Aggregation capacity range prediction method and device for rapid frequency modulation of distributed resources, and storage medium

Abstract

The invention provides an aggregation capacity range prediction method for rapid frequency modulation of distributed resources. The method comprises the following steps: acquiring operation data of each single device in a distributed resource cluster; inputting the operation data of each single device into a pre-constructed two-stage robust optimization model based on Camp; and performing iterative solution on the two-stage robust optimization model by a CG algorithm to obtain an aggregation capacity range of rapid frequency modulation of the distributed resources in the cluster. According to the aggregation capacity range prediction method for the rapid frequency modulation of the distributed resources, on the premise of considering network constraints of a power distribution network where a distributed resource cluster is located and adjustment constraints of single equipment, the frequency modulation capacities of the distributed resources in the cluster at all moments are dynamically aggregated by using a two-stage robust optimization model; predicting an aggregation capacity range of rapid frequency modulation of the distributed resources; effective transfer of distributed resources and optimal configuration of power system resources are facilitated, the stability of a power grid is improved, and the problem that risks exist when a decentralized control mode is adopted is solved.

Description

Technical Field

[0001] The invention relates to a method, a device and a storage medium for predicting the aggregate capacity range of distributed resource rapid frequency modulation, and belongs to the field of power system dispatching. Background Art

[0002] As the proportion of renewable energy in the power system increases, the replacement of thermal power generation by non-synchronous power sources such as wind and solar power leads to insufficient inertia of the power grid, which increases the frequency change rate after disturbance. At the same time, the higher regulation demand brought by the uncertainty and volatility of its output brings challenges to the safe, stable and economical operation of the power system. Distributed resources are widely distributed, small in scale, and have rapid response capabilities. They can adjust their own electricity consumption behavior to reduce peak-to-valley differences, promote the consumption of new energy, and quickly inject active power to limit the frequency drop speed before the primary frequency regulation response, avoiding low-frequency load shedding in the system, showing huge regulation potential. Therefore, how to achieve aggregated control of massive heterogeneous distributed resources with uncertain output, promote their interaction with the power grid and support the optimization of power system operation has become the key to tapping the regulation potential of distributed resources.

[0003] At present, the aggregate calculation of distributed resource frequency regulation capacity usually adopts the method of simply adding up the individual capacities within the cluster. This method has many disadvantages. First, for the distributed resource cluster within the regional power grid, its response output is interactive, and different distributed resources influence each other. When considering the power grid topology, this interaction is particularly prominent. The simple addition aggregate capacity calculation method obviously ignores the relationship between resources and network constraints, which may lead to over-regulation or under-regulation problems; secondly, the output of resources such as wind power, photovoltaics and electric vehicles has strong uncertainty. The simple addition method will make the aggregate calculation results of distributed resource frequency regulation capacity unreliable. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method for predicting the aggregate capacity range of distributed resource rapid frequency modulation, which adopts a two-stage robust optimization model to deal with the challenge of parameter uncertainty in the optimization problem.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] In the first aspect, the present invention provides a method for predicting the aggregate capacity range of distributed resource rapid frequency modulation, including: obtaining the operating data of each single device in the distributed resource cluster; inputting the operating data of each single device into a pre-constructed two-stage robust optimization model, decomposing the two-stage robust optimization model into a main problem and a sub-problem through the C&CG algorithm, and performing iterative solution to obtain the aggregate capacity range of distributed resource rapid frequency modulation. The main problem is used to solve the aggregate capacity range of distributed resource rapid frequency modulation; the dual sub-problem is used to solve the feasibility of distributed resource aggregate frequency modulation capacity.

[0007] Furthermore, the construction of the two-stage robust optimization model includes:

[0008] Construct the frequency modulation capacity model of each single device in the distributed resource cluster;

[0009] A two-stage robust optimization model is constructed based on the frequency regulation capacity model of each single device, including:

[0010] The first phase optimization goal is to maximize the aggregate capacity range of distributed resource rapid frequency modulation.

[0011] The upper and lower limits of the aggregate capacity range of distributed resources at each moment are used as the first-stage decision variables;

[0012] Obtaining an uncertainty set based on the decision variables in the first stage;

[0013] The second phase optimization goal is to ensure the feasibility of distributed resource aggregation frequency modulation capacity.

[0014] The frequency modulation capacity of each single device in the distributed resource cluster at each moment is used as the decision variable in the second stage;

[0015] Setting constraints for the first-stage optimization objective and the second-stage optimization objective includes:

[0016] The constraints of the optimization objective in the first stage include: active power balance constraints and network constraints;

[0017] The constraints of the second stage optimization objective include: active power balance constraints and operation constraints;

[0018] A two-stage robust optimization model is constructed according to the first-stage decision variables, the first-stage optimization objectives, the constraints of the first-stage optimization objectives, the uncertainty set, the second-stage decision variables, the second-stage optimization objectives and the constraints of the second-stage optimization objectives.

[0019] Furthermore, obtaining the uncertainty set according to the decision variables in the first stage includes:

[0020] Set the preset time period The distributed resource aggregate frequency modulation capacity at each moment in is linearly expressed by the first-stage decision variable corresponding to that moment, and the expression is: ; in, is an uncertain variable, x t ∈ [ 0 ， 1 ] , is the upper limit of the aggregate capacity range at each moment, It is the lower limit of the aggregate capacity range at each moment.

[0021] Depend on The uncertain variables corresponding to all moments in Constructing an uncertain set .

[0022] Furthermore, the two-stage robust optimization model is: ; Where T is the transpose operation, It is the frequency modulation capacity of each single device in the distributed resource cluster at each moment.

[0023] constraint It is the active power balance equation constructed based on the DC power flow model; and are the coefficient matrix and constant vector constituting the active power balance constraint respectively;

[0024] constraint Frequency modulation power constraints and energy constraints for each single device in the distributed resource cluster, is the coefficient matrix, is a constant vector.

[0025] Furthermore, the two-stage robust optimization model is decomposed into a main problem and sub-problems, including: The main problem is: ; The sub-problems are: .

[0026] Furthermore, the iterative solution to obtain the aggregate capacity range of the distributed resource rapid frequency modulation includes: S51: Reconstruct the subproblem according to the strong duality theory to obtain the dual subproblem; S52: As a sub-problem scenario and initialize it, and initialize the number of iterations ; S53: Input the current scenario of the sub-problem into the main problem, solve the main problem in combination with the active power balance constraints of each distributed resource and the network constraints, and obtain the aggregate capacity range corresponding to the current scenario ; S54: The aggregate capacity range to be obtained Input the dual subproblem to solve the feasibility of the current distributed resource aggregation frequency modulation capacity: In response to any constraint of the dual sub-problem not being satisfied, it is determined that the current distributed resource aggregation frequency modulation capacity is not feasible, and then the process goes to S55; In response to satisfying all constraints of the dual sub-problem, it is determined that the current distributed resource aggregation frequency modulation capacity is feasible, and the iteration ends; S55: , and update the subproblem scenarios and main problem constraints , then go to S53.

[0027] Furthermore, the subproblems are reconstructed according to the strong duality theory to obtain dual subproblems, including:

[0028] According to the strong duality theory, the subproblem is reconstructed and expressed as follows: ; In the formula, and is the dual variable.

[0029] The uncertain variable The value limit is ;

[0030] use The nonlinear term in the objective function Perform linearization processing, including: Defining non-negative set variables and ; ; ; ; Introduce a new collection variable: ; ; use replace , replace , and introduce the following constraints: in, .

[0031] The dual subproblem is: .

[0032] In a second aspect, the present invention provides an electronic device including a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method for predicting the aggregate capacity range of rapid frequency modulation of distributed resources as described in the first aspect is implemented.

