Virtual power plant multi-time scale adjustable capacity dynamic checking method

Abstract

A kind of virtual power plant multi-time scale adjustable capacity dynamic checking method, comprising: constructing wind power and photovoltaic power generation output model;Establish the charge-discharge model of energy storage system;Establish the model of electric vehicle scheduling potential;Based on the wind power and photovoltaic power generation output model, the charge-discharge model of energy storage system and the model of electric vehicle scheduling potential, through addition, the virtual power plant multi-time scale adjustable capacity model is formed as actual delineation model;Establish the optimization model with the minimum deviation of the adjustable capacity range of virtual power plant reported to power grid and the adjustable capacity range of actual delineation model as objective function, considering grid constraint and operating state constraint, realize virtual power plant multi-time scale adjustable capacity dynamic checking.The present application considers grid constraint, market demand, operating state, user's wishes and other multi-dimensional influence factors, and can effectively reflect the multi-time scale adjustable capacity of virtual power plant and dynamic checking method.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system analysis, in particular to a virtual power plant multi-time scale adjustable capacity dynamic checking method. BACKGROUND

[0002] In recent years, a large number of distributed resources have appeared in the power grid system, and their randomness, volatility and over-dispersed characteristics have increased the complexity and regulation difficulty of the system, which has posed a challenge 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. The virtual power plant can aggregate various dispersed source-load-storage resources, form a resource cluster, and be uniformly scheduled, so as to fully utilize the output characteristics of distributed power sources, energy storage systems and electric vehicles and other dispersed resources, and improve the flexibility, safety and economy of power grid regulation. Without changing the grid-connected mode of various distributed power sources, through advanced coordination control technology, intelligent computing technology and information communication technology, different types of elements such as distributed power sources, electric vehicles and energy storage systems are aggregated, and the coordinated and optimized operation of multiple distributed energy sources is realized through the upper-layer 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, the virtual power plant aggregates multiple distributed resources to form a resource cluster and is uniformly scheduled. At present, the research on the optimal scheduling of virtual power plants mainly falls into three categories: virtual power plant scheduling based on robust stochastic optimization, virtual power plant scheduling based on conditional value at risk 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 within a deterministic set, and the optimal solution of robust optimization has a certain consistency in the adverse effects caused by any element in the set, but the calculation is relatively complex and slow, and iteration needs to be performed again at each system state change; the virtual power plant scheduling based on conditional value at risk can select the investment portfolio that brings the maximum benefit under a certain risk level in advance, but it cannot provide the extent of loss; the virtual power plant scheduling based on artificial intelligence technology breaks free from the shackles of complex models, directly maps from input to output through learning from historical data, and can make direct and fast decisions, but a large amount of difficult-to-obtain data is needed for pre-training.

[0004] In summary, the existing virtual power plant optimal scheduling has less research on systems that simultaneously contain wind power generation, photovoltaic power generation and electric vehicles. SUMMARY

[0005] To solve the above problems, the present application provides a virtual power plant multi-time scale adjustable capacity dynamic checking method, which provides technical support for unified scheduling of the power grid.

[0006] The technical solutions adopted by the application are as follows:

[0007] A virtual power plant multi-time scale adjustable capacity dynamic checking method 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] According to the stored electric energy, the charging and discharging power and the charging and discharging efficiency of the energy storage system in the virtual power plant, a charging and discharging model of the energy storage system is established;

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

[0011] Based on the model 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, the virtual power plant multi-time scale adjustable capacity model is composed by adding to form an actual description model, so as to describe the adjustable capacity of the entire virtual power plant;

[0012] An optimization model considering the grid constraint and operation state constraint is established, taking the absolute value of the deviation between the adjustable capacity range of the virtual power plant reported to the grid and the adjustable capacity range of the actual description model as the objective function, to realize the dynamic checking of the virtual power plant multi-time scale adjustable capacity.

