Wind power resource virtual electric quantity collaborative support regulation and control method based on virtual power plant

Abstract

The invention provides a wind power resource virtual electric quantity collaborative support regulation and control method based on a virtual power plant, and the method comprises the steps: building a virtual electric quantity equivalent model of each fan of a wind power plant for simulating the charging and discharging electric quantity of a storage battery based on the wind speed and rotating speed operation characteristics of a single fan; according to the virtual electric quantity constraint of the virtual electric quantity equivalent model of each fan of the wind power plant, establishing a wind power plant minimum adjustable virtual electric quantity evaluation model; based on the wind power plant minimum adjustable virtual electric quantity evaluation model, a wind power plant economic calculation model with the minimum cost as the optimization target is established, and the particle swarm optimization is applied to the wind power plant economic calculation model to obtain the optimal virtual electric quantity. According to the method, the virtual electric quantity equivalent model for simulating the electric quantity of the traditional hybrid energy storage equipment is established for wind power resources, and flexible resources are scheduled through unified control parameters, so that the system effectively simplifies the scheduling parameters of the virtual power plant, and the power scheduling and standby evaluation difficulty of the virtual power plant is greatly simplified.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual wind power storage control, and in particular to a method for collaboratively supporting and regulating virtual electricity volume of wind power resources based on a virtual power plant. Background Art

[0002] At present, with the increase in the proportion of wind power in the power system, the total inertia level of the system has decreased, posing a serious threat to frequency stability. In addition, virtual power plants integrate resources such as distributed power sources, energy storage equipment and controllable loads to achieve flexible scheduling and efficient utilization of electric energy, thereby improving the safety and stability of the power grid. In order to solve the above problems and deal with the state perception differentiation, resource control difficulties and inertia support evaluation brought about by a large number of heterogeneous resources, this paper establishes a virtual power plant-based wind power resource virtual electricity collaborative support technology to explore the dispatchable potential of wind power resources, homogenize wind power resources, evaluate the minimum threshold for wind farm resources to participate in collaborative scheduling, establish an optimal economic model and construct a system optimization control structure based on particle swarm algorithm for solution. By unifying the control parameters, the virtual power plant can optimize the source and storage collaborative optimization by optimizing the virtual electricity and improve the inertial support for wind power. Summary of the invention

[0003] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a method for collaborative support and regulation of virtual electricity of wind power resources based on a virtual power plant, which can unify the control parameters of the virtual power plant energy collaborative support, reduce the difficulty of collaborative operation of heterogeneous resources, and improve the economy of wind power regulation in realizing the inertial energy support of wind turbines.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A wind power resource virtual electricity collaborative support and control method based on a virtual power plant, comprising:

[0006] Based on the wind speed and rotation speed operating characteristics of a single wind turbine, a virtual power equivalent model of each wind turbine in the wind farm is established to simulate the battery charging and discharging power;

[0007] According to the virtual power constraints of the virtual power equivalent model of each wind turbine in the wind farm, a minimum adjustable virtual power evaluation model of the wind farm is established;

[0008] Based on the wind farm minimum adjustable virtual power evaluation model, a wind farm economic calculation model with minimizing cost as the optimization goal is established, and the particle swarm algorithm is applied to the wind farm economic calculation model to obtain the optimal virtual power.

[0009] Preferably, based on the wind speed and rotation speed operating characteristics of a single wind turbine, a virtual power equivalent model of each wind turbine in a wind farm simulating the battery charging and discharging power is established, including:

[0010] Combined with the wind turbine operation characteristics, the wind turbine rotor energy storage is reflected by measuring the wind speed, and the wind turbine rotor energy storage relationship with wind speed as a parameter is established;

[0011] The expression of the fan rotor energy storage relationship is:

[0012]

