A virtual power plant power regulation method, system, device and medium based on hybrid order damping control

Abstract

The application is suitable for the technical field of power regulation, and provides a virtual power plant power regulation method, system, equipment and medium based on mixed-order damping control, which comprises the following steps: on the basis of the output and the difference in the adjustment rate of the internal heterogeneous distributed resources of the virtual power plant, 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; selecting the consistency control variables of the distributed wind and light cluster and the distributed energy storage cluster respectively; constructing an initial virtual power plant power regulation model according to the consistency control variables; constructing a mixed-order variable consensus protocol based on the uncertainty of the distributed wind and light output, and optimizing the initial virtual power plant power regulation model by using the mixed-order variable consensus protocol to generate a target virtual power plant power regulation strategy. The application improves the real-time performance and control accuracy of wind and light storage coordination regulation, and helps to fully exert the reactive power and active power regulation potential of demand side resources.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power regulation, in particular to a virtual power plant power regulation method, system, device and medium based on hybrid order containment control. BACKGROUND

[0002] 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 consensus algorithm is to update the state variables of local nodes through information interaction between local nodes and adjacent nodes, so that the state variables of nodes converge to a stable common value. In addition to the traditional consensus problem, in recent years, an extended consensus problem has emerged, namely the grouping consensus control strategy. Unlike the traditional consensus control problem, the grouping consensus is that multiple agents in the control system converge to different consensus values according to subgroups, which can be divided into dynamic and static types.

[0003] As a new type of resource aggregator, virtual power plant can use advanced information communication technology and software system to realize the aggregation and coordinated optimization of multiple types of demand side distributed resources such as distributed photovoltaic, wind power, energy storage and flexible load. Since the multiple types of distributed resources contained in the virtual power plant have obvious differences in physical characteristics and dynamic characteristics, how to give consideration to the coordinated control of active power and reactive power response of the virtual power plant and realize the power flow integration regulation of the heterogeneous resources in the virtual power plant has become a problem to be solved. SUMMARY

[0004] The embodiment of the present application provides a virtual power plant power regulation method, system, device and medium based on hybrid order containment control, which is used for solving the problem of giving consideration to the coordinated control of active power and reactive power response of the virtual power plant.

[0005] The first aspect of the embodiment of the present application provides a virtual power plant power regulation method based on hybrid order containment control, comprising:

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

[0007] Consensus control variables of distributed wind and light clusters and distributed energy storage clusters are selected respectively;

[0008] An initial virtual power plant power regulation model is constructed according to the consensus control variables;

[0009] A hybrid order variable consensus protocol is constructed based on the uncertainty of distributed wind and light output, and the initial virtual power plant power regulation model is optimized by using the hybrid order variable consensus protocol to generate a target virtual power plant power regulation strategy.

[0010] Further, the difference between the output and the adjustment rate of the internal heterogeneous distributed resources in the virtual power plant is used to quantitatively analyze the reactive power adjustment capability of the distributed wind-solar power generation resource and the active power adjustment capability of the distributed energy storage resource, including:

[0011] The expression of the reactive power adjustment capability is:

[0012]

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

[0014] The expression of the active power adjustment capability is:

[0015] S(t) = S(t-Δt) + ΔS(t)

[0016]

[0017] In the formula, 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 amount at time t, P ESS (t) is the energy storage active power at time t, C ESS is the energy storage device capacity, and Δt is the time interval.

[0018] Further, the consistent control variables of the distributed wind-solar cluster and the distributed energy storage cluster are selected respectively, including:

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

[0020] The consistent control variable of the distributed wind-solar cluster is:

[0021]

[0022] In the formula, 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 consistent control protocol of the distributed wind-solar cluster.

[0023] The consistent control variable of the distributed energy storage cluster is:

[0024]

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

[0026] Furthermore, the step of constructing the initial virtual power plant power regulation model based on the consistent control variables includes:

[0027] In the consensus control subgroups of the distributed wind and solar clusters and the distributed energy storage clusters, a leader-follower consensus control mode is adopted, and a distributed restraint control group consensus protocol is designed based on the state information of the agent itself and its neighbors.

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

[0029]

[0030] In the formula: the initial value η0(0), ρ1 is the positive feedback gain of the active power control subgroup, and C ESS,i Let Δp be the energy storage capacity at node i, Δt be the control time interval, and ΔP be the energy storage capacity at node i. goal For the desired controlled active power, SOC i,max This is the upper limit of SOC.

[0031] Furthermore, the distributed restraint control group consensus protocol designed based on the state information of the agent itself and its neighbors includes:

[0032]

[0033] In the formula: γ k (t), γ i γ(t) and γ0(t) are the ratios of the reactive power regulation power and the maximum reactive power adjustable rate of the k-th, i-th, and leader wind-solar inverters, respectively; d i The coefficients of the degree matrix of the network topology; ε1, ε2, and ε3 are all coupling parameters greater than 0, and the adjacency matrix A = [a ij ]∈R n×n This describes the communication connection relationships between resource nodes within the system. If the k-th resource node can receive information from the i-th resource node, then a ik >0, otherwise a ik =0.

