Optimized dispatching method for virtual power plant

Abstract

Disclosed in the present invention is an optimized dispatching method for a virtual power plant, which method is applied to a virtual power plant dispatching control center in a virtual power plant dispatching system. The method comprises: predicting next-day wind power and photovoltaic output values and power demand conditions of electric vehicles in a load aggregator on the day ahead; acquiring a next-day output scheme reported by each output unit in a virtual power plant dispatching system, wherein the next-day output scheme is determined by each output unit on the basis of a day-ahead electricity selling price, electric-vehicle charging and discharging strategies and the output characteristics and costs of other output units; and performing coordinated optimization on the basis of the next-day output schemes reported by the output units and a virtual power plant optimized dispatching model which takes demand responses and environmental costs into account, so as to obtain a final next-day output plan, and issuing the final next-day output plan to each output unit, such that each output unit performs execution on the basis of the final next-day output plan. The present invention makes load dispatching in a virtual power plant more flexible.

Description

A virtual power plant optimization scheduling method Technical Field

[0001] The present invention relates to the technical field of virtual power plant scheduling, and in particular to a virtual power plant optimization scheduling method. Background Art

[0002] The continued application of digital energy technologies can enhance management, monitoring, and support capabilities within the energy sector, effectively promoting high-quality development. Virtual power plants (VPPs) in digital energy can organically integrate diverse energy sources to form a multi-energy, complementary energy system. This helps resolve scheduling issues arising from the operational characteristics of diverse power sources, effectively improving energy utilization and contributing to the achievement of carbon peak and carbon neutrality goals.

[0003] As traditional energy-based power generation is gradually phased out, the development of clean energy generation presents an opportunity to reduce environmental pollution. Therefore, clean energy generation will inevitably become the primary component of the future grid's energy supply. However, clean energy generation output is volatile and random. Electric vehicles, as a flexible resource, possess both source and load properties and can better meet the flexibility needs of virtual power plants (VPPs). Therefore, they can be aggregated and uniformly regulated within VPPs. With the increasing penetration of new energy, reducing the environmental costs of various types of harmful gases and fully tapping into the flexible operational capabilities of the demand side have become key issues that must be addressed for VPPs to achieve low-carbon economic dispatch.

[0004] For example, Chinese patent CN202210828831.2 discloses a virtual power plant optimization scheduling method and device. However, it does not take into account the flexible resource of electric vehicles. In the virtual power plant optimization model that considers demand-side response, this technology only considers the time-of-use electricity price strategy based on price-based demand-side response to regulate the electricity consumption mode of the user-side load, and does not consider the incentive-based demand-side response strategy and the participation of electric vehicles in the virtual power plant optimization scheduling. Electric vehicles, as a new type of distributed energy storage system, can provide energy storage capacity support and power supply. Virtual power plants can use the energy storage capacity of electric vehicles to balance the power load, reduce the difference between power peaks and valleys, and reduce the load pressure of the power system. However, there is a problem of a single scheduling method.

[0005] Summary of the Invention

[0006] In view of this, in order to overcome the above problems, one or more embodiments of this specification provide a virtual power plant optimization scheduling method.

[0007] To achieve the above objectives, one or more embodiments of this specification provide the following technical solutions:

[0008] The present invention provides a virtual power plant optimization scheduling method, which is applied to a virtual power plant scheduling control center in a virtual power plant scheduling system. The method includes:

[0009] Predict the wind power and photovoltaic output values ​​and electric vehicle electricity demand in load agents for the next day;

[0010] Obtaining the next-day output plan reported by each output unit in the virtual power plant dispatch system. The next-day output plan is determined by each output unit based on the day-ahead electricity price, electric vehicle charging and discharging strategy, and the output characteristics and costs of other output units;

[0011] Coordinated optimization is performed based on the next-day power generation plan reported by the output unit and the virtual power plant optimization scheduling model that considers demand response and environmental costs to obtain a final next-day power generation plan, which is then sent to each output unit so that each output unit executes according to the final next-day power generation plan.

[0012] Preferably, the method further comprises:

[0013] The actual operating status of each output unit during the day is obtained, and the gas turbine units and electric vehicles are optimized and adjusted according to the wind power, photovoltaic output and the daily electricity sales price to reduce the deviation between the actual output of the virtual power plant and the final next-day output plan.

