Dynamic checking method for multi-time-scale adjustable capability of virtual power plant

Abstract

The invention discloses a multi-time-scale adjustable capability dynamic checking method for a virtual power plant. The method comprises the following steps: constructing a wind power and photovoltaic power generation output model; establishing a charging and discharging model of the energy storage system; establishing an electric vehicle scheduling potential model; based on a wind power and photovoltaic power generation output model, a charging and discharging model of an energy storage system and an electric vehicle scheduling potential model, a virtual power plant multi-time scale adjustable capability model is formed through addition and serves as an actual description model; and establishing an optimization model which takes the absolute value of the deviation between the adjustable capacity range of the virtual power plant reported to the power grid and the adjustable capacity range of the actual description model as a target function and considers the power grid constraint and the operation state constraint, and realizing dynamic checking of the multi-time-scale adjustable capacity of the virtual power plant. According to the method, multi-dimensional influence factors such as power grid constraints, market demands, operation states and user willingness are considered, and the multi-time-scale adjustable capability and the dynamic checking method of the virtual power plant can be effectively reflected.

Description

Technical Field

[0001] The present invention relates to the technical field of power system analysis, and in particular to a method for dynamically checking the multi-time-scale adjustable capacity of a virtual power plant. Background Art

[0002] In recent years, a large number of distributed resources have appeared in the power grid system. Their randomness, volatility and over-dispersion have increased the complexity of the system and the difficulty of regulation, posing challenges to the safe and reliable operation of the power grid. However, the access of large-scale distributed resources also provides more load support for the power grid, and the user side has the potential to participate in the regulation of the power grid. Virtual power plants can aggregate various decentralized source-load-storage resources to form resource clusters and conduct unified scheduling, thereby making full use of the output characteristics of decentralized resources such as distributed power sources, energy storage systems and electric vehicles, and improving the flexibility, safety and economy of power grid regulation. Under the premise of not changing the grid connection mode of various distributed power sources, various types of components such as distributed power sources, electric vehicles, energy storage systems, etc. are aggregated through advanced coordinated control technology, intelligent computing technology and information and communication technology, and the coordinated and optimized operation of multiple distributed energy sources is realized through the upper-level software algorithm. Therefore, it is of great significance to study the optimal scheduling of virtual power plants.

[0003] The optimal scheduling of virtual power plants, that is, virtual power plants aggregate multiple distributed resources to form resource clusters for unified scheduling. At present, the research on the optimal scheduling of virtual power plants is mainly divided into three categories: virtual power plant scheduling based on robust stochastic optimization, virtual power plant scheduling based on conditional risk value, and virtual power plant scheduling based on artificial intelligence technology. Among them, the virtual power plant scheduling based on robust stochastic optimization limits all possible values ​​of uncertainty to a deterministic set. The optimal solution of robust optimization has a certain consistency in the adverse effects that any element in the set may cause, but the calculation is relatively complex and slow. Every time the system state changes, it needs to be iterated and derived again; the virtual power plant scheduling based on conditional risk value can select the investment portfolio that brings the maximum return under a certain risk level in advance, but it cannot provide the degree of loss; the virtual power plant scheduling based on artificial intelligence technology breaks away from the constraints of complex models, and directly maps from input to output through learning historical data, which can make direct and fast decisions, but requires a large amount of difficult-to-obtain data for pre-training.

[0004] In summary, there is little research on the existing virtual power plant optimization scheduling for systems that simultaneously include wind power generation, photovoltaic power generation and electric vehicles. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a method for dynamically checking the multi-time-scale adjustable capacity of a virtual power plant to provide technical support for the unified dispatching of power grids.

[0006] The technical solution adopted by the present invention is as follows:

[0007] A method for dynamically checking the multi-timescale adjustable capacity of a virtual power plant comprises the following steps:

[0008] Based on the power output characteristics of wind power and photovoltaic power generation, a model of wind power and photovoltaic power generation output is constructed;

[0009] Establish a charging and discharging model of the energy storage system according to the stored electric energy, charging and discharging power and charging and discharging efficiency of the energy storage system in the virtual power plant;

[0010] Based on the energy and power boundary representation method and conversion mechanism of the adjustable capacity of electric vehicle regulation, a model of electric vehicle dispatch potential is established;

[0011] Based on the models of wind power and photovoltaic power generation output, the charging and discharging model of the energy storage system, and the model of the electric vehicle scheduling potential, a multi-time scale adjustable capacity model of the virtual power plant is added together as the actual characterization model to characterize the adjustable capacity of the entire virtual power plant.

