Intelligent demand management system and method for virtual power plants based on photovoltaic output forecast

Abstract

The present application discloses an intelligent demand management system and method for a virtual power plant based on photovoltaic output prediction, which relates to the technical field of virtual power plants. The operating demand data corresponding to each time period on the demand side are imported into an operating demand analysis strategy, and the load pre-demand response index corresponding to each time period on the demand side is calculated and obtained; whether the load demand in each time period on the demand side is met is judged according to the load pre-demand response index corresponding to each time period on the demand side; the historical operating data of each interruptible load on the demand side is input into the interruptible load influence model on the demand side, and the stability influence index corresponding to each interruptible load on the demand side is output; each interruptible load on the demand side is adjusted according to the stability influence index corresponding to each interruptible load on the demand side. In the absence of human intervention, the system realizes dynamic regulation and gives priority to adjusting the interruptible load, completes intelligent demand management, avoids excessive concentration of power demand, reduces the pressure on the power grid, improves energy utilization efficiency, and makes the power supply more green and sustainable.

Description

Technical Field

[0001] The present application belongs to the field of virtual power plants, and more specifically to an intelligent demand management system and method for virtual power plants based on photovoltaic output forecasting. Background Art

[0002] In recent years, due to the pressure of energy structure transformation brought about by large-scale access of new energy and changes in power load characteristics, the problem of flexibility resource gap in power system regulation demand has become prominent; in order to avoid power shortages caused by excessive concentration of power demand and to improve energy utilization efficiency, virtual power plant demand management systems have come into being; but in the existing technology, the virtual power plant demand management system still requires human intervention to realize the regulation of interruptible loads, and it is impossible to perform intelligent demand management of the virtual power plant without human intervention, which reduces the energy clearing capacity of the substation in the virtual power plant and reduces energy utilization efficiency. In order to solve the problems raised by this background technology, this application designs an intelligent demand management system and method for virtual power plants based on photovoltaic output prediction. Summary of the Invention

[0003] In response to the above-mentioned technical deficiencies, this application proposes an intelligent demand management system and method for a virtual power plant based on photovoltaic output forecasting.

[0004] To solve the above technical problems, the present invention adopts the following technical solution: This application provides an intelligent demand management method for a virtual power plant based on photovoltaic output forecasting, which includes the following specific steps:

[0005] S1. Preset a data unit time interval, and periodically obtain the operation demand data corresponding to each time period of the demand side and the historical demand side interruptible load operation data according to the data unit time interval;

[0006] S2. Import the operation demand data corresponding to each time period of the demand side into the operation demand analysis strategy, and calculate and obtain the load pre-demand response index corresponding to each time period of the demand side;

[0007] S3. Determine whether the load demand in each time period of the demand side is met based on the load pre-demand response index corresponding to each time period of the demand side. If the load demand is met, the process ends; if the load demand is not met, proceed to S4.

[0008] S4. Establish a demand-side interruptible load influence model, input historical demand-side interruptible load operation data into the demand-side interruptible load influence model, output the stability influence index corresponding to each demand-side interruptible load, and adjust each interruptible load on the demand side according to the stability influence index corresponding to each demand-side interruptible load.

[0009] It should be noted that, as a preferred technical solution for the intelligent demand management system and method of a virtual power plant based on photovoltaic output forecasting, the specific steps of S1 are:

[0010] S11, presetting a data unit time interval, and periodically collecting data according to the data unit time interval;

[0011] S12. Obtaining operation demand data corresponding to each time period on the demand side from a database, wherein the operation demand data corresponding to each time period on the demand side includes power demand forecast data and supply side meteorological impact value data;

[0012] S13. Obtaining historical operating data of each interruptible load on the demand side corresponding to each time period on the demand side from a database, wherein the historical operating data of each interruptible load on the demand side corresponding to each time period on the demand side includes historical operating power average value data, interruptible duration data, and production reduction rate data varying with interruption duration;

[0013] S14. Storing the collected data in a storage component for use in the analysis process.

[0014] It should be noted that, as a preferred technical solution for the intelligent demand management system and method of a virtual power plant based on photovoltaic output forecasting, S2 includes the following specific steps: importing the power demand forecast data corresponding to each time period on the demand side and the meteorological impact value data on the supply side into the load pre-demand response index calculation formula to calculate the load pre-demand response index corresponding to each time period on the demand side, wherein the load pre-demand response index calculation formula corresponding to the tth time period on the demand side is: , where t is the number corresponding to each time period on the demand side, and t is any one of 1 to N. is the power demand forecast data corresponding to the t-th time period on the demand side, t is the number corresponding to each time period on the demand side, and t is any item from 1 to N. is the minimum power demand corresponding to the demand side, is the maximum power demand corresponding to the demand side, a is the weight of the power demand data, is the supply-side meteorological impact value data corresponding to the t-th time period on the demand side, It is a reference value for the meteorological impact value on the supply side. It should be noted that this formula analyzes the load demand response index by combining the power demand forecast data corresponding to each time period on the demand side and the meteorological impact value data on the supply side, thereby improving the accuracy of the load demand response index.

[0015] It should be noted that, as the preferred technical solution for the intelligent demand management system and method of the virtual power plant based on photovoltaic output forecast, the S3 includes the following steps: comparing the load demand response index corresponding to each time period on the demand side with the set load demand response index threshold; if the load demand response index corresponding to a certain time period on the demand side is greater than or equal to the set load demand response index threshold, it is determined that the load demand is not met and S4 is performed; if the load demand response index corresponding to a certain time period on the demand side is less than the set load demand response index threshold, it is determined that the load demand is met.

[0016] It should be noted that, as a preferred technical solution for the intelligent demand management system and method for a virtual power plant based on photovoltaic output forecasting, the specific steps of S4 are:

[0017] S41. Obtaining the number of inflection points corresponding to each interruptible load on the demand side from the historical operating power average data of each time period corresponding to each interruptible load on the demand side;

[0018] S42, obtaining a production impact index corresponding to each interruptible load on the demand side from the interruptible duration data corresponding to each interruptible load on the demand side and the production reduction rate data varying with the interruption duration;

[0019] S43, obtaining a stability impact index corresponding to each interruptible load on the demand side from the number of inflection points corresponding to each interruptible load on the demand side and the production impact index;

[0020] S44. Arrange the stability impact indexes corresponding to the interruptible loads on the demand side in ascending order, and adjust the interruptible loads on the demand side according to the arrangement result of the stability impact indexes corresponding to the interruptible loads on the demand side.

[0021] It should be noted that, as a preferred technical solution for the intelligent demand management system and method of a virtual power plant based on photovoltaic output forecasting, the specific step of S41 is: importing the historical operating power average value data of each time period corresponding to each interruptible load on the demand side into the inflection point number calculation formula to calculate the number of inflection points corresponding to each interruptible load on the demand side, wherein the inflection point number calculation formula corresponding to the j-th interruptible load on the demand side is: ,in, is the number of inflection points corresponding to the jth interruptible load at the demand end, is the average power value of the jth interruptible load at the demand end in the t+1th time period, is the average power of the jth interruptible load at the demand end corresponding to the tth time period. It should be noted that the meaning of this formula is: whenever When n=n+1, that is, when the power average value of the j-th interruptible load at the demand end in the t+1-th time period increases or decreases by more than or equal to 50% compared with the power average value in the t-th time period, the power average change point is marked as the inflection point.

[0022] It should be noted that, as a preferred technical solution for the intelligent demand management system and method of a virtual power plant based on photovoltaic output forecasting, the specific step of S42 is: importing the interruptible duration data corresponding to each interruptible load on the demand side and the production reduction rate data that varies with the interruption duration into the production impact index calculation formula to calculate the production impact index corresponding to each interruptible load on the demand side, wherein the production impact index calculation formula corresponding to the j-th interruptible load on the demand side is: ,in, is the interruption duration corresponding to the jth interruptible load on the demand side, is the production reduction rate corresponding to the jth interruptible load on the demand side as the interruption duration changes, This is the allowable value of the production reduction rate. It should be noted that this formula comprehensively analyzes the impact of interruption duration and production reduction rate on the production situation on the demand side by combining the interruptible duration data corresponding to each interruptible load on the demand side and the production reduction rate data that changes with the interruption duration, thereby improving the accuracy of the production impact index.

