Virtual power plant power regulation and control method, system and equipment based on mixed-order containment control, and medium

Abstract

The invention is suitable for the technical field of power regulation and control, and provides a virtual power plant power regulation and control method, system and device based on mixed-order containment control and a medium. Quantitatively analyzing the reactive power regulation capability of the distributed wind and light power generation resources and the active power regulation capability of the distributed energy storage resources; respectively selecting consistency control variables of the distributed wind and light cluster and the distributed energy storage cluster; constructing an initial virtual power plant power regulation and control model according to the consistency control variable; and constructing a mixed-order variable consensus protocol based on the uncertainty of distributed wind and light output, and optimizing the initial virtual power plant power regulation and control model by using the mixed-order variable consensus protocol to generate a target virtual power plant power regulation and control strategy. According to the invention, the real-time performance and the control precision of wind and light storage coordinated regulation are improved, and the reactive power and active power regulation potentials of demand side resources can be brought into full play.

Description

Technical Field

[0001] The present invention relates to the technical field of power regulation, and in particular to a virtual power plant power regulation method, system, equipment and medium based on mixed-order control. Background Art

[0002] The consensus algorithm is a commonly used method in distributed control, which has the advantages of simplicity, easy implementation, and fast calculation speed. The essence of the consensus algorithm is to update the state variables of the local node through information exchange between the local node and the adjacent nodes, so that the state variables of the node converge to a stable common value. In addition to the traditional consistency problem, an extended consistency problem has emerged in recent years, namely the group consistency control strategy. Different from the traditional consistency control problem, the group consistency is that the multi-agent in the control system converges to different consistency values ​​according to subgroups, which can be divided into two categories: dynamic and static.

[0003] As a new type of resource aggregator, virtual power plants can use advanced information and communication technologies and software systems to achieve the aggregation and coordinated optimization of multiple types of demand-side distributed resources such as distributed photovoltaics, wind power, energy storage, and flexible loads. Since the multiple types of distributed resources contained in virtual power plants have obvious differences in physical and dynamic characteristics, how to take into account the coordinated control of the active power and reactive power responses of virtual power plants and realize the integrated control of the power flow of heterogeneous resources within virtual power plants has become an urgent problem to be solved. Summary of the invention

[0004] The embodiments of the present application provide a virtual power plant power regulation method, system, device and medium based on hybrid-order control, which are used to solve the problem of coordinated control of active power and reactive power response of the virtual power plant.

[0005] A first aspect of an embodiment of the present application provides a virtual power plant power control method based on mixed-order pinning control, comprising:

[0006] Based on the differences in output and regulation rate of heterogeneous distributed resources within the virtual power plant, the reactive regulation capability of distributed wind and solar power generation resources and the active regulation capability of distributed energy storage resources are quantitatively analyzed;

[0007] Select the consistency control variables of the distributed wind and solar cluster and the distributed energy storage cluster respectively;

[0008] Constructing an initial virtual power plant power regulation model according to the consistency control variables;

[0009] Based on the uncertainty of distributed wind and solar power output, a hybrid-order variable consensus protocol is constructed, and the initial virtual power plant power control model is optimized using the hybrid-order variable consensus protocol to generate a target virtual power plant power control strategy.

[0010] Furthermore, based on the differences in output and regulation rate of heterogeneous distributed resources within the virtual power plant, the reactive regulation capability of distributed wind and solar power generation resources and the active regulation capability of distributed energy storage resources are quantitatively analyzed, including:

[0011] The expression of reactive power regulation capability is:

[0012]

[0013] Where: is the maximum adjustable reactive capacity of the photovoltaic inverter, P PV is the active power output of photovoltaic power generation, S INV is the rated capacity of the PV inverter;

[0014] The expression of active power regulation capability is:

[0015] S(t)=S(t-Δt)+ΔS(t)

[0016]

[0017] Where: S(t) is the energy storage SOC at time t, S(t-Δt) is the energy storage SOC at time t-Δt, ΔS(t) is the energy storage SOC change at time t, P ESS (t) is the active power of energy storage at time t, C ESS is the capacity of the energy storage device, and Δt is the time interval.

[0018] Furthermore, the selection of consistency control variables of the distributed wind and solar cluster and the distributed energy storage cluster respectively includes:

[0019] The reactive response rate of the wind-solar inverter is selected as the consistency control variable of the distributed wind-solar cluster, and the change of the energy storage SOC is selected as the consistency control variable of the distributed energy storage cluster;

[0020] Consistency control variables of distributed wind and solar clusters:

[0021]

[0022] Where ΔQ i ∈R n is the reactive power absorbed or emitted by the i-th wind-solar inverter, v i ∈R n is the reactive power response rate of the i-th wind-solar inverter, u i (t) represents the consistency control protocol of the distributed wind and solar cluster;

[0023] Consistency control variables of distributed energy storage clusters:

[0024]

[0025] Where: ΔS i ∈R n is the SOC change of the i-th energy storage device, w i (t) is the consistency control protocol of the distributed energy storage cluster.

[0026] Furthermore, constructing an initial virtual power plant power regulation model according to the consistency control variable includes:

[0027] The leader-follower consistency control mode is adopted in the consistency control subgroups of the distributed wind-solar cluster and the distributed energy storage cluster, and a distributed pinning control group consistency protocol is designed according to the state information of the intelligent agent itself and its neighbors.

[0028] Furthermore, the leader-follower consistency control mode includes:

[0029]

[0030] Where: the initial value η0(0), ρ1 is the positive feedback gain of the active power control subgroup, C ESS,i is the energy storage capacity at node i, Δt is the control time interval, ΔP goal is the active power to be regulated, SOC i,max is the upper limit of SOC.

[0031] Furthermore, the group consistency protocol for distributed pinning control is designed based on the state information of the agent itself and its neighbors, including:

[0032]

[0033] Where: γ k (t), γ i d(t) and γ0(t) are the ratios of the reactive power regulation of the kth, ith and leader wind-solar inverters to their maximum reactive regulation rate, respectively; i is the degree matrix coefficient of the network topology structure; ε1, ε2 and ε3 are all coupling parameters greater than 0, and the adjacency matrix A = [a ij ]∈R n×n It describes the communication connection relationship between each resource node in the system. If the kth resource node can receive the information of the ith resource node, then a ik >0, otherwise a ik =0.

[0034] Furthermore, the hybrid-order variable consensus protocol is constructed based on the uncertainty of distributed wind and solar power output, and the hybrid-order variable consensus protocol is used to optimize the initial virtual power plant power regulation model to generate a target virtual power plant power regulation strategy, including:

[0035]

[0036] Where: Ω i is the consistency correction, which is used to measure the corresponding uncertainty of the wind and solar cluster, v i,max , μ Δ and γ Δ They are respectively the consistency control variables of the distributed wind and solar clusters in the system.

[0037] A second aspect of an embodiment of the present application provides a virtual power plant power control system based on hybrid-order pinning control, including:

[0038] The power regulation capability analysis unit is used to quantitatively analyze the reactive regulation capability of distributed wind and solar power generation resources and the active regulation capability of distributed energy storage resources based on the differences in output and regulation rate of heterogeneous distributed resources within the virtual power plant;

[0039] A consistency control variable selection unit, used to select consistency control variables of a distributed wind and solar cluster and a distributed energy storage cluster respectively;

[0040] An initial virtual power plant power regulation model construction unit, used to construct an initial virtual power plant power regulation model according to the consistency control variables;

[0041] A target virtual power plant power control strategy generation unit is used to construct a hybrid order variable consensus protocol based on the uncertainty of distributed wind and solar power output, and use the hybrid order variable consensus protocol to optimize the initial virtual power plant power control model to generate a target virtual power plant power control strategy

[0042] A third aspect of the embodiments of the present application provides a computer device, including:

[0043] memories, transceivers, processors, and bus systems;

[0044] Wherein, the memory is used to store programs;

[0045] The processor is used to execute the program in the memory, including executing the virtual power plant power control method based on hybrid order pinning control as described in any one of the above;

[0046] The bus system is used to connect the memory and the processor so that the memory and the processor can communicate with each other.