[0013] Where: E k is the kinetic energy of the fan rotor; J s is the inherent inertia of the fan; ω r is the real-time angular velocity of the fan rotor; opt is the optimal tip speed ratio of the fan; R t is the fan blade radius; ω max is the maximum speed of the fan when it is running stably; ν is the real-time wind speed captured by the fan; ν min ν is the cut-in wind speed in the maximum power tracking area of ​​the wind farm wind turbine; max The wind speed of the wind farm wind turbines during rated operation;

[0014] According to the wind turbine rotor energy storage relationship, a virtual electric quantity equivalent model of each wind turbine in the wind farm is established;

[0015] The expression of the virtual power equivalent model of each wind turbine in the wind farm is:

[0016]

[0017] Where: subscript i is the state parameter corresponding to the i-th fan; Q virw (t) is the total virtual power of the wind farm; U B (t) is the battery terminal voltage; SoC g (t) is the overall equivalent charge state parameter of the fan; ω r (t) is the real-time mechanical angular velocity of the fan rotor;

[0018] In the virtual power equivalent model of each wind turbine in the wind farm, determining the equivalent state of charge parameters of the wind farm;

[0019] The expression of the state of charge parameter of each wind turbine in the wind farm is:

[0020]

[0021] In the formula, ω rn is the rated speed of the fan; E g is the rotor kinetic energy at the real-time speed of the fan; E gn is the rotor kinetic energy of the fan at rated speed.

[0022] Preferably, the expression of the wind farm minimum adjustable virtual power evaluation model is:

[0023]

[0024] Where RoCoF is the rate of change of system frequency; f is the system frequency; ω e is the synchronous angular velocity of the system; f0 is the initial frequency of the system; H w The inertia time constant of the fan; S NW Rated capacity of the fan; ΔP d is the disturbance power; RoCoF max Specifies the maximum rate of frequency change for the system.

[0025] Preferably, the objective function of the wind farm economic calculation model is:

[0026]

[0027] Among them, C wf C is the fan air adjustment cost; wi is the wind turbine operation and maintenance cost; C bess is the total operation and maintenance cost of the hybrid energy storage system; C B C is the battery life loss cost; b K is the total operation and maintenance cost of the battery in the hybrid energy storage system; w is the mixing adjustment coefficient; K m K is the operation and maintenance cost of mixed unit electricity; b P is the operation and maintenance cost per unit of electricity of the hybrid energy storage system; wo , P w are the original output power of the fan after capturing the maximum wind speed during period t and the output power of the fan after adjustment; P bchar , P bdis are the charging and discharging power of the battery pack during period t respectively.

[0028] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0029] The present invention provides a method for collaboratively supporting and controlling virtual electricity of wind power resources based on a virtual power plant, including: establishing a virtual electricity equivalent model of each wind turbine in a wind farm that simulates the charging and discharging electricity of a battery based on the wind speed and rotational speed operating characteristics of a single wind turbine; establishing a minimum adjustable virtual electricity evaluation model for the wind farm based on the virtual electricity constraints of the virtual electricity equivalent model of each wind turbine in the wind farm; establishing a wind farm economic calculation model with minimizing cost as the optimization goal based on the minimum adjustable virtual electricity evaluation model of the wind farm, and applying a particle swarm algorithm to the wind farm economic calculation model to obtain the optimal virtual electricity. The present invention establishes a virtual electricity equivalent model that simulates the electricity of traditional hybrid energy storage equipment for wind power resources, and through unified control parameter scheduling flexibility resources, the system effectively simplifies the scheduling parameters of the virtual power plant, thereby greatly simplifying the difficulty of power scheduling and standby evaluation of the virtual power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0031] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0032] Figure 2 It is a structural block diagram of a method for coordinated optimization control of wind power resources and hybrid energy storage in a virtual power plant based on virtual power instruction parameters provided by an embodiment of the present invention;

[0033] Figure 3 It is the power equipment topology structure of the wind storage resources of the virtual power plant provided by the embodiment of the invention;

[0034] Figure 4 are specific parameters of controllable resources of the virtual power plant provided in the embodiment of the present invention;

