Virtual power plant optimization scheduling method

Abstract

The invention discloses a virtual power plant optimal scheduling method which is applied to a virtual power plant scheduling control center in a virtual power plant scheduling system, and the method comprises the steps: predicting the wind power and photovoltaic output values of the next day and the electricity demand condition of an electric vehicle in a load agent in the day; obtaining a next-day output scheme reported by each output unit in the virtual power plant dispatching system, wherein the next-day output scheme is determined by each output unit according to the day-ahead electricity selling price, the electric vehicle charging and discharging strategy and the output characteristics and cost of other output units; and performing coordinated optimization according to the next-day output scheme reported by the output unit and the virtual power plant optimization scheduling model considering the demand response and the environment cost to obtain a final next-day output plan, and issuing the final next-day output plan to each output unit, so that each output unit executes according to the final next-day output plan. According to the invention, virtual power plant load scheduling is more flexible.

Description

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] With the continuous application of energy digital technology, it is possible to improve the management, supervision and support level of the energy field and effectively promote high-quality development in the energy field. The virtual power plant (VPP) in energy digitalization can organically integrate different energy sources to form a multi-energy complementary energy system, promote the solution of scheduling problems caused by the operating characteristics of different power sources, and effectively improve energy utilization.

[0003] Since the power generation mode based on traditional energy will be gradually eliminated, and the development of clean energy power generation provides an opportunity to reduce environmental pollution, clean energy power generation will inevitably become the main structure of the energy supply side of the power grid in the future. However, the power generation output of clean energy is volatile and random, and electric vehicles, as a flexible resource, have both source and load attributes, which can better meet the flexibility needs of virtual power plants. Therefore, electric vehicles can be aggregated and uniformly regulated in virtual power plants. With the gradual increase in the penetration rate of new energy, how to reduce the environmental costs of various types of harmful gases and fully tap the flexible operation capabilities of the demand side has become a key issue that needs to be urgently solved for VPP to achieve low-carbon economic scheduling.

[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 considering 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 user-side loads, and does not consider the incentive-based demand-side response strategy and electric vehicles participating in the virtual power plant optimization scheduling. As a new type of distributed energy storage system, electric vehicles 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. There is a problem of a single scheduling method. Summary of the invention

[0005] 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.

[0006] To achieve the above objectives, one or more embodiments of this specification provide the following technical solutions:

[0007] 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 comprises:

[0008] 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;

[0009] 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;

[0010] 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.

[0011] Preferably, the method further comprises:

[0012] 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.

[0013] Preferably, 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 manner:

[0014] 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;

[0015] The virtual power plant optimization scheduling model is established based on wind power model, electric vehicle, energy storage equipment, gas turbine model and load.

[0016] Preferably, the output power corresponding to the wind power model includes:

[0017]

[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]

[0021] 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.

[0022] Preferably, the quadratic function expression corresponding to the gas turbine model includes:

[0023]

[0024] 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.

[0025] Preferably, the objective function for maximizing net profit corresponding to the virtual power plant optimization scheduling model includes:

[0026]

[0027] 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.

[0028] Preferably, the revenue of the virtual power plant at time t includes:

[0029] Y(t)=G1(t)(P wt (t)+P pv (t)+P gt (t)+P evdis (t)+P evc (t))

[0030] Where T represents 24 moments in a day, t = 1, 2, 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.

[0031] Preferably, the electric vehicle uses the revenue at time t after the price demand response, including:

[0032] Y dr (t) = P evc (t)F t -P evc,0 (t)F t0

[0033] 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

[0034] Use electricity prices before and after price-based demand response.

[0035] Preferably, the operating management expenses of each output unit of the virtual power plant at time t include:

[0036] 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))

[0037] 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.

[0038] The beneficial effects of the present invention are:

[0039] The present invention aims at the relationship between electric vehicle demand response and environmental cost, 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 operation of the virtual power plant 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 cost of the gas turbine is considered. Wind power and photovoltaics, as clean energy, do not pollute the environment. Only the operating costs of the two are considered, and 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 respectively, and an optimization scheduling model for 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

[0040] Figure 1 is a schematic diagram of the architecture of a virtual power plant dispatching system provided by an exemplary embodiment of the present invention;

[0041] Figure 2 is a flow chart of a virtual power plant optimization scheduling method provided by an exemplary embodiment of the present invention;

[0042] Figure 3 It is a flowchart of a method for establishing a virtual power plant optimization scheduling model taking into account demand response and environmental costs, provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the technical solution in the embodiments of the present invention is further described in detail through the following embodiments and in combination with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the invention.

