A method and device for verifying adjustable capacity declared by a virtual power plant

Abstract

The present invention relates to a method and device for verifying the declared adjustable capacity of a virtual power plant, and belongs to the technical field of verification of the adjustable capacity of a virtual power plant. The method comprises the following steps: determining the type of distributed resources of the virtual power plant, covering virtual generator sets, adjustable loads and virtual energy storage. Collecting relevant load data, and outputting the load regulation capabilities of the adjustable loads, virtual energy storage and virtual generator sets respectively. Based on these load regulation capabilities, formulate constraints that constrain the regulation capacity of the distributed resources of the virtual power plant. Construct an objective function with the goal of maximizing the sum of the regulation capacity, and solve it in combination with the constraints to obtain the adjustable capacity of the virtual power plant. Obtain the declared adjustable capacity of the virtual power plant, verify it with the obtained adjustable capacity and output the result. The present invention fully taps the flexibility of distributed resources, unifies the management of various types of resources, and realizes flexible deployment under different working conditions.

Description

Technical Field

[0001] The present invention relates to a method and device for verifying the adjustable capacity declared by a virtual power plant, belonging to the technical field of adjustable capacity verification of virtual power plants. Background Art

[0002] With the acceleration of energy transition and the widespread integration of distributed energy resources, virtual power plants (VPPs), virtual entities that coordinate and control distributed generation, demand-side response, and energy storage resources, are becoming increasingly important in the power system. By integrating distributed resources, VPPs can flexibly participate in power market transactions and grid operation and dispatch, providing power support and ancillary services to the grid, improving energy efficiency and the reliability and stability of grid operations. Accurately calibrating the adjustable capacity of VPPs is crucial during their operation. Adjustable capacity directly impacts the VPP's ability to participate in grid dispatch and market transactions, and determines its value and role in the power system. However, the adjustable capacity of VPPs is affected by multiple factors, including the type, characteristics, operating status, and coordination of distributed resources.

[0003] Existing technologies, such as the Chinese invention patent application with publication number CN119093497A, disclose a virtual power plant capacity control and operation optimization method, system, and related equipment. The method includes: constructing a mathematical model with the installed capacity and operating efficiency of resources as target variables, with the mathematical model targeting the lowest annual economic cost; determining the constraints of the mathematical model, including energy balance constraints, upper and lower limits of resource installed capacity constraints, and resource interaction constraints; introducing source-storage resource capacity constraints to ensure sufficient energy storage capacity to balance energy fluctuations; constructing a virtual power plant interactive operation optimization model under resource capacity constraints; determining source-load resource interaction constraints and incentive-based demand response constraints; determining the optimal solution of the virtual power plant interactive operation optimization model based on the source-load resource interaction constraints and incentive-based demand response constraints, and verifying and optimizing the results. However, the above patent only introduces a simple source-storage resource capacity constraint, namely, the total capacity of the energy storage system must be greater than or equal to the minimum energy storage capacity, and fails to fully consider the various technical constraints and dynamic characteristics of energy storage resources in actual operation. In addition, the above patent did not separately evaluate the regulation capabilities of different types of distributed resources, but only generally constructed a mathematical model with the installed capacity and operating efficiency of the resources as target variables, lacking detailed consideration of the regulation capabilities of each resource. Summary of the Invention

[0004] In order to solve the above problems existing in the prior art, the present invention proposes a method and device for verifying the adjustable capacity declared by a virtual power plant.

[0005] The technical solutions of the present invention are as follows:

[0006] In one aspect, the present invention provides a method for verifying the adjustable capacity of a virtual power plant, comprising the following steps:

[0007] Determine the types of distributed resources in the virtual power plant, including virtual generators, adjustable loads, and virtual energy storage;

[0008] Collect load-related data of adjustable load, virtual energy storage, and virtual generator set, and output the load regulation capability of adjustable load, virtual energy storage, and virtual generator set respectively;

[0009] Based on the load regulation capabilities of each distributed resource, formulate the constraints on the regulation capacity of each distributed resource in the virtual power plant;

[0010] Constructing an objective function with the goal of maximizing the sum of the regulation capacity, and solving the objective function based on the constraint conditions to obtain the adjustable capacity of the virtual power plant;

[0011] Obtain the declared adjustable capacity of the virtual power plant, verify the declared adjustable capacity based on the adjustable capacity of the virtual power plant, and output the verification result.