[0014] Preferably, the virtual power plant dispatch control center includes a virtual power plant optimization dispatch model that considers demand response and environmental costs. The virtual power plant optimization dispatch model is constructed in the following manner:

[0015] Modeling each output unit of the virtual power plant to obtain a wind power model, a photovoltaic power generation system, and a gas turbine model;

[0016] The virtual power plant optimization scheduling model is established based on wind power model, electric vehicle, energy storage equipment, gas turbine model and load.

[0017] Preferably, the output power corresponding to the wind power model includes:

[0018] Among them, v i 、v o and v r are the cut-in speed, cut-out speed and rated speed of the wind turbine respectively; a, b, c are the output coefficients of the wind turbine; p r is the rated power.

[0019] Preferably, the output power corresponding to the photovoltaic power generation system includes:

[0020] Among them, PSTC is the maximum output power of the component under standard test conditions; k1 represents the temperature coefficient of the component; T r and T c Represent the reference temperature and photovoltaic panel temperature respectively; G STC and G ING They are the irradiance under standard test conditions and estimated output respectively.

[0021] Preferably, the quadratic function expression corresponding to the gas turbine model includes:

[0022] Among them, P m (t) represents the power generation of the micro gas turbine at time t; ρ m , γ m , α m is the unit operating cost coefficient of the gas turbine.

[0023] Preferably, the objective function for maximizing net profit corresponding to the virtual power plant optimization scheduling model includes:

[0024] Among them, Y(t) is the profit of the virtual power plant at time t, Y dr (t) is the income of electric vehicles at time t after price demand response, C G (t) is the operating and management cost of each output unit of the virtual power plant at time t, C M is the fuel cost generated by the gas turbine output at time t, D(t) is the cost of the virtual power plant purchasing electricity from the main grid, and C dis (t) is the sum of the electric vehicle discharge benefit and battery loss cost.

[0025] Preferably, the revenue of the virtual power plant at time t includes:

[0026] Y(t)=G1(t)(P wt (t)+P pv (t)+P gt (t)+P evdis (t)+P evc (t))

[0027] Where T represents 24 moments in a day, t = 1, 2, 3, 3, T; G(t) is the day-ahead electricity price at moment t; P wind (t), P pv (t), P gt (t), P evdis (t), P evc (t) are the wind power, photovoltaic, gas turbine, electric vehicle output and electric vehicle load at time t.

[0028] Preferably, the electric vehicle uses the revenue at time t after the price demand response, including:

[0029] Y dr (t) = P evc (t)F t -P evc,0 (t)F t0

[0030] Among them, P evc,0 (t)P evc (t) is the electric vehicle load before and after the price demand response at time t, F t0 、F t is time t

[0031] 、

[0032] Use electricity prices before and after price-based demand response.

[0033] Preferably, the operating management expenses of each output unit of the virtual power plant at time t include:

[0034] C G (t) = X wt P wt (t)+X pv P pv (t)+X gt P m (t)+X ev (P evc (t)+P evdis (t))

[0035] Among them, X wt 、X pv 、X m 、X ev They are the operation and management cost coefficients of wind power, photovoltaic output units, gas turbines and electric vehicles respectively.

[0036] The beneficial effects of the present invention are:

[0037] The present invention aims at the relationship between electric vehicle demand response and environmental costs, fully considers the harmful gases released by gas turbine output and eliminates the uncertainty of new energy output, and proposes a virtual power plant optimization scheduling model. The model adds electric vehicles to the operation of the virtual power plant in the form of distributed energy storage, and takes into account environmental costs such as harmful gas emissions from gas turbines. At the same time, the operating costs of gas turbines are considered. Wind power and photovoltaics, as clean energy, do not cause pollution to the environment. Only the operating costs of the two are considered. The maximum operating profit of the virtual power plant is used as the optimization scheduling analysis objective function. Through the virtual power plant, multiple types of energy are integrated and coordinated and optimized, the output of each type of energy is obtained separately, and an optimization scheduling model of a virtual power plant including wind power, photovoltaics, gas turbines and electric vehicles is constructed, making the load scheduling of the virtual power plant more flexible. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] FIG1 is a schematic diagram of the architecture of a virtual power plant scheduling system provided by an exemplary embodiment of the present invention;

[0039] FIG2 is a flow chart of a virtual power plant optimization scheduling method provided by an exemplary embodiment of the present invention;