[0013] Further, 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, comprising:

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

[0015] The wind turbine includes a wind wheel and a generator. First, the wind energy is converted into mechanical energy by the wind wheel machine, 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, which is approximately represented by a piecewise function. The model of independent wind power output is represented as follows:

[0016] (1)

[0017] Wherein, is the actual wind speed, is the cut-in wind speed, is the rated wind speed, is the cut-out wind speed, is the wind power output expression when the actual wind speed is between the cut-in wind speed and the rated wind speed, The commonly used expression of is ; is the wind power output; rated wind power output;

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

[0019] The principle of photovoltaic power generation is the photovoltaic effect produced by sunlight shining on the P-N junction of a photovoltaic cell, thereby realizing the conversion of solar energy to electrical energy. In an ideal case, the output power of a photovoltaic cell is represented as:

[0020] (2)

[0021] wherein, is the maximum output power of the photovoltaic cell under standard light intensity 1000 W / m 2 and standard temperature 25℃; is the actual light intensity; is the standard light intensity; is the power temperature coefficient; represents the working temperature of the photovoltaic cell; is the standard cell temperature;

[0022] The output power of the ideal photovoltaic cell is corrected to the model of photovoltaic power generation independent power output by using an empirical formula as:

[0023] (3)

[0024] wherein, is the total area of the photovoltaic array; is the total photoelectric conversion rate of the photovoltaic power station.

[0025] Further, the charge-discharge model of the energy storage system is established according to the stored energy of the energy storage system in the virtual power plant and the charge-discharge power and charge-discharge efficiency, which specifically includes:

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

[0027] (4)

[0028] wherein, is the stored energy of the energy storage system at time t; is the charge-discharge power at time t, with the discharging power being positive; is the charge-discharge efficiency; is the length of the charge-discharge time.

[0029] Further, the method and conversion mechanism for representing the energy and power boundaries of the adjustable capacity based on the electric vehicle regulation, establishes a model of the electric vehicle scheduling potential, specifically including:

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

[0031] (5)

[0032] wherein, represents the battery power of the electric vehicle i at time t; represents the total energy;

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

[0034]

[0035] wherein and are the maximum and minimum charging and discharging power of the electric vehicle i at time t, , and are the aggregated total maximum and minimum charging and discharging power of the electric vehicles;

[0036] In the aggregation process, the time of each electric vehicle to arrive at the charging pile, the time to leave, the arrival power, the departure power, the battery capacity, as well as the number and distribution of the electric vehicle cluster are considered, and through comprehensive analysis and calculation, the overall scheduling potential range of the electric vehicle cluster can reflect the overall capacity of the large-scale electric vehicle cluster.

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

[0038]

[0039] wherein, , are the upper and lower limits of the adjustable capacity of the virtual power plant in each period; is the wind power generation; is the photovoltaic power generation; , are the upper and lower limits of the charging and discharging power of the energy storage system in each period; , are the upper and lower limits of the charging and discharging power of the electric vehicle in each period.

[0040] Further, the objective function of the optimization model is:

[0041] (9) ​

[0042] wherein, , are the upper and lower limits of the virtual power plant adjustable capacity reported to the power grid for each time period.

[0043] Further, the power grid constraints include power flow constraints, voltage amplitude constraints, current amplitude constraints, the operating state constraints include wind power output constraints, photovoltaic power output constraints, power balance constraints, virtual power plant output constraints, virtual power plant ramping constraints, energy storage system storage energy constraints and charge-discharge power constraints, energy upper and lower bound constraints of the aggregated electric vehicle cluster, power upper and lower bound constraints of the aggregated electric vehicle cluster, state of charge constraints of the electric vehicle battery.

[0044] Further, the power grid constraints and the operating state constraints are as follows:

[0045] Power flow constraints:

[0046] (10)

[0047] (11)

[0048] (12)

[0049] wherein, is a set representing lines in the power grid; and represent the active power and the reactive power of the distributed power supply at node i respectively; and represent the active load and the reactive load at node i respectively; , , , and represent the active power, the reactive power, the square of the current amplitude, the resistance and the inductive reactance value on the line (i, j) respectively; represents the square of the voltage amplitude at node i;

[0050] Voltage amplitude constraints:

[0051]

[0052] wherein, and represent the lower limit and the upper limit of the node voltage copy respectively; represents the square of the voltage amplitude of the balance node;

[0053] Current amplitude constraints:

[0054] (14)

[0055] where, represents the maximum current of line (i, j). In the current amplitude constraint, when represents that the line is closed, in which case the line is allowed to have current passing through; otherwise, it represents that the line is open, in which case the maximum value of the current square is 0, i.e., no current can pass through the line;

[0056] Wind power output constraint:

[0057] (15);

[0058] where, , are the upper and lower limits of wind power, respectively;

[0059] Photovoltaic power output constraint:

[0060] (16);

[0061] where, , are the upper and lower limits of photovoltaic power, respectively.