[0012] An optimization model with the objective function of minimizing the absolute value of the deviation between the adjustable capacity range of the virtual power plant reported to the power grid and the adjustable capacity range of the actual characterization model is established. The grid constraints and operating status constraints are considered to realize the dynamic verification of the multi-time scale adjustable capacity of the virtual power plant.

[0013] Furthermore, the wind power and photovoltaic power generation output models are constructed based on the power output characteristics of wind power and photovoltaic power generation, including:

[0014] (1) Based on the power output characteristics of wind power, a model of independent wind power output is constructed, including:

[0015] A wind turbine includes a wind rotor and a generator. The wind turbine first converts the kinetic energy of wind into mechanical energy, which acts on the generator to drive it to rotate, realizing the conversion of mechanical energy into electrical energy, and finally outputting electrical energy. The output power of the wind turbine is closely related to the wind speed and is approximately expressed by a piecewise function. The model of independent wind power output is expressed as follows:

[0016]

[0017] Where, v is the actual wind speed, v CI is the cut-in wind speed, v R is the rated wind speed, v CO is the cut-out wind speed, f(v) is the wind power output power expression when the actual wind speed is between the cut-in wind speed and the rated wind speed, and the commonly used expression of f(v) is f(v)=P R (vv CI ) / (v R -v CI );Pw is the wind power output power; P R is the rated wind power output power;

[0018] (2) Based on the power output characteristics of photovoltaic power generation, a model of independent output of photovoltaic power generation is constructed, including:

[0019] The principle of photovoltaic power generation is the photovoltaic effect generated by sunlight shining on the PN junction of photovoltaic cells, thereby realizing the conversion of solar energy into electrical energy. In an ideal situation, the output power of photovoltaic cells is expressed as:

[0020]

[0021] Where P STC The photovoltaic cell is under the standard light intensity of 1000W / m 2 and the maximum output power at standard temperature 25°C; S is the actual light intensity; S STC is the standard light intensity; k is the power temperature coefficient; T c Indicates the operating temperature of the photovoltaic cell; T r is the standard battery temperature;

[0022] The model of correcting the output power of photovoltaic cells in the ideal situation to the independent output power of photovoltaic power generation using the empirical formula is:

[0023] P v =SAη(3)

[0024] Where A is the total area of ​​the photovoltaic array; η is the total photoelectric conversion rate of the photovoltaic power station.

[0025] Furthermore, the charging and discharging model of the energy storage system is established according to the stored electric energy, charging and discharging power and charging and discharging efficiency of the energy storage system in the virtual power plant, specifically including:

[0026] The stored electric energy of the energy storage system is related to its initial value and the charging and discharging process. The dynamic change of the stored electric energy during charging and discharging is related to the charging and discharging power and efficiency. The charging and discharging model of the energy storage system is established as follows:

[0027] E b (t+1)=E b (t)-P b (t)η b Δt (4)

[0028] Among them, E b (t) is the electric energy stored in the energy storage system at time t; P b (t) is the charge and discharge power at time t, with the discharge power being positive; η b is the charge and discharge efficiency; Δt is the charge and discharge time length.