[0040] FIG3 is a flowchart of a method for establishing a virtual power plant optimization scheduling model considering demand response and environmental costs, provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the following embodiments and the accompanying drawings are used to further describe the technical solutions in the embodiments of the present invention. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] FIG1 is a schematic diagram of the architecture of a virtual power plant dispatch system provided by an exemplary embodiment of the present invention. As shown in FIG1 , distributed power sources such as photovoltaic generators, wind turbines, and gas turbines, as well as distributed energy storage and internal loads, are integrated into a virtual power plant. The coordinated interaction of distributed power sources and energy storage systems during user-side regulation and virtual power plant operation constructs the virtual power plant dispatch architecture. The virtual power plant dispatch system may include a main grid 102, a virtual power plant dispatch control center 104, and corresponding output units. Output is generally short for output power in a power system, so an output unit can be considered a unit that can output a certain output power. Specifically, it may include a wind turbine 106, a photovoltaic generator 108, a gas turbine 110, and an electric vehicle 112 within a target area, as shown in FIG1 . It should be noted that the present invention creatively incorporates electric vehicles, adding them to the virtual power plant operation in the form of distributed energy storage. As a new type of distributed energy storage system, electric vehicles can provide energy storage support and power supply. Virtual power plants can leverage the energy storage capacity of electric vehicles to balance power loads, reduce the difference between peak and valley power levels, and reduce load pressure on the power system.

[0043] A virtual power plant (VPP) is a power coordination and management system that uses advanced information and communication technologies and software systems to aggregate and coordinate distributed energy resources (DERs), such as distributed generators (DGs), energy storage systems, controllable loads, and electric vehicles, to participate in electricity markets and grid operations as a specialized power plant. The core concepts of VPPs can be summarized as "communication" and "aggregation." Key VPP technologies include coordinated control, smart metering, and information and communication technologies. The most attractive feature of VPPs is their ability to aggregate DERs to participate in electricity and ancillary service markets, providing management and ancillary services for distribution and transmission networks.

[0044] FIG2 is a flow chart of a virtual power plant optimization scheduling method provided by an exemplary embodiment of the present invention, which may specifically include the following steps:

[0045] Step 202: Forecast the wind power and photovoltaic output values ​​and the electric vehicle electricity demand in the load agent for the next day.

[0046] The virtual power plant dispatch and control center predicts the next day's wind and photovoltaic output values, as well as the electric vehicle electricity demand from load agents. As shown in Figure 1, the wind power and photovoltaic output forecasts shown in Figure 1 represent the forecasts. Electric vehicle electricity demand also requires a forecast.

[0047] Step 204: Obtain the next-day output plan reported by each output unit. The next-day output plan is determined by each output unit based on the day-ahead electricity price, the electric vehicle charging and discharging strategy, and the output characteristics and costs of other output units.

[0048] Each output unit can optimize its next-day output plan based on the day-ahead electricity price, electric vehicle charging and discharging strategies, and the output characteristics and costs of other output units to maximize the virtual power plant's revenue. This optimized plan can then be submitted to the virtual power plant control center as the next-day output plan. For example, as shown in Figure 1, a gas turbine unit can submit its day-ahead output plan to the virtual power plant control center.

[0049] Step 206: Coordinate and optimize the next-day power generation plan reported by the power unit and the virtual power plant optimization scheduling model that considers demand response and environmental costs to obtain a final next-day power generation plan, and send it to each power unit so that each power unit executes according to the final next-day power generation plan.

[0050] The virtual power plant control center can coordinate and optimize based on the plans reported by each output unit of the virtual power plant, and reasonably arrange the final next-day output plan of the virtual power plant and each output unit.

[0051] In one embodiment, to increase the actual daily operating revenue of the virtual power plant, the virtual power plant dispatch and control center can obtain the actual daily operating status of each output unit and, based on the wind power and photovoltaic output and the daily electricity price, optimize and adjust the gas turbine units and electric vehicles to reduce the deviation between the actual output of the virtual power plant and the final next-day output plan. In other words, during the actual daily operation of the virtual power plant, the dispatch and control center can optimize and adjust the gas turbine units and electric vehicles based on the wind power and photovoltaic output and the daily electricity price to reduce the deviation between the actual output of the virtual power plant and the day-ahead output plan, thereby increasing the actual daily operating revenue of the virtual power plant.

[0052] In order to better understand the establishment and application of the virtual power plant optimization scheduling model of demand response and environmental cost described in the present invention, a detailed introduction is given below.