[0062] Power balance constraint:

[0063] (17);

[0064] where, is the number of terminal users inside the virtual power plant; is the power demand of the terminal user;

[0065] Virtual power plant output constraint:

[0066] (18);

[0067] Virtual power plant ramp constraint:

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

[0069] (19)

[0070] (20)

[0071] where,

[0072] The maximum upward ramp constraint and the minimum downward ramp constraint of the virtual power plant should be limited by the active power upper limit and the active power lower limit of the adjacent two dispatching periods:

[0073] (21)

[0074] (22)

[0075] The storage energy constraints and charge-discharge power constraints of the energy storage system are as follows:

[0076] (23);

[0077] (24);

[0078] Wherein, , are the upper and lower limits of the storage energy of the energy storage system respectively; , are the upper limits of charge and discharge power respectively;

[0079] The upper and lower bound constraints of the energy of the aggregated electric vehicle cluster after aggregation are as follows:

[0080] (25);

[0081] The upper and lower bound constraints of the power of the aggregated electric vehicle cluster after aggregation are as follows:

[0082] (26);

[0083] The state of charge constraint of the electric vehicle battery is as follows:

[0084] (27);

[0085] Wherein, is the state of charge of the battery of the electric vehicle at time t, , represent the arrival time and departure time of the electric vehicle at the charging station respectively, , are the upper and lower limits of the state of charge respectively.

[0086] The present application has the following beneficial effects:

[0087] The present application can effectively check the dispatchable capacity of a virtual power plant containing wind turbines, photovoltaic power generation and electric vehicles, realize coordinated and optimized operation of multiple distributed energy sources through the software algorithm of the upper layer, and improve the flexibility, safety and economy of power grid regulation. BRIEF DESCRIPTION OF DRAWINGS

[0088] Figure 1 is the flow chart of the virtual power plant multi-time scale dispatchable capacity dynamic checking method proposed in the present application. DETAILED DESCRIPTION

[0089] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be clearly and completely described below.

[0090] Please refer to Figure 1 The embodiment of the present application provides a virtual power plant multi-time scale adjustable capacity dynamic checking method, comprising the following steps:

[0091] Step (1): based on the power output characteristics of wind power and photovoltaic power generation, a model of independent power output of wind power and photovoltaic power generation is constructed;

[0092] The wind turbine mainly consists of a wind wheel and a generator. First, the kinetic energy of wind is converted into mechanical energy by the wind turbine, 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 can be approximately represented by a piecewise function.

[0093] (1)

[0094] wherein, is the actual wind speed, is the cut-in wind speed, is the rated wind speed, is the cut-out wind speed. 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 is is the wind power output power; is the rated wind power output power.

[0095] The principle of photovoltaic power generation is that the photovoltaic effect produced by sunlight shining on the P-N junction of a photovoltaic cell realizes the conversion of solar energy into electrical energy. The output power of an ideal photovoltaic cell can be represented as:

[0096] (2)

[0097] wherein, is the maximum output power of the photovoltaic cell under standard light intensity (1000 W / m 2 ) and standard temperature (25℃); is the actual light intensity; is the standard light intensity; is the power temperature coefficient; represents the working temperature of the photovoltaic cell; is the standard cell temperature.

[0098] It can be corrected by using an empirical formula:

[0099] (3)

[0100] wherein, is the total area of the photovoltaic array; is the total photoelectric conversion rate of the photovoltaic power station.

[0101] Step (2): establishing a charging and discharging model of the energy storage system according to the stored electric energy of the energy storage system in the virtual power plant and the charging and discharging power and charging and discharging efficiency;

[0102] 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 re-discharging power and charging and discharging efficiency. Therefore, the charging and discharging model of the energy storage system is established as follows:

[0103] (4)

[0104] wherein, is the stored electric energy of the energy storage system at time t; is the charging and discharging power at time t, with the discharging power being positive; is the charging and discharging efficiency.

[0105] Step (3): based on the energy and power boundary representation method and conversion mechanism of the adjustable capacity of the electric vehicle regulation, a model of the electric vehicle scheduling potential is established;

[0106] On the basis of analyzing the electric vehicle user's electricity demand and charging habits, considering the relationship between the electric vehicle charging and discharging power and the electric vehicle battery SoC size, the battery capacity and the electric vehicle charging and discharging power, the size of the scheduling potential that each electric vehicle can provide at the corresponding time is determined, and the flexible adjustment capability range of a single electric vehicle is obtained. However, the energy of a single electric vehicle is too small to be schedulable. Therefore, the model is expanded to a large-scale electric vehicle cluster. For a large-scale electric vehicle cluster, the adjustment capability model of each electric vehicle can be aggregated from bottom to top by using the Minkowski addition. The Minkowski addition allows the adjustment capability of different electric vehicles to be combined into a whole flexible adjustment capability model, forming a scaled model.