[0029] Furthermore, the energy and power boundary representation method and conversion mechanism based on the adjustable capacity of electric vehicle regulation is used to establish a model of electric vehicle scheduling potential, which specifically includes:

[0030] First, from the energy perspective, the upper and lower bounds of energy for large-scale electric vehicles are aggregated, as shown in formula (5):

[0031]

[0032] Among them, e i (t) represents the battery capacity of electric vehicle i at time t; E i (t) represents the total energy;

[0033] Secondly, from the perspective of power, the upper and lower bounds of the power of large-scale electric vehicles are aggregated, as shown in formula (6):

[0034] P i，max (t) = ∑p i，max (t), P i，min (t) = ∑p i，min (6)

[0035] where p i，max (t), p i，min (t) are the maximum and minimum charging and discharging power of electric vehicle i at time t, P i，max (t), P i，min (t) are the total maximum and minimum charging and discharging power of the electric vehicle after polymerization;

[0036] During the aggregation process, the time of arrival at the charging station, departure time, arrival power, departure power, battery capacity, and the number and distribution of electric vehicle clusters are taken into account for each electric vehicle. Through comprehensive analysis and calculation, the overall scheduling potential range of the electric vehicle cluster is obtained, which can reflect the overall capacity of large-scale electric vehicle clusters.

[0037] Furthermore, the calculation formula of the multi-time scale adjustable capability model of the virtual power plant is:

[0038]

[0039] P vpp (t) = P w +P v +P b，min (t)+P i，min (t) (8)

[0040] in, P vpp (t) is the upper and lower limits of the adjustable capacity of the virtual power plant in each period; Pw For wind power generation; v For photovoltaic power generation; P b，max (t), P b，min (t) are the upper and lower limits of the charging and discharging power of the energy storage system in each period; P i，max (t), P i，min (t) are the upper and lower limits of the charging and discharging power of electric vehicles in each period.

[0041] Furthermore, the objective function of the optimization model is:

[0042]

[0043] in, F (t) The upper and lower limits of the adjustable capacity of the virtual power plant reported to the power grid for each time period.

[0044] Furthermore, the power grid constraints include power flow constraints, voltage amplitude constraints, and current amplitude constraints; the operating status constraints include wind power output constraints, photovoltaic power output constraints, power balance constraints, virtual power plant output constraints, virtual power plant climbing constraints, energy storage system storage power constraints and charging and discharging power constraints, energy upper and lower limit constraints of the aggregated electric vehicle cluster, power upper and lower limit constraints of the aggregated electric vehicle cluster, and charge state constraints of electric vehicle batteries.

[0045] Furthermore, the power grid constraint and the operating state constraint are specifically as follows:

[0046] Power flow constraints:

[0047]

[0048] Where V is the set of lines in the power grid; P DGi and Q DGi They represent the active power and reactive power of the distributed generation at node i respectively; P Li and Q Li Represent the active load and reactive load at node i respectively; P ij , Q ij , l ij 、r ij and x ij Represent the active power, reactive power, square of current amplitude, resistance and inductive reactance on line (i, d) respectively; represents the square of voltage amplitude at node i;

[0049] Voltage amplitude constraint:

[0050]

[0051] u1=1 (14)

[0052] Among them, U min and U max They represent the lower and upper limits of node voltage replication respectively; u1 represents the square of the voltage amplitude of the equilibrium node;

[0053] Current amplitude constraint:

[0054]

[0055] Among them, I ijmax represents the maximum current of line (i, d). In the current amplitude constraint, when α ij =1 means the line is closed, and current is allowed to flow through the line; otherwise, the line is disconnected, and the maximum value of the square of the current is 0, that is, no current can flow through the line;

[0056] Wind power output constraints:

[0057] P w，min ≤P w ≤P w，max (16)

[0058] Among them, P w，max , P w，min They are the upper and lower limits of wind power generation respectively;

[0059] Photovoltaic power generation output constraints:

[0060] P v，min ≤P v ≤P v，max (17)

[0061] Among them, P v，max , P v，min They are the upper and lower limits of photovoltaic power generation respectively.

[0062] Power balance constraints:

[0063]

[0064] Where L is the number of terminal users within the virtual power plant; P l，t The power requirements of end users;

[0065] Virtual power plant output constraints:

[0066]

[0067] Virtual power plant ramping constraints:

[0068] The difference between the upper and lower limits of active power in two adjacent dispatching periods should be less than the maximum upward climbing constraint and the minimum downward climbing constraint respectively:

[0069]

[0070] Among them, are the upper and lower limits of active power of the virtual power plant, the maximum upward climbing constraint and the minimum downward climbing constraint, respectively;

[0071] The maximum upward ramp constraint and minimum downward ramp constraint of the virtual power plant should be subject to the upper and lower limits of active power in two adjacent dispatch periods:

[0072]

[0073] The energy storage system's storage energy constraints and charging and discharging power constraints are as follows:

[0074] E b，min ≤E b (t)≤E b，max (24)-P bc，min ≤P b (t)≤P bd，max (25)

[0075] Among them, E b，min 、E b，max are the upper and lower limits of the energy stored in the energy storage system; P bc，min , P bd，max They are the upper limits of charge and discharge power respectively;

[0076] The energy upper and lower bounds of the electric vehicle cluster after aggregation are:

[0077] E i，min ≤E i (t)≤E i，max (26)

[0078] The upper and lower bounds of the power of the electric vehicle cluster after aggregation:

[0079] P i，min ≤P i (t)≤P i，max , t arrive ≤t≤t leave (27)

[0080] State of charge constraints for electric vehicle batteries:

[0081] SoC min ≤SoC i，t ≤SoC max (28)

[0082] Among them, SoC i，t is the state of charge of the battery of the electric vehicle at time t, t arrive ,t leaveRespectively represent the time when the electric vehicle arrives at the charging station and leaves the charging station, SoC max 、SoC min are the upper and lower limits of the state of charge respectively.

[0083] The present invention has the following beneficial effects:

[0084] The present invention can effectively verify the dispatchability of a virtual power plant containing wind turbines, photovoltaic power generation and electric vehicles, and realize the coordinated and optimized operation of multiple distributed energy sources through upper-level software algorithms, thereby improving the flexibility, safety and economy of power grid regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 The present invention provides a flowchart of a method for dynamically verifying the multi-time-scale adjustable capability of a virtual power plant. DETAILED DESCRIPTION

[0086] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below.

[0087] See also Figure 1 The embodiment of the present invention provides a method for dynamically checking the multi-time scale adjustable capacity of a virtual power plant, comprising the following steps:

[0088] Step (1): Based on the power output characteristics of wind power and photovoltaic power generation, a model of independent output of wind power and photovoltaic power generation is constructed;

[0089] A wind turbine is mainly composed of a wind rotor and a generator. First, the kinetic energy of the wind is converted into mechanical energy through the wind turbine, which acts on the generator to drive it to rotate, thereby realizing the conversion of mechanical energy into electrical energy and finally outputting electrical energy.

[0090] The output power of a wind turbine is closely related to wind speed and can be approximated by a piecewise function.

[0091]

[0092] Where, v is the actual wind speed, v CI is the cut-in wind speed, v R is the rated wind speed, V CO is the cut-out wind speed. f(v) is the wind power output power expression when the actual wind speed is between the cut-in wind speed and the rated wind speed. The commonly used expression of f(v) is f(v)=P R (vv CI ) / (v R -v CI );P w is the wind power output power; P R is the rated wind power output power.

[0093] The principle of photovoltaic power generation is the photovoltaic effect generated by sunlight shining on the PN junction of photovoltaic cells, thereby realizing the conversion of solar energy into electrical energy. In an ideal situation, the output power of photovoltaic cells can be expressed as:

[0094]

[0095] Where P STC It is the photovoltaic cell under standard light intensity (1000W / m 2 ) and the maximum output power at standard temperature (25°C); S is the actual light intensity; S STC is the standard light intensity; k is the power temperature coefficient; T c Indicates the operating temperature of the photovoltaic cell; T r is the standard battery temperature.

[0096] The empirical formula can be corrected as follows:

[0097] P v =SAη (3)

[0098] Where A is the total area of ​​the photovoltaic array; η is the total photoelectric conversion rate of the photovoltaic power station.

[0099] Step (2): establishing a charging and discharging model of the energy storage system according to the stored electric energy, charging and discharging power and charging and discharging efficiency of the energy storage system in the virtual power plant;

[0100] The stored electric energy of the energy storage system is mainly related to its initial value and the charging and discharging process. The dynamic change of the stored electric energy during charging and discharging is related to the charging and discharging efficiency of the heavy discharge power. Therefore, the charging and discharging model of the energy storage system is established as follows:

[0101] E b (t+1)=E b (t)-P b (t)η b Δt (4)

[0102] Among them, E b (t) is the electric energy stored in the energy storage system at time t; P b (t) is the charge and discharge power at time t, with the discharge power being positive; η b is the charge and discharge efficiency.