[0012] Preferably, load-related data of the adjustable load is collected, and load characteristics of the load-related data of the adjustable load are extracted. An adjustable load vector is constructed based on the load characteristics. The adjustable load vector and the load-related data of the adjustable load are used as inputs of a support vector regression model to output the load regulation capability of the adjustable load.

[0013] Preferably, load-related data of the virtual energy storage is collected, energy storage characteristics of the load-related data of the virtual energy storage are extracted, the energy storage characteristics are used as input of a decision tree model, and the load regulation capability of the virtual energy storage is output.

[0014] Preferably, load-related data of the virtual generator set, including historical load, is collected, and linearization processing is performed on the historical load;

[0015] Decomposing the linearized historical load into the virtual generator set adjustable load and the virtual generator set non-adjustable load;

[0016] For the adjustable load of virtual generator set, the adjustable load of virtual generator set is used as the input of autoregressive integral moving average model, and the load regulation capability of virtual generator set is output.

[0017] Preferably, based on the load regulation capability of each distributed resource, a constraint condition for regulating capacity of each distributed resource in the virtual power plant is formulated, which can be expressed as follows:

[0018] ;

[0019] ;

[0020] Where, represents the constraints, Indicates the Virtual power plant Distributed resources in The adjustment capacity of the moment, Indicates the Virtual power plant Distributed resources in Load regulation capability at all times, Indicates the Virtual power plants in The proportion of virtual energy storage that can be reduced at the moment; Indicates the Virtual power plant Distributed resources in A binary variable indicating whether the moment is adjusted, Indicates the Virtual power plants in The maximum adjustment rate at the moment, Indicates the The maximum adjustment time of a virtual power plant, Indicates the Virtual power plant Distributed resources in The adjustment capacity of the moment, Indicates the adjustment cycle, represents the virtual power plant index, Represents the time index, Indicates the quantity index of distributed resources.

[0021] Preferably, an objective function is constructed with the goal of maximizing the sum of the regulation capacity, which can be expressed as follows:

[0022] ;

[0023] ;

[0024] Where, Indicates the adjustment cycle, represents the virtual power plant index, Represents the time index, represents the maximum function, Indicates the Virtual power plant Distributed resources in The adjustment capacity of the moment, Indicates the quantity index of distributed resources.

[0025] Preferably, the declared adjustable capacity of the virtual power plant is obtained, and the declared adjustable capacity is verified based on the adjustable capacity of the virtual power plant, which can be expressed as follows:

[0026] ;

[0027] ;

[0028] Where, Represents a judgment operator, Indicates the Virtual power plants in The declaration of adjustable capacity at the moment, Indicates the Virtual power plants in The virtual power plant can adjust its capacity at any time. Indicates the The resource combination factor of a virtual power plant, Indicates the The conservative factor of a virtual power plant, express The default option if the operation does not work. Indicates the adjustment cycle, represents the virtual power plant index, Represents a time index.

[0029] Preferably, the resource combination factor is expressed as:

[0030] ;

[0031] Where, Indicates the distributed resource type index, Indicates the number of distributed resource types, Indicates the Virtual power plant The load regulation capability of distributed resources and The proportion of the total load regulation capacity of virtual power plants, Indicates the Virtual power plant Uncertainty coefficient of distributed resources.

[0032] Preferably, the conservative factor is expressed as:

[0033] ;

[0034] ;

[0035] Where, represents the conservative adjustment coefficient, Indicates the historical reporting deviations of virtual power plants.

[0036] On the other hand, the present invention further provides an electronic device having a computer program stored thereon, which, when executed by a processor, implements the method for verifying the adjustable capacity declared by a virtual power plant as described in any embodiment of the present invention.

[0037] The present invention has the following beneficial effects:

[0038] 1. By identifying distributed resources in a virtual power plant, such as virtual generators, adjustable loads, and virtual energy storage, the present invention can fully understand the resource composition of the virtual power plant, thereby managing various resources in a targeted manner and improving resource utilization efficiency.

[0039] 2. The present invention fully exploits the flexibility of distributed resources, bringing adjustable loads, virtual energy storage, and virtual generator sets into unified management, enabling virtual power plants to flexibly allocate various resources under different operating conditions, enhancing the virtual power plant's ability to respond to power system demands, and better adapting to dynamic changes in the power market.