[0035] Figure 5 is a virtual power plant load forecasting diagram provided by an embodiment of the present invention;

[0036] Figure 6 It is a measured wind speed diagram of a virtual power plant wind farm provided by an embodiment of the present invention;

[0037] Figure 7 is a virtual power adjustable potential assessment result of wind power resources provided by an embodiment of the present invention;

[0038] Figure 8 It is the optimization result of the virtual power value of each wind power resource of the virtual power plant provided by the embodiment of the present invention;

[0039] Fig. 9 is a dynamic response curve of a virtual power plant hybrid energy storage system provided by an embodiment of the present invention;

[0040] Fig.10 It is the cumulative total daily cost curve of the virtual power plant provided by the embodiment of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] The purpose of the present invention is to provide a method for collaboratively supporting and controlling virtual electricity of wind power resources based on a virtual power plant, establish a virtual electricity equivalent model that simulates the electricity of traditional hybrid energy storage equipment for wind power resources, and schedule flexible resources through unified control parameters, so that the system can effectively simplify the scheduling parameters of the virtual power plant, thereby greatly simplifying the difficulty of power scheduling and standby evaluation of the virtual power plant.

[0043] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] Figure 1 A flow chart of a method provided by an embodiment of the present invention, such as Figure 1 As shown, the present invention provides a wind power resource virtual quantity collaborative support and control method based on a virtual power plant, comprising:

[0045] Step 100: Based on the wind speed and rotation speed operating characteristics of a single wind turbine, a virtual power equivalent model of each wind turbine in the wind farm simulating the battery charging and discharging power is established;

[0046] Step 200: establishing a minimum adjustable virtual power evaluation model for the wind farm according to the virtual power constraints of the virtual power equivalent model of each wind turbine in the wind farm;

[0047] Step 300: Based on the wind farm minimum adjustable virtual power evaluation model, a wind farm economic calculation model with minimizing cost as the optimization goal is established, and the particle swarm algorithm is applied to the wind farm economic calculation model to obtain the optimal virtual power.

[0048] At present, with the increase in the proportion of wind power in the power system, the total inertia level of the system has decreased, posing a serious threat to frequency stability. In addition, virtual power plants integrate resources such as distributed power sources, energy storage equipment and controllable loads to achieve flexible scheduling and efficient utilization of electric energy, thereby improving the safety and stability of the power grid. In order to solve the above problems and deal with the state perception differentiation, resource management difficulties and inertia support evaluation brought about by a large number of heterogeneous resources, this paper establishes a virtual power plant virtual electricity collaborative support system to explore the dispatchable potential of wind power resources, homogenize wind power resources, establish an optimal economic model and construct a system optimization control structure based on particle swarm algorithm for solution. By unifying the control parameters, the virtual power plant can complete the coordinated optimization of source and storage through virtual electricity collaborative support, and improve the inertial support for wind power.

[0049] Wind power resources are characterized by randomness, volatility, and intermittency. Traditionally, accurate wind power forecasts are required for wind farms. However, the reality is that there is still a difference between the wind power forecast and the actual output, which has a certain impact on grid control and security. In order to improve the aggregated control of wind power resources by virtual power plants, combined with the operating characteristics of wind turbines, the wind turbine rotor energy storage is reflected by the measured wind speed, and the wind turbine rotor energy storage relationship with wind speed as a parameter is established, which can be expressed as:

[0050]

[0051] Where: E k is the kinetic energy of the fan rotor; J s is the inherent inertia of the fan; ω r is the real-time angular velocity of the fan rotor; opt is the optimal tip speed ratio of the wind turbine; ν is the real-time wind speed captured by the wind turbine; R t is the fan blade radius; ν min is the wind speed at which the wind turbine enters the maximum power tracking zone, ν max is the wind speed of the wind farm wind turbine during rated operation; ω max It is the maximum speed of the fan when it is running stably.