[0103] Step (3): Establish a model of electric vehicle dispatching potential based on the energy and power boundary representation method and conversion mechanism of the adjustable capacity of electric vehicle regulation;

[0104] Based on the analysis of the electricity demand and charging habits of electric vehicle users, the relationship between the charging and discharging power of electric vehicles and the size of the electric vehicle battery SoC, and the battery capacity and the charging and discharging power of electric vehicles is considered to determine the scheduling potential that each electric vehicle can provide at the corresponding time, and obtain the flexible adjustment capacity range of a single electric vehicle. However, the energy of a single electric vehicle is too small to be dispatchable. Therefore, the model is extended to large-scale electric vehicle clusters. For large-scale electric vehicle clusters, bottom-up aggregation can be achieved by Minkowski summing the adjustment capacity model of each electric vehicle. The Minkowski summation method allows the adjustment capabilities of different electric vehicles to be combined into an overall flexible adjustment capacity model to form a scaled model.

[0105] First, from the energy perspective, the upper and lower bounds of the energy of large-scale electric vehicles are aggregated, as shown in formula (5).

[0106]

[0107] Secondly, from the power perspective, the upper and lower bounds of the power of large-scale electric vehicles are aggregated, as shown in formula (6).

[0108] P i,max (t) = ∑p i,max (t),P i,min (t) = ∑p i,min (t) (6)

[0109] The aggregation process takes into account the arrival time, departure time, arrival power, departure power, battery capacity and other parameters of each electric vehicle, as well as the number and distribution of electric vehicle clusters. By comprehensively analyzing and calculating this information, the overall dispatch potential range of the electric vehicle cluster can better reflect the overall capacity of large-scale electric vehicle clusters.

[0110] Step (4): The charging and discharging models of wind power generation, photovoltaic power generation, and energy storage systems and the electric vehicle scheduling potential model obtained in steps (1), (2), and (3) are added together to form a multi-time scale adjustable capacity model of the virtual power plant to characterize the adjustable capacity of the entire virtual power plant, that is, the wind and solar power output plus the upper and lower adjustable capacity of electric vehicles and energy storage systems.

[0111] The calculation formula of the multi-time scale adjustable capability model of the virtual power plant is:

[0112]

[0113] P vpp (t) = P w +P v +Pb，min (t)+P i，min (t) (8)

[0114] in, P vpp (t) is the upper and lower limits of the adjustable capacity of the virtual power plant in each period; P w For wind power generation; v For photovoltaic power generation; P b，max (t), P b，min (t) are the upper and lower limits of the charging and discharging power of the energy storage system in each period; P i，max (t), P i，min (t) are the upper and lower limits of the charging and discharging power of electric vehicles in each period.

[0115] Step (5): Establish an optimization model that takes minimizing the absolute value of the deviation between the reported adjustable capacity range of the virtual power plant and the adjustable capacity range of the actual characterization model as the objective function, taking into account constraints such as grid constraints and operating status.

[0116] The objective function of the optimization model is as follows:

[0117]

[0118] in, F (t) is the upper and lower limits of the adjustable capacity of the virtual power plant reported to the power grid for each period. The objective function is to minimize the absolute value of the deviation between the adjustable capacity range of the virtual power plant reported to the power grid and the adjustable capacity range of the actual characterization model.

[0119] Power flow constraints:

[0120]

[0121]

[0122] Wherein, V represents the set of lines in the power grid (which can be understood as a directed graph); P DGi and Q DGi They represent the active power and reactive power of the distributed generation at node i respectively; P Li and Q Li Represent the active load and reactive load at node i respectively; P ij , Q ij , l ij 、r ij and x ij Respectively represent the active power, reactive power, square of current amplitude, resistance and inductance value on line (i, j); represent the square of voltage amplitude at node i.

[0123] Voltage amplitude constraint:

[0124]

[0125] u1=1(14)

[0126] Among them, U min and U max They represent the lower and upper limits of node voltage replication respectively; u1 represents the square of the voltage amplitude of the balanced node.