[0040] 3. The present invention collects load-related data of various distributed resources and uses methods such as support vector regression model, decision tree model and autoregressive integral moving average model to output the load regulation capabilities of adjustable load, virtual energy storage and virtual generator sets respectively, thereby realizing accurate quantitative evaluation of the regulation capabilities of various distributed resources in the virtual power plant, and providing an accurate basis for the subsequent formulation of regulation capacity constraints and construction of objective functions.

[0041] 4. The present invention formulates constraints based on the load regulation capabilities of each distributed resource, and constructs an objective function with the goal of maximizing the sum of the regulation capacities. By solving the objective function, the adjustable capacity of the virtual power plant is obtained, and then the declared adjustable capacity is verified. This can ensure that the declared adjustable capacity of the virtual power plant matches the actual regulation capability, improve the accuracy of the regulation capacity of the virtual power plant, and avoid problems such as imbalance in electricity supply and demand and unfair market transactions caused by inflated or low declared capacity.

[0042] 5. The present invention ensures that the virtual power plant can stably provide power regulation services within the range of its declared adjustable capacity through reasonable regulation capacity verification, avoiding problems such as grid frequency fluctuations and voltage instability caused by insufficient or false regulation capabilities of the virtual power plant, improving the overall stability and reliability of the power system, and playing a positive supporting role in the safe and stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The present invention is a flowchart for implementing the method. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0045] It should be understood that the step numbers used herein are only for convenience of description and are not intended to limit the order in which the steps are to be executed.

[0046] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0047] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0048] The term "and / or" refers to and includes any and all possible combinations of one or more of the associated listed items.

[0049] Example 1:

[0050] See also Figure 1 The present invention provides a method for verifying the adjustable capacity declared by a virtual power plant, comprising the following steps:

[0051] Determine the types of distributed resources in the virtual power plant, including virtual generators, adjustable loads, and virtual energy storage;

[0052] Among them, virtual generator sets refer to distributed energy resources that simulate the behavior of traditional generator sets, including renewable energy power generation facilities such as solar photovoltaic and wind power.

[0053] Adjustable loads include transferable loads based on electricity prices and interruptible loads based on incentives. Reasonable scheduling of adjustable loads is conducive to maximizing resource utilization. For transferable loads based on electricity prices, the operating time period is adjusted according to high or low electricity prices, including:

[0054] 1. Shift loads from high-price periods (e.g., daytime) to low-price periods (e.g., late at night);

[0055] 2. When the power grid issues a peak electricity price warning, completely shut down or significantly reduce the interruptible load.

[0056] For incentive-based interruptible loads, when the reliability of the power grid system is affected or the power consumption reaches its peak, interruption instructions are issued to power users to effectively reduce the power grid load and achieve the effect of peak shaving and valley filling.

[0057] Virtual energy storage is not a traditional physical energy storage device (such as batteries or pumped hydro). Instead, it is an energy balancing mechanism achieved through intelligent management and optimization technologies. Its core is to leverage the flexibility of demand-side resources (such as adjustable loads and virtual generators) to simulate the charging and discharging behavior of physical energy storage, thereby optimizing the energy flow of the power system, achieving peak load shifting, and increasing the utilization rate of renewable energy.

[0058] Collect load-related data of adjustable load, virtual energy storage, and virtual generator set, and output the load regulation capability of adjustable load, virtual energy storage, and virtual generator set respectively;

[0059] Based on the load regulation capabilities of each distributed resource, formulate the constraints on the regulation capacity of each distributed resource in the virtual power plant;

[0060] Constructing an objective function with the goal of maximizing the sum of the regulation capacity, and solving the objective function based on the constraint conditions to obtain the adjustable capacity of the virtual power plant;

[0061] Obtain the declared adjustable capacity of the virtual power plant, verify the declared adjustable capacity based on the adjustable capacity of the virtual power plant, and output the verification result.

[0062] In at least one embodiment, the objective function can be solved by using an optimization solver in the mathematical calculation and simulation software MATLAB.

[0063] Preferably, the adjustable load is collected at The load-related data at the moment is obtained, and the load characteristics of the load-related data of the adjustable load are extracted. The adjustable load vector is constructed based on the load characteristics. The adjustable load vector and the load-related data of the adjustable load are used as inputs of the support vector regression model, and the load regulation capability of the adjustable load is output, which is expressed as follows:

[0064] ;

[0065] Where, Indicates the The adjustable load of a virtual power plant is Load regulation capability at all times, represents the adjustable load vector, Indicates the data index, Indicates the number of load-related data for adjustable loads, Indicates the The Lagrange multiplier corresponding to the relevant data of the adjustable load, represents the kernel function, Indicates the The relevant data of the adjustable load, Indicates the offset.