[0052] Virtual power plants aggregate a large number of distributed resources. In order to better regulate the response frequency of many resources and realize the inertial support of wind power resources, a method of collaboratively managing source-storage resources based on virtual electricity is proposed to deal with large deviations in load forecasts, and priority is given to power-density resources such as wind power and supercapacitors with fast power response capabilities to complete short-term inertial response. In the early stage of frequency recovery, energy-density resources with long-term power support capabilities can be called to compensate for wind turbine speed. Although virtual power plants can aggregate resources of different response types, the flexible management of a large number of heterogeneous resources requires the consistency of various types of resources for equivalent modeling.

[0053] Most of the resources aggregated in virtual power plants are equipped with power electronic converters to ensure the flexibility and controllability of resources and have the ability to respond quickly. In order to solve the problem of coordinated operation of multiple types of flexible resources and provide inertia support for wind power resources, it is necessary to integrate the control parameters of virtual power plant collaborative support to provide the system with overall state perception and evaluation methods. In order to realize the model of wind turbine simulation similar to battery power as the control parameter, a wind farm wind turbine virtual power equivalent model is established, which can be expressed as:

[0054]

[0055] Where: subscript i is the state parameter corresponding to the i-th fan; Q virw (t) is the total virtual power of the wind farm; U B (t) is the battery terminal voltage; SoC g (t) is the overall equivalent charge state parameter of the fan; ω r (t) is the real-time mechanical angular velocity of the wind turbine rotor. Similarly, when n=1, it is the corresponding single wind turbine virtual power equivalent model.

[0056] In the virtual power equivalent model, in order to reflect the virtual power parameters only through the measured wind speed and rotation speed, according to the definition of the state of charge parameter, the state of charge parameter of the wind farm equivalent can be expressed as:

[0057]

[0058] Where, the subscript i is the state parameter corresponding to the i-th fan; ω rn is the rated speed of the fan, E g is the rotor kinetic energy at the real-time speed of the fan; E gn is the rotor kinetic energy at the rated speed of the fan. Similarly, when n=1, it is the equivalent charge state of the corresponding single fan after adjustment.

[0059] Therefore, according to the above analysis, a rotor energy storage model of multiple wind turbines with different wind speeds in a wind farm can be established. By monitoring the wind speed, the virtual energy reserve of each wind turbine can be evaluated, so that wind turbines at different wind speeds can be integrated into the economic operation of the virtual power plant in an orderly manner and supported by the coordinated support of virtual electricity.

[0060] Figure 2 It is a structural block diagram of a method for coordinated optimization control of wind power resources and hybrid energy storage in a virtual power plant based on virtual power instructions according to an embodiment of the present invention;

[0061] The current main technical idea is to introduce system safety and stability constraints into the traditional economic dispatch model. Based on the virtual power evaluation model with equivalent virtual power constraints, an effective threshold for evaluating the participation of each wind turbine in collaborative support is established, so that multiple wind power resources can be coordinated with hybrid energy storage to flexibly participate in system dispatch under certain constraints;

[0062] In order to realize the inertia support characteristics of wind power resources in wind farms, taking a single wind turbine as an example, a minimum virtual power evaluation model for wind turbines is established to effectively evaluate the support potential of wind turbines to realize the flexible support of inertia by virtual power plants, which can be expressed as:

[0063]

[0064] Where: RoCoF is the rate of change of system frequency; f is the system frequency; ω e is the synchronous angular velocity of the system; f0 is the initial frequency of the system; H w The inertia time constant of the fan; S NW Rated capacity of the fan; ΔP d is the disturbance power; RoCoF max The maximum frequency change rate is specified for the system, which is generally set to 0.5Hz / s.