[0107] First, the upper and lower bounds of the energy of the scaled electric vehicle are aggregated from the energy point of view, as shown in formula (5).

[0108] (5)

[0109] Second, the upper and lower bounds of the power of the scaled electric vehicle are aggregated from the power point of view, as shown in formula (6).

[0110] ;

[0111] The parameters of each electric vehicle, such as the time of arriving at the charging pile, the time of leaving, the arriving electric quantity, the leaving electric quantity, the battery capacity, and the number and distribution of the electric vehicle cluster are considered in the aggregation process. Through comprehensive analysis and calculation of the information, the overall scheduling potential range of the electric vehicle cluster can better reflect the overall capacity of the large-scale electric vehicle cluster.

[0112] Step (4): The wind power generation, photovoltaic power generation, and charging and discharging model of the energy storage system and the electric vehicle scheduling potential model obtained in steps (1), (2), and (3) are added to form a virtual power plant multi-time scale adjustable capacity model to depict the adjustable capacity of the entire virtual power plant, that is, the superimposed output of wind and light and the up and down adjustable capacity of electric vehicles and energy storage systems.

[0113] The calculation formula of the virtual power plant multi-time scale adjustable capacity model is:

[0114]

[0115] Among them, , are the upper and lower limits of the adjustable capacity of the virtual power plant in each period; is the wind power generation; is the photovoltaic power generation; , are the upper and lower limits of the charging and discharging power of the energy storage system in each period; , are the upper and lower limits of the charging and discharging power of the electric vehicle in each period.

[0116] Step (5): An optimization model is established to minimize the absolute value of the deviation between the reported virtual power plant adjustable capacity range and the adjustable capacity range of the actual depiction model, considering the grid constraints and operating state constraints.

[0117] The objective function of the optimization model is as follows:

[0118] (9)

[0119] Among them, , are the upper and lower limits of the virtual power plant adjustable capacity reported to the grid in each period. The objective function minimizes the absolute value of the deviation between the virtual power plant adjustable capacity reported to the grid and the adjustable capacity range of the actual depiction model.

[0120] Power flow constraints:

[0121] (10)

[0122] (11)

[0123] (12)

[0124] where, is a set of lines (can be understood as a directed graph) representing the power grid; and represent the active power and reactive power of the distributed power source at node i, respectively; and represent the active load and reactive load at node i, respectively; , , , and represent the active power, reactive power, square of current amplitude, resistance and reactance of line (i, j), respectively; represents the square of the voltage amplitude at node i.

[0125] Voltage amplitude constraint:

[0126]

[0127] where, and represent the lower limit and upper limit of the node voltage replica, respectively; represents the square of the voltage amplitude of the balanced node.

[0128] Current amplitude constraint:

[0129] (14)

[0130] where, represents the maximum current of line (i, j). In the current amplitude constraint, when represents that the line is closed, and at this time the line is allowed to have current passing through; otherwise, it represents that the line is open, and at this time the maximum value of the current square is 0, that is, no current can pass through the line.

[0131] Wind power output constraint:

[0132] (15);

[0133] where, , are the upper and lower limits of wind power, respectively.

[0134] Photovoltaic power output constraint:

[0135] (16);

[0136] where, , Upper and lower limits of photovoltaic power generation, respectively.

[0137] Power balance constraint:

[0138] (17)

[0139] wherein, is the number of terminal users inside the virtual power plant; is the power demand of the terminal user.

[0140] Virtual power plant output constraint:

[0141] (18)

[0142] Virtual power plant ramping constraint:

[0143] The difference between the active upper limit and the active lower limit of the adjacent two scheduling periods should be less than the maximum upward ramping constraint and the minimum downward ramping constraint, respectively:

[0144] (19)

[0145] (20)

[0146] wherein, and are the active upper limit and the active lower limit of the virtual power plant, respectively; and are the maximum upward ramping constraint and the minimum downward ramping constraint, respectively.

[0147] In addition, the maximum upward ramping constraint and the minimum downward ramping constraint of the virtual power plant should be limited by the active upper limit and the active lower limit of the adjacent two scheduling periods:

[0148] (21)

[0149] (22)

[0150] The energy storage system storage energy constraint and charge-discharge power constraint are as follows:

[0151] (23)

[0152] (24)

[0153] wherein, , and are the upper and lower limits of the energy storage system storage energy, respectively; , and are the upper limits of charge and discharge power, respectively.

