehicles in this area are uniformly allocated according to the needs of the grid, so as to realize orderly charging and discharging.

[0085] Based on the V2G-based multi-tenant virtual power plant scheduling method and service end of the present invention, in addition to improving the stability of the power grid, electric car owners can also obtain income from the electric energy they export, which can significantly help car owners reduce the cost of ownership during the life cycle of the vehicle. It is also one of the key factors in promoting the global use of electric vehicles, greatly improving the way of producing, storing and using energy, and helping to accelerate the realization of zero-emission driving.

[0086] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A V2G-based multi-tenant virtual power plant scheduling method, characterized in that: Includes steps: S1. Acquire information of all target vehicles connected to the power grid within a preset time period, use the target vehicle cluster as a virtual power plant, and each virtual power plant forms a microgrid; S2. Obtain target information uploaded by each target vehicle, including off-grid time, target power, and initial power, and perform charging and discharging scheduling control on the target vehicle through a model predictive control algorithm based on the target information; The output power of the target vehicles in the microgrid is prioritized to meet the power needs of the target vehicles in the same microgrid; In step S2, based on the target information, the target vehicle is scheduled to be discharged and charged by a model predictive control algorithm, including: S21. Obtain battery data, power grid data, and user behavior data through a prediction model based on information of the target vehicle; S22, constructing a multi-objective optimization model with economic benefit maximization and grid stability as objectives and battery charge and discharge restrictions, maximum power restrictions of charging infrastructure and target power as constraints based on the target information, the battery data, the grid data and the user behavior data; The objective function for maximizing economic benefits is constructed as follows: ； Where T represents the prediction time window, t represents time, is the electricity purchase price, is the electricity selling price, is the charging and discharging power of the electric vehicle; The grid stability is ensured by minimizing the peak-to-valley difference, and the objective function is constructed as: ； in, represents the average power of the grid, Indicates the total power of the power grid; S23, solving the multi-objective optimization model by a rolling optimization algorithm, and performing charge and discharge scheduling control according to the solution results.

2. A V2G-based multi-tenant virtual power plant scheduling method according to claim 1, characterized in that: The constraints of battery charging and discharging are: ； in, and Indicates the minimum and maximum SOC of the battery; The constraint condition of the maximum power limit of the charging infrastructure is: ； in, and represents the minimum and maximum charge and discharge power of the electric vehicle, SOC(t) represents the battery SOC at time t, P EV (t) represents the charging and discharging power of the electric vehicle at time t.

3. The V2G-based multi-tenant virtual power plant scheduling method according to claim 1 is characterized in that: The prediction model includes a vehicle battery model and a power grid model; The vehicle battery model describes the relationship between the battery state of charge and the charging and discharging power over time and is used to predict battery data; The power grid model is used to predict power grid data, which includes power grid load, renewable energy power generation and power grid electricity price.

4. The V2G-based multi-tenant virtual power plant scheduling method according to claim 1 is characterized in that: Step S2 also includes the steps of: S24, acquiring actual values ​​of the battery data and the grid data in real time, and performing feedback update on the prediction model.

5. The V2G-based multi-tenant virtual power plant scheduling method according to claim 1 is characterized in that: The charge and discharge scheduling control includes at least one of a price-based discharge strategy, a frequency and voltage regulation service, a peak shaving and valley filling strategy, and a battery health and life management strategy; Price-based discharge strategy: adjust the discharge timing according to the grid electricity price or demand response signal, and discharge to the grid when the electricity price is high or during peak electricity consumption; Frequency and voltage regulation services: Automatically adjust the discharge rate according to the frequency or voltage changes of the power grid; Peak shaving and valley filling strategy: During the peak load period of the power grid, each target vehicle is controlled to discharge in a centralized manner, and charged during the low load period; Battery health and life management strategy: Control the battery discharge depth, frequency and temperature based on the battery SOC and SOH.

6. A V2G-based multi-tenant virtual power plant dispatching service terminal, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: S1. Acquire information of all target vehicles connected to the power grid within a preset time period, use the target vehicle cluster as a virtual power plant, and each virtual power plant forms a microgrid; S2. Obtain target information uploaded by each target vehicle, including off-grid time, target power, and initial power, and perform charging and discharging scheduling control on the target vehicle through a model predictive control algorithm based on the target information; The output power of the target vehicles in the microgrid is prioritized to meet the power needs of the target vehicles in the same microgrid; In step S2, based on the target information, the target vehicle is scheduled to be discharged and charged by a model predictive control algorithm, including: S21. Obtain battery data, power grid data, and user behavior data through a prediction model based on information of the target vehicle; S22, constructing a multi-objective optimization model with economic benefit maximization and grid stability as objectives and battery charge and discharge restrictions, maximum power restrictions of charging infrastructure and target power as constraints based on the target information, the battery data, the grid data and the user behavior data; The objective function for maximizing economic benefits is constructed as follows: ； Where T represents the prediction time window, t represents time, is the electricity purchase price, is the electricity selling price, is the charging and discharging power of the electric vehicle; The grid stability is ensured by minimizing the peak-to-valley difference, and the objective function is constructed as: ； in, represents the average power of the grid, Indicates the total power of the power grid; S23, solving the multi-objective optimization model by a rolling optimization algorithm, and performing charge and discharge scheduling control according to the solution results.

7. The V2G-based multi-tenant virtual power plant dispatching service terminal according to claim 6 is characterized in that: The constraints of battery charging and discharging are: ； in, and Indicates the minimum and maximum SOC of the battery; The constraint condition of the maximum power limit of the charging infrastructure is: ； in, and represents the minimum and maximum charge and discharge power of the electric vehicle, SOC(t) represents the battery SOC at time t, P EV (t) represents the charging and discharging power of the electric vehicle at time t.

8. The V2G-based multi-tenant virtual power plant dispatching service terminal according to claim 6 is characterized in that: The prediction model includes a vehicle battery model, a power grid model, and a user behavior model; The vehicle battery model describes the relationship between the battery state of charge and the charging and discharging power over time and is used to predict battery data; The power grid model is used to predict power grid data, wherein the power grid data includes power grid load, renewable energy power generation and power grid electricity price; The user behavior model is used to predict the user behavior data of the vehicle user, including charging habits and travel plans, based on the historical travel data of the target vehicle.

9. The V2G-based multi-tenant virtual power plant dispatching service terminal according to claim 6, characterized in that: Step S2 also includes the steps of: S24, acquiring actual values ​​of the battery data and the grid data in real time, and performing feedback update on the prediction model.

10. The V2G-based multi-tenant virtual power plant dispatching service terminal according to claim 6, characterized in that: The charge and discharge scheduling control includes at least one of a price-based discharge strategy, a frequency and voltage regulation service, a peak shaving and valley filling strategy, and a battery health and life management; Price-based discharge strategy: adjust the discharge timing according to the grid electricity price or demand response signal, and discharge to the grid when the electricity price is high or during peak electricity consumption; Frequency and voltage regulation services: Automatically adjust the discharge rate according to the frequency or voltage changes of the power grid; Peak shaving and valley filling strategy: During the peak load period of the power grid, each target vehicle is controlled to discharge in a centralized manner, and charged during the low load period; Battery health and life management: Control the battery discharge depth, frequency and temperature based on the battery SOC and SOH.

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