[0047] A fourth aspect of an embodiment of the present application provides a readable storage medium, including instructions, which, when executed on a computer, enables the computer to execute a virtual power plant power regulation method based on hybrid-order control as described in any one of the above.

[0048] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0049] 1. The power control strategy of a virtual power plant based on hybrid-order control starts from the differences in the output and regulation rates of heterogeneous distributed resources within the virtual power plant, selects the consistent control variables of distributed wind and solar clusters and energy storage clusters to participate in the active power and reactive power regulation, and comprehensively considers the active and reactive collaborative control coupling relationship between different types of flexible resources and controllable devices.

[0050] 2. The power control strategy of a virtual power plant based on hybrid-order control of the present invention comprehensively considers the reactive power regulation capability of distributed wind and solar power generation resources and the active power regulation capability of distributed energy storage resources, constructs a power control strategy of a virtual power plant based on a hybrid-order control algorithm, and realizes the integrated coordinated control of active power and reactive power of distributed wind, solar and energy storage resources within the virtual power plant.

[0051] 3. The power control strategy of a virtual power plant based on hybrid-order restraint control of the present invention takes into account the uncertainty of the output of distributed wind and solar resources. On the basis of constructing a hybrid-order variable consensus protocol that takes uncertainty into account, the power control strategy of a virtual power plant based on a hybrid-order restraint control algorithm is improved, thereby improving the real-time and control accuracy of the coordinated control of wind, solar and storage, and helping to give full play to the reactive and active regulation potential of demand-side resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic flow chart of an embodiment of a virtual power plant power regulation method based on mixed-order control in the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] In this embodiment, the virtual power plant power control method based on mixed-order pinning control is used to improve the real-time performance and control accuracy of wind, solar and energy storage coordinated control. The implementation method in this embodiment can be implemented in the system, on the server, or on the terminal, without specific limitation.

[0055] Embodiment 1

[0056] See also Figure 1 , an embodiment of a virtual power plant power control method based on hybrid order control in the present invention comprises the following steps:

[0057] S11. Based on the differences in output and regulation rate of heterogeneous distributed resources within the virtual power plant, quantitative analysis is conducted on the reactive regulation capability of distributed wind and solar power generation resources and the active regulation capability of distributed energy storage resources;

[0058] In this embodiment, from the perspective of the differences in output and regulation rate of heterogeneous distributed resources within the virtual power plant, the reactive regulation capability of distributed wind and solar power generation resources and the active regulation capability of distributed energy storage resources are quantitatively analyzed.

[0059] 1. Distributed wind and solar power generation resources are mainly connected to the grid through inverters. Their reactive power regulation capability depends on the control strategy and performance of the inverter, which changes dynamically with the active power output of wind and solar power generation. Taking photovoltaic inverters as an example, their reactive power regulation capability is expressed as:

[0060]

[0061] Where: is the maximum adjustable reactive capacity of the photovoltaic inverter, P PV is the active power output of photovoltaic power generation, S INV is the rated capacity of the PV inverter.

[0062] 2. The active regulation capability of distributed energy storage resources needs to comprehensively consider its device capacity and energy storage state of charge (SOC). The relationship between the adjustable active capacity of the energy storage device and the energy storage SOC is expressed as:

[0063] S(t)=S(t-Δt)+ΔS(t)

[0064]

[0065] Where: S(t) is the energy storage SOC at time t, S(t-Δt) is the energy storage SOC at time t-Δt, ΔS(t) is the energy storage SOC change at time t, P ESS (t) is the active power of energy storage at time t, C ESS is the capacity of the energy storage device, and Δt is the time interval.

[0066] S12. Select the consistency control variables of the distributed wind and solar cluster and the distributed energy storage cluster respectively;

[0067] In this embodiment, the active and reactive coordinated control coupling between different types of flexible resources and controllable devices is considered, and the consistency control variables of the distributed wind and solar clusters and the energy storage clusters are selected respectively, including the reactive power response rate of the wind and solar inverters (second-order consistency variable) and the charge state of the energy storage resources (first-order consistency variable), to construct a virtual power plant power regulation strategy based on a mixed-order restraint control algorithm.

[0068] 1. In order to maximize the reactive power regulation capability of the wind-solar inverter, taking the wind-solar inverter i as an example, the reactive power response rate v of the wind-solar inverter is selected. i As the consistency control variable of the distributed wind and solar cluster, it is expressed as:

[0069]

[0070] Where ΔQ i ∈R n is the reactive power absorbed or emitted by the i-th wind-solar inverter, v i ∈R n is the reactive power response rate of the i-th wind-solar inverter, u i (t) represents the consistency control protocol of the distributed wind and solar cluster.

[0071] Define the reactive power response rate v of the i-th wind-solar inverter i (t) and its maximum reactive adjustable rate v i,max The ratio is μ i , the reactive power regulation of the i-th wind-solar inverter and its maximum reactive adjustable rate v i,max The ratio is γ i , expressed as:

[0072]

[0073] In order to ensure that all wind and solar inverters bear the task of power regulation fairly, each inverter should participate in reactive power regulation in the same proportion.

[0074] Each inverter participates in reactive power regulation in the same proportion, expressed as:

[0075]

[0076] Where: v Δ and γ Δ It is the consistency control variable of the distributed wind and solar cluster in the system.

[0077] Therefore, the second-order control mathematical model of the i-th wind-solar inverter in the system can be further derived, which is expressed as:

[0078]

[0079] 2. In order to take into account the active power regulation capability of each energy storage device and the state of charge at different times, the SOC change of the energy storage ΔS is selected i As a state variable, it is expressed as:

[0080]

[0081] Where: ΔS i ∈R n is the SOC change of the i-th energy storage device, w i (t) is the consistency control protocol of the distributed energy storage cluster.

[0082] Define the SOC change ΔS of the i-th energy storage device i (t) and its upper limit SOC i,max The ratio is η i , expressed as:

[0083]

[0084] In order to ensure that all energy storage devices bear the task of power regulation fairly, each energy storage device should participate in active power regulation in the same proportion.

[0085] Each energy storage device participates in active power regulation in the same proportion, expressed as:

[0086] η1=η2=…=η n =η Δ

[0087] Where: η Δ It is the consistency control variable of the distributed energy storage cluster in the system.

[0088] From this, the first-order control mathematical model of the i-th energy storage device in the system can be further derived, which is expressed as:

[0089]

[0090] S13. Constructing an initial virtual power plant power regulation model based on the consistency control variables;

[0091] The leader-follower consistency control mode is adopted in the second-order control subgroup of the distributed wind-solar cluster and the first-order control subgroup of the distributed energy storage cluster, and a group consistency protocol of distributed pinning control is designed according to the state information of the intelligent agent itself and its neighbors.

[0092] The leader-follower consistency control mode includes:

[0093] Taking the first-order control subgroup of a distributed energy storage cluster as an example, there is a virtual leader node in the control subgroup that satisfies the following formula:

[0094]

[0095] Where: the initial value η0(0), ρ1 is the positive feedback gain of the active power control subgroup, C ESS,i is the energy storage capacity at node i, Δt is the control time interval, ΔP goal is the active power that is expected to be regulated.

[0096] Further derive the consistency variable μ Δ satisfy:

[0097]

[0098] Design a group consistency protocol for distributed pinning control, as follows:

[0099]

[0100] Where: γ k (t), γ i d(t) and γ0(t) are the ratios of the reactive power regulation of the kth, ith and leader wind-solar inverters to their maximum reactive regulation rate, respectively; i is the degree matrix coefficient of the network topology structure; ε1, ε2 and ε3 are all coupling parameters greater than 0, and the adjacency matrix A = [a ij ]∈R n×n It describes the communication connection relationship between each resource node in the system. If the kth resource node can receive the information of the ith resource node, then a ik >0, otherwise a ik =0.

[0101] S14. Construct a hybrid-order variable consensus protocol based on the uncertainty of distributed wind and solar power output, and use the hybrid-order variable consensus protocol to optimize the initial virtual power plant power control model to generate a target virtual power plant power control strategy.