[0066] In at least one embodiment, the kernel function It is a linear kernel function, and the similarity is obtained by calculating the inner product of the adjustable load vector and the load-related data of the adjustable load. The adjustable load vector is a one-dimensional vector arranged in order based on load characteristics. The load-related data of the adjustable load includes historical electricity consumption data, ambient temperature data, and time information data. The load characteristics include average daily temperature, maximum temperature, minimum temperature, time period index, date type index, load value of the same period of the previous day, and load value of the same period of the previous week. The load characteristics are extracted by the smart meter acquisition system.

[0067] Preferably, the virtual energy storage is collected The load-related data at the moment is used to extract the energy storage characteristics of the load-related data of the virtual energy storage, and the energy storage characteristics are used as the input of the decision tree model to output the load regulation capability of the virtual energy storage, which is expressed as follows:

[0068] ;

[0069] Where, Indicates the Virtual energy storage in a virtual power plant Load regulation capability at all times, represents the prediction function of the decision tree model, Represents the energy storage characteristics, Represents a decision tree.

[0070] In at least one embodiment, the load-related data of the virtual energy storage includes charging session data, operation data, and time information, and the energy storage characteristics include time period characteristics, date type, historical load characteristics, and charging behavior characteristics.

[0071] Preferably, load-related data of the virtual generator set is collected, including historical loads, and the historical loads are linearized and expressed as follows:

[0072] ;

[0073] Where, represents the historical load of the virtual generator set after linearization, 、 represents the linear optimization parameter, Represents the historical load of the virtual generator set;

[0074] Decomposing the linearized historical load into the virtual generator set adjustable load and the virtual generator set non-adjustable load;

[0075] For the adjustable load of virtual generator set, the adjustable load of virtual generator set is used as the input of autoregressive integral moving average model, and the output is the The virtual generator sets of a virtual power plant are Load regulation capability at all times.

[0076] The historical load of virtual generators exhibits nonlinear and multi-timescale coupling characteristics. Linearization processing, through Taylor expansion or piecewise linear approximation, transforms the nonlinear load model into a system of linear equations, significantly reducing computational complexity.

[0077] In at least one embodiment, the decomposition method includes:

[0078] 1. Divide the load of basic equipment such as production lines, safety systems, and lighting systems that must operate continuously into the non-adjustable load of virtual generator sets;

[0079] 2. Divide the air-conditioning system, non-critical production equipment, energy storage system, etc. into adjustable loads of virtual generator sets.

[0080] The non-adjustable load of the virtual generator set is not processed in this embodiment due to its non-adjustable nature.

[0081] Preferably, based on the load regulation capability of each distributed resource, a constraint condition for regulating capacity of each distributed resource in the virtual power plant is formulated, which can be expressed as follows:

[0082] ;

[0083] ;

[0084] Where, represents the constraints, Indicates the Virtual power plant Distributed resources in The adjustment capacity of the moment, Indicates the Virtual power plant Distributed resources in Load regulation capability at all times, Indicates the Virtual power plants in The proportion of virtual energy storage that can be reduced at the moment; Indicates the Virtual power plant Distributed resources in A binary variable indicating whether the moment is adjusted, When it is 1, it is regulated. When it is 0, it means it is not adjusted. Indicates the Virtual power plants in The maximum adjustment rate at the moment, Indicates the The maximum adjustment time of a virtual power plant, Indicates the Virtual power plant Distributed resources in The adjustment capacity of the moment, Indicates the adjustment cycle, represents the virtual power plant index, Represents the time index, Indicates the quantity index of distributed resources.

[0085] In at least one embodiment, the types of distributed resources of a virtual power plant include virtual generator sets, adjustable loads, and virtual energy storage. However, the number of distributed resources can be multiple. For example, a virtual power plant has 5 virtual generator sets, 3 adjustable loads, and 6 virtual energy storages. The maximum value is 14, but the number of distributed resource types In this embodiment, the value is 3.

[0086] At this time, if The value is 1-5, then Indicates the The virtual generator sets of a virtual power plant are The load regulation capability at all times, if The value is 6-8, then Indicates the The adjustable load of a virtual power plant is The load regulation capability at all times, if The value is 9-14, then Indicates the Virtual energy storage in a virtual power plant Load regulation capability at all times.