[0033] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for predicting the aggregate capacity range of rapid frequency modulation of distributed resources as described in the first aspect is implemented.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] (1) The method for predicting the range of aggregated capacity of rapid frequency modulation of distributed resources provided by the present invention takes into account the network constraints of the distribution network where the distributed resource cluster is located and the regulation constraints of the single device, and uses a two-stage robust optimization model to dynamically aggregate the aggregated capacity of the rapid frequency modulation of distributed resources at each moment, thereby obtaining a method for predicting the range of aggregated capacity of rapid frequency modulation of distributed resources; this method is conducive to promoting the effective mobilization of distributed resources and the optimal allocation of power system resources, thereby improving the stability of the power grid and solving the problem of risks in the decentralized control method used in the prior art;

[0036] (2) The method for predicting the range of aggregated capacity for rapid frequency modulation of distributed resources provided by the present invention decomposes the traditional robust optimization model into a main problem and sub-problems, and uses strong duality to transform the dual optimization problem of the sub-problems into a single optimization problem. In the process of iteratively solving the sub-problems, active power balance is used as a constraint to ensure that the aggregated capacity in this iteration can be successfully deaggregated, so that the aggregated capacity for rapid frequency modulation of distributed resources not only ensures the feasibility of deaggregation, but also ensures the optimality and robustness of the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic diagram of iterative solution of the two-stage robust optimization model in Example 2 of the present invention. DETAILED DESCRIPTION

[0038] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0039] Example 1

[0040] According to a first aspect of the present invention, the present invention provides a method for predicting the aggregate capacity range of distributed resource rapid frequency modulation, such as Figure 1 As shown, the method comprises the following steps:

[0041] Step 1: Obtain the operating data of each single device in the distributed resource cluster.

[0042] Step 2: Input the operating data of each single device into a pre-built two-stage robust optimization model.

[0043] Specifically, the construction process of the two-stage robust optimization model includes:

[0044] Step 01: Build a frequency modulation capacity model for each single device in the distributed resource cluster.

[0045] Specifically, the distributed resource cluster includes wind turbines, photovoltaic units, energy storage equipment, electric vehicle aggregators EVA and HVAC.

[0046] Preferably, the wind turbine generator set adopts the double-fed wind turbine DFIG which is widely used in actual scenarios, and the frequency modulation capacity of the double-fed wind turbine DFIG is modeled. The power upper and lower limits should be met: ; In the formula, , is the number of wind turbines, and They are the lower and upper limits of the frequency regulation power of wind turbines respectively.

[0047] Preferably, for a photovoltaic unit, its frequency modulation power The power upper and lower limits should be met: ; In the formula, , is the number of photovoltaic units, and They are respectively the lower and upper limits of the frequency regulation power of the photovoltaic unit.

[0048] Preferably, the frequency modulation power of battery energy storage and energy Both upper and lower constraints should be met: ; in, ; In the formula, , is the amount of battery energy storage, and are the lower and upper limits of the frequency modulation power of battery energy storage, and are the lower and upper limits of battery storage energy, respectively. is the energy reserve at the previous moment t-1, is the efficiency factor that simulates the loss of stored energy over time.

[0049] At the same time, the energy storage of the energy storage battery after participating in the frequency modulation should meet the following requirements: ; In the formula, This is the energy reserve at the initial moment of frequency modulation. It is the energy reserve for the end of frequency modulation.

[0050] Preferably, the frequency modulation power of the electric vehicle and state of charge should satisfy the upper and lower limit constraints: ; ; In the formula, , is the number of electric vehicles, and are the lower and upper limits of the frequency modulation power of electric vehicles, and They are the lower and upper limits of the state of charge of electric vehicles respectively.

[0051] Preferably, the HVAC participates in the power system frequency modulation by adjusting the operating frequency of the compressor. The upper and lower constraints should be met: ; Consider the indoor temperature at time t The upper and lower limit constraints are: ; and ; In the formula, , is the number of HVAC, and are the lower and upper limits of HVAC frequency modulation power, respectively. and are the lower and upper limits of the indoor temperature, is the indoor temperature at time t-1, is the outdoor temperature at time t, is a parameter that represents the thermal characteristics of a building. It is a parameter that represents the thermal characteristics of the environment.

[0052] Step 02: Construct a two-stage robust optimization model based on the frequency regulation capacity model of each single device, including:

[0053] Step 021: Maximize the aggregate capacity range of distributed resources for rapid frequency modulation As the optimization target of the first stage, the upper limit of the aggregate capacity range of distributed resources at each moment is and lower limit as the first-stage decision variable.

[0054] Step 022: Based on the first stage decision variables and Get the uncertainty set ,include:

[0055] Set the preset time period Distributed resource aggregation frequency modulation capacity at each moment The first-stage decision variable corresponding to this moment and Linear expression: ; in, is an uncertain variable, x ∈ [ 0 ， 1 ] .

[0056] Depend on The uncertain variables corresponding to all moments in Constructing an uncertain set .

[0057] Step 023: The second stage optimization goal is to ensure the feasibility of the distributed resource aggregation frequency modulation capacity, and the frequency modulation capacity of each single device in the distributed resource cluster at each time. as the second-stage decision variable.

[0058] Step 024: Setting constraints for the first-stage optimization target and the second-stage optimization target, including: the constraints for the first-stage optimization target include: active power balance constraints and network constraints; the constraints for the second-stage optimization target include: active power balance constraints and operation constraints.

[0059] Step 025: Construct a two-stage robust optimization model based on the first-stage decision variables, the first-stage optimization target, the constraints of the first-stage optimization target, the uncertainty set, the second-stage decision variables, the second-stage optimization target and the constraints of the second-stage optimization target, specifically: ; in, It is the active power balance equation constructed based on the DC power flow model; It is the frequency modulation power constraint and energy constraint of each distributed resource unit.

[0060] Step 3: Decompose the two-stage robust optimization model into a main problem and sub-problems through the C&CG algorithm.

[0061] Specifically, the main problem is used to solve the aggregate capacity range of distributed resource rapid frequency modulation ,include: ; in, To maximize the sum of aggregated frequency modulation capacity in each time period.

[0062] The dual subproblem is used to solve the feasibility of distributed resource aggregation frequency modulation capacity, including: ;

[0063] Step 4: Iterate the main problem and sub-problems to obtain the aggregate capacity range for rapid frequency regulation of distributed resources, including:

[0064] Step 41: Reconstruct the subproblem according to the strong duality theory, and the expression is: ; In the formula, and is the dual variable, T is the transposition operation;

[0065] because and The feasible domains of the subproblems are disjoint, so the optimal solution to the objective function of the subproblem must be in the uncertain set The optimal must be equal to 0 or equal to 1, so the uncertain variable is The value limit is ;

[0066] use The nonlinear term in the objective function Perform linearization processing, including:

[0067] Defining non-negative set variables and ; ; ; ;

[0068] Introduce a new collection variable: ; ;

[0069] use replace , replace , and introduce the following constraints: ; ; in, ;

[0070] The dual subproblem is: ;

[0071] Step 42: As a sub-problem scenario and initialize it, and initialize the number of iterations ;

[0072] Step 43: Bring the current scenario of the sub-problem into the main problem as a known quantity, solve the main problem in combination with the active power balance constraints of each distributed resource and the network constraints, and obtain the aggregate capacity range corresponding to the current scenario ;

[0073] Step 44: Get the aggregate capacity range The feasibility of solving the current aggregate frequency modulation capacity is solved by bringing it into the dual sub-problem as a known quantity.

[0074] Specifically, Figure 1As shown, in this embodiment, a judgment parameter is set and parameter preset thresholds ,like In the process of solving the dual subproblem, if any constraint of the dual subproblem is not satisfied, then return ;like If all constraints of the dual subproblem are satisfied, then return .

[0075] In response to , it is determined that the current aggregate frequency modulation capacity is not feasible, and then go to step 45, that is, the scenario in the sub-problem at this time is added as a bad scenario to the main problem for the next iteration. The main problem accepts the bad scenario returned by the sub-problem, which means solving a new aggregate capacity range under the consideration of the bad scenario; otherwise, it is determined that the current aggregate frequency modulation capacity is feasible, the iteration ends, and the aggregate capacity range under the worst scenario is output, and the solution ends.