[0023] It should be noted that, as a preferred technical solution for the intelligent demand management system and method of a virtual power plant based on photovoltaic output forecasting, the specific step of S43 is: importing the number of inflection points and the production impact index corresponding to each interruptible load on the demand side into the stability impact index calculation formula to calculate the stability impact index corresponding to each interruptible load on the demand side, wherein the stability impact index calculation formula corresponding to the j-th interruptible load on the demand side is: ,in, is the reference value of the set inflection point number, and b is the weight of the inflection point number ratio.

[0024] An intelligent demand management system and method for a virtual power plant based on photovoltaic output forecasting is implemented based on the above-mentioned intelligent demand management method for a virtual power plant based on photovoltaic output forecasting, and specifically includes an operation data acquisition module, a load pre-demand analysis module, a load pre-demand judgment module, and an interruption load adjustment module, wherein the operation data acquisition module is used to preset a data unit time interval, and periodically acquire the operation demand data corresponding to each time period of the demand side and the historical operation data of each interruptible load of the demand side according to the data unit time interval;

[0025] The load pre-demand analysis module is used to import the operation demand data corresponding to each time period of the demand side into the operation demand analysis strategy, and calculate and obtain the load pre-demand response index corresponding to each time period of the demand side;

[0026] The load pre-demand judgment module is used to judge whether the load demand in each time period of the demand side is met according to the load pre-demand response index corresponding to each time period of the demand side. If the load demand is met, the process ends; if the load demand is not met, the process proceeds to S4;

[0027] The interruption load adjustment module is used to establish an interruptible load influence model on the demand side, input historical operation data of each interruptible load on the demand side into the interruptible load influence model on the demand side, output the stability influence index corresponding to each interruptible load on the demand side, and adjust each interruptible load on the demand side according to the stability influence index corresponding to each interruptible load on the demand side.

[0028] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0029] The processor executes the above-mentioned intelligent demand management method of the virtual power plant based on photovoltaic output prediction by calling the computer program stored in the memory.

[0030] A computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned intelligent demand management method for a virtual power plant based on photovoltaic output prediction.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention presets a data unit time interval, and periodically obtains the operation demand data corresponding to each time period of the demand side and the historical operation data of each interruptible load on the demand side according to the data unit time interval; the operation demand data corresponding to each time period of the demand side is imported into the operation demand analysis strategy, and the load pre-demand response index corresponding to each time period of the demand side is calculated and obtained; whether the load demand in each time period of the demand side is met is judged according to the load pre-demand response index corresponding to each time period of the demand side, if the load demand is met, then the process ends; if the load demand is not met, then S4 is performed; a demand side interruptible load influence model is established, the historical operation data of each interruptible load on the demand side is input into the demand side interruptible load influence model, and the stability influence index corresponding to each interruptible load on the demand side is output; each interruptible load on the demand side is adjusted according to the stability influence index corresponding to each interruptible load on the demand side; in the absence of human intervention, the system realizes dynamic regulation and preferentially adjusts the interruptible load, completes intelligent demand management, avoids excessive concentration of power demand, reduces the pressure on the power grid, improves energy utilization efficiency, and makes the power supply more green and sustainable. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings;

[0033] Figure 1 This is a schematic diagram of the overall process of the intelligent demand management method of the virtual power plant based on photovoltaic output forecasting in this application.

[0034] Figure 2 This is a flow chart of step S4 of the intelligent demand management method for a virtual power plant based on photovoltaic output forecasting in this application.

[0035] Figure 3 This is a schematic diagram of the overall framework of the intelligent demand management system and method of the virtual power plant based on photovoltaic output prediction in this application.

[0036] Figure 4 Implement a scene graph for this application. DETAILED DESCRIPTION

[0037] To better understand the present application, various aspects of the present application will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are merely descriptions of exemplary embodiments of the present application and are not intended to limit the scope of the present application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0038] In the accompanying drawings, the size, dimensions, and shapes of elements have been slightly adjusted for ease of illustration. The drawings are for illustration only and are not drawn strictly to scale. As used herein, the terms "substantially," "approximately," and similar terms are intended to indicate approximation, not degree, and are intended to illustrate the inherent variations in measured or calculated values that would be recognized by one of ordinary skill in the art. Furthermore, in this application, the order in which the various steps are described does not necessarily represent the order in which these steps would occur in actual operation, unless otherwise specified or inferred from the context. It should also be understood that expressions such as "comprises," "including," "having," "includes," and / or "comprising" are open-ended, not closed-ended, expressions in this specification, indicating the presence of the stated features, elements, and / or components, but do not exclude the presence of one or more other features, elements, components, and / or combinations thereof. Furthermore, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features, not just the individual elements in the list. Furthermore, when describing embodiments of the present application, the use of "may" means "one or more embodiments of the present application." Furthermore, the term "exemplary" is intended to refer to an example or illustration. Unless otherwise specified, all words used herein (including engineering terms and scientific and technological terms) have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that, unless otherwise specified in this application, words defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0039] In order to solve the technical problems raised in the background technology, this application provides a preferred embodiment: please refer to Figure 4As shown, the implementation scenario of this embodiment is demonstrated. The implementation scenario is: data is collected from the data acquisition terminal, the data is transmitted to the data processing terminal, the data processing terminal analyzes and calculates to obtain the stability impact index corresponding to each interruptible load on the demand side, and each interruptible load on the demand side is adjusted according to the stability impact index corresponding to each interruptible load on the demand side.

[0040] The specific contents of this embodiment are:

[0041] like Figure 1 As shown, the intelligent demand management system and method of a virtual power plant based on photovoltaic output forecasting includes the following specific steps:

[0042] S1. Preset a data unit time interval, and periodically obtain the operation demand data corresponding to each time period of the demand side and the historical demand side interruptible load operation data according to the data unit time interval;

[0043] In this embodiment, the specific steps of S1 are:

[0044] S11, presetting a data unit time interval, and periodically collecting data according to the data unit time interval;

[0045] S12. Obtaining operation demand data corresponding to each time period on the demand side from a database, wherein the operation demand data corresponding to each time period on the demand side includes power demand forecast data and supply side meteorological impact value data;

[0046] S13. Obtaining historical operating data of each interruptible load on the demand side corresponding to each time period on the demand side from a database, wherein the historical operating data of each interruptible load on the demand side corresponding to each time period on the demand side includes historical operating power average value data, interruptible duration data, and production reduction rate data varying with interruption duration;

[0047] S14. Storing the collected data in a storage component for use in the analysis process.

[0048] S2. Import the operation demand data corresponding to each time period of the demand side into the operation demand analysis strategy, and calculate and obtain the load pre-demand response index corresponding to each time period of the demand side;

[0049] In this embodiment, S2 includes the following specific steps: importing the power demand forecast data corresponding to each time period on the demand side and the meteorological impact value data on the supply side into the load pre-demand response index calculation formula to calculate the load pre-demand response index corresponding to each time period on the demand side, wherein the load pre-demand response index calculation formula corresponding to the t-th time period on the demand side is: , where t is the number corresponding to each time period on the demand side, and t is any one of 1 to N. is the power demand forecast data corresponding to the t-th time period on the demand side, t is the number corresponding to each time period on the demand side, and t is any item from 1 to N. is the minimum power demand corresponding to the demand side, is the maximum power demand corresponding to the demand side, a is the weight of the power demand data, is the supply-side meteorological impact value data corresponding to the t-th time period on the demand side, It is a reference value for the meteorological impact value on the supply side. It should be noted that this formula analyzes the load demand response index by combining the power demand forecast data corresponding to each time period on the demand side and the meteorological impact value data on the supply side, thereby improving the accuracy of the load demand response index.

[0050] S3. Determine whether the load demand in each time period of the demand side is met based on the load pre-demand response index corresponding to each time period of the demand side. If the load demand is met, the process ends; if the load demand is not met, proceed to S4.

[0051] In this embodiment, the specific steps of S3 are: comparing the load demand response index corresponding to each time period on the demand side with the set load demand response index threshold; if the load demand response index corresponding to a certain time period on the demand side is greater than or equal to the set load demand response index threshold, it is determined that the load demand is not met and S4 is performed; if the load demand response index corresponding to a certain time period on the demand side is less than the set load demand response index threshold, it is determined that the load demand is met.

[0052] S4. Establish a demand-side interruptible load influence model, input historical demand-side interruptible load operation data into the demand-side interruptible load influence model, output the stability influence index corresponding to each demand-side interruptible load, and adjust each interruptible load on the demand side according to the stability influence index corresponding to each demand-side interruptible load.

[0053] like Figure 2 As shown, in this embodiment, the specific steps in S4 are:

[0054] S41. Obtaining the number of inflection points corresponding to each interruptible load on the demand side from the historical operating power average data of each time period corresponding to each interruptible load on the demand side;

[0055] S42, obtaining a production impact index corresponding to each interruptible load on the demand side from the interruptible duration data corresponding to each interruptible load on the demand side and the production reduction rate data varying with the interruption duration;

[0056] S43, obtaining a stability impact index corresponding to each interruptible load on the demand side from the number of inflection points corresponding to each interruptible load on the demand side and the production impact index;

[0057] S44. Arrange the stability impact indexes corresponding to the interruptible loads on the demand side in ascending order, and adjust the interruptible loads on the demand side according to the arrangement result of the stability impact indexes corresponding to the interruptible loads on the demand side.

[0058] In this embodiment, the specific step of S41 is: importing the historical operating power average value data of each time period corresponding to each interruptible load on the demand side into the inflection point number calculation formula to calculate the number of inflection points corresponding to each interruptible load on the demand side, wherein the inflection point number calculation formula corresponding to the j-th interruptible load on the demand side is: ,in, is the number of inflection points corresponding to the jth interruptible load at the demand end, is the average power value of the jth interruptible load at the demand end in the t+1th time period, is the average power of the jth interruptible load at the demand end corresponding to the tth time period. It should be noted that the meaning of this formula is: whenever When n=n+1, that is, when the power average value of the t+1th time period corresponding to the jth interruptible load on the demand side increases or decreases by more than or equal to 50% compared with the power average value of the tth time period, the power average change point is marked as an inflection point; for example, the calculation process of the number of inflection points is illustrated by taking an example. If, when calculating the number of inflection points corresponding to a certain interruptible load, the setting value of t is 15 minutes, the power average value of the t+1th time period corresponding to a certain interruptible load on the demand side is 1kW, and the power average value of the tth time period is 2kW, then the transition time point between the tth time period and the t+1th time period corresponding to this interruptible load on the demand side is marked as an inflection point, and the number of inflection points is increased by 1 on the original basis.

[0059] In this embodiment, the specific step of S42 is: importing the interruptible duration data corresponding to each interruptible load on the demand side and the production reduction rate data that varies with the interruption duration into the production impact index calculation formula to calculate the production impact index corresponding to each interruptible load on the demand side, wherein the production impact index calculation formula corresponding to the j-th interruptible load on the demand side is: ,in, is the interruption duration corresponding to the jth interruptible load on the demand side, is the production reduction rate corresponding to the jth interruptible load on the demand side as the interruption duration changes, is the set allowable value of the production reduction rate. It should be noted that this formula comprehensively analyzes the interruptible time data corresponding to each interruptible load on the demand side and the production reduction rate data that changes with the interruption time to analyze the impact of the interruption time and the production reduction rate on the production situation on the demand side, thereby improving the accuracy of the production impact index. For example, an example is given to illustrate the impact of the interruption of the interruptible load on the production situation on the demand side. In a certain coating workshop, if the phosphating solution stagnates for more than 30 minutes, it will lead to abnormal crystallization and the adhesion of the electrophoretic paint will decrease by 25% to 30%. If the paint supply pipeline stagnates for more than 8 hours, the paint will settle and stratify.

[0060] In this embodiment, the specific step of S43 is: importing the number of inflection points and the production impact index corresponding to each interruptible load on the demand side into the stability impact index calculation formula to calculate the stability impact index corresponding to each interruptible load on the demand side, wherein the stability impact index calculation formula corresponding to the j-th interruptible load on the demand side is: ,in, is the set reference value of the number of inflection points, b is the weight of the proportion of the number of inflection points. It should be noted that this formula comprehensively analyzes the stability impact index corresponding to each interruptible load based on the number of inflection points and the production impact index corresponding to each interruptible load on the demand side. In order to improve the accuracy of the stability impact index, the reference value of the number of inflection points and the weight of the proportion of the number of inflection points are set at the same time to jointly analyze the impact of the number of inflection points on the stability index.

[0061] It should be noted here that the setting parameters (such as weights and thresholds, etc.) in this embodiment need to be set by those skilled in the art based on relevant experiments. The specific experimental method is: obtain the operating demand data corresponding to each time period on the demand side and the historical demand side interruptible load operation data, substitute them into the steps in this embodiment to calculate the load pre-demand response index and the stability impact index, import the calculation results of the load pre-demand response index and the stability impact index into the fitting software for continuous fitting, and output the setting parameters (such as weights and thresholds, etc.) with the highest compliance with the load pre-demand response index and the stability impact index.

[0062] According to the above implementation content, this embodiment has the following advantages over the prior art: This embodiment presets a data unit time interval, and periodically obtains the operating demand data corresponding to each time period of the demand side and the historical operating data of each interruptible load on the demand side according to the data unit time interval; imports the operating demand data corresponding to each time period of the demand side into the operating demand analysis strategy, and calculates and obtains the load pre-demand response index corresponding to each time period of the demand side; determines whether the load demand in each time period of the demand side is met based on the load pre-demand response index corresponding to each time period of the demand side; if the load demand is met, the process ends; if the load demand is not met, the process proceeds to S4; establishes a demand side interruptible load influence model, inputs the historical operating data of each interruptible load on the demand side into the demand side interruptible load influence model, outputs the stability impact index corresponding to each interruptible load on the demand side, and adjusts each interruptible load on the demand side based on the stability impact index corresponding to each interruptible load on the demand side. In the absence of human intervention, the system realizes dynamic control and prioritizes the adjustment of the interruptible load, completes intelligent demand management, avoids excessive concentration of power demand, reduces pressure on the power grid, improves energy utilization efficiency, and makes the power supply more green and sustainable.

[0063] like Figure 3As shown, this embodiment also provides an intelligent demand management system and method for a virtual power plant based on photovoltaic output forecasting, which is implemented based on the above-mentioned intelligent demand management method for a virtual power plant based on photovoltaic output forecasting, and specifically includes an operation data acquisition module, a load pre-demand analysis module, a load pre-demand judgment module and an interruption load adjustment module, wherein the operation data acquisition module is used to preset a data unit time interval, and periodically obtain the operation demand data corresponding to each time period of the demand side and the historical operation data of each interruptible load of the demand side according to the data unit time interval; the load pre-demand analysis module is used to import the operation demand data corresponding to each time period of the demand side into the operation demand In the analysis strategy, the load pre-demand response index corresponding to each time period on the demand side is calculated and obtained; the load pre-demand judgment module is used to judge whether the load demand in each time period on the demand side is met according to the load pre-demand response index corresponding to each time period on the demand side. If the load demand is met, it ends; if the load demand is not met, S4 is performed; the interruption load adjustment module is used to establish an interruptible load influence model on the demand side, input the historical operation data of each interruptible load on the demand side into the interruptible load influence model on the demand side, output the stability influence index corresponding to each interruptible load on the demand side, and adjust each interruptible load on the demand side according to the stability influence index corresponding to each interruptible load on the demand side.

[0064] The above-mentioned specific steps for each unit module in the intelligent demand management system of the virtual power plant based on photovoltaic output prediction of this application to realize the corresponding functions can be referred to the various steps in the embodiment of the intelligent demand management method of the virtual power plant based on photovoltaic output prediction above, and will not be repeated here.

[0065] This embodiment further provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0066] The processor executes the above-mentioned intelligent demand management method of the virtual power plant based on photovoltaic output prediction by calling the computer program stored in the memory.

[0067] The memory can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 310 can include a program storage area and a data storage area. The program storage area can store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the method for intelligent demand management of a virtual power plant based on photovoltaic output forecasting provided in the above embodiment. The data storage area can store data related to the method for intelligent demand management of a virtual power plant based on photovoltaic output forecasting provided in the above embodiment.

[0068] The processor may include one or more processing cores. The processor executes instructions, programs, code sets, or instruction sets stored in memory, accesses data stored in memory, and performs the various functions and processes data of the present application. The processor may be at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field-programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, and a microprocessor. It is understood that for different devices, the electronic components used to implement the above-mentioned processor functions may also be other, and the embodiments of the present application are not specifically limited thereto.

[0069] A communication bus may also be included. This communication bus may include a path for transmitting information between the aforementioned components. Examples of communication buses include the PCI (Peripheral Component Interconnect) bus and the EISA (Extended Industry Standard Architecture) bus. Communication buses can be categorized as address buses, data buses, and control buses.

[0070] This embodiment further proposes a computer-readable storage medium storing instructions. When the instructions are executed on a computer, the computer executes the above-mentioned intelligent demand management method for a virtual power plant based on photovoltaic output prediction.

[0071] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0072] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0073] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0074] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of application of this application is not limited to technical solutions formed by a specific combination of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned application concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. An intelligent demand management method for a virtual power plant based on photovoltaic output forecasting, characterized in that: include: S1. Preset a data unit time interval, and periodically obtain the operation demand data corresponding to each time period of the demand side and the historical demand side interruptible load operation data according to the data unit time interval; S2. Import the operation demand data corresponding to each time period of the demand side into the operation demand analysis strategy, and calculate and obtain the load pre-demand response index corresponding to each time period of the demand side; The S2 includes the following specific steps: importing the power demand forecast data corresponding to each time period on the demand side and the meteorological impact value data on the supply side into the load pre-demand response index calculation formula to calculate the load pre-demand response index corresponding to each time period on the demand side, wherein the load pre-demand response index calculation formula corresponding to the t-th time period on the demand side is: , where t is the number corresponding to each time period on the demand side, and t is any one of 1 to N. is the power demand forecast data corresponding to the tth time period on the demand side, is the minimum power demand corresponding to the demand side, is the maximum power demand corresponding to the demand side, a is the weight of the power demand data, is the supply-side meteorological impact value data corresponding to the t-th time period on the demand side, It is the reference value of meteorological impact value on the supply side; S3. Determine whether the load demand in each time period of the demand side is met based on the load pre-demand response index corresponding to each time period of the demand side. If the load demand is met, the process ends; if the load demand is not met, proceed to S4. S4. Establish a demand-side interruptible load influence model, input historical demand-side interruptible load operation data into the demand-side interruptible load influence model, output the stability influence index corresponding to each demand-side interruptible load, and adjust each demand-side interruptible load according to the stability influence index corresponding to each demand-side interruptible load. The specific steps of S4 are: S41. Obtaining the number of inflection points corresponding to each interruptible load on the demand side from the historical operating power average data of each time period corresponding to each interruptible load on the demand side; S42, obtaining a production impact index corresponding to each interruptible load on the demand side from the interruptible duration data corresponding to each interruptible load on the demand side and the production reduction rate data varying with the interruption duration; S43, obtaining a stability impact index corresponding to each interruptible load on the demand side from the number of inflection points corresponding to each interruptible load on the demand side and the production impact index; S44. Arrange the stability impact indexes corresponding to the interruptible loads on the demand side in ascending order, and adjust the interruptible loads on the demand side according to the arrangement result of the stability impact indexes corresponding to the interruptible loads on the demand side.

2. The method for intelligent demand management of a virtual power plant based on photovoltaic output forecasting according to claim 1, characterized in that: The S3 includes the following steps: comparing the load pre-demand response index corresponding to each time period on the demand side with the set load pre-demand response index threshold; if the load pre-demand response index corresponding to a certain time period on the demand side is greater than or equal to the set load pre-demand response index threshold, it is determined that the load demand is not met and S4 is performed; if the load pre-demand response index corresponding to a certain time period on the demand side is less than the set load pre-demand response index threshold, it is determined that the load demand is met.

3. The method for intelligent demand management of a virtual power plant based on photovoltaic output forecasting according to claim 2, characterized in that: The specific step of S41 is: importing the historical operating power average value data of each time period corresponding to each interruptible load on the demand side into the inflection point number calculation formula to calculate the number of inflection points corresponding to each interruptible load on the demand side, wherein the inflection point number calculation formula corresponding to the j-th interruptible load on the demand side is: ,in, is the number of inflection points corresponding to the jth interruptible load at the demand end, is the average power value of the jth interruptible load at the demand end in the t+1th time period, is the average power of the jth interruptible load at the demand end corresponding to the tth time period. When n=n+1, that is, when the power average value of the j-th interruptible load at the demand end in the t+1-th time period increases or decreases by more than or equal to 50% compared with the power average value in the t-th time period, the power average change point is marked as the inflection point.

4. The method for intelligent demand management of a virtual power plant based on photovoltaic output forecasting according to claim 3, characterized in that: The specific step of S42 is: importing the interruptible duration data corresponding to each interruptible load on the demand side and the production reduction rate data varying with the interruption duration into the production impact index calculation formula to calculate the production impact index corresponding to each interruptible load on the demand side, wherein the production impact index calculation formula corresponding to the j-th interruptible load on the demand side is: ,in, is the interruption duration corresponding to the jth interruptible load on the demand side, is the production reduction rate corresponding to the jth interruptible load on the demand side as the interruption duration changes, It is the allowable value of the set production reduction rate.

5. The method for intelligent demand management of a virtual power plant based on photovoltaic output forecasting according to claim 4, characterized in that: The specific step of S43 is: importing the number of inflection points and the production impact index corresponding to each interruptible load on the demand side into the stability impact index calculation formula to calculate the stability impact index corresponding to each interruptible load on the demand side, wherein the stability impact index calculation formula corresponding to the j-th interruptible load on the demand side is: ,in, is the reference value of the set inflection point number, and b is the weight of the inflection point number ratio.

6. An intelligent demand management system for a virtual power plant based on photovoltaic output forecasting, which is used to implement the intelligent demand management method for a virtual power plant based on photovoltaic output forecasting according to any one of claims 1 to 5, characterized in that: It specifically includes an operation data acquisition module, a load pre-demand analysis module, a load pre-demand judgment module and an interruption load adjustment module, wherein the operation data acquisition module is used to obtain the operation demand data corresponding to each time period of the demand side and the historical operation data of each interruptible load of the demand side; The load pre-demand analysis module is used to import the operation demand data corresponding to each time period of the demand side into the operation demand analysis strategy, and calculate and obtain the load pre-demand response index corresponding to each time period of the demand side; The load pre-demand judgment module is used to judge whether the load demand in each time period of the demand side is met according to the load pre-demand response index corresponding to each time period of the demand side. If the load demand is met, the process ends; if the load demand is not met, the process proceeds to S4; The interruption load adjustment module is used to establish an interruptible load influence model on the demand side, input historical operation data of each interruptible load on the demand side into the interruptible load influence model on the demand side, output the stability influence index corresponding to each interruptible load on the demand side, and adjust each interruptible load on the demand side according to the stability influence index corresponding to each interruptible load on the demand side.

7. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the intelligent demand management method of a virtual power plant based on photovoltaic output prediction as described in any one of claims 1 to 5 by calling the computer program stored in the memory.

8. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the intelligent demand management method for a virtual power plant based on photovoltaic output prediction as described in any one of claims 1 to 5.

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