[0087] like If it is 1, it means a virtual generator set. If it is 2, it means the load is adjustable. If it is 3, it means virtual energy storage.

[0088] Preferably, an objective function is constructed with the goal of maximizing the sum of the regulation capacity, which can be expressed as follows:

[0089] ;

[0090] ;

[0091] Where, Indicates the adjustment cycle, represents the virtual power plant index, Represents the time index, represents the maximum function, Indicates the Virtual power plant Distributed resources in The adjustment capacity of the moment, Indicates the quantity index of distributed resources.

[0092] Preferably, the declared adjustable capacity of the virtual power plant is obtained, and the declared adjustable capacity is verified based on the adjustable capacity of the virtual power plant, which can be expressed as follows:

[0093] ;

[0094] ;

[0095] Where, Indicates the judgment operator, used to judge Is it established? Indicates the Virtual power plants in The declaration of adjustable capacity at the moment, Indicates the Virtual power plants in The virtual power plant can adjust its capacity at any time. Indicates the The resource combination factor of a virtual power plant, Indicates the The conservative factor of a virtual power plant, express The default option if the operation does not work. Indicates the adjustment cycle, represents the virtual power plant index, Represents a time index.

[0096] Preferably, the resource combination factor is expressed as:

[0097] ;

[0098] Where, Indicates the distributed resource type index, Indicates the number of distributed resource types, Indicates the Virtual power plant The load regulation capability of distributed resources and The proportion of the total load regulation capacity of virtual power plants, Indicates the Virtual power plant Uncertainty coefficient of distributed resources.

[0099] The said The total load regulation capacity of the virtual power plants is The load regulation capabilities of all distributed resources in a virtual power plant are summed. In this embodiment, the load regulation capabilities of 14 distributed resources are summed.

[0100] The said Virtual power plant The load regulation capability of the distributed resources is, if If it is 1, it indicates a virtual generator set, which is the sum of the load regulation capabilities of the five distributed resources whose distributed resource type is the virtual generator set.

[0101] Preferably, the conservative factor is expressed as:

[0102] ;

[0103] ;

[0104] Where, represents the conservative adjustment coefficient, Indicates the historical reporting deviations of virtual power plants.

[0105] Implementation scenario:

[0106] The distributed resource management system (DERMS) collects load-related data of three types of distributed resources in the area in real time. Based on the load-related data, the load of each distributed resource is predicted. Adjustable time:

[0107] Adjustable load capacity: 285 MW;

[0108] Virtual energy storage adjustable capacity: 165 MW;

[0109] Virtual generator set adjustable capacity: 320 MW;

[0110] Taking into account the multiple constraints of various distributed resources, such as the predicted power limit, the scheduling time limit, and the ramp rate limit, the distributed resources’ adjustable capacity is aggregated and scheduled according to the objective function, and the first Virtual power plants in The adjustable capacity of the virtual power plant at this moment is 685MW.

[0111] Before the virtual power plant participates in the power market or system dispatch, A virtual power plant The declared adjustable capacity at the time is 580 MW. The declared adjustable capacity is checked for compliance according to the method described in the present invention to ensure that the declared value does not exceed the actual adjustable capacity available. It can be expressed as:

[0112] ;

[0113] ;

[0114] For the resource combination factor, we set the following parameters based on the resource composition of the virtual power plant:

[0115] Table 1 Adjustment capacity ratio

[0116]

[0117] Table 2 Uncertainty coefficient table

[0118]

[0119] According to Table 1 and Table 2, we can get Resource combination factors of a virtual power plant .

[0120] For the conservative coefficient, based on the load-related data of the virtual power plant over the past seven days, we compared the declared capacity with the actual aggregated capacity, as shown in Table 3:

[0121] Table 3 Relative deviation table

[0122]

[0123] ;

[0124] Considering the importance of safe and stable operation of the power system, we set the conservative adjustment coefficient as:

[0125] ;

[0126] Calibration Factor ;

[0127] According to the obtained calibration factor, the declared value 580MW<685*0.97*0.94=624.583, and the calibration result is normal.

[0128] Example 2:

[0129] This embodiment provides an electronic device having a computer program stored thereon. When the computer program is executed by a processor, the method for verifying the adjustable capacity declared by a virtual power plant as described in any embodiment of the present invention is implemented.