[0127] Current amplitude constraint:

[0128]

[0129] Among them, I ijmax represents the maximum current of line (i, j). In the current amplitude constraint, when α ij =1, it means the line is closed, and current is allowed to pass through the line; otherwise, it means the line is disconnected, and the maximum value of the square of the current is 0, that is, no current can pass through the line.

[0130] Wind power output constraints:

[0131] P w，min ≤P w ≤P w，max (16)

[0132] Among them, P w，max , P w，min They are the upper and lower limits of wind power generation respectively.

[0133] Photovoltaic power generation output constraints:

[0134] P v，min ≤P v ≤P v，max (17)

[0135] Among them, P v，max , P v，min They are the upper and lower limits of photovoltaic power generation respectively.

[0136] Power balance constraints:

[0137]

[0138] Where L is the number of terminal users within the virtual power plant; P l，t The power requirements of the end user.

[0139] Virtual power plant output constraints:

[0140]

[0141] Virtual power plant ramping constraints:

[0142] The difference between the upper and lower limits of active power in two adjacent dispatching periods should be less than the maximum upward climbing constraint and the minimum downward climbing constraint respectively:

[0143]

[0144] Among them, are the upper and lower limits of active power of the virtual power plant; are the maximum upward climbing constraint and the minimum downward climbing constraint.

[0145] In addition, the maximum upward ramp constraint and the minimum downward ramp constraint of the virtual power plant should be subject to the upper and lower limits of the active power in two adjacent dispatch periods:

[0146]

[0147] The energy storage system's storage energy constraints and charging and discharging power constraints are as follows:

[0148] E b，min ≤E b (t)≤E b，max (twenty four)

[0149] -P bc，min ≤P b (t)≤P bd，max (25)

[0150] Among them, E b，min 、E b，max are the upper and lower limits of the energy stored in the energy storage system; P bc，min , P bd，max They are the upper limits of charge and discharge power respectively.

[0151] The energy upper and lower bounds of the electric vehicle cluster after aggregation are:

[0152] E i，min ≤E i (t)≤E i，max (26)

[0153] The upper and lower bounds of the power of the electric vehicle cluster after aggregation:

[0154] P i，min ≤P i (t)≤P i，max (27)

[0155] State of charge constraints for electric vehicle batteries:

[0156] SoC min ≤SoC i，t ≤SoC max (28)

[0157] Among them, SoC i，t is the state of charge of the battery of the electric vehicle at time t, SoC max 、SoC min are the upper and lower limits of the state of charge respectively.

[0158] The present invention takes into account multi-dimensional influencing factors such as grid constraints, market demand, operating status, and user willingness, and can effectively reflect the multi-time scale adjustable capability and dynamic verification method of the virtual power plant.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for dynamically checking the multi-timescale adjustable capacity of a virtual power plant, characterized in that: The following steps are involved: Based on the power output characteristics of wind power and photovoltaic power generation, a model of wind power and photovoltaic power generation output is constructed; Establish a charging and discharging model of the energy storage system according to the stored electric energy, charging and discharging power and charging and discharging efficiency of the energy storage system in the virtual power plant; Based on the energy and power boundary representation method and conversion mechanism of the adjustable capacity of electric vehicle regulation, a model of electric vehicle dispatch potential is established; Based on the models of wind power and photovoltaic power generation output, the charging and discharging model of the energy storage system, and the model of the electric vehicle scheduling potential, a multi-time scale adjustable capacity model of the virtual power plant is added together as the actual characterization model to characterize the adjustable capacity of the entire virtual power plant. An optimization model with the objective function of minimizing the absolute value of the deviation between the adjustable capacity range of the virtual power plant reported to the power grid and the adjustable capacity range of the actual characterization model is established. The grid constraints and operating status constraints are considered to realize the dynamic verification of the multi-time scale adjustable capacity of the virtual power plant.