[0044] Figure 1 is a schematic diagram of the architecture of a virtual power plant dispatching system provided by an exemplary embodiment of the present invention. Figure 1 As shown, 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 synergy of distributed power sources and energy storage systems during user-side regulation and virtual power plant operation constructs a dispatching architecture for the virtual power plant. The virtual power plant dispatching system may include a main power grid 102, a virtual power plant dispatching control center 104, and corresponding output units. Output is generally an abbreviation for output power in the power system, so the output unit can be considered as a unit that can output a certain output power. Specifically, it may include Figure 1The wind turbine 106, photovoltaic unit 108, gas turbine unit 110 and electric vehicle 112 in the target area are shown. It should be noted that the present invention creatively adds electric vehicles and adds electric vehicles to the operation of the virtual power plant in the form of distributed energy storage. 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 power loads, reduce the difference between power peaks and valleys, and reduce the load pressure of the power system.

[0045] A virtual power plant is a power coordination management system that uses advanced information and communication technologies and software systems to achieve the aggregation and coordinated optimization of distributed energy resources DER (Distributed Energy Resource) such as distributed power sources DG (distributed generator), energy storage systems, controllable loads, and electric vehicles, so as to participate in the power market and power grid operation as a special power plant. The core of the concept of virtual power plants can be summarized as "communication" and "aggregation". The key technologies of virtual power plants mainly include coordinated control technology, smart metering technology, and information and communication technology. The most attractive function of virtual power plants is that they can aggregate DER to participate in the operation of the power market and ancillary service market, and provide management and ancillary services for distribution and transmission networks.

[0046] Figure 2 1 is a flowchart of a virtual power plant optimization scheduling method provided by an exemplary embodiment of the present invention, which may specifically include the following steps:

[0047] Step 202: forecast the wind power and photovoltaic output values ​​and the electric vehicle power demand in the load agent for the next day.

[0048] The virtual power plant dispatching and control center predicts the wind power and photovoltaic output values ​​and the electric vehicle power demand of the load agents for the next day. Figure 1 As shown, Figure 1 The wind power day-ahead output forecast and photovoltaic day-ahead output forecast shown are forecast situations, and the electricity demand of electric vehicles also needs to be forecasted.

[0049] Step 204: Obtain the next-day power generation plan reported by each power generation unit, wherein the next-day power generation plan is determined by each power generation unit according to the day-ahead electricity price, the electric vehicle charging and discharging strategy, and the output characteristics and costs of other power generation units.

[0050] Each output unit can optimize the next day's output plan according to the day-ahead electricity price, electric vehicle charging and discharging strategy, and the output characteristics and costs of other output units to maximize the benefits of the virtual power plant, and report the optimized plan as the next day's output plan to the virtual power plant control center. Figure 1As shown, the gas turbine unit can report the gas turbine day-ahead output plan to the virtual power plant control center.

[0051] 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.

[0052] The virtual power plant control center can coordinate and optimize according to 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.

[0053] In one embodiment, in order to improve the actual operating income of the virtual power plant during the day, the virtual power plant dispatching and control center can obtain the actual operating status of each output unit during the day, and optimize and adjust the gas turbine units and electric vehicles according to the wind power, photovoltaic output and the daily electricity price, so as to reduce the deviation between the actual output of the virtual power plant and the final next-day output plan. In other words, when the virtual power plant is actually operating during the day, the dispatching and control center can optimize and adjust the gas turbines and electric vehicles according to the wind power, photovoltaic output and the daily electricity price, so as to reduce the deviation between the actual output of the virtual power plant and the day-ahead output plan, and improve the actual operating income of the virtual power plant during the day.

[0054] 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.

[0055] Figure 3 It 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, which can be constructed in the following manner:

[0056] 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.

[0057] 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 wt are different. They are as follows:

[0058]

[0059] 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.

[0060] 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 It is expressed as:

[0061]

[0062] 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.