[0053] FIG3 is a flowchart of a method for establishing a virtual power plant optimization scheduling model considering demand response and environmental costs, provided by an exemplary embodiment of the present invention. Specifically, the method can be constructed in the following manner:

[0054] Step 302: Model the output units of the virtual power plant to obtain a wind power model, a photovoltaic power generation system, and a gas turbine model.

[0055] It is generally believed that wind power generation follows the Weibull distribution, that is, at different wind speeds, the output power P of the wind turbine is wtare different. Specifically as follows:

[0056] Among them, v i 、v o and v r are the cut-in speed, cut-out speed and rated speed of the wind turbine respectively; a, b, c are the output coefficients of the wind turbine; p r is the rated power.

[0057] For photovoltaic power generation systems, the main factors affecting photovoltaic power generation output are solar panel temperature, ambient temperature and solar radiation intensity. Photovoltaic system output power P pv Expressed as:

[0058] Among them, P STC is the maximum output power of the component under standard test conditions; k1 represents the temperature coefficient of the component; T r and T c Represent the reference temperature and photovoltaic panel temperature respectively; G STC and G ING They are the irradiance under standard test conditions and estimated output respectively.

[0059] As for the gas turbine model, it mainly consists of two parts: fuel cost and maintenance cost. When optimizing the system, the model can be expressed in the form of a quadratic function:

[0060] Among them, P m (t) represents the power generation of the micro gas turbine at time t; ρ m , γ m , α m is the unit operating cost coefficient of the gas turbine.

[0061] Gas turbine power generation cost, environmental cost includes environmental value cost and penalty cost, the function is as follows

[0062] Among them, C H (t) is the environmental cost of pollutant emissions from gas turbine power generation; α is the operating cost coefficient of wind power output; P MT (t) is the power output of the gas turbine during period t; W gt,r is the emission of the rth type of pollutant gas emitted by the gas turbine power generation output; H r 、Y r , are the environmental value and penalty of the rth gas respectively.

[0063] Step 304: Establish the virtual power plant optimization scheduling model based on the wind power model, electric vehicle, energy storage equipment, gas turbine model, and load.

[0064] A virtual power plant optimization scheduling model can be constructed based on wind power generation, electric vehicles, energy storage equipment, gas turbines, and loads. The virtual power plant takes the maximum net profit as its objective function, as shown in formula (5):

[0065] Among them, Y(t) is the profit of the virtual power plant at time t, Y dr (t) is the income of electric vehicles at time t after price demand response, C G (t) is the operating and management cost of each output unit of the virtual power plant at time t, C M is the fuel cost generated by the gas turbine output at time t, D(t) is the cost of the virtual power plant purchasing electricity from the main grid, and C dis (t) is the sum of the electric vehicle discharge benefit and battery loss cost.

[0066] The revenue Y(t) of the virtual power plant at time t includes:

[0067] Y(t)=G1(t)(P wt (t)+P pv (t)+P gt (t)+P evdis (t)+P evc (t)) (6)

[0068] Where, T represents 24 moments in a day, t = 1, 2, 3, 3, T; G(t) is the day-ahead electricity price at moment t; P wind (t), P pv (t), P gt (t), P evdis (t), P evc (t) are the wind power, photovoltaic, gas turbine, electric vehicle output and electric vehicle load at time t.

[0069] The benefit Y of electric vehicle use at time t after price demand response dr (t) are as follows:

[0070] Y dr (t) = P evc (t)F t -P evc,0 (t)F t0 (7)

[0071] Where, P evc,0 (t), P evc (t) is the electric vehicle load before and after the price demand response at time t, F t0 、F t is time t

[0072] Use electricity prices before and after price-based demand response.

[0073] The operating and management costs C of each output unit of the virtual power plant at time t G (t) are as follows:

[0074] C G (t) = X wt P wt (t)+X pv P pv (t)+X gt P m (t)+X ev (P evc (t)+P evdis (t)) (8)

[0075] Among them, X wt 、X pv 、X m 、X ev They are the operation and management cost coefficients of wind power, photovoltaic output units, gas turbines and electric vehicles respectively.

[0076] The penalty cost D(t) for the deviation between the virtual power plant's day-ahead output plan and actual output is as follows:

[0077] D(t)=G2(t)|ΔP vpp (t)| (9)

[0078] ΔP vpp (t) = P′(t) - P wt (t)-P pv (t)-P m (t)-P evf (t) (10)

[0079] Where G2(t) is the unit electricity purchase cost of the virtual power plant in the main grid at time t; ΔP vpp (t) is the deviation between the planned output and actual output of the virtual power plant at time t; P′(t) is the planned output at time t.

