Virtual power plant intelligent demand management system and method based on photovoltaic output prediction

Abstract

The invention discloses a virtual power plant intelligent demand management system and method based on photovoltaic output prediction, and relates to the technical field of virtual power plants, operation demand data corresponding to each time period of a demand end is imported into an operation demand analysis strategy, and a load pre-demand response index corresponding to each time period of the demand end is calculated; according to the load pre-demand response index corresponding to each time period of the demand end, judging whether the load demand in each time period of the demand end is satisfied; and inputting the historical operation data of each interruptible load of the demand end into the demand end interruptible load influence model, outputting a stability influence index corresponding to each interruptible load of the demand end, adjusting each interruptible load in the demand end according to the stability influence index corresponding to each interruptible load of the demand end, and determining the interruptible load in the demand end without human intervention. The system achieves dynamic regulation and control, preferentially adjusts interruptible loads, completes intelligent demand management, avoids excessive concentration of power demands, reduces the power grid pressure, improves the utilization efficiency of energy, and enables power supply to be greener and more sustainable.

Description

Technical Field

[0001] This application belongs to the field of virtual power plants, specifically a virtual power plant intelligent demand management system and method based on photovoltaic output prediction. Background Art

[0002] In recent years, due to the pressure of energy structure transformation brought about by the large-scale access of new energy and the transformation of power load characteristics, the problem of the gap in flexible resources in the regulation demand of the power system has become prominent; in order to avoid the situation of insufficient power supply caused by over-concentration of power demand and improve the energy utilization efficiency, a virtual power plant demand management system has emerged; however, in the existing virtual power plant demand management system, it is still necessary to manually intervene to control interruptible loads, and it is impossible to perform intelligent demand management on the virtual power plant without manual intervention, which reduces the energy clearing ability of the substations in the virtual power plant and the energy utilization efficiency. To solve the problems raised in this background art, this application designs a virtual power plant intelligent demand management system and method based on photovoltaic output prediction. Summary of the Invention

[0003] In view of the above technical deficiencies, this application proposes a virtual power plant intelligent demand management system and method based on photovoltaic output prediction.

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

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

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

[0007] S3. Judge whether the load demand in each time period at the demand side is satisfied according to the load pre-demand response index corresponding to each time period at the demand side. If the load demand is satisfied, the process ends; if the load demand is not satisfied, go to step four;

[0008] S4. Establish an interruptible load impact model at the demand side, input the operation data of each interruptible load in the historical demand side into the interruptible load impact model at the demand side, output the stability impact index corresponding to each interruptible load at the demand side, and adjust each interruptible load at the demand side according to the stability impact index corresponding to each interruptible load at the demand side.

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

[0010] S11. Preset a data unit time interval, and collect data periodically according to the data unit time interval;

[0011] S12. Obtain the operation demand data corresponding to each time period at the demand side from the database, where the operation demand data corresponding to each time period at the demand side includes power demand prediction data and power supply side meteorological influence value data;

[0012] S13. Obtain the historical operation data of each interruptible load at the demand side corresponding to each time period at the demand side from the database, where the historical operation data of each interruptible load at the demand side corresponding to each time period at the demand side includes historical average operation power data, interruptible duration data, and production reduction rate data varying with the interruptible duration;

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

[0014] It should be noted that, as an optimal technical solution of the intelligent demand management system and method for a virtual power plant based on photovoltaic power output prediction, S2 includes the following specific steps: Import the power demand prediction data and power supply side meteorological influence value data corresponding to each time period at the demand side into the load pre-demand response index calculation formula to calculate the load pre-demand response index corresponding to each time period at the demand side. Among them, the calculation formula for the load pre-demand response index corresponding to the t-th time period at the demand side is: , where t is the number corresponding to each time period at the demand side, and t is any item from 1 to N, is the power demand prediction data corresponding to the t-th time period at the demand side, t is the number corresponding to each time period at 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 ratio, is the power supply side meteorological influence value data corresponding to the t-th time period at the demand side, is the reference value of the power supply side meteorological influence value. It should be noted that this formula comprehensively analyzes the power demand prediction data and power supply side meteorological influence value data corresponding to each time period at the demand side to calculate the load pre-demand response index, improving the accuracy of the load pre-demand response index.