[0034] Furthermore, the construction of a hybrid-order variable consensus protocol based on the uncertainty of distributed wind and solar power output, and the optimization of the initial virtual power plant power regulation model using the hybrid-order variable consensus protocol to generate a target virtual power plant power regulation strategy, includes:

[0035]

[0036] Where: Ω i v is a consistency correction measure used to assess the corresponding uncertainty in wind-solar clusters. i,max μ Δ and γ Δ These are the consistency control variables for the distributed wind and solar clusters in the system.

[0037] A second aspect of this application provides a virtual power plant power regulation system based on hybrid-order restraint control, comprising:

[0038] The power regulation capability analysis unit is used to quantitatively analyze the reactive power regulation capability of distributed wind and solar power generation resources and the active power 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] The consistency control variable selection unit is used to select the consistency control variables for distributed wind and solar clusters and distributed energy storage clusters, respectively.

[0040] An initial virtual power plant power regulation model construction unit is used to construct an initial virtual power plant power regulation model based on the consistency control variables.

[0041] The target virtual power plant power regulation 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 to optimize the initial virtual power plant power regulation model using the hybrid-order variable consensus protocol to generate the target virtual power plant power regulation strategy.

[0042] A third aspect of this application provides a computer device, including:

[0043] Memory, transceiver, processor, and bus system;

[0044] The memory is used to store programs;

[0045] The processor is used to execute programs in the memory, including executing the virtual power plant power regulation method based on hybrid-level restraint control as described in any of the above-mentioned methods;

[0046] The bus system is used to connect the memory and the processor to enable communication between the memory and the processor.

[0047] A fourth aspect of this application provides a readable storage medium including instructions that, when executed on a computer, cause the computer to perform the virtual power plant power regulation method based on hybrid-level restraint control as described in any of the preceding embodiments.

[0048] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0049] 1. This invention is based on a virtual power plant power regulation strategy of hybrid-stage restraint control. Starting from the perspective of the differences in output and regulation rate of heterogeneous distributed resources within the virtual power plant, it selects consistent control variables for active and reactive power regulation of distributed wind and solar clusters and energy storage clusters respectively, and comprehensively considers the active and reactive power coordinated control coupling relationship between different types of flexible resources and controllable equipment.

[0050] 2. This invention is based on a virtual power plant power regulation strategy using hybrid-order restraint control. It 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. It constructs a virtual power plant power regulation strategy based on a hybrid-order restraint control algorithm to achieve integrated coordinated regulation of active and reactive power of distributed wind, solar and energy storage within the virtual power plant.

[0051] 3. The present invention is based on a virtual power plant power regulation strategy of hybrid-order constraint control, which takes into account the uncertainty of distributed wind and solar power output. On the basis of constructing a hybrid-order variable consensus protocol that takes into account uncertainty, the present invention improves the virtual power plant power regulation strategy based on hybrid-order constraint control algorithm, thereby improving the real-time performance and control accuracy of wind, solar and energy storage coordinated regulation, and helping to fully utilize the reactive and active power regulation potential of demand-side resources. Attached Figure Description

[0052] Figure 1 This is a schematic flowchart of an embodiment of a virtual power plant power regulation method based on hybrid-order restraint control in this invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0054] The virtual power plant power regulation method based on hybrid-order restraint control in this embodiment is used to improve the real-time performance and control accuracy of coordinated wind, solar, and energy storage regulation. The implementation method in this embodiment can be implemented in the system, on the server, or on the terminal; no specific limitation is made.

[0055] Example 1

[0056] Please seeFigure 1 An embodiment of a virtual power plant power regulation method based on hybrid-order restraint control in this invention includes the following steps:

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

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

[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 inverter's control strategy and performance, and dynamically changes with the active power output of wind and solar power generation. Taking a photovoltaic inverter as an example, its reactive power regulation capability is expressed as:

[0060]

[0061] In the formula: P is the maximum adjustable reactive power capacity of the photovoltaic inverter. PV For the positive contribution of photovoltaic power generation, S INV This refers to the rated capacity of the photovoltaic inverter.

[0062] 2. The active power 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 power capability of the energy storage device and the energy storage SOC is expressed as follows:

[0063] S(t) = S(t―Δt) + ΔS(t)

[0064]

[0065] In the formula: S(t) is the stored energy SOC at time t, S(t―Δt) is the stored energy SOC at time t―Δt, ΔS(t) is the change in stored energy SOC at time t, and P ESS (t) represents the active power of the stored energy at time t, C ESS Δt represents the energy storage device capacity, and Δt represents the time interval.