[0080] The sum of electric vehicle discharge benefits and battery loss costs C f (t) are as follows:

[0081] Among them, C d,n is the average loss cost per unit discharge of the nth electric vehicle; N evf (t) is the number of electric vehicles in the discharging state at time t; 0.5G1(t) is the unit discharge benefit of electric vehicles.

[0082] In fact, based on the embodiments shown in Figures 1, 2 and 3, simplification can also be made, that is, it is considered that the present invention realizes the scheduling of virtual power plants through three core steps, namely, step 1 is the virtual power plant optimization scheduling architecture, step 2 is the modeling of typical VPP equipment (such as wind turbines, photovoltaic units, gas turbine units, etc.), and step 3 is to construct a virtual power plant optimization scheduling model considering demand response and environmental costs.

[0083] Furthermore, based on actual conditions, it's necessary to set constraints on the model to impose certain restrictions and achieve desired scheduling results. Specifically, the intraday scheduling constraints for virtual power plants can include power balance constraints, photovoltaic output constraints, wind power output constraints, gas turbine output constraints, and electric vehicle charging and discharging constraints. These constraints are described in detail below.

[0084] The power balance constraint can be shown as follows:

[0085] P′(t)=P wt (t)+P pv (t)+P m (t)+P evdis (t)+△P vpp (t) (12)

[0086] The PV output constraint can be shown as follows:

[0087] P pv,min ≤P pv (t)≤P pv,max (13)

[0088] The wind power output constraint can be shown as follows:

[0089] P wt,min ≤P wt (t)≤P wt,max (14)

[0090] Among them, P wt,max 、P wt,min are the maximum and minimum output values ​​of the wind turbine respectively.

[0091] The electric vehicle charging and discharging constraints can be shown as follows:

[0092] 0≤P evc,n (t)≤P evc,max (18)

[0093] 0≤P evf,n (t)≤P evf,max (19)

[0094] SOC ev,min (t)≤SOC ev,n (t)≤SOC ev,max (t) (23)

[0095] Among them, P evc (t), P evf (t) are the total charging and discharging power of the electric vehicle at time t; N evcn (t), N evfn (t) are the number of electric vehicles in the charging and discharging states at time t; P evc,n (t), P evf,n (t) are the charging and discharging power of the nth electric vehicle at time t; P evc,max 、P evf,max are the maximum charging and discharging power limits of electric vehicles; K evc,n (t), K evf,n (t) are 0-1 variables corresponding to the charging and discharging power of the nth electric vehicle; t arr,n , t dep,n are the end time and start time of the trip of the nth electric vehicle respectively; SOC ev,n (t) is the state of charge of the nth electric vehicle at time t; η evc ,η evf are the charging and discharging efficiency of electric vehicles respectively; C is the battery capacity of electric vehicles; △t is the optimal scheduling time step; SOC ev,max (t), SOC ev,min (t) are the maximum and minimum state of charge of the electric vehicle, respectively.

[0096] Based on the above embodiments, the present invention aims at the relationship between electric vehicle demand response and environmental costs, fully considers the fact that gas turbine output will release harmful gases to the outside, and excludes factors such as uncertainty in new energy output, and provides a virtual power plant optimization scheduling model. The model adds electric vehicles to the virtual power plant operation in the form of distributed energy storage, and takes environmental costs such as harmful gas emissions from gas turbines into account in the model. At the same time, the operating costs of gas turbines are considered. Wind power and photovoltaics, as clean energy, do not cause pollution to the environment. Only the operating costs of the two are considered. The maximum operating profit of the virtual power plant is used as the objective function of the optimization scheduling analysis. Through the virtual power plant, multiple types of energy are integrated and coordinated to optimize, and the output of each type of energy is obtained separately to construct an optimization scheduling model for a virtual power plant containing wind power, photovoltaics, gas turbines and electric vehicles.

[0097] In an exemplary embodiment of the present invention, an electronic device is provided, which includes a processor, a network interface, a memory, a non-volatile memory, and an internal bus, and may also include hardware required for other functions. The electronic device is configured with a virtual switch, which reads the corresponding computer program from the non-volatile memory into the memory and then runs it, and the computer program executes the above method when it runs. Of course, in addition to software implementation, the present invention does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0098] The embodiment described above is only a preferred solution of the present invention and does not limit the present invention in any form. Other variations and modifications are possible without exceeding the technical solution described in the claims.

