Virtual power plant charging and discharging scheduling method based on electric vehicle aggregation

Abstract

The invention relates to the technical field of power dispatching, in particular to a virtual power plant charging and discharging dispatching method based on electric vehicle aggregation, which comprises the following steps of: constructing a driving characteristic model based on user behaviors of electric vehicles, and simulating and generating typical travel time periods and charging loads of the electric vehicles through a Monte Carlo method; constructing a power generation model and a transfer load model; setting different vehicle types to participate in virtual power plant scheduling; and constructing a virtual power plant overall operation cost minimization objective function based on different vehicle types. The method solves the problem that an existing method does not consider battery loss factors of different vehicle types, so that the energy-saving effect is poor.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, and in particular to a virtual power plant charging and discharging dispatching method based on electric vehicle aggregation. Background Technology

[0002] With the continuous increase in the proportion of renewable energy sources such as wind power and photovoltaic power in the power system, the randomness and volatility of their output have placed higher demands on grid dispatch. At the same time, electric vehicles, as a mobile and bidirectionally adjustable load resource, are showing a rapid growth trend in their stock and are gradually gaining the potential to participate in power system dispatch. Treating electric vehicles as an important component of virtual power plants, and through reasonable aggregation and optimized control, enabling them to serve grid operation while ensuring users' travel needs, has become a key focus in the field of power system optimization dispatch.

[0003] The proposed method (publication number CN116231638A) reads the planned power grid topology to obtain the number of load nodes, important load nodes, branches, switchable lines, substations, and 500kV substations. With the objective of minimizing the sum of load shedding amounts at each fault, constraints are constructed to obtain a planned power grid important user power supply path decision model. A multivariate decision tree is used to transform the planned power grid important user power supply path decision model into a MILP model. This method primarily addresses user-side power safety issues but cannot solve the charging and discharging optimization problem for new energy vehicles. Furthermore, the decision tree model suffers from insufficient stability, overfitting, and weak generalization ability.

[0004] In addition, existing technologies often employ physical measures to address the degradation rate of electric vehicles, such as maintaining a suitable temperature, preheating the battery, improving driving habits, improving battery materials, and implementing intelligent BMS management. Summary of the Invention

[0005] To address the shortcomings of existing methods, this invention constructs a virtual power plant charging and discharging optimization model that integrates electric vehicle aggregation scheduling, given the strong fluctuations in power output from new energy sources such as wind and solar and the uncertainty of electric vehicle charging and discharging behavior. This significantly reduces the battery degradation rate of electric vehicles and better achieves grid load balance and reduces system operating costs.

[0006] The technical solution adopted in this invention is: a virtual power plant charging and discharging scheduling method based on electric vehicle aggregation, comprising the following steps: Step 1: Construct a driving characteristic model based on electric vehicle user behavior, and use the Monte Carlo method to simulate and generate typical travel periods and charging loads for electric vehicles; Step 2: Construct the power generation model and load transfer model; As a preferred embodiment of the present invention, the power generation model includes: wind power, photovoltaic power, gas turbine, and energy storage.

[0007] Step 3: Configure different vehicle models to participate in virtual power plant dispatching; As a preferred embodiment of the present invention, the vehicle models include: BYD, Nissan, Mitsubishi and BMW.

[0008] Step 4: Construct an objective function to minimize the overall operating cost of the virtual power plant based on different vehicle models; In a preferred embodiment of the present invention, the formula for the objective function is: (10) In the formula, The cost and profit of buying and selling electricity in the market; For gas turbine fuel costs; To compensate for interruptible load costs; To compensate for the cost of transferable load; Cost of electric vehicle battery wear and tear.

[0009] This invention constructs an optimized scheduling model aimed at minimizing overall operating costs and combines it with a time-of-use pricing mechanism to effectively guide electric vehicles to charge during off-peak hours and discharge during peak hours. This enables the regulation and response to fluctuations in renewable energy output, enhancing the flexibility and economy of power grid operation. It not only helps to achieve peak shaving and valley filling and load balancing in the power system, but also improves the efficiency of the coordinated utilization of distributed resources and the absorption capacity of renewable energy, providing important support for building an efficient, reliable, and low-carbon modern power system.