[0065] In summary, based on the virtual energy storage resources obtained in step 100, the objective function of the optimal economic calculation model that can be designed is as follows:

[0066]

[0067] Where, the subscript i is the state parameter corresponding to the i-th fan; C wf C is the fan air adjustment cost; wi is the wind turbine operation and maintenance cost; C bess is the total operation and maintenance cost of the hybrid energy storage system; C B C is the battery life loss cost; b The total operation and maintenance cost of the battery in the hybrid energy storage system; K w is the regulation coefficient of the hybrid energy storage system; K m K is the operation and maintenance cost per unit of electricity of the hybrid energy storage system; b P is the operation and maintenance cost per unit of electricity of the hybrid energy storage system; wo , P w are the original output power of the fan after capturing the maximum wind speed during period t and the output power of the fan after adjustment; P bchar , P bdis are respectively the charging and discharging power of the battery group in period t; according to the relationship between the flexible controllable load and the rated power, performance parameters for evaluating the virtual energy storage configuration capacity and operating status are obtained; the performance parameters at least include the virtual power.

[0068] Based on the optimal economic model, a system optimization control structure based on a particle swarm algorithm is used to solve the obtained virtual electricity value, and this is used as a parameter to allocate virtual instructions to flexible and controllable resources in the virtual power plant to coordinate the output of equipment in the virtual power plant.

[0069] According to the virtual power value, the output power of multiple wind turbines and hybrid energy storage devices is adjusted to the reference power respectively, so as to participate in the power balance in the virtual power plant.

[0070] Based on this, the present invention proposes a wind storage resource control method based on virtual power plant virtual power instruction collaborative optimization. The specific design block diagram is as follows: Figure 2 As shown, by establishing a wind turbine virtual power equivalent model, the minimum adjustable virtual power of each wind turbine in the wind farm is evaluated, and then the virtual power value is solved in combination with the optimal economic model established in step 300, and this is used as a parameter to allocate virtual instructions to the flexible and controllable resources in the virtual power plant to coordinate the output of the virtual power plant equipment.

[0071] Figure 3 It is the power equipment topology structure of the wind and energy storage resources of the virtual power plant in the embodiment of the present invention. The embodiment of the present invention builds a typical virtual power plant aggregation wind farm and energy storage resource operation example in the Matlab / Simulink simulation environment. The example optimization scheduling cycle is 24h, and the simulation step length is set to 1h per unit cycle. It contains traditional hybrid energy storage systems, multiple permanent magnet direct-drive wind turbines, and loads based on the predictable virtual power plant. The specific parameters of the computing power equipment are as follows Figure 4 As shown in Figure 2, the virtual power plant's day-ahead load forecast is as follows: Figure 5 As shown. Observe the comparison of the effects of traditional hybrid energy storage control and the virtual power plant virtual power collaborative support parameters proposed in this invention on equipment output load balancing and economic optimization. The actual wind speed measurement diagram of each wind turbine in the wind farm is shown in Figure 6 As shown in , the minimum adjustable virtual power demand of wind power at a certain moment can be evaluated through the current operating status of the wind turbine and parameters such as wind speed, rotation speed and related constraints, such as Figure 7 As shown in the figure, it can be seen that the minimum adjustable virtual power has positive and negative values. When it is positive, it means that there is a positive deviation in power and the frequency of the wind power virtual power needs to be increased. When the adjustable virtual power is not adjustable at this moment, hybrid energy storage is needed to coordinate and support the increase in frequency. When it is negative, it means that there is surplus power and there may be problems with wind power absorption. The frequency needs to be lowered and, if necessary, wind power can be absorbed through hybrid energy storage.

[0072] In a specific example, the wind turbine virtual power value adjustment effect is as follows: Figure 8As shown in the figure, the system analyzes the system dispatching instructions for each time period and knows that the virtual power of wind turbines in the whole time period is not much different, and there are some differences between different wind turbines. Therefore, wind turbines can coordinate and compensate each other to maintain the power stability between wind power resources, on the one hand to reduce the burden of hybrid energy storage, and on the other hand to reduce the operation and maintenance cost of hybrid energy storage. In the time period of 6:00-14:00, the virtual power of wind power resources is relatively low, but at the same time, this time period is also the peak period of electricity consumption, and energy support from hybrid energy storage is needed to smooth the fluctuation of load power and slow down the fluctuation of frequency. However, since the electricity load is in the valley period at night, wind power resources can be used to charge the hybrid energy storage with the stored virtual power energy storage to achieve valley filling, and this part of the power can be used to make up for the lack of virtual power of wind power resources during the peak period of the day.