Claims

1. A virtual power plant optimization scheduling method, characterized in that: A virtual power plant dispatch control center applied to a virtual power plant dispatch system, the method comprising: Predict the wind power and photovoltaic output values ​​and the electric vehicle electricity demand in the load agent for the next day in the day ahead; Obtain the next-day output plan reported by each output unit in the virtual power plant dispatching system, where the next-day output plan is determined by each output unit based on the day-ahead electricity price, electric vehicle charging and discharging strategy, and output characteristics and costs of other output units; Coordinated optimization is performed based on the next-day power generation plan reported by the power unit and the virtual power plant optimization scheduling model that considers demand response and environmental costs to obtain a final next-day power generation plan, which is then sent to each power unit so that each power unit executes according to the final next-day power generation plan.

2. A virtual power plant optimization scheduling method according to claim 1, characterized in that: The method further comprises: The actual operating status of each output unit during the day is obtained, and the gas turbine units and electric vehicles are optimized and adjusted according to the wind power, photovoltaic output and the daily electricity sales price to reduce the deviation value between the actual output of the virtual power plant and the final next-day output plan.

3. A virtual power plant optimization scheduling method according to claim 1, characterized in that: The virtual power plant dispatch control center includes a virtual power plant optimization dispatch model that takes into account demand response and environmental costs. The virtual power plant optimization dispatch model is constructed in the following way: Modeling each output unit of the virtual power plant to obtain a wind power model, a photovoltaic power generation system and a gas turbine model; The virtual power plant optimization scheduling model is established based on wind power model, electric vehicle, energy storage equipment, gas turbine model and load.

4. A virtual power plant optimization scheduling method according to claim 3, characterized in that: The output power corresponding to the wind power model includes: Among them, v i 、v o and v r are the cut-in speed, cut-out speed and rated speed of the wind turbine respectively; a, b, c are the output coefficients of the wind turbine; p r is the rated power.

5. A virtual power plant optimization scheduling method according to claim 3, characterized in that: The output power corresponding to the photovoltaic power generation system includes: Among them, P STC is the maximum output power of the component under standard test conditions; k1 represents the temperature coefficient of the component; T r and T c Represent the reference temperature and photovoltaic panel temperature respectively; G STC and G ING They are the irradiance under standard test conditions and estimated output respectively.

6. A virtual power plant optimization scheduling method according to claim 3, characterized in that: The quadratic function expression corresponding to the gas turbine model includes: Among them, P m (t) represents the power generation of the micro gas turbine at time t; ρ m , γ m , α m is the unit operating cost coefficient of the gas turbine.

7. A virtual power plant optimization scheduling method according to claim 3, characterized in that: The objective function for maximizing net profit corresponding to the virtual power plant optimization scheduling model includes: Among them, Y(t) is the revenue of the virtual power plant at time t, Y dr (t) is the revenue of electric vehicles based on price demand response at time t, C G (t) is the operating and management cost of each output unit of the virtual power plant at time t, C M is the fuel cost generated by the gas turbine output at time t, D(t) is the cost of the virtual power plant purchasing electricity from the main grid, and C dis (t) is the sum of the electric vehicle discharge benefit and battery loss cost.

8. A virtual power plant optimization scheduling method according to claim 7, characterized in that: The virtual power plant's revenue at time t includes: Y(t)=G1(t)(P wt (t)+P pv (t)+P gt (t)+P evdis (t)+P evc (t)) Among them, T represents 24 moments in a day. G(t) is the day-ahead electricity price at time t; are the wind power, photovoltaic, gas turbine, electric vehicle output and electric vehicle load at time t, respectively.

9. A virtual power plant optimization scheduling method according to claim 7, characterized in that: The benefits of using electric vehicles at time t after price demand response include: Y dr (t) = P evc (t)F t -P evc,0 (t)F t0 Among them, P evc,0 (t), P evc (t) is the electric vehicle load before and after the price demand response at time t, F t0 、F t Use the electricity prices before and after the price-based demand response for time t.

10. A virtual power plant optimization scheduling method according to claim 7, characterized in that: The operating and management costs of each output unit of the virtual power plant at time t include: G (t) = X wt P wt (t)+X pv P pv (t)+X gt P m (t)+X ev (P evc (t)+P evdis (t)) Among them, X wt , X pv , X m , X ev They are the operation and management cost coefficients of wind power, photovoltaic output units, gas turbines and electric vehicles respectively.

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