[0010] In a preferred embodiment of the present invention, the formula for the cost of electric vehicle battery degradation is as follows: ; In the formula, This is the weighting coefficient for the loss penalty; For electric vehicles i In t Discharge power at any given moment; ; ; In the formula, Indicates the first The cost of replacing a vehicle's battery; Indicates battery capacity; Indicates the rated number of cycles; This represents the average state of electric vehicle batteries. express SOC upper limit ； express SOC Lower limit.

[0011] In a preferred embodiment of the present invention, power balance constraints are applied to the virtual power plant, using the following formula: ; In the formula, For wind power in Output power during the time period; For photovoltaics Output power during the time period; For gas turbines Output power during the time period; For VPP in Power purchased and sold from the power grid during specific time periods; For energy storage Discharge power over a given period of time; For energy storage The charging power at that time; for The total discharge power of electric vehicles during the time period; for Total charging power of electric vehicles during a given time period; for The total power demand of the period.

[0012] In a preferred embodiment of the present invention, the electric vehicle is subjected to charging and discharging constraints, as shown in the formula: ; In the formula, for t The number of electric vehicles that can be controlled by the virtual power plant during a given time period; for The equivalent power output of electric vehicles during a given time period; This represents the average state of charge of the electric vehicle at the previous moment. Electricity consumption per unit distance of electric vehicles; This represents the average driving range of an electric vehicle. for The number of electric vehicles newly added to the system during a given period.

[0013] In a preferred embodiment of the present invention, the objective function of minimizing overall operating cost and the power balance constraint are combined to obtain the MILP model. CPLEX is then used to solve the MILP model to obtain the charging and discharging strategies and power output strategies of different vehicle models.

[0014] As a preferred embodiment of the present invention, a virtual power plant charging and discharging scheduling system based on electric vehicle aggregation includes: a memory for storing instructions executable by a processor; and a processor for executing the instructions to implement a virtual power plant charging and discharging scheduling method based on electric vehicle aggregation.

[0015] As a preferred embodiment of the present invention, a computer-readable medium storing computer program code implements a virtual power plant charging and discharging scheduling method based on electric vehicle aggregation when executed by a processor.

[0016] The beneficial effects of this invention are: 1. The non-linear penalty weight ω(SOC) and the unit cycle cost differentiated by vehicle model are used to determine the SOC. These two core elements enable precise perception and hierarchical protection of the operating range of heterogeneous electric vehicle batteries, thereby allowing the scheduling strategy to meet the grid demand while forming differentiated operating characteristics based on the physical and economic characteristics of each vehicle's battery: "vehicles with large capacity discharge more, while vehicles with small capacity use shallow cycles to preserve lifespan." 2. The method of this invention significantly reduces the degradation rate of different models of new energy vehicles; 3. Employing MILP and combining it with solvers such as CPLEX for efficient solutions, the system ultimately outputs the optimal output scheme for each type of resource at each time period, ensuring system reliability and playing a key role in load shaving and valley filling and efficient consumption of new energy. Attached Figure Description

[0017] Figure 1 This is a flowchart of the virtual power plant charging and discharging scheduling method based on electric vehicle aggregation according to the present invention; Figure 2 It aggregates the charging and discharging results of electric vehicles; Figure 3 It aggregates the energy stored in electric vehicles; Figure 4 This is a loss perception map of all existing vehicle models; Figure 5 This is a full-vehicle model wear perception map of the present invention; Figure 6 These are peak shaving and valley filling effect diagrams for four types of electric vehicles. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments. The drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0019] like Figure 1 As shown, a virtual power plant charging and discharging scheduling method based on electric vehicle aggregation includes the following steps: Step 1: Construct a driving characteristic model based on electric vehicle user behavior data, and simulate and generate typical travel periods and charging loads for electric vehicles using the Monte Carlo method; The present invention analyzes that the charging and discharging patterns of electric vehicles are mainly affected by the spatiotemporal distribution characteristics of electric vehicles, which are mainly influenced by the driving patterns of electric vehicles (the end time of daily driving and the daily driving mileage). According to the analysis of the driving patterns of electric vehicles, the probability density expression of the starting charging time of electric vehicles is shown in Equation (1); the probability density expression of the driving mileage of electric vehicles is shown in Equation (2). (1) In the formula, and Let be the expected value and standard deviation of the probability density function, respectively. =17.6; (2) In the formula, and Let $\begin{bmatrix}$ and $\begin{bmatrix}$ be the expected value and the standard deviation of the probability density function, respectively. =3.2, =0.88.

[0020] This invention extracts the basic travel behavior characteristics of electric vehicles based on this set of data, and uses this as a basis to model and analyze the daily travel patterns of electric vehicles, providing data support and behavioral reference for subsequent charging load prediction and optimized scheduling.

[0021] The main methods for estimating the charging load of electric vehicles include analysis based on travel behavior, random simulation, and statistical methods based on charging station data. Given the obstacles to obtaining actual travel data, this invention adopts the Monte Carlo simulation method to randomly generate the charging state parameters of each electric vehicle, calculate its load demand in different time intervals, and then overlay the load distribution of each vehicle to construct the overall charging load curve under the actual situation.

[0022] The charging station data includes: vehicle type, charging start and end time, single trip distance, initial and target battery state of charge, charging method, charging location and time period.

[0023] Step 2: Modeling wind power, photovoltaics, gas turbines, energy storage, and loads; Wind power model: (3) In the formula, This refers to the power output under natural wind speed. It is the rated power of the wind turbine, that is, the output power at the rated wind speed; The cut-in wind speed of the fan; This refers to the cut-off velocity of the fan. is the rated wind speed of the wind turbine, that is, the wind speed at which the wind turbine outputs its rated power; v is the actual cut-in wind speed of the wind turbine.

[0024] Photovoltaic model: (4) In the formula, Light intensity (W / m); The light intensity is Output power (kW) at that time; The light level is under standard test conditions (light intensity of 1000 W / m2, temperature of 25 degrees Celsius). This represents the maximum power generation under standard test conditions. The power temperature coefficient; The temperature of the photovoltaic cell; This is a reference temperature.

[0025] Gas turbine model: (5) In the formula, For gas turbines Output power at any given moment; The power generation efficiency of the gas turbine; The low calorific value of natural gas is taken as 9.7 (kWh / m3) in this embodiment. This represents the volume of natural gas in the micro gas turbine.

[0026] Energy storage model: (6) In the formula, The remaining charge of the battery at a certain moment; For battery capacity; when When =1, it indicates that the battery is fully charged; when When the value is 0, the battery is in a fully discharged state.

[0027] Interruptible load model: (7) In the formula, For the first One interruptible load user in The actual interruptible power of interruptible loads during the time period; For the first The maximum interruption limit for each interruptible user; for Total load demand during the time period.

[0028] Transferable load model: (8) (9) In the formula, for The actual operating power of the readily movable load; for The original load demand at any given moment.

[0029] Step 3: Based on the characteristics of electric vehicles, such as capacity and charging speed, set up four models—BYD, BMW Mini, Mitsubishi, and Nissan—to participate in virtual power plant scheduling. The capacity, charging speed, and other features of the four vehicle models are configured as follows: BYD's single unit has a capacity of 57 kWh, and the charging power is relatively high during the initial charging period; The Nissan unit has a capacity of 24 kWh, with a higher charging power during the middle period. Mitsubishi's single unit has a capacity of 16 kWh, and the charging power fluctuation is relatively balanced. The BMW Mini has a single-unit charging capacity of 35 kWh, and the charging power fluctuates significantly. Step 4: Establish an optimization model that minimizes the overall operating cost of a virtual power plant containing four electric vehicle models. This involves using the objective function of minimizing the total system operating cost, and optimizing scheduling based on the characteristics of controllable and uncontrollable users. The objective function is: (10) In the formula: To minimize the operating costs of the virtual power plant; The cost and profit of buying and selling electricity in the market; For gas turbine fuel costs; To compensate for interruptible load costs; To compensate for the cost of transferable load; Cost of electric vehicle battery wear and tear.

[0030] The formula for the cost-benefit of buying and selling electricity in the market is: (11) (12) (13) (14) In the formula, For the power purchased; It is the electricity purchase price; This refers to the power output for electricity sales. It refers to the electricity price. For whether or not to purchase electricity, it is a 0-1 decision variable; For whether or not to sell electricity, it is a 0-1 decision variable. This refers to the maximum permissible power output for purchasing and selling electricity.

[0031] The fuel cost of a gas turbine is calculated using the following formula: (15) (16) In the formula, The unit fuel cost for power generation by gas turbines; For natural gas prices; For the output of the gas turbine; For gas turbine power generation efficiency; It represents the lower heating value of natural gas.

[0032] Interruptible load compensation cost: (17) In the formula, For the first The unit compensation price for each interruptible load user. For the first The user in the first The amount of electricity interrupted during each period.

[0033] Cost of transferable load compensation: (18) In the formula, For the degree of load transfer operation, for The actual operating power of the readily movable load. for The original load demand at any given moment.

[0034] Cost of battery degradation during a given period and The depth of discharge of an electric vehicle is related to the time period and can be obtained from the electric vehicle discharge cost function curve, as shown in the formula: (19) In the formula, This is the weighting coefficient for the loss penalty; For electric vehicles i In t Discharge power at any given moment.

[0035] (20) (twenty one) In the formula, Indicates the first The cost of replacing a vehicle's battery; Indicates battery capacity, Indicates the rated number of cycles; This represents the average level of the electric vehicle battery state; in this embodiment... =, =; Apply virtual power plant constraints: 1. Power balance constraint, the formula is: (19) In the formula, For wind power in Output power during the time period; For photovoltaics Output power during the time period; For gas turbines Output power during the time period; For VPP in Power purchased and sold from the power grid during specific time periods For energy storage Discharge power over a given period of time; For energy storage The charging power at that time; for The total discharge power of electric vehicles during the time period; for Total charging power of electric vehicles during a given time period; for The total power demand of the period.

[0036] This invention considers the different characteristics of electric vehicles from different brands when accessing the network, and provides a more comprehensive solution for electric vehicle charging and discharging than the traditional electric vehicle scheduling problem (which is of a uniform type).

[0037] 2. Wind power output constraint, the formula is: (20) In the formula, This represents the lower limit of wind power output. This is the upper limit of wind power output.

[0038] 3. Photovoltaic output constraint, the formula is: (twenty one) In the formula, This represents the lower limit of photovoltaic power output. This is the upper limit for photovoltaic power output.

[0039] 4. Gas turbine output constraint, the formula is: (twenty two) In the formula, This represents the lower limit of the gas turbine's output. This represents the upper limit of the gas turbine's output.

[0040] 5. Electric vehicle charging and discharging constraints, the formula is: (twenty three) (twenty four) (25) (26) (27) (28) (29) In the formula, for Total charging power of electric vehicles during a given time period; for The total discharge power of electric vehicles during the time period; The charging power for a single electric vehicle; This refers to the discharge power of a single electric vehicle. Let t represent the number of electric vehicles that the virtual power plant can control during time period t. , Electric vehicles State variables of charging and discharging during a specific time period; for The equivalent power output of electric vehicles during a given time period; for Average state of charge of electric vehicles over a given period; This represents the average state of charge of the electric vehicle at the previous moment. Electricity consumption per unit distance of electric vehicles; This represents the average driving range of an electric vehicle. for The number of electric vehicles newly added to the system during the period; , These represent the minimum and maximum states of charge of an electric vehicle, respectively. The state of charge of electric vehicles that need to travel during time period t; This refers to the expected charge level of an electric vehicle owner when using the vehicle.

[0041] Formula (27) takes into account the dynamic characteristics of the vehicle (vehicle entry and exit), captures the change of the new member on the overall energy level of the system, reduces the computational complexity, and aggregates many individual EVs to represent the change of the overall energy SOC of the system.

[0042] 6. The capacity and charge / discharge constraints of energy storage batteries are given by the following formula: (30) (31) (32) (33) (34) In the formula, for The charge level of the energy storage battery during a given period; , These are the minimum and maximum capacities of the energy storage battery, respectively. , These are the state variables of the energy storage battery during charging and discharging; , These are the charging and discharging efficiencies, respectively. , The points represent the minimum and maximum output power for charging the energy storage battery, respectively. , These represent the minimum and maximum output power of the energy storage battery, respectively.

[0043] The MILP model is obtained by simultaneously solving the objective function of minimizing overall operating costs and the power balance constraint. The optimization solver CPLEX is then used to quickly solve the MILP model to obtain the charging and discharging strategies for different vehicle models and the output strategies of the power supply system. In other words, the optimal output scheme for each type of resource in each time period is finally output to ensure the reliable operation of the system and play a key role in load peak shaving and valley filling and efficient consumption of new energy.

[0044] Experimental procedure: The virtual power plant (VPP) considered in the example consists of a 10MW wind farm, an 8MW photovoltaic power station, a 5MW gas turbine, an 8MW energy storage battery, and 800 electric vehicles. The four vehicle types participating in the dispatch are: a BYD (57kW·h), a Nissan (24kW·h), a Mitsubishi (16kW·h), and a BMW Mini (35kW·h). The electricity market adopts the Hebei North Power Grid time-of-use pricing for industrial and commercial use, with peak pricing from 18-21 hours (see Table 1). The model considers four different types of electric vehicles participating in the virtual power plant dispatch, with a total of 800 vehicles of these four aggregated electric vehicle types participating in the dispatch.

[0045] Table 1 Time-of-use electricity prices in the Hebei North Power Grid

[0046] like Figure 2 , 3Aggregating electric vehicles (EVs) for virtual power plant dispatch demonstrates significant advantages in peak shaving and valley filling, as well as system coordination. From a charging and discharging perspective, the EV group exhibits a regular pattern of "charging during off-peak hours and discharging during peak hours," responding rapidly to changes in electricity prices and load. The charging and discharging power is distributed orderly and rhythmically over time, demonstrating a highly coordinated control strategy. This centralized dispatch significantly reduces the peak-to-valley difference in the power grid, enhancing the stability and flexibility of system operation. Simultaneously, the aggregated EVs possess a large adjustable capacity, enabling them to serve as effective energy storage resources for ancillary services such as frequency and voltage regulation. The energy storage curve exhibits periodic fluctuations, and the charging and discharging process is stable, reflecting the dispatch system's precise control over the EV status, which helps improve the predictability of system operation and the executability of dispatch plans. Compared to decentralized charging and discharging behavior, centralized management not only improves resource coordination efficiency but also reduces the interference of uncertainties on power grid operation, verifying the practical feasibility and application value of EVs as virtual power plant dispatch resources. Figure 4 A comparison chart of perceived wear and tear across all vehicle models ( (using empirical values) Figure 5 The full-vehicle loss perception comparison chart (Equation 20) shows that the scheduling strategy of this invention can fully perceive the battery's operating range. Experimental results show that the BYD E6 and BMW Mini maintain their SoC in the healthy zone of over 50% throughout the day due to their high battery margin. For smaller capacity models such as the Mitsubishi i-MiEV, the model strictly limits their discharge depth to the yellow warning zone, prohibiting them from entering the severe loss zone below 20%. This hierarchical protection mechanism ensures that when heterogeneous electric vehicle groups participate in virtual power plant scheduling, they can collaboratively complete the grid's ancillary service tasks based on their own physical limitations without sacrificing long-term battery life.

[0047] Furthermore, comparative analysis revealed a significant decrease in the daily battery capacity degradation rate for all models. BMW's performance improvement was the most remarkable, with its degradation rate decreasing from [previous figure]. The percentage dropped significantly. The optimization rate reached 44.1%; Nissan and Mitsubishi also achieved [the same results]. ‰ (approximately 33.3%) and The decline was approximately 28.6% (‰); even for BYD, with a relatively small base, the decline rate was... Further optimization to ‰ ‰ (an increase of approximately 5.3%).

[0048] Figure 6The data visually demonstrates how four types of electric vehicles achieve significant peak shaving and valley filling effects under the influence of electricity price signals, utilizing battery charging and discharging characteristics: During the off-peak / flat electricity price periods in the early morning and midday, the blue curve is higher than the red curve, indicating that vehicles are charging intensively to "fill the valley"; while during the peak electricity consumption periods in the morning and evening (especially 18-22h), the blue curve drops significantly below the red curve, indicating that vehicles are discharging to the grid via V2G to perform "peak shaving"; among them, BYD models, with the largest single-vehicle capacity (57kWh), demonstrate the strongest load regulation depth.

[0049] This invention attempts to construct a driving characteristic model of electric vehicles based on actual travel and charging data, starting from user behavior. It then uses the Monte Carlo method to simulate typical travel times and load characteristics to improve the accuracy and flexibility of the modeling. Building upon this, it incorporates wind power, photovoltaics, gas turbines, energy storage devices, and various loads into a unified scheduling modeling framework to construct a virtual power plant structure that operates collaboratively across multiple sources. Furthermore, it considers the capacity, charging power, and usage characteristics of electric vehicles from different brands and models. For example, four typical models—BYD, BMW Mini, Mitsubishi, and Nissan—are designated as independent scheduling units participating in the joint optimization of the virtual power plant to enhance the model's adaptability and precision.

[0050] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A virtual power plant charging and discharging scheduling method based on electric vehicle aggregation, characterized in that, Includes the following steps: Step 1: Construct a driving characteristic model based on electric vehicle user behavior, and use the Monte Carlo method to simulate and generate typical travel periods and charging loads for electric vehicles; Step 2: Construct the power generation model and load transfer model; Step 3: Configure different vehicle models to participate in virtual power plant dispatching; Step 4: Construct an objective function to minimize the overall operating cost of the virtual power plant based on different vehicle models.

2. The virtual power plant charging and discharging scheduling method based on electric vehicle aggregation according to claim 1, characterized in that, The formula for the objective function is: ； In the formula, The cost and profit of buying and selling electricity in the market; For gas turbine fuel costs; To compensate for interruptible load costs; To compensate for the cost of transferable load; Cost of electric vehicle battery wear and tear.

3. The virtual power plant charging and discharging scheduling method based on electric vehicle aggregation according to claim 2, characterized in that, The formula for the cost of electric vehicle battery degradation is: ； In the formula, This is the weighting coefficient for the loss penalty; For electric vehicles i In t Discharge power at any given moment; ； ； In the formula, Indicates the first The cost of replacing a vehicle's battery; Indicates battery capacity; Indicates the rated number of cycles; This represents the average state of electric vehicle batteries. express SOC upper limit ； express SOC Lower limit.

4. The virtual power plant charging and discharging scheduling method based on electric vehicle aggregation according to claim 1, characterized in that, The power balance constraint for the virtual power plant is given by the following formula: ； In the formula, For wind power in Output power during the time period; For photovoltaics Output power during the time period; For gas turbines in Output power during the time period; For VPP in Power purchased and sold from the power grid during specific time periods; For energy storage Discharge power over a given period of time; For energy storage The charging power at that time; for The total discharge power of electric vehicles during the time period; for Total charging power of electric vehicles during a given time period; for The total power demand of the period.

5. The virtual power plant charging and discharging scheduling method based on electric vehicle aggregation according to claim 1, characterized in that, The formula for constraining the charging and discharging of electric vehicles is as follows: ； In the formula, for t The number of electric vehicles that can be controlled by the virtual power plant during a given time period; for The equivalent power output of electric vehicles during a given time period; This represents the average state of charge of the electric vehicle at the previous moment. Electricity consumption per unit distance of electric vehicles; This represents the average driving range of an electric vehicle. for The number of electric vehicles newly added to the system during a given period.

6. The virtual power plant charging and discharging scheduling method based on electric vehicle aggregation according to claim 1, characterized in that, The MILP model is obtained by simultaneously solving the objective function of minimizing overall operating costs and the power balance constraint. CPLEX is then used to solve the MILP model to obtain the charging and discharging strategies and power output strategies of different vehicle models.

7. The virtual power plant charging and discharging scheduling method based on electric vehicle aggregation according to claim 1, characterized in that, The power generation models include: wind power, photovoltaic power, gas turbines, and energy storage.

8. The virtual power plant charging and discharging scheduling method based on electric vehicle aggregation according to claim 1, characterized in that, Models include: BYD, Nissan, Mitsubishi, and BMW.

9. A virtual power plant charging and discharging scheduling system based on electric vehicle aggregation, characterized in that, include: Memory is used to store instructions that can be executed by the processor; A processor for executing instructions to implement the virtual power plant charging and discharging scheduling method based on electric vehicle aggregation as described in any one of claims 1-8.

10. A computer-readable medium storing computer program code, characterized in that, The computer program code, when executed by a processor, implements the virtual power plant charging and discharging scheduling method based on electric vehicle aggregation as described in any one of claims 1-8.

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