[0015] It should be noted that, as an optimal technical solution of the intelligent demand management system and method for a virtual power plant based on photovoltaic output prediction, the step S3 includes the following steps: comparing the load demand response index corresponding to each time period at the demand side with the set load demand response index threshold. If the load demand response index corresponding to a certain time period at 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 step four is performed; if the load demand response index corresponding to a certain time period at 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 an optimal technical solution of the intelligent demand management system and method for a virtual power plant based on photovoltaic output prediction, the specific steps of the step S4 are as follows:

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

[0018] S42. Obtain the production impact index corresponding to each interruptible load at the demand side from the interruptible duration data corresponding to each interruptible load at the demand side and the production reduction rate data varying with the interruptible duration;

[0019] S43. Obtain the stability impact index corresponding to each interruptible load at the demand side from the number of inflection points and the production impact index corresponding to each interruptible load at the demand side;

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

[0021] It should be noted that, as an optimal technical solution of the intelligent demand management system and method for a virtual power plant based on photovoltaic output prediction, the specific steps of the step S41 are as follows: import the average historical operating power data of each time period corresponding to each interruptible load at the demand side into the inflection point number calculation formula to calculate the number of inflection points corresponding to each interruptible load at the demand side. Among them, the inflection point number calculation formula corresponding to the jth interruptible load at the demand side is: , where is the number of inflection points corresponding to the jth interruptible load at the demand side, is the average power value of the (t + 1)th time period corresponding to the jth interruptible load at the demand side, is the average power value of the tth time period corresponding to the jth interruptible load at the demand side. It should be noted that the meaning of this formula is: whenever , n = n + 1, that is, when the increase or decrease of the average power value of the (t + 1)th time period corresponding to the jth interruptible load at the demand side compared with the average power value of the tth time period is greater than or equal to 50%, the power value change point is marked as an inflection point.

[0022] It should be noted that, as an optimal technical solution of the intelligent demand management system and method for a virtual power plant based on photovoltaic output prediction, the specific steps of S42 are as follows: Import the interruptible duration data corresponding to each interruptible load at the demand side and the production reduction rate data that changes with the interruptible duration into the production impact index calculation formula to calculate the production impact index corresponding to each interruptible load at the demand side. Among them, the production impact index calculation formula corresponding to the j-th interruptible load at the demand side is: , where is the interruptible duration corresponding to the j-th interruptible load at the demand side, is the production reduction rate that changes with the interruptible duration corresponding to the j-th interruptible load at the demand side, is the set allowable value of the production reduction rate. It should be noted that in this formula, the interruptible duration data corresponding to each interruptible load at the demand side and the production reduction rate data that changes with the interruptible duration are comprehensively analyzed to analyze the impact of the interruptible duration and the production reduction rate on the production situation at the demand side, improving the accuracy of the production impact index.

[0023] It should be noted that, as an optimal technical solution of the intelligent demand management system and method for a virtual power plant based on photovoltaic output prediction, the specific steps of S43 are as follows: Import the inflection point quantity and the production impact index corresponding to each interruptible load at the demand side into the stability impact index calculation formula to calculate the stability impact index corresponding to each interruptible load at the demand side. Among them, the stability impact index calculation formula corresponding to the j-th interruptible load at the demand side is: , where is the set reference value of the inflection point quantity, and b is the weight of the inflection point quantity ratio.

[0024] The intelligent demand management system and method for a virtual power plant based on photovoltaic output prediction are implemented based on the above intelligent demand management method for a virtual power plant based on photovoltaic output prediction. It specifically includes an operation data acquisition module, a load pre-demand analysis module, a load pre-demand judgment module, and an interruptible load adjustment module. Among them, the operation data acquisition module is used to preset the data unit time interval and periodically acquire the operation demand data corresponding to each time period at the demand side and the operation data of each interruptible load at the historical 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 at the demand side into the operation demand analysis strategy to calculate and obtain the load pre-demand response index corresponding to each time period at the demand side;

[0026] The load pre-demand judgment module is used to judge whether the load demand within each time period at the demand side is satisfied according to the load pre-demand response index corresponding to each time period at the demand side. If the load demand is satisfied, it ends. If the load demand is not satisfied, it proceeds to step four;

[0027] The interruptible load regulation module is used to establish an impact model of the demand-side interruptible load. Input the operation data of each interruptible load at the demand side in history into the impact model of the demand-side interruptible load, output the stability impact index corresponding to each interruptible load at the demand side, and adjust each interruptible load at the demand side according to the stability impact index corresponding to each interruptible load at the demand side.

[0028] An electronic device includes a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory.

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

[0030] A computer-readable storage medium stores instructions. When the instructions run on a computer, the computer is made to execute the above-mentioned intelligent demand management method of the virtual power plant based on photovoltaic power 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 at the demand side and the operation data of each interruptible load at the demand side in history according to the data unit time interval; Import the operation demand data corresponding to each time period at the demand side into the operation demand analysis strategy, and calculate and obtain the load pre-demand response index corresponding to each time period at the demand side; Judge whether the load demand within each time period at the demand side is met according to the load pre-demand response index corresponding to each time period at the demand side. If the load demand is met, end. If the load demand is not met, proceed to step four; Establish an impact model of the demand-side interruptible load. Input the operation data of each interruptible load at the demand side in history into the impact model of the demand-side interruptible load, output the stability impact index corresponding to each interruptible load at the demand side, and adjust each interruptible load at the demand side according to the stability impact index corresponding to each interruptible load at the demand side. Without human intervention, the system realizes dynamic regulation, preferentially adjusts interruptible loads, completes intelligent demand management, avoids excessive concