Internal resource calling method, device, medium and product based on virtual power plant participating in peak load regulation scenario

Abstract

The present application discloses a method, device, medium and product for calling internal resources based on a virtual power plant participating in a peak-shaving scenario, and relates to the field of resource calling. The method includes obtaining all internal resource objects of the virtual power plant and corresponding peak-shaving technical indicators; determining corresponding weights using an entropy weight method based on historical data of the peak-shaving technical indicators; judging whether the installed capacity of the virtual power plant is greater than the product of the virtual power plant's peak-shaving threshold and the regional power grid's demand for peak-shaving; if greater than, determining a comprehensive evaluation index of the peak-shaving potential of each internal resource object based on the peak-shaving technical indicators of the internal resource objects at the current moment and the corresponding weights; making corresponding calls based on the ranking results of the comprehensive evaluation index of the peak-shaving potential of each internal resource object; and if less than or equal to, not calling the virtual power plant control to participate in peak-shaving. The present application can improve the accuracy and efficiency of calling internal resource objects based on a virtual power plant participating in a peak-shaving scenario.

Description

Technical Field

[0001] The present application relates to the field of resource calling, and in particular to an internal resource calling method, device, medium and product based on a virtual power plant participating in a peak-shaving scenario. Background Art

[0002] In recent years, peak loads for electrical appliances such as air conditioners and electric vehicles have become increasingly prominent. Furthermore, the distribution of power resources such as photovoltaic and wind power varies in both time and space, staggering peak loads. The intertwined temporal and spatial complexity of the supply side and the evolving structure of the demand side have led to increasingly significant imbalances in the balance between power supply and demand. A virtual power plant (VPP) is not a physical power plant. Instead, it breaks down the physical boundaries between power plants and power users in the traditional power system. It is an intelligent energy system that integrates and optimizes distributed energy resources (such as solar, wind, and energy storage), traditional energy resources (such as thermal and hydropower), and user-side load resources. Through intelligent technologies and systems, VPPs enable coordinated scheduling and optimized operation of various energy resources to improve energy efficiency, reduce energy production costs, and minimize environmental impact. They integrate distributed demand-side resources and enable flexible, precise, and intelligent interaction with the power grid, helping to smooth peak and valley load variations and enhance grid security. Through demand-side management, we can solve the problem of large-scale access to new energy, effectively promote the transformation of the power system to clean energy, and promote the transformation from "source follows load" to "source, grid and load interaction", thereby enhancing the system's supply and demand balance capabilities.

[0003] The current strategies and algorithms for calling internal resources of virtual power plants mainly include the following: (1) Optimal scheduling algorithm, which uses mathematical optimization methods to find the optimal scheduling scheme for internal resources of virtual power plants, with the goal of maximizing resource utilization efficiency or minimizing costs. However, the modeling of each internal resource object in this method is complex and the computational complexity is large. (2) Predictive scheduling algorithm, which uses historical data and model predictions to predict resource demand and supply in the future and formulate corresponding scheduling strategies. This method has high requirements for data prediction accuracy and large data requirements. (3) Rule-based scheduling algorithm, which formulates resource scheduling strategies according to set rules and constraints, such as allocating resources according to priority, time period, etc. This method is highly operational, but currently lacks comprehensive consideration of the ability to evaluate the internal resource potential of peak-shaving scenarios. (4) Intelligent optimization algorithm, which uses intelligent optimization algorithms such as evolutionary algorithms, genetic algorithms, and simulated annealing algorithms to find the optimal scheduling scheme for internal resources of virtual power plants. The model built by this method is relatively complex and the computational complexity is large.