[0076] Step 45: , and update the subproblem scenarios and main problem constraints , then go to step 43.

[0077] Example 2

[0078] Based on Example 1, this example provides an application of the aggregate capacity range predicted by the method of the present invention in the frequency regulation auxiliary service market declaration.

[0079] Distributed resource aggregators participate in the frequency regulation ancillary service market as market players and declare aggregated adjustable capacity, that is, declare upward capacity and downward capacity.

[0080] like Figure 1 As shown, the aggregator can calculate the declared upward adjustment capacity and downward adjustment capacity according to the solution result of Example 1, including: ; ; ; in, is the base point power, To increase capacity, To reduce capacity.

[0081] Example 3

[0082] On the basis of Example 1, this embodiment provides that after the bidding results in the frequency modulation market are announced, the distributed resource aggregator decomposes the aggregated frequency modulation capacity in Example 1 into individual distributed resources according to the bidding results to form an initial scheduling plan for the distributed resources.

[0083] Specifically, by assigning different objective functions to sub-problems and considering different characteristics according to actual needs, the aggregation results are decomposed to form an initial distributed resource scheduling plan that meets actual needs.

[0084] In this embodiment, economic characteristics are taken into consideration, and the goal is to minimize the operating cost. The problem is constructed as follows: Object . minutes x t [ F w ( x t ) + F pv ( x t ) + F it ( x t ) + F ev ( x t ) + F HVAC ( x t )] s . t . p 0 reg = Dx t + g ∥ Ex ( x ) ∥ ≤ s , x ∈ U 1 Wx ( x ) ≤ w , ∀ x ∈ U 1 ; in, is the aggregated FM capacity of the deaggregation, ; To respond to the operating costs of wind turbines during the dispatch period, In response to the operation cost of the photovoltaic unit during the dispatch period, In response to the operation cost of energy storage equipment during the dispatch period, In response to the operating costs of electric vehicles during the dispatch period, The operating costs of HVAC for the response scheduling period.

[0085] By solving the operation constraints, network constraints and feasibility constraints of each distributed resource, the frequency modulation power tracking plan for each resource with the lowest operation cost is finally obtained.

[0086] Example 4

[0087] According to a second aspect of the present invention, there is also provided an electronic device, comprising a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method for predicting the aggregate capacity range of rapid frequency modulation of distributed resources as described in Example 1 is implemented.

[0088] Example 5

[0089] According to a third aspect of the present invention, a computer storage medium is also provided, on which a computer program is stored, and when the computer program is executed by a processor, the method for predicting the aggregate capacity range of distributed resource rapid frequency modulation as described in Example 1 is implemented. The storage medium may include, for example, a storage component of a tablet computer, a hard disk of a computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer storage medium may be any combination of one or more computer storage media.

[0090] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for predicting the aggregate capacity range of distributed resource rapid frequency modulation, characterized in that: include: Obtain the operating data of each single device in the distributed resource cluster; Inputting the operation data of each single device into a pre-built two-stage robust optimization model, decomposing the two-stage robust optimization model into a main problem and sub-problems through a C&CG algorithm, and performing iterative solving to obtain an aggregate capacity range for rapid frequency modulation of distributed resources; The main problem is used to solve the aggregate capacity range of distributed resource rapid frequency regulation; the dual sub-problem is used to solve the feasibility of distributed resource aggregate frequency regulation capacity.