[0102] The variable consistency correction is obtained from the probability distribution of wind and solar power output uncertainty. Assuming that the output uncertainty of the distributed wind and solar power cluster obeys the normal distribution, the value of the correction can be defined as the confidence of the normal distribution.

[0103] Considering the output response uncertainty of the distributed wind and solar clusters in the virtual power plant, an improved distributed traction control protocol with variable consensus variables is constructed, which is expressed as:

[0104]

[0105] Where: Ω i It is a consistency correction, which is used to measure the corresponding uncertainty of the wind and solar cluster.

[0106] Based on the constructed virtual power plant power regulation strategy using a mixed-order pinning control algorithm, an improved distributed traction control protocol with variable consensus variables is introduced, as shown in the following formula:

[0107]

[0108] By considering the uncertainty of the output of distributed wind and solar resources, on the basis of constructing a hybrid-order variable consensus protocol that takes uncertainty into consideration, the hybrid-order variable consensus protocol is used to optimize the initial virtual power plant power control model, and finally the optimal virtual power plant power control strategy is generated.

[0109] Embodiment 2

[0110] An embodiment of a virtual power plant power control system based on hybrid order pinning control in the present invention includes the following steps:

[0111] The power regulation capability analysis unit is used to quantitatively analyze the reactive regulation capability of distributed wind and solar power generation resources and the active regulation capability of distributed energy storage resources based on the differences in output and regulation rate of heterogeneous distributed resources within the virtual power plant;

[0112] A consistency control variable selection unit, used to select consistency control variables of a distributed wind and solar cluster and a distributed energy storage cluster respectively;

[0113] An initial virtual power plant power regulation model building unit, used to build an initial virtual power plant power regulation model according to consistency control variables;

[0114] The target virtual power plant power control strategy generation unit is used to construct a hybrid-order variable consensus protocol based on the uncertainty of distributed wind and solar power output, and use the hybrid-order variable consensus protocol to optimize the initial virtual power plant power control model to generate a target virtual power plant power control strategy.

[0115] For the specific definition of the system, please refer to the definition of the method above, which will not be repeated here. Each module in the above system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0116] Embodiment 3

[0117] The present invention provides a computer device, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the above method are implemented.

[0118] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0119] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored. In addition, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0120] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0121] It can be understood 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, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A virtual power plant power control method based on mixed-order pinning control, characterized in that: include: Based on the differences in output and regulation rate of heterogeneous distributed resources within the virtual power plant, the reactive regulation capability of distributed wind and solar power generation resources and the active regulation capability of distributed energy storage resources are quantitatively analyzed; Select the consistency control variables of the distributed wind and solar cluster and the distributed energy storage cluster respectively; Constructing an initial virtual power plant power regulation model according to the consistency control variables; Based on the uncertainty of distributed wind and solar power output, a hybrid-order variable consensus protocol is constructed, and the initial virtual power plant power control model is optimized using the hybrid-order variable consensus protocol to generate a target virtual power plant power control strategy.

2. The power control method of a virtual power plant based on mixed-order pinning control according to claim 1 is characterized in that: The method is based on the differences in output and regulation rate of heterogeneous distributed resources within the virtual power plant, and quantitatively analyzes the reactive regulation capability of distributed wind and solar power generation resources and the active regulation capability of distributed energy storage resources, including: The expression of reactive power regulation capability is: Where: is the maximum adjustable reactive capacity of the photovoltaic inverter, P PV is the active power output of photovoltaic power generation, S INV is the rated capacity of the PV inverter; The expression of active power regulation capability is: S(t)=S(t-Δt)+ΔS(t) Where: S(t) is the energy storage SOC at time t, S(t-Δt) is the energy storage SOC at time t-Δt, ΔS(t) is the energy storage SOC change at time t, P ESS (t) is the active power of energy storage at time t, C ESS is the capacity of the energy storage device, and Δt is the time interval.