[0130] In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can represent: a, b, c, a and b, a and c, b and c or a and b and c, where a, b, c can be single or multiple.

[0131] Those skilled in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented using a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0132] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0133] In the several embodiments provided in this application, if any function 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 this application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), magnetic disk or optical disk, and other media that can store program code.

[0134] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention's description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for verifying the adjustable capacity of a virtual power plant, characterized in that: The following steps are involved: Determine the types of distributed resources in the virtual power plant, including virtual generators, adjustable loads, and virtual energy storage; Collecting load-related data of each distributed resource, and using different single models to obtain load regulation capabilities of the load-related data for different types of distributed resources; Based on the load regulation capabilities of each distributed resource, formulate the constraints on the regulation capacity of each distributed resource in the virtual power plant; Constructing an objective function with the goal of maximizing the sum of the regulation capacity, and solving the objective function based on the constraint conditions to obtain the adjustable capacity of the virtual power plant; Obtain the declared adjustable capacity of the virtual power plant, and verify the declared adjustable capacity based on the adjustable capacity of the virtual power plant, which can be expressed as follows: ； ； Where, Represents a judgment operator, Indicates the Virtual power plants in The declaration of adjustable capacity at the moment, Indicates the Virtual power plants in The virtual power plant's adjustable capacity at any given moment, Indicates the The resource combination factor of a virtual power plant, Indicates the The conservative factor of a virtual power plant, express The default option if the operation does not work. Indicates the adjustment cycle, represents the virtual power plant index, Represents a time index; The resource combination factor is expressed as follows: ； Where, Indicates the distributed resource type index, Indicates the number of distributed resource types, Indicates the The first virtual power plant The load regulation capability of distributed resources and The proportion of the total load regulation capacity of virtual power plants, Indicates the The first virtual power plant Uncertainty coefficient of distributed resources; The conservative factor is expressed as follows: ； ； Where, represents the conservative adjustment coefficient, Indicates the historical reporting deviations of virtual power plants; Output the verification results.

2. The method for verifying the adjustable capacity declared by a virtual power plant according to claim 1 is characterized in that: Collect load-related data of the adjustable load, extract load characteristics of the load-related data of the adjustable load, construct an adjustable load vector based on the load characteristics, use the adjustable load vector and the load-related data of the adjustable load as inputs of a support vector regression model, and output the load regulation capability of the adjustable load.

3. The method for verifying the adjustable capacity declared by a virtual power plant according to claim 1 is characterized in that: Collect load-related data of the virtual energy storage, extract energy storage characteristics of the load-related data of the virtual energy storage, use the energy storage characteristics as input of the decision tree model, and output the load regulation capability of the virtual energy storage.

4. The method for verifying the adjustable capacity declared by a virtual power plant according to claim 1 is characterized in that: Collecting load-related data of the virtual generator set, including historical load, and performing linearization processing on the historical load; Decomposing the linearized historical load into the virtual generator set adjustable load and the virtual generator set non-adjustable load; For the adjustable load of virtual generator set, the adjustable load of virtual generator set is used as the input of autoregressive integral moving average model, and the load regulation capability of virtual generator set is output.

5. The method for verifying the adjustable capacity declared by a virtual power plant according to claim 1 is characterized in that: Based on the load regulation capability of each distributed resource, the constraint conditions for the regulation capacity of each distributed resource in the virtual power plant are formulated and expressed as follows: ； Where, represents the constraints, Indicates the The first virtual power plant Distributed resources in The adjustment capacity of the moment, Indicates the The first virtual power plant Distributed resources in Load regulation capability at all times, Indicates the Virtual power plants in The proportion of virtual energy storage that can be reduced at the moment; Indicates the Virtual power plant Distributed resources in A binary variable indicating whether the moment is adjusted, Indicates the Virtual power plants in The maximum adjustment rate at the moment, Indicates the The maximum adjustment time of a virtual power plant, Indicates the Virtual power plant Distributed resources in The adjustment capacity of the moment, Indicates the quantity index of distributed resources.

6. The method for verifying the adjustable capacity declared by a virtual power plant according to claim 1 is characterized in that: The objective function is constructed to maximize the sum of the regulation capacity, which can be expressed as follows: ； Where, represents the maximum function, Indicates the Virtual power plant Distributed resources in The adjustment capacity of the moment, Indicates the quantity index of distributed resources.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for verifying the adjustable capacity declared by a virtual power plant as described in any one of claims 1 to 6 is implemented.

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