[0063] 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:

[0064]

[0065] 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.

[0066] Gas turbine power generation cost, environmental cost includes environmental value cost and penalty cost, the function is as follows

[0067]

[0068] 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 pollutant gas discharged by the gas turbine power generation output; H r , Y r , are the environmental value and penalty of the rth gas respectively.

[0069] 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.

[0070] Based on wind power generation, electric vehicles, energy storage equipment, gas turbines, and loads, a virtual power plant optimization scheduling model can be constructed. The virtual power plant takes the maximum net profit as its objective function, as shown in formula (5):

[0071]

[0072] 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.

[0073] The revenue Y(t) of the virtual power plant at time t includes:

[0074] Y(t)=G1(t)(P wt (t)+P pv (t)+P gt (t)+P evdis (t)+P evc (t)) (6)

[0075] Where, T represents 24 moments in a day, t = 1, 2, 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.

[0076] The benefit Y of electric vehicle use at time t after price demand response dr (t) are as follows:

[0077] Y dr (t) = P evc (t)F t -P evc,0 (t)F t0 (7)

[0078] 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 Use the electricity prices before and after the price-based demand response for time t.

[0079] The operation and management cost C of each output unit of the virtual power plant at time t G (t) are as follows:

[0080] CG (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)

[0081] 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.

[0082] The penalty cost D(t) for the deviation between the virtual power plant's day-ahead output plan and actual output is as follows:

[0083] D(t)=G2(t)|ΔP vpp (t)| (9)

[0084] ΔP vpp (t) = P′(t) - P wt (t)-P pv (t)-P m (t)-P evf (t) (10)

[0085] Where G2(t) is the unit power 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 the actual output of the virtual power plant at time t; P′(t) is the planned output before time t.

[0086] The sum of electric vehicle discharge benefits and battery loss costs C f (t) are as follows:

[0087]

[0088] 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.

[0089] In fact, based on Figure 1 , Figure 2 and Figure 3The embodiment shown can also be simplified, 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.

[0090] Furthermore, in combination with the actual situation, it is also necessary to set constraints on the model, so as to impose certain restrictions and obtain the expected dispatch results. Specifically, the intraday dispatch constraints of the virtual power plant may include power balance constraints, photovoltaic output constraints, wind power output constraints, gas turbine output constraints, and electric vehicle charging and discharging constraints. The present invention describes the above constraints in detail below.

[0091] The power balance constraint can be shown as follows:

[0092] P′(t)=P wt (t)+P pv (t)+P m (t)+P evdis (t)+△P vpp (t) (12)

[0093] The PV output constraint can be shown as follows:

[0094] P pv,min ≤P pv (t)≤P pv,max (13)

[0095] The wind power output constraint can be shown as follows:

[0096] P wt,min ≤P wt (t)≤P wt,max (14)

[0097] Among them, P wt,max , P wt,min They are the maximum and minimum output of wind turbines respectively.

[0098] The electric vehicle charging and discharging constraints can be shown as follows:

[0099]

[0100]

[0101] 0≤P evc,n (t)≤P evc,max (18)

[0102] 0≤P evf,n (t)≤P evf,max (19)

[0103]

[0104]

[0105]

[0106] SOC ev,min (t)≤SOC ev,n (t)≤SOC ev,max (t) (23)

[0107] 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; SOC ev,n (t) is the charge state 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.

[0108] Based on the above embodiments, the present invention aims at the relationship between electric vehicle demand response and environmental costs, fully considers that the output of gas turbines will release harmful gases to the outside, and excludes factors such as uncertainty in the output of new energy, and provides 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 environmental costs such as harmful gas emissions from gas turbines into account in the model. At the same time, the operating costs of the gas turbines are considered. Wind power and photovoltaics, as clean energy, do not pollute the environment. Only the operating costs of the two are considered, and 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, and the output of each type of energy is obtained separately to construct an optimization scheduling model for a virtual power plant including wind power, photovoltaics, gas turbines and electric vehicles.

[0109] 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, and the virtual switch 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 the 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 to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0110] The above-described embodiment is only a preferred solution of the present invention and does not limit the present invention in any form. There are other variations and modifications 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 benefits at time t include: 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; P e 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: 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)) 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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