3. The power control method of a virtual power plant based on mixed-order pinning control according to claim 1 is characterized in that: The consistency control variables of the distributed wind and solar cluster and the distributed energy storage cluster are respectively selected, including: The reactive response rate of the wind-solar inverter is selected as the consistency control variable of the distributed wind-solar cluster, and the change of the energy storage SOC is selected as the consistency control variable of the distributed energy storage cluster; Consistency control variables of distributed wind and solar clusters: Where ΔQ i ∈R n is the reactive power absorbed or emitted by the i-th wind-solar inverter, v i ∈R n is the reactive power response rate of the i-th wind-solar inverter, u i (t) represents the consistency control protocol of the distributed wind and solar cluster; Consistency control variables of distributed energy storage clusters: Where: ΔS i ∈R n is the SOC change of the i-th energy storage device, w i (t) is the consistency control protocol of the distributed energy storage cluster.

4. The power control method of a virtual power plant based on mixed-order pinning control according to claim 1 is characterized in that: The constructing of an initial virtual power plant power regulation model according to the consistency control variables includes: The leader-follower consistency control mode is adopted in the consistency control subgroups of the distributed wind-solar cluster and the distributed energy storage cluster, and a distributed pinning control group consistency protocol is designed according to the state information of the intelligent agent itself and its neighbors.

5. The power control method of a virtual power plant based on mixed-order pinning control according to claim 4 is characterized in that: The leader-follower consistency control mode includes: Where: the initial value η0(0), ρ1 is the positive feedback gain of the active power control subgroup, C ESS,i is the energy storage capacity at node i, Δt is the control time interval, ΔP goal is the active power to be regulated, SOC i,max is the upper limit of SOC.

6. The power control method of a virtual power plant based on mixed-order pinning control according to claim 4 is characterized in that: The group consistency protocol for distributed pinning control is designed based on the state information of the agent itself and its neighbors, including: Where: γ k (t), γ i d(t) and γ0(t) are the ratios of the reactive power regulation of the kth, ith and leader wind-solar inverters to their maximum reactive regulation rate, respectively; i is the degree matrix coefficient of the network topology structure; ε1, ε2 and ε3 are all coupling parameters greater than 0, and the adjacency matrix A = [a ij ]∈R n×n It describes the communication connection relationship between each resource node in the system. If the kth resource node can receive the information of the ith resource node, then a ik >0, otherwise a ik =0.

7. The power control method of a virtual power plant based on hybrid order pinning control according to any one of claims 1 to 6, characterized in that: The hybrid-order variable consensus protocol is constructed based on the uncertainty of distributed wind and solar power output, and the hybrid-order variable consensus protocol is used to optimize the initial virtual power plant power regulation model to generate a target virtual power plant power regulation strategy, including: Where: Ω i is the consistency correction, which is used to measure the corresponding uncertainty of the wind and solar cluster, v i,max , μ Δ and γ Δ They are respectively the consistency control variables of the distributed wind and solar clusters in the system.

8. A virtual power plant power control system based on mixed-order pinning control, characterized in that: include: The power regulation capability analysis unit is used to quantitatively analyze the reactive regulation capability of distributed wind and solar power generation resources and the active regulation capability of distributed energy storage resources based on the differences in output and regulation rate of heterogeneous distributed resources within the virtual power plant; A consistency control variable selection unit, used to select consistency control variables of a distributed wind and solar cluster and a distributed energy storage cluster respectively; An initial virtual power plant power regulation model construction unit, used to construct an initial virtual power plant power regulation model according to the consistency control variables; The target virtual power plant power control strategy generation unit is used to construct a hybrid-order variable consensus protocol based on the uncertainty of distributed wind and solar power output, and use the hybrid-order variable consensus protocol to optimize the initial virtual power plant power control model to generate a target virtual power plant power control strategy.

9. A computer device, characterized in that: include: memories, transceivers, processors, and bus systems; Wherein, the memory is used to store programs; The processor is used to execute the program in the memory, including executing the virtual power plant power regulation method based on hybrid order pinning control according to any one of claims 1 to 7; The bus system is used to connect the memory and the processor so that the memory and the processor can communicate with each other.

10. A readable storage medium, characterized in that: The method comprises instructions which, when executed on a computer, enable the computer to execute the virtual power plant power regulation method based on hybrid-order control as claimed in any one of claims 1 to 7.

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