[0004] Therefore, there is an urgent need to provide a method for calling internal resource objects based on virtual power plants participating in peak-shaving scenarios that can accurately and efficiently realize this. Summary of the Invention

[0005] The purpose of this application is to provide an internal resource calling method, equipment, medium and product based on the virtual power plant participating in the peak-shaving scenario, which can improve the accuracy and efficiency of internal resource object calling based on the virtual power plant participating in the peak-shaving scenario.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides an internal resource calling method based on a virtual power plant participating in a peak-shaving scenario, the internal resource calling method based on a virtual power plant participating in a peak-shaving scenario comprising:

[0008] Get all internal resource objects of the virtual power plant;

[0009] Determine peak-shaving technical indicators for different types of internal resource objects; the peak-shaving technical indicators include: up-shaving response duration, down-shaving response duration, peak-shaving capacity, valley-filling capacity, and ramp rate;

[0010] Based on the historical data of peak-shaving technical indicators of different types of internal resource objects, the entropy weight method is used to determine the weight corresponding to each peak-shaving technical indicator;

[0011] Obtain the current peak load demand of the regional power grid and the installed capacity of the virtual power plant;

[0012] Determine whether the installed capacity of the virtual power plant is greater than the product of the virtual power plant's participation in peak regulation and the regional power grid's demand for peak regulation;

[0013] If it is greater than, then determine the comprehensive evaluation index of peak-shaving potential of each internal resource object based on the peak-shaving technical indicators of the internal resource object at the current moment and the corresponding weight; make the corresponding call based on the ranking result of the comprehensive evaluation index of peak-shaving potential of each internal resource object; then make the call at the next moment until the required call time is reached;

[0014] If it is less than or equal to, the virtual power plant control will not be called to participate in peak regulation.

[0015] Optionally, determine peak-shaving technical indicators for different types of internal resource objects, including:

[0016] Using formula C fp_n (u)=P max_n (u)-P base_n (u) Determine the peak capacity C that can be cut at the historical moment u fp_n (u);

[0017] Using formula C fv_n (u)=P base_n (u)-P min_n(u ) Determine the valley-filling capacity C at historical time u fv_n (u);

[0018] Among them, P max_n (u) is the maximum technical output of the nth internal callable resource object at historical time u, P min_n (u) is the minimum technical output of the nth internal callable resource object at the historical moment u, P base_n (u) is the baseline load at historical time u.

[0019] Optionally, if the above value is greater than , then determining a comprehensive evaluation index of peak-shaving potential of each internal resource object according to the peak-shaving technical index of the internal resource object at the current moment and the corresponding weight, and the above also includes:

[0020] Normalize the peak-shaving technical indicators of internal resource objects at the current moment.

[0021] Optionally, the normalization process is specifically as follows:

[0022]

[0023] Among them, x* is the normalized value of the peak-shaving technical indicators, x min 、x max are the minimum and maximum values ​​of the peak-shaving technical indicators respectively, and x is the peak-shaving technical indicator.

[0024] Optionally, if the above value is greater than , a comprehensive evaluation index of the peak-shaving potential of each internal resource object is determined based on the peak-shaving technical indicators of the internal resource object at the current moment and the corresponding weight, specifically including:

[0025] Using the formula Determine the comprehensive evaluation index X of the peak load potential of the nth internal resource object n ;

[0026] Among them, T * fup_n (t) is the normalized duration of the up-regulation response at time t T fup_n (t), T * fdn_n (t) is the normalized duration of the regulatory response at time t, C * fp_n (t) is the normalized clipping capacity C at time t fp_n (t), C * fv_n (t) is the normalized valley filling capacity C at time t fv_n (t), V * f_n(t) is the normalized climbing rate V at time t f_n (t), w 1_n 、w 2_n 、w 3_n 、w 4_n and w 5_n T * fup_n (t), T * fdn_n (t), C * fp_n (t), C * fv_n (t) and V * f_n (t)The corresponding weight.

[0027] Optionally, performing corresponding calls according to the ranking results of the comprehensive evaluation indicators of peak-shaving potential of each internal resource object specifically includes:

[0028] Sort the comprehensive evaluation index of peak load regulation potential of each internal resource object from large to small to obtain the sorting result;

[0029] Determine the calling order of internal resource objects based on the sorting results;

[0030] Calls are made sequentially according to the calling order of internal resource objects.

[0031] In a second aspect, the present application provides an internal resource calling device based on a virtual power plant participating in a peak-shaving scenario, the internal resource calling device based on a virtual power plant participating in a peak-shaving scenario comprising:

[0032] Internal resource object acquisition module, used to obtain all internal resource objects of the virtual power plant;

[0033] A peak-shaving technical indicator determination module is used to determine peak-shaving technical indicators for different types of internal resource objects; the peak-shaving technical indicators include: up-shaving response duration, down-shaving response duration, peak-shaving capacity, valley-filling capacity, and ramp rate;

[0034] A weight determination module is used to determine the weight corresponding to each peak-shaving technical indicator based on the historical data of peak-shaving technical indicators of different types of internal resource objects using the entropy weight method;

[0035] The demand and installed capacity acquisition module is used to obtain the peak load demand of the regional power grid and the installed capacity of the virtual power plant at the current moment;

[0036] A peak-shaving period determination module is used to determine whether the installed capacity of the virtual power plant is greater than the product of the virtual power plant's peak-shaving participation threshold and the regional power grid's demand for peak-shaving;

[0037] The calling module is used to determine the comprehensive evaluation index of peak-shaving potential of each internal resource object according to the peak-shaving technical indicators of the internal resource object at the current moment and the corresponding weight if it is greater than; make the corresponding call according to the ranking result of the comprehensive evaluation index of peak-shaving potential of each internal resource object; and then make the call at the next moment until the required call time is reached;

[0038] The uncalled module is used to not call the virtual power plant control to participate in peak regulation if it is less than or equal to.

[0039] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the internal resource calling method based on the virtual power plant participating in the peak-shaving scenario.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the internal resource calling method based on the virtual power plant participating in the peak-shaving scenario.

[0041] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the internal resource calling method based on the virtual power plant participating in the peak-shaving scenario.

[0042] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0043] The present application provides an internal resource calling method, equipment, medium and product based on the virtual power plant participating in the peak-shaving scenario. The peak-shaving period is determined by the current demand for peak-shaving of the regional power grid and the installed capacity of the virtual power plant, and the potential of the peak-shaving technical indicators of the internal callable resource objects of the virtual power plant is evaluated based on the peak-shaving technical indicators and the entropy weight method; the internal resource objects are called in sequence according to the sorting results of the potential evaluation results (comprehensive evaluation indicators of peak-shaving potential) to meet the peak-shaving demand. The present application takes into account the peak-shaving demand scenario and can give priority to calling better resources to meet the peak-shaving demand, and has good operability. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1This is a flow chart of an internal resource calling method based on a virtual power plant participating in a peak-shaving scenario in one embodiment of the present application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0047] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0048] In an exemplary embodiment, Figure 1 As shown, a method for calling internal resources based on a virtual power plant participating in a peak load regulation scenario is provided, the method comprising the following S101 to S107.

[0049] S101, obtain all internal resource objects of the virtual power plant; the nth internal resource object is represented by S n (n=1,2,3...,N);

[0050] Among them, the internal resource object S n Including distributed photovoltaic, distributed wind power, energy storage, gas turbines, industrial loads, air conditioning loads, etc.;

[0051] S102, determining peak-shaving technical indicators for different types of internal resource objects; the peak-shaving technical indicators include: up-shaving response duration, down-shaving response duration, peak-shaving capacity, valley-filling capacity, and ramp rate;

[0052] Among them, the duration of the upward regulation response is the duration of continuous participation in upward regulation during the peak-shaving period; the duration of the downward regulation response is the duration of continuous participation in downward regulation during the peak-shaving period;

[0053] S102 specifically includes:

[0054] Using formula C fp_n (u)=P max_n (u)-P base_n (u) Determine the peak capacity C that can be cut at the historical moment u fp_n (u);

[0055] Using formula C fv_n (u)=P base_n (u)-P min_n (u) Determine the valley-filling capacity C at historical time ufv_n (u);

[0056] Among them, P max_n (u) is the maximum technical output of the nth internal callable resource object at historical time u, P min_n (u) is the minimum technical output of the nth internal callable resource object at the historical moment u, P base_n (u) is the baseline load at the historical time u; the baseline load is the average output of the same time in the previous five days when not participating in peak load regulation;

[0057] S103, determining the weight corresponding to each peak-shaving technical indicator using an entropy weight method based on historical data of peak-shaving technical indicators of different types of internal resource objects;

[0058] The historical data of the peak-shaving technical indicators include the peak-shaving technical indicators of various types of internal resource objects at a historical time u within a past historical period T.

[0059] S104, obtaining the peak load demand R of the regional power grid at the current moment f (t) and the installed capacity of the virtual power plant P vir (t), P vir (t) is the sum of the rated powers of the internal resource objects;

[0060] S105, determine whether the installed capacity of the virtual power plant is greater than the product of the virtual power plant's participation in peak regulation and the regional power grid's demand for peak regulation; that is, determine whether P vir Is (t) greater than εR? f (t); where ε represents the threshold for virtual power plants to participate in peak load regulation (0<ε<1);

[0061] S106, if it is greater than, then adjust the response duration according to the peak load type technical indicators of the internal resource object at the current moment (up T fup_n (t), duration of down-regulation response T fdn_n (t), clipping capacity C fp_n (t), valley filling capacity C fv_n (t) and the climbing rate V f_n (t)) and the corresponding weight to determine the comprehensive evaluation index of the peak-shaving potential of each internal resource object; make a corresponding call according to the ranking result of the comprehensive evaluation index of the peak-shaving potential of each internal resource object; then make a call at the next moment until the required call time is reached;

[0062] Among them, using C fp_n (t) = P max_n (t)-P base_n (t) Determine the peak capacity that can be cut C fp_n (t);

[0063] Using formula C fv_n (t) = P base_n (t)-P min_n (t) Determine the valley filling capacity C fv_n (t);

[0064] Among them, P max_n (t) is the maximum technical output of the nth internal callable resource object at time t, P min_n (t) is the minimum technical output of the nth internal callable resource object at time t, P base_n (t) is the baseline load of the nth internal callable resource object at time t;

[0065] In another exemplary embodiment of the present application, using the formula Normalize the peak-shaving technical indicators of the internal resource objects at the current moment; where x* is the normalized value of the peak-shaving technical indicators, x min 、x max are the minimum and maximum values ​​of the peak-shaving technical indicators respectively, and x is the peak-shaving technical indicator.

[0066] Using the formula Determine the comprehensive evaluation index X of the peak load potential of the nth internal resource object n ;

[0067] Among them, T * fup_n (t) is the normalized duration of the up-regulation response at time t T fup_n (t), T * fdn_n (t) is the normalized duration of the regulatory response at time t, C * fp_n (t) is the normalized clipping capacity C at time t fp_n (t), C * fv_n (t) is the normalized valley filling capacity C at time t fv_n (t), V * f_n (t) is the normalized climbing rate V at time t f_n (t), w 1_n 、w 2_n 、w 3_n 、w 4_n and w 5_n T * fup_n (t), T * fdn_n (t), C * fp_n (t), C* fv_n (t) and V * f_n (t)The corresponding weight.

[0068] S106 specifically includes:

[0069] Sort the comprehensive evaluation index of peak load regulation potential of each internal resource object from large to small to obtain the sorting result;

[0070] Determine the calling order of internal resource objects based on the sorting results;

[0071] Calls are made sequentially according to the calling order of internal resource objects.

[0072] S106 specifically involves prioritizing resource objects with greater comprehensive evaluation indicators of peak-shaving potential to participate in peak-shaving, until the peak-shaving demand can be fully met or all internal resource objects are fully utilized;

[0073] S107: If it is less than or equal to, the virtual power plant control will not be called to participate in peak regulation.