[0154] Energy upper and lower bound constraints of the aggregated electric vehicle cluster:

[0155] (25)

[0156] Power upper and lower bound constraints of the post-aggregated electric vehicle cluster:

[0157] (26)

[0158] State of charge constraints of the electric vehicle battery:

[0159] (27)

[0160] wherein, is the state of charge of the battery of the electric vehicle at time t, , are the upper and lower bounds of the state of charge, respectively.

[0161] The application considers grid constraints, market demand, operating state, user willingness and other multi-dimensional influencing factors, and can effectively reflect the multi-time scale adjustable capacity and dynamic checking method of the virtual power plant.

[0162] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit it, although the present application has been described in detail with reference to the above examples, those skilled in the art should understand that: the specific embodiments of the present application can still be modified or replaced by the equivalent, without departing from the spirit and scope of the present application, any modification or equivalent replacement, which should be covered within the protection scope of the claims of the present application.

Claims

1. A method for dynamic verification of the adjustable capacity of a virtual power plant across multiple time scales, characterized in that, Includes the following steps: Based on the power output characteristics of wind power and photovoltaic power generation, models of wind power and photovoltaic power generation output are constructed; A charging and discharging model of the energy storage system is established based on the stored electrical 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 vehicles, a model of the scheduling potential of electric vehicles is established. Based on the power output models of wind and solar power generation, the charging and discharging models of energy storage systems, and the dispatching potential models of electric vehicles, a multi-timescale adjustable capacity model of a virtual power plant is formed by combining them as a practical model to characterize the adjustable capacity of the entire virtual power plant. An optimization model is established 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 model. This model considers power grid constraints and operating state constraints to achieve dynamic verification of the adjustable capacity of the virtual power plant across multiple time scales. The calculation formula for the virtual power plant multi-timescale adjustable capacity model is as follows: ； in, , The upper and lower limits of the adjustable capacity of the virtual power plant for each time period; For wind power generation; For photovoltaic power generation; , These represent the upper and lower limits of the charging and discharging power of the energy storage system for each time period; , These represent the upper and lower limits of the charging and discharging power of electric vehicles for each time period; The objective function of the optimization model is: (9)； in, , The upper and lower limits of the virtual power plant's adjustable capacity are reported to the power grid for each time period; The grid constraints include power flow constraints, voltage amplitude constraints, and current amplitude constraints. The operating state constraints include wind power output constraints, photovoltaic power output constraints, power balance constraints, virtual power plant output constraints, virtual power plant ramping constraints, energy storage system energy storage constraints and charging / discharging power constraints, energy upper and lower bound constraints of the aggregated electric vehicle cluster, power upper and lower bound constraints of the aggregated electric vehicle cluster, and state of charge constraints of electric vehicle batteries.

2. The method for dynamic verification of the multi-timescale adjustable capability of a virtual power plant according to claim 1, characterized in that, The model for the output of wind and solar power, based on the power output characteristics of wind and solar power, includes: (1) Based on the power output characteristics of wind power, a model for independent wind power output is constructed, specifically including: A wind turbine consists of a wind turbine and a generator. First, the wind turbine converts the kinetic energy of the wind into mechanical energy, which then powers the generator to rotate, thus converting mechanical energy into electrical energy, ultimately outputting electrical power. The output power of a wind turbine is closely related to wind speed and can be approximated using a piecewise function. The model for the independent output of wind power is as follows: (1)； in, This refers to the actual wind speed. To cut into wind speed, Rated wind speed, To cut off the wind speed, This is the expression for the wind power output when the actual wind speed is between the cut-in wind speed and the rated wind speed. The commonly used expression is ; This refers to the output power of wind power. This refers to the rated wind power output. (2) Based on the power output characteristics of photovoltaic power generation, a model for independent output of photovoltaic power generation is constructed, specifically including: The principle of photovoltaic power generation is the photovoltaic effect generated when sunlight shines on the PN junction of a photovoltaic cell, thereby converting solar energy into electrical energy. Ideally, the output power of a photovoltaic cell can be expressed as: (2)； In the formula, Photovoltaic cells under standard light intensity of 1000W / m 2 And the maximum output power at a standard temperature of 25℃; This is the actual light intensity; It is the standard light intensity; It is the power temperature coefficient; Indicates the operating temperature of the photovoltaic cell; This is the standard battery temperature; The model for correcting the output power of an ideal photovoltaic cell to the independent output power of photovoltaic power generation using empirical formulas is as follows: (3)； In the formula, It is the total area of ​​the photovoltaic array; It is the total photoelectric conversion efficiency of the photovoltaic power station.

3. The method for dynamic verification of the multi-timescale adjustable capability of a virtual power plant according to claim 1, characterized in that, The process of establishing a charging and discharging model for the energy storage system based on the stored electrical energy, charging and discharging power, and charging and discharging efficiency in the virtual power plant specifically includes: The stored electrical energy of an energy storage system is related to its initial value and the charging and discharging process. The dynamic change of the stored electrical energy during charging and discharging is related to the charging and discharging power and charging and discharging efficiency. The charging and discharging model of the energy storage system is established as follows: (4)； in, The energy storage system stores electrical energy at time t; Let t be the charging and discharging power at time t, with the discharging power being positive; For charge and discharge efficiency; This refers to the charging / discharging time.

4. The method for dynamic verification of the multi-timescale adjustable capability of a virtual power plant according to claim 3, characterized in that, The method for representing energy and power boundaries and the conversion mechanism based on the adjustable capacity of electric vehicles, and the model for establishing the scheduling potential of electric vehicles, specifically include: First, the upper and lower bounds of the energy of large-scale electric vehicles are aggregated from an energy perspective, as shown in formula (5): (5)； in, This represents the battery charge of electric vehicle i at time t; Represents total energy; Secondly, the upper and lower bounds of the power of large-scale electric vehicles are aggregated from the perspective of power, as shown in formula (6): ； in Let be the maximum and minimum charging and discharging power of electric vehicle i at time t, respectively. , These represent the aggregated maximum and minimum total charging and discharging power of the electric vehicle, respectively. During the aggregation process, the arrival time, departure time, arrival battery level, departure battery level, battery capacity, and the number and distribution of electric vehicle clusters for each electric vehicle are considered. Through comprehensive analysis and calculation, the overall scheduling potential range of the electric vehicle cluster can reflect the overall capacity of a large-scale electric vehicle cluster.

5. The method for dynamic verification of the multi-timescale adjustable capability of a virtual power plant according to claim 1, characterized in that, The specific power grid constraints and the operational state constraints are as follows: Current constraints: (10)； (11)； (12)； in, To represent the set of lines in a power grid; and These represent the active power and reactive power of the distributed power source at node i, respectively. and These represent the active load and reactive load at node i, respectively. , , , and These represent the active power, reactive power, square of current amplitude, resistance, and inductive reactance on line (i, j), respectively. This represents the square of the voltage magnitude at node i; Voltage amplitude constraint: ； in, and These represent the lower and upper limits of node voltage replication, respectively; Current amplitude constraint: (14)； in, Represents the maximum current in line (i, j). In the current amplitude constraint, when... A time indicates that the circuit is closed, allowing current to flow through it; otherwise, it indicates that the circuit is open, and the maximum value of the square of the current is 0, meaning that no current can flow through the circuit. Wind power output constraints: （15）； in, , These are the upper and lower limits for wind power generation, respectively. Photovoltaic power generation output constraints: （16）； in, , These are the upper and lower limits for photovoltaic power generation; Power balance constraints: (17)； in, This represents the number of end-users within the virtual power plant. For the power requirements of end users; Virtual power plant output constraints: (18)； Virtual power plant ramp-up constraints: The difference between the upper and lower limits of active power in two adjacent scheduling periods should be less than the maximum upward ramp constraint and the minimum downward ramp constraint, respectively. (19)； (20)； Wherein, are the upper and lower limits of active power for the virtual power plant, respectively; and are the maximum upward ramp constraint and the minimum downward ramp constraint, respectively. The maximum upward ramp constraint and minimum downward ramp constraint of the virtual power plant should be limited by the upper and lower limits of active power in two adjacent dispatch periods: (21)； (22)； The energy storage system's energy storage constraints and charge / discharge power constraints are as follows: （23）； （24）； in, , These are the upper and lower limits of the electrical energy that the energy storage system can store; , These are the upper limits for charging and discharging power, respectively. Energy upper and lower bound constraints of the aggregated electric vehicle cluster: (25)； Power upper and lower bound constraints for the aggregated electric vehicle cluster: (26)； State of charge constraints for electric vehicle batteries: (27)； in, Let be the state of charge of the electric vehicle's battery at time t. , These represent the time the electric vehicle arrives at the charging station and the time it leaves the charging station, respectively. , These represent the upper and lower limits of the state of charge, respectively.

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