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

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

[0068] 1. To maximize the reactive power regulation capability of wind and solar inverters, taking wind and solar inverter i as an example, the reactive power response rate v of the wind and solar inverter is selected. i As a consistency control variable for a distributed wind and solar cluster, it is represented as:

[0069]

[0070] In the formula, ΔQ i ∈R n v is the reactive power absorbed or generated by the i-th wind-solar inverter. i ∈R n Let u be the reactive power response rate of the i-th wind-solar inverter. 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 adjustable reactive power rate v i,max The ratio is μ i The reactive power regulation capacity and its maximum reactive power adjustable rate v of the i-th wind-solar inverter i,max The ratio is γ i , represented as:

[0072]

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

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

[0075]

[0076] In the formula: v Δ and γ Δ This is the consistency control variable for the distributed wind and solar clusters 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, expressed as:

[0078]

[0079] 2. 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 ΔS of the energy storage is selected. i As a state variable, it is represented as:

[0080]

[0081] Where: ΔS i ∈R n Let w be the change in SOC of the i-th energy storage device. i (t) represents the consistency control protocol for 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 SOC i,max The ratio is η i , represented as:

[0083]

[0084] To ensure that all energy storage devices fairly undertake the task of power regulation, 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, as shown below:

[0086] η1=η2=…=η n =η Δ

[0087] In the formula: η Δ This is the consistency control variable for the distributed energy storage cluster in the system.

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

[0089]

[0090] S13. Construct an initial virtual power plant power regulation model based on consistent control variables;

[0091] In both the second-order control subgroup of the distributed wind and solar cluster and the first-order control subgroup of the distributed energy storage cluster, a leader-follower consensus control mode is adopted, and a distributed restraint control group consensus protocol is designed based on the state information of the agent i itself and its neighbors.

[0092] The leader-follower consistency control model specifically includes:

[0093] Taking a first-order control subgroup of a distributed energy storage cluster as an example, there exists a virtual leader node in the control subgroup, satisfying the following equation:

[0094]

[0095] In the formula: the initial value η0(0), ρ1 is the positive feedback gain of the active power control subgroup, and C ESS,i Let ΔP be the energy storage capacity at node i, Δt be the control time interval, and ΔP be the energy storage capacity at node i. goal The active power to be controlled.

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

[0097]

[0098] Design a distributed check control group consensus protocol, specifically as follows:

[0099]

[0100] In the formula: γ k (t), γ i γ(t) and γ0(t) are the ratios of the reactive power regulation power and the maximum reactive power adjustable rate of the k-th, i-th, and leader wind-solar inverters, respectively; d i The coefficients of the degree matrix of the network topology; ε1, ε2, and ε3 are all coupling parameters greater than 0, and the adjacency matrix A = [a ij ]∈R n×n This describes the communication connection relationships between resource nodes within the system. If the k-th resource node can receive information from the i-th resource node, then a ik >0, otherwise a ik =0.

[0101] S14. 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 regulation model is optimized using the hybrid-order variable consensus protocol to generate the target virtual power plant power regulation strategy.

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

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

[0104]

[0105] Where: Ω i This is a consistency correction measure used to assess the corresponding uncertainty of wind-solar clusters.

[0106] Based on the constructed virtual power plant power regulation strategy employing a hybrid-order traction control algorithm, an improved distributed traction control protocol with variable consensus variables is introduced, as follows:

[0107]

[0108] By considering the uncertainty of distributed wind and solar power output, and based on the construction of a hybrid-order variable consensus protocol that takes uncertainty into account, the initial virtual power plant power regulation model is optimized using the hybrid-order variable consensus protocol, and finally the optimal virtual power plant power regulation strategy is generated.

[0109] Example 2

[0110] An embodiment of a virtual power plant power regulation system based on hybrid-order restraint control in this invention includes the following steps:

[0111] The power regulation capability analysis unit is used to quantitatively analyze the reactive power regulation capability of distributed wind and solar power generation resources and the active power 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] The consistency control variable selection unit is used to select the consistency control variables for distributed wind and solar clusters and distributed energy storage clusters, respectively.

[0113] The initial virtual power plant power regulation model construction unit is used to construct the initial virtual power plant power regulation model based on consistent control variables.

[0114] The target virtual power plant power regulation 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 to optimize the initial virtual power plant power regulation model using the hybrid-order variable consensus protocol to generate the target virtual power plant power regulation strategy.

[0115] For specific limitations regarding the system, please refer to the method limitations described above, which will not be repeated here. Each module in the above system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0116] Example 3

[0117] The present invention provides a computer device, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor executes the computer-readable instructions to perform the steps of the method described above.

[0118] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0119] In the embodiments provided by this invention, it should be understood that the division of units is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units may be combined into one unit, one unit may be split into multiple units, or some features may be ignored. Furthermore, the functional units in the various embodiments of this invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The integrated unit described above can be implemented in hardware or as a software functional unit.

[0120] If the integrated unit is implemented as 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0121] It is understood that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A virtual power plant power regulation method based on hybrid-order restraint control, characterized in that, include: Based on the differences in output and regulation rate of heterogeneous distributed resources within a virtual power plant, this study quantitatively analyzes the reactive power regulation capability of distributed wind and solar power generation resources and the active power regulation capability of distributed energy storage resources. Consistency control variables were selected for distributed wind and solar clusters and distributed energy storage clusters, respectively. An initial virtual power plant power regulation model is constructed based on the aforementioned consistency control variables; A hybrid-order variable consensus protocol is constructed based on the uncertainty of distributed wind and solar power output, and the initial virtual power plant power regulation model is optimized using the hybrid-order variable consensus protocol to generate a target virtual power plant power regulation strategy; the construction of the hybrid-order variable consensus protocol based on the uncertainty of distributed wind and solar power output, and the optimization of the initial virtual power plant power regulation model using the hybrid-order variable consensus protocol to generate a target virtual power plant power regulation strategy, includes: In the formula: This is a consistency correction measure used to assess the corresponding uncertainty in wind-solar clusters. , and These are the consistency control variables for the distributed wind and solar clusters in the system; , , The first The, the The ratio of the reactive power regulation power to the maximum reactive power adjustable rate of individual and leading wind and solar inverters; These are the degree matrix coefficients of the network topology; , and All are coupling parameters greater than 0, adjacency matrix This describes the communication connection relationships between various resource nodes within the system. If the... The resource node can receive the first Information about each resource node, then ,otherwise .

2. The virtual power plant power regulation method based on hybrid-order restraint control according to claim 1, characterized in that, Based on the differences in output and regulation rates of heterogeneous distributed resources within a virtual power plant, the method quantifies and analyzes the reactive power regulation capability of distributed wind and solar power generation resources and the active power regulation capability of distributed energy storage resources, including: The expression for reactive power regulation capability: In the formula: This represents the maximum adjustable reactive power capacity of the photovoltaic inverter. Contributing to photovoltaic power generation This refers to the rated capacity of the photovoltaic inverter. The expression for active power regulation capability: In the formula: for Energy storage SOC at any time for Energy storage SOC at any time for Changes in SOC of stored energy at any given time for Continuous energy storage active power, For the capacity of the energy storage device, For time intervals.

3. The virtual power plant power regulation method based on hybrid-order restraint control according to claim 1, characterized in that, The consistency control variables selected for distributed wind and solar clusters and distributed energy storage clusters respectively include: The reactive power response rate of the wind and solar inverters is selected as the consistency control variable for the distributed wind and solar clusters, and the change in the SOC of the energy storage is selected as the consistency control variable for the distributed energy storage clusters. Consistency control variables for distributed wind-solar clusters: In the formula, For the first The reactive power absorbed or generated by each wind and solar inverter For the first The reactive power response rate of a wind-solar inverter. This refers to the consistency control protocol for distributed wind and solar power clusters. Consistency control variables for distributed energy storage clusters: In the formula: For the first The change in SOC of an energy storage device It is a consistency control protocol for distributed energy storage clusters.

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

5. The virtual power plant power regulation method based on hybrid-order restraint control according to claim 4, characterized in that, The leader-follower consistency control model includes: Where: initial value , For the positive feedback gain of the active power control subgroup, For nodes The capacity of the energy storage It controls the time interval. For the active power to be controlled, This is the upper limit of SOC.

6. A virtual power plant power regulation system based on hybrid-order restraint control, characterized in that, The method described by any one of claims 1-5 includes: The power regulation capability analysis unit is used to quantitatively analyze the reactive power regulation capability of distributed wind and solar power generation resources and the active power 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. The consistency control variable selection unit is used to select the consistency control variables for distributed wind and solar clusters and distributed energy storage clusters, respectively. An initial virtual power plant power regulation model construction unit is used to construct an initial virtual power plant power regulation model based on the consistency control variables. The target virtual power plant power regulation 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 to optimize the initial virtual power plant power regulation model using the hybrid-order variable consensus protocol to generate the target virtual power plant power regulation strategy.

7. A computer device, characterized in that, include: Memory, transceiver, processor, and bus system; 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-level restraint control as described in any one of claims 1 to 5; The bus system is used to connect the memory and the processor to enable communication between the memory and the processor.

8. A readable storage medium, characterized in that, The instructions, when executed on a computer, cause the computer to perform the virtual power plant power regulation method based on hybrid-stage restraint control as described in any one of claims 1 to 5.

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