[0074] Based on the same inventive concept, an embodiment of the present application further provides an internal resource calling device based on a virtual power plant participating in peak-shaving scenario for implementing the internal resource calling method based on a virtual power plant participating in peak-shaving scenario involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution described in the above method. Therefore, the specific limitations of one or more embodiments of the internal resource calling device based on a virtual power plant participating in peak-shaving scenario provided below can be found in the above limitations of the internal resource calling method based on a virtual power plant participating in peak-shaving scenario, and will not be repeated here.

[0075] In an exemplary embodiment, an internal resource calling device based on a virtual power plant participating in a peak load regulation scenario is provided, including:

[0076] Internal resource object acquisition module, used to obtain all internal resource objects of the virtual power plant;

[0077] A peak-shaving technical indicator determination module is used to determine peak-shaving technical indicators for different types of internal resource objects; the peak-shaving technical indicators include: up-shaving response duration, down-shaving response duration, peak-shaving capacity, valley-filling capacity, and ramp rate;

[0078] A weight determination module is used to determine the weight corresponding to each peak-shaving technical indicator based on the historical data of peak-shaving technical indicators of different types of internal resource objects using the entropy weight method;

[0079] The demand and installed capacity acquisition module is used to obtain the peak load demand of the regional power grid and the installed capacity of the virtual power plant at the current moment;

[0080] A peak-shaving period determination module is used to determine whether the installed capacity of the virtual power plant is greater than the product of the virtual power plant's peak-shaving participation threshold and the regional power grid's demand for peak-shaving;

[0081] The calling module is used to determine the comprehensive evaluation index of peak-shaving potential of each internal resource object according to the peak-shaving technical indicators of the internal resource object at the current moment and the corresponding weight if it is greater than; make the corresponding call according to the ranking result of the comprehensive evaluation index of peak-shaving potential of each internal resource object; and then make the call at the next moment until the required call time is reached;

[0082] The uncalled module is used to not call the virtual power plant control to participate in peak regulation if it is less than or equal to.

[0083] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements an internal resource calling method based on a virtual power plant participating in a peak-shaving scenario.

[0084] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0085] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0086] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0087] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0088] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0089] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0090] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for calling internal resources based on a virtual power plant participating in a peak load regulation scenario, characterized in that: The internal resource calling method based on the virtual power plant participating in the peak load regulation scenario includes: Get all internal resource objects of the virtual power plant; Determine peak-shaving technical indicators for different types of internal resource objects; the peak-shaving technical indicators include: up-shaving response duration, down-shaving response duration, peak-shaving capacity, valley-filling capacity, and ramp rate; Based on the historical data of peak-shaving technical indicators of different types of internal resource objects, the entropy weight method is used to determine the weight corresponding to each peak-shaving technical indicator; Obtain the current peak load demand of the regional power grid and the installed capacity of the virtual power plant; Determine whether the installed capacity of the virtual power plant is greater than the product of the virtual power plant's participation in peak regulation and the regional power grid's demand for peak regulation; Normalize the peak-shaving technical indicators of internal resource objects at the current moment; If it is greater than, then determine the comprehensive evaluation index of peak-shaving potential of each internal resource object based on the peak-shaving technical indicators of the internal resource object at the current moment and the corresponding weight; make the corresponding call based on the ranking result of the comprehensive evaluation index of peak-shaving potential of each internal resource object; then make the call at the next moment until the required call time is reached; If it is less than or equal to, the virtual power plant control will not be called to participate in peak regulation; If the above value is greater than , the comprehensive evaluation index of peak-shaving potential of each internal resource object is determined based on the peak-shaving technical indicators of the internal resource object at the current moment and the corresponding weight, specifically including: Using the formula Determine the comprehensive evaluation index X of the peak load potential of the nth internal resource object n ; Among them, T * fup_n (t) is the normalized duration of the up-regulation response at time t T fup_n (t), T * fdn_n (t) is the normalized duration of the regulatory response at time t, C * fp_n (t) is the normalized clipping capacity C at time t fp_n (t), C * fv_n (t) is the normalized valley filling capacity C at time t fv_n (t), V * f_n (t) is the normalized climbing rate V at time t f_n (t), w 1_n 、w 2_n 、w 3_n 、w 4_n and w 5_n T * fup_n (t), T * fdn_n (t), C * fp_n (t), C * fv_n (t) and V * f_n (t)The corresponding weight.