2. The method for predicting the aggregate capacity range of distributed resource rapid frequency modulation according to claim 1, characterized in that: The construction of the two-stage robust optimization model includes: Construct the frequency modulation capacity model of each single device in the distributed resource cluster; A two-stage robust optimization model is constructed based on the frequency regulation capacity model of each single device, including: The first phase optimization goal is to maximize the aggregate capacity range of distributed resource rapid frequency modulation. The upper and lower limits of the aggregate capacity range of distributed resources at each moment are used as the first-stage decision variables; Obtaining an uncertainty set based on the decision variables in the first stage; The second phase optimization goal is to ensure the feasibility of distributed resource aggregation frequency modulation capacity. The frequency modulation capacity of each single device in the distributed resource cluster at each moment is used as the decision variable in the second stage; Setting constraints for the first-stage optimization objective and the second-stage optimization objective includes: The constraints of the optimization objective in the first stage include: active power balance constraints and network constraints; The constraints of the second stage optimization objective include: active power balance constraints and operation constraints; A two-stage robust optimization model is constructed according to the first-stage decision variables, the first-stage optimization objectives, the constraints of the first-stage optimization objectives, the uncertainty set, the second-stage decision variables, the second-stage optimization objectives and the constraints of the second-stage optimization objectives.

3. The method for predicting the aggregate capacity range of distributed resource rapid frequency modulation according to claim 2, characterized in that: The step of obtaining an uncertainty set according to the decision variables in the first stage includes: Set the preset time period The distributed resource aggregate frequency modulation capacity at each moment in is linearly expressed by the first-stage decision variable corresponding to that moment, and the expression is: ； in, is an uncertain variable, , is the upper limit of the aggregate capacity range at each moment, is the lower limit of the aggregate capacity range at each moment; Depend on The uncertain variables corresponding to all moments in Constructing an uncertain set .

4. The method for predicting the aggregate capacity range of distributed resource rapid frequency modulation according to claim 3 is characterized in that: The two-stage robust optimization model is: ； Where, T is the transpose operation; The frequency modulation capacity of each single device in the distributed resource cluster at each moment; constraint It is the active power balance equation constructed based on the DC power flow model; and are the coefficient matrix and constant vector constituting the active power balance constraint respectively; constraint Frequency modulation power constraints and energy constraints for each single device in the distributed resource cluster, is the coefficient matrix, is a constant vector.

5. The method for predicting the aggregate capacity range of distributed resource rapid frequency modulation according to claim 4, characterized in that: Decomposing the two-stage robust optimization model into a main problem and sub-problems includes: The main problem is: ； The sub-problems are: 。 6. The method for predicting the aggregate capacity range of distributed resource rapid frequency modulation according to claim 5, characterized in that: The iterative solution to obtain the aggregate capacity range of the distributed resource rapid frequency modulation includes: S51: Reconstruct the subproblem according to the strong duality theory to obtain the dual subproblem; S52: As a sub-problem scenario and initialize it, and initialize the number of iterations ; S53: Input the current scenario of the sub-problem into the main problem, solve the main problem in combination with the active power balance constraints of each distributed resource and the network constraints, and obtain the aggregate capacity range corresponding to the current scenario ; S54: The aggregate capacity range to be obtained Input the dual sub-problem to solve the feasibility of the current distributed resource aggregation frequency modulation capacity, In response to any constraint of the dual sub-problem not being satisfied, it is determined that the current distributed resource aggregation frequency modulation capacity is not feasible, and then the process goes to S55; In response to satisfying all constraints of the dual sub-problem, it is determined that the current distributed resource aggregation frequency modulation capacity is feasible, and the iteration ends; S55: , and update the subproblem scenarios and main problem constraints , then go to S53.

7. The method for predicting the aggregate capacity range of distributed resource rapid frequency modulation according to claim 6, characterized in that: The subproblems are reconstructed according to the strong duality theory to obtain dual subproblems, including: According to the strong duality theory, the subproblem is reconstructed and expressed as follows: ； In the formula, and is the dual variable; The uncertain variable The value limit is ; use The nonlinear term in the objective function Perform linearization processing, including: Defining non-negative set variables and ; ； ； ； Introduce a new collection variable: ； ； use replace , replace , and introduce the following constraints: ； ； in, ; The dual subproblem is: 。 8. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method for predicting the aggregate capacity range of distributed resource rapid frequency modulation according to any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for predicting the aggregate capacity range of distributed resource rapid frequency modulation according to any one of claims 1 to 7 is implemented.

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