2. The method for dynamically checking the multi-timescale adjustable capability of a virtual power plant according to claim 1, characterized in that: The model of wind power and photovoltaic power generation output is constructed based on the power output characteristics of wind power and photovoltaic power generation, including: (1) Based on the power output characteristics of wind power, a model of independent wind power output is constructed, including: A wind turbine includes a wind rotor and a generator. The wind turbine first converts the kinetic energy of wind into mechanical energy, which acts on the generator to drive it to rotate, realizing the conversion of mechanical energy into electrical energy, and finally outputting electrical energy. The output power of the wind turbine is closely related to the wind speed and is approximately expressed by a piecewise function. The model of independent wind power output is expressed as follows: Where, v is the actual wind speed, v CI is the cut-in wind speed, v R is the rated wind speed, v CO is the cut-out wind speed, f(v) is the wind power output power expression when the actual wind speed is between the cut-in wind speed and the rated wind speed, and the commonly used expression of f(v) is f(v)=P R (vv CI ) / (v R -v CI );P w is the wind power output power; P R is the rated wind power output power; (2) Based on the power output characteristics of photovoltaic power generation, a model of independent output of photovoltaic power generation is constructed, including: The principle of photovoltaic power generation is the photovoltaic effect generated by sunlight shining on the PN junction of photovoltaic cells, thereby realizing the conversion of solar energy into electrical energy. In an ideal situation, the output power of photovoltaic cells is expressed as: Where P STC The photovoltaic cell is under the standard light intensity of 1000W / m 2 and the maximum output power at standard temperature 25°C; S is the actual light intensity; S STC is the standard light intensity; k is the power temperature coefficient; T c Indicates the operating temperature of the photovoltaic cell; T r is the standard battery temperature; The model of correcting the output power of photovoltaic cells in the ideal situation to the independent output power of photovoltaic power generation using the empirical formula is: P v =SAη (3) Where A is the total area of ​​the photovoltaic array; η is the total photoelectric conversion rate of the photovoltaic power station.

3. The method for dynamically checking the multi-timescale adjustable capacity of a virtual power plant according to claim 3 is characterized in that: The charging and discharging model of the energy storage system is established according to the stored electric energy, charging and discharging power and charging and discharging efficiency of the energy storage system in the virtual power plant, specifically including: The stored electric energy of the energy storage system is related to its initial value and the charging and discharging process. The dynamic change of the stored electric energy during charging and discharging is related to the charging and discharging power and efficiency. The charging and discharging model of the energy storage system is established as follows: From b (t+1)=E b (t)-P b (t)η b Δt (4) Among them, E b (t) is the electric energy stored in the energy storage system at time t; P b (t) is the charge and discharge power at time t, with the discharge power being positive; η b is the charge and discharge efficiency; Δt is the charge and discharge time length.

4. The method for dynamically checking the multi-timescale adjustable capability of a virtual power plant according to claim 3 is characterized in that: The energy and power boundary representation method and conversion mechanism based on the adjustable capacity of electric vehicle regulation are used to establish a model of electric vehicle scheduling potential, which specifically includes: First, from the energy perspective, the upper and lower bounds of energy for large-scale electric vehicles are aggregated, as shown in formula (5): Among them, e i (t) represents the battery capacity of electric vehicle i at time t; E i (t) represents the total energy; Secondly, from the perspective of power, the upper and lower bounds of the power of large-scale electric vehicles are aggregated, as shown in formula (6): P i，max (t)=∑p i,max (t),P i,min (t)=∑p i,min (6) where p i,max (t),p i,min (t) are the maximum and minimum charging and discharging power of electric vehicle i at time t, P i,max (t),P i,min (t) are the total maximum and minimum charging and discharging power of the electric vehicle after polymerization; During the aggregation process, the time of arrival at the charging station, departure time, arrival power, departure power, battery capacity, and the number and distribution of electric vehicle clusters are taken into account for each electric vehicle. Through comprehensive analysis and calculation, the overall scheduling potential range of the electric vehicle cluster is obtained, which can reflect the overall capacity of large-scale electric vehicle clusters.