2. The internal resource calling method based on the virtual power plant participating in the peak load regulation scenario according to claim 1 is characterized in that: Determine the peak-shaving technical indicators for different types of internal resource objects, including: Using formula C fp_n (u)=P max_n (u)-P base_n (u) Determine the peak capacity C that can be cut at the historical moment u fp_n (u); Using formula C fv_n (u)=P base_n (u)-P min_n (u) Determine the valley-filling capacity C at historical time u fv_n (u); Among them, P max_n (u) is the maximum technical output of the nth internal callable resource object at historical time u, P min_n (u) is the minimum technical output of the nth internal callable resource object at the historical moment u, P base_n (u) is the baseline load at historical time u.

3. The internal resource calling method based on the virtual power plant participating in the peak load regulation scenario according to claim 1 is characterized in that: The normalization process is as follows: Among them, x* is the normalized value of the peak-shaving technical indicators, x min 、x max are the minimum and maximum values ​​of the peak-shaving technical indicators respectively, and x is the peak-shaving technical indicator.

4. The internal resource calling method based on the virtual power plant participating in the peak load regulation scenario according to claim 1 is characterized in that: The corresponding call is made according to the ranking result of the comprehensive evaluation index of the peak-shaving potential of each internal resource object, specifically including: Sort the comprehensive evaluation index of peak load regulation potential of each internal resource object from large to small to obtain the sorting result; Determine the calling order of internal resource objects based on the sorting results; Calls are made sequentially according to the calling order of internal resource objects.

5. An internal resource calling device based on a virtual power plant participating in a peak-shaving scenario, used to implement the internal resource calling method based on a virtual power plant participating in a peak-shaving scenario according to any one of claims 1 to 4, characterized in that: The internal resource calling device based on the virtual power plant participating in the peak load regulation scenario includes: Internal resource object acquisition module, used to obtain all internal resource objects of the virtual power plant; A peak-shaving technical indicator determination module is used to determine peak-shaving technical indicators for different types of internal resource objects; the peak-shaving technical indicators include: up-shaving response duration, down-shaving response duration, peak-shaving capacity, valley-filling capacity, and ramp rate; A weight determination module is used to determine the weight corresponding to each peak-shaving technical indicator based on the historical data of peak-shaving technical indicators of different types of internal resource objects using the entropy weight method; The demand and installed capacity acquisition module is used to obtain the peak load demand of the regional power grid and the installed capacity of the virtual power plant at the current moment; A peak-shaving period determination module is used to determine whether the installed capacity of the virtual power plant is greater than the product of the virtual power plant's peak-shaving participation threshold and the regional power grid's demand for peak-shaving; The calling module is used to determine the comprehensive evaluation index of peak-shaving potential of each internal resource object according to the peak-shaving technical indicators of the internal resource object at the current moment and the corresponding weight if it is greater than; make the corresponding call according to the ranking result of the comprehensive evaluation index of peak-shaving potential of each internal resource object; and then make the call at the next moment until the required call time is reached; The uncalled module is used to not call the virtual power plant control to participate in peak regulation if it is less than or equal to.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the internal resource calling method based on a virtual power plant participating in a peak-shaving scenario as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the internal resource calling method based on the virtual power plant participating in the peak-shaving scenario described in any one of claims 1 to 4 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the internal resource calling method based on the virtual power plant participating in the peak-shaving scenario described in any one of claims 1 to 4 is implemented.

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