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

Abstract

The invention discloses an internal resource calling method and device based on a virtual power plant participation peak regulation scene, a medium and a product, and relates to the field of resource calling, and the method comprises the steps: obtaining all internal resource objects of a virtual power plant and corresponding peak regulation technical indexes; according to historical data of the peak regulation type technical indexes, determining corresponding weights by adopting an entropy weight method; judging whether the installation of the virtual power plant is greater than the product of the peak regulation participation threshold of the virtual power plant and the peak regulation demand of the regional power grid; if yes, determining a peak regulation potential comprehensive evaluation index of each internal resource object according to the peak regulation type technical index of the internal resource object at the current moment and the corresponding weight; performing corresponding calling according to the sorting result of the peak regulation potential comprehensive evaluation indexes of each internal resource object; and if not, not calling the virtual power plant control to participate in peak regulation. According to the method, the accuracy and the efficiency of calling the internal resource object based on the virtual power plant participating in the peak regulation scene can be improved.

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 load regulation scenario. Background Art

[0002] In recent years, the peak load characteristics of power equipment such as air conditioners and electric vehicles have become increasingly prominent. At the same time, the distribution of power resources such as photovoltaics and wind power has temporal and spatial differences, and they appear at different times from the peak load of power consumption. The temporal and spatial complexity of the supply side is intertwined with the evolution of the demand-side structure, resulting in an increasingly significant contradiction in the balance of power supply and demand. A virtual power plant is not a physical power plant. It breaks the physical boundaries between power plants and power users in the traditional power system. It refers to an intelligent energy system that integrates and optimizes distributed energy resources (such as solar energy, wind energy, energy storage, etc.), traditional energy resources (such as thermal power, hydropower, etc.) and user-side load resources. Through intelligent technologies and systems, virtual power plants can achieve coordinated scheduling and optimized operation of various energy resources to improve energy utilization efficiency, reduce energy production costs, and reduce environmental impact. It can integrate decentralized resources on the demand side and achieve flexible, precise, and intelligent interaction with the power grid, which helps to stabilize the peak-to-valley difference of the power grid load and improve the level of power 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 amount of calculation is large. (2) Predictive scheduling algorithm, which predicts resource demand and supply in the future based on historical data and model predictions, and formulates 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 amount of calculation 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 implement them. Summary of the invention

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

[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-regulation response duration, down-regulation response duration, peak-cutting capacity, valley-filling capacity, and ramp rate;

[0010] According to 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 load regulation and the regional power grid's demand for peak load regulation;

[0013] If it is greater than, then determine the comprehensive evaluation index of the 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; make the corresponding call according to the sorting result of the comprehensive evaluation index of the 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 load 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 time 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 the historical time u fv_n (u);

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

[0019] Optionally, if the above is greater than, then the comprehensive evaluation index of the peak-shaving potential of each internal resource object is determined 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 the 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 is greater than, then the comprehensive evaluation index of the peak-shaving potential of each internal resource object is determined according to the peak-shaving technical index 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, the corresponding calling according to the ranking result of the comprehensive evaluation index of the peak load potential of each internal resource object specifically includes:

[0028] The comprehensive evaluation index of the peak load potential of each internal resource object is sorted from large to small to obtain a sorting result;

[0029] Determine the calling order of internal resource objects according to the sorting results;

[0030] Calls are made sequentially according to the calling order of the 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 load regulation scenario, and the internal resource calling device based on a virtual power plant participating in a peak load regulation scenario includes:

[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 the peak-shaving technical indicators of different types of internal resource objects; the peak-shaving technical indicators include: the duration of the upward adjustment response, the duration of the downward adjustment response, the peak-cutting capacity, the valley-filling capacity and the climbing rate;

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

[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 judgment module is used to judge whether the installed capacity of the virtual power plant is greater than the product of the peak-shaving threshold of the virtual power plant and the peak-shaving demand of the regional power grid;

[0037] The calling module is used to determine the comprehensive evaluation index of the 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 if it is greater than; make a corresponding call according to the sorting result of the comprehensive evaluation index of the peak-shaving potential of each internal resource object; and then make a call at the next moment until the required calling 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, including: 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 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, device, medium and product based on the virtual power plant participating in the peak-shaving scenario. The peak-shaving period is determined by the peak-shaving demand of the regional power grid at the current moment 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 are evaluated based on the peak-shaving technical indicators and the entropy weight method; the internal resource objects are called in sequence to meet the peak-shaving demand according to the sorting results of the potential evaluation results (comprehensive evaluation indicators of peak-shaving potential). 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 drawings required for use in the embodiments will be briefly introduced below. 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 paying creative work.

[0045] Figure 1A flowchart 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 the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] In order to make the above-mentioned objects, 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, and the method includes the following S101 to S107. Among them:

[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 of different types of internal resource objects; the peak-shaving technical indicators include: up-regulation response duration, down-regulation response duration, peak-cutting 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 time u fp_n (u);

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

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

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

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

[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, judging whether the installed capacity of the virtual power plant is greater than the product of the virtual power plant's participation in peak load regulation and the regional power grid's demand for peak load regulation; i.e. judging P vir Is (t) greater than εR? f (t); where ε represents the threshold of virtual power plant participation in peak load regulation (0<ε<1);

[0061] S106: If it is greater than, then adjust the response duration according to the peak load technical indicators of the internal resource objects at the current moment (upward adjustment response duration T fup_n (t), down-regulation response duration T fdn_n (t), cuttable peak capacity C fp_n (t), valley filling capacity C fv_n (t) and the climbing rate V f_n (t)) and the corresponding weights to determine the comprehensive evaluation index of the peak-shaving potential of each internal resource object; make corresponding calls according to the ranking results of the comprehensive evaluation index of the peak-shaving potential of each internal resource object; and then make calls at the next moment until the required calling time is reached;

[0062] Among them, using C fp_n (t) = P max_n (t)-P base_n (t) Determine the cuttable peak capacity 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] The comprehensive evaluation index of the peak load potential of each internal resource object is sorted from large to small to obtain a sorting result;

[0070] Determine the calling order of internal resource objects according to the sorting results;

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

[0072] S106 specifically involves first calling resource objects with large 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 called;

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

[0074] Based on the same inventive concept, the embodiment of the present application also provides an internal resource calling device based on the virtual power plant participating in the peak-shaving scenario for implementing the internal resource calling method based on the virtual power plant participating in the peak-shaving scenario involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the internal resource calling device based on the virtual power plant participating in the peak-shaving scenario provided below can refer to the limitations of the internal resource calling method based on the virtual power plant participating in the peak-shaving scenario above, 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, comprising:

[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 of different types of internal resource objects; the peak-shaving technical indicators include: up-regulation response duration, down-regulation response duration, peak-cutting 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 by using an entropy weight method based on historical data of peak-shaving technical indicators of different types of internal resource objects;

[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 judgment module is used to judge whether the installed capacity of the virtual power plant is greater than the product of the peak-shaving threshold of the virtual power plant and the peak-shaving demand of the regional power grid;

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

[0082] The uncalled module is used to not call the virtual power plant control to participate in peak load 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 for short) 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, an internal resource calling method based on a virtual power plant participating in a peak-shaving scenario is implemented.

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

[0085] In an exemplary embodiment, a computer program product is provided, including a computer program, and 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 of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed 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 the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can 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 can 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 database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0089] The technical features of the above embodiments may be arbitrarily combined. 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 article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will 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. An internal resource calling method 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-regulation response duration, down-regulation response duration, peak-cutting capacity, valley-filling capacity, and ramp rate; According to 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 load regulation and the regional power grid's demand for peak load regulation; If it is greater than, then determine the comprehensive evaluation index of the 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; make the corresponding call according to the sorting result of the comprehensive evaluation index of the 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 load regulation.

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 of 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 historical time u fp_n (u); Using formula C fv_n (u) = P base_n (u)-P min_n (u) Determine the valley capacity C at historical time u fv_n (u); Among them, P max_n (u) is the maximum technical output of the nth internally callable resource object at historical time u, P min_n (u) is the minimum technical output of the nth internally callable resource object at historical time 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: If the above is greater than, then the comprehensive evaluation index of the peak-shaving potential of each internal resource object is determined 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: Normalize the peak-shaving technical indicators of the internal resource objects at the current moment.

4. The internal resource calling method based on the virtual power plant participating in the peak load regulation scenario according to claim 3 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.

5. The internal resource calling method based on the virtual power plant participating in the peak load regulation scenario according to claim 3 is characterized in that: If it is greater than, then the comprehensive evaluation index of the peak-shaving potential of each internal resource object is determined according to 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.

6. 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 calling according to the ranking result of the comprehensive evaluation index of the peak load potential of each internal resource object specifically includes: The comprehensive evaluation index of the peak load potential of each internal resource object is sorted from large to small to obtain a sorting result; Determine the calling order of internal resource objects according to the sorting results; Calls are made sequentially according to the calling order of the internal resource objects.

7. An internal resource calling device based on a virtual power plant participating in a peak load regulation scenario, 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 of different types of internal resource objects; the peak-shaving technical indicators include: up-regulation response duration, down-regulation response duration, peak-cutting capacity, valley-filling capacity, and ramp rate; A weight determination module is used to determine the weight corresponding to each peak-shaving technical indicator by using an entropy weight method based on historical data of peak-shaving technical indicators of different types of internal resource objects; 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 judgment module is used to judge whether the installed capacity of the virtual power plant is greater than the product of the peak-shaving threshold of the virtual power plant and the peak-shaving demand of the regional power grid; The calling module is used to determine the comprehensive evaluation index of the 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 if it is greater than; make a corresponding call according to the sorting result of the comprehensive evaluation index of the peak-shaving potential of each internal resource object; and then make a call at the next moment until the required calling 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.

8. 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 1-6.

9. 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 6 is implemented.

10. 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 6 is implemented.

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