[0073] In the above specific example, the hybrid energy storage power state adjustment effect is as follows: Fig. 9 As shown in the figure, the system cost of the collaborative support of various resources in the virtual power plant is as follows: Fig.10 As shown. Combined Figure 6 and Fig. 9 It can be seen that under the control of traditional hybrid energy storage, at 2:00, the wind speed is high, the wind power runs to the upper limit, and the load at night consumes less power from the virtual power plant. The supercapacitor absorbs power to the upper limit and then exits operation. The battery absorbs power, and the maximum power rises to 77.94% of the rated capacity. The daily system revenue is greatly affected by the wind and solar output status. However, under the control of hybrid virtual energy storage, the load accepts communication instructions to adjust the power at 2:00. While simplifying the system control parameters, it effectively utilizes the flexible and controllable load to store energy, so that the supercapacitor charge state is always maintained at an adjustable state of about 60%, and avoids frequent charging and discharging of the battery, extending its life. At the same time, multiple wind power coordinates and complements each other. Fig.10 It can be seen that this reduces the system's daily cost by 23.5%, which can alleviate the capacity configuration of hybrid energy storage to a certain extent and is beneficial to the economic operation of the system.

[0074] The present invention provides a method for collaboratively supporting and controlling virtual electricity of wind power resources based on a virtual power plant. Compared with the traditional method, first: construct a wind turbine virtual electricity model and parameters based on the hybrid energy storage operation characteristics; then evaluate the minimum virtual electricity threshold of each wind turbine unit in the wind farm resources, and propose a virtual power plant economic optimization method based on virtual electricity constraints. This method can improve the system economy and simplify the difficulty of operation and scheduling by optimizing virtual electricity instructions and collaboratively optimizing the collaborative scheduling of virtual power plant resources. The present invention provides a method for calling multiple types of flexible resources through virtual electricity, which can effectively evaluate the adjustable potential of wind power resources, reduce the difficulty of collaborative scheduling of the system, reduce the system operation and maintenance costs, and improve the overall perception and collaborative operation level of the system, which can effectively solve the shortcomings of traditional energy control based on hybrid energy storage.

[0075] The beneficial effects of the present invention are as follows:

[0076] First, a virtual electricity equivalent model is established for wind power resources to simulate the electricity of traditional hybrid energy storage equipment. By scheduling flexible resources through unified control parameters, the system can effectively simplify the scheduling parameters of virtual power plants, thereby greatly simplifying the difficulty of power scheduling and backup evaluation of virtual power plants.

[0077] Second, by integrating the characteristics of virtual power plants, virtual power is used as a parameter for unified regulation to aggregate cross-regional flexible and controllable resources for collaborative support without the need to configure additional equipment on site. Communication technology is used to collect real-time monitoring status, and unified and coordinated resource scheduling is performed in the energy control center. According to the real-time operating status of the system, the adjustable energy reserve of the wind storage system is evaluated, providing a scientific basis for the unified scheduling of flexible and controllable resource clusters and traditional energy storage by the control center;

[0078] Third, the control method is based on the premise that the frequency of the entire power system operates within a stable range. In addition, the system power constraints are taken into account. The adjustable potential of wind power resources is evaluated through the minimum virtual power of wind power resources. With the goal of minimizing the cost of the virtual power plant, the control plan of each wind turbine and hybrid energy storage of the wind farm resources is determined. On the basis of meeting the virtual power constraints, it not only ensures the stable operation of the system, but also reduces the comprehensive operation and maintenance costs of wind turbines and hybrid energy storage equipment, thereby improving the economic operation potential of the system.

[0079] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0080] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A wind power resource virtual electricity collaborative support and control method based on a virtual power plant, characterized in that: include: Based on the wind speed and rotation speed operating characteristics of a single wind turbine, a virtual power equivalent model of each wind turbine in the wind farm is established to simulate the battery charging and discharging power; According to the virtual power constraints of the virtual power equivalent model of each wind turbine in the wind farm, a minimum adjustable virtual power evaluation model of the wind farm is established; Based on the wind farm minimum adjustable virtual power evaluation model, a wind farm economic calculation model with minimizing cost as the optimization goal is established, and the particle swarm algorithm is applied to the wind farm economic calculation model to obtain the optimal virtual power.

2. The wind power resource virtual quantity collaborative support and control method based on virtual power plant according to claim 1 is characterized in that: Based on the wind speed and rotation speed operating characteristics of a single wind turbine, a virtual power equivalent model of each wind turbine in a wind farm is established to simulate the battery charging and discharging power, including: Combined with the wind turbine operation characteristics, the wind turbine rotor energy storage is reflected by measuring the wind speed, and the wind turbine rotor energy storage relationship with wind speed as a parameter is established; The expression of the fan rotor energy storage relationship is: Where: E k is the kinetic energy of the fan rotor; J s is the inherent inertia of the fan; ω r is the real-time angular velocity of the fan rotor; opt is the optimal tip speed ratio of the fan; R t is the fan blade radius; ω max is the maximum speed of the fan when it is running stably; ν is the real-time wind speed captured by the fan; ν min ν is the cut-in wind speed in the maximum power tracking area of ​​the wind farm wind turbine; max The wind speed of the wind farm wind turbines during rated operation; According to the wind turbine rotor energy storage relationship, a virtual electric quantity equivalent model of each wind turbine in the wind farm is established; The expression of the virtual power equivalent model of each wind turbine in the wind farm is: Where: subscript i is the state parameter corresponding to the i-th fan; Q virw (t) is the total virtual power of the wind farm; U B (t) is the battery terminal voltage; SoC g (t) is the overall equivalent charge state parameter of the wind turbine; ω r (t) is the real-time mechanical angular velocity of the fan rotor; In the virtual power equivalent model of each wind turbine in the wind farm, determining the state of charge parameters of the wind farm equivalent; The expression of the state of charge parameter of each wind turbine in the wind farm is: In the formula, ω rn is the rated speed of the fan; E g is the rotor kinetic energy at the real-time speed of the fan; E gn is the rotor kinetic energy of the fan at rated speed.

3. The wind power resource virtual quantity collaborative support and control method based on virtual power plant according to claim 2 is characterized in that: The expression of the wind farm minimum adjustable virtual power evaluation model is: Where RoCoF is the rate of change of system frequency; f is the system frequency; ω e is the synchronous angular velocity of the system; f0 is the initial frequency of the system; H w The inertia time constant of the fan; S NW Rated capacity of the fan; ΔP d is the disturbance power; RoCoF max Specifies the maximum rate of frequency change for the system.

4. The wind power resource virtual quantity collaborative support and control method based on virtual power plant according to claim 2 is characterized in that: The objective function of the wind farm economic calculation model is: Among them, C wf C is the fan air adjustment cost; wi is the wind turbine operation and maintenance cost; C bess is the total operation and maintenance cost of the hybrid energy storage system; C B C is the battery life loss cost; b K is the total operation and maintenance cost of the battery in the hybrid energy storage system; w is the mixing adjustment coefficient; K m K is the operation and maintenance cost of mixed unit electricity; b P is the operation and maintenance cost per unit of electricity of the hybrid energy storage system; wo , P w are the original output power of the fan after capturing the maximum wind speed during period t and the output power of the fan after adjustment; P bchar , P bdis are the charging and discharging power of the battery pack during period t respectively.

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