5. The method for dynamically checking the multi-timescale adjustable capability of a virtual power plant according to claim 4, characterized in that: The calculation formula of the multi-time scale adjustable capability model of the virtual power plant is: in, P vpp (t) is the upper and lower limits of the adjustable capacity of the virtual power plant in each period; P w For wind power generation; v For photovoltaic power generation; P b,max (t), P b,min (t) are the upper and lower limits of the charging and discharging power of the energy storage system in each period; P i,max (t), P i,min (t) are the upper and lower limits of the charging and discharging power of electric vehicles in each period.

6. The method for dynamically checking the multi-timescale adjustable capability of a virtual power plant according to claim 5, characterized in that: The objective function of the optimization model is: in, F (t) The upper and lower limits of the adjustable capacity of the virtual power plant reported to the power grid for each time period.

7. The method for dynamically checking the multi-timescale adjustable capability of a virtual power plant according to claim 5, characterized in that: The power grid constraints include flow constraints, voltage amplitude constraints, and current amplitude constraints; the operating status constraints include wind power output constraints, photovoltaic power output constraints, power balance constraints, virtual power plant output constraints, virtual power plant climbing constraints, energy storage system storage power constraints and charging and discharging power constraints, energy upper and lower limit constraints of the aggregated electric vehicle cluster, power upper and lower limit constraints of the aggregated electric vehicle cluster, and charge state constraints of electric vehicle batteries.

8. The method for dynamically checking the multi-timescale adjustable capability of a virtual power plant according to claim 7, characterized in that: The grid constraints and the operating state constraints are specifically as follows: Power flow constraints: Where V is the set of lines in the power grid; P DGi and Q DGi They represent the active power and reactive power of the distributed generation at node i respectively; P Li and Q Li Represent the active load and reactive load at node i respectively; P ij , Q ij , l ij 、r ij and x ij Represent the active power, reactive power, square of current amplitude, resistance and inductive reactance on line (i, j) respectively; represents the square of voltage amplitude at node i; Voltage amplitude constraint: u1＝1 (14) Among them, U min and U max They represent the lower and upper limits of node voltage replication respectively; u1 represents the square of the voltage amplitude of the equilibrium node; Current amplitude constraint: Among them, I ijmax represents the maximum current of line (i, j). In the current amplitude constraint, when α ij =1 means the line is closed, and current is allowed to flow through the line; otherwise, the line is disconnected, and the maximum value of the square of the current is 0, that is, no current can flow through the line; Wind power output constraints: P w,min ≤P w ≤P w,max (16) Among them, P w,max , P w,min They are the upper and lower limits of wind power generation respectively; Photovoltaic power generation output constraints: P v,min ≤P v ≤P v,max (17) Among them, P v,max , P v,min They are the upper and lower limits of photovoltaic power generation respectively. Power balance constraints: Where L is the number of terminal users within the virtual power plant; P l,t The power requirements of end users; Virtual power plant output constraints: Virtual power plant ramping constraints: The difference between the upper and lower limits of active power in two adjacent dispatching periods should be less than the maximum upward climbing constraint and the minimum downward climbing constraint respectively: Among them, are the upper and lower limits of active power of the virtual power plant, the maximum upward climbing constraint and the minimum downward climbing constraint, respectively; The maximum upward ramp constraint and minimum downward ramp constraint of the virtual power plant should be subject to the upper and lower limits of active power in two adjacent dispatch periods: The energy storage system's storage energy constraints and charging and discharging power constraints are as follows: E b,min ≤E b (t)≤E b,max (24) -P bc,min ≤P b (t)≤P bd,max (25) Among them, E b,min 、E b,max are the upper and lower limits of the energy stored in the energy storage system; P bc,min , P bd,max They are the upper limits of charge and discharge power respectively; The energy upper and lower bounds of the electric vehicle cluster after aggregation are: E i,min ≤E i (t)≤E i,max (26) The upper and lower bounds of the power of the electric vehicle cluster after aggregation: P i,min ≤P i (t)≤P i,max ,t arrive ≤t≤t leave (27) State of charge constraints for electric vehicle batteries: SoC min ≤SoC i,t ≤SoC max (28) Among them, SoC i,t is the state of charge of the battery of the electric vehicle at time t, t arrive ,t leave Respectively represent the time when the electric vehicle arrives at the charging station and leaves the charging station, SoC max 、SoC min are the upper and lower limits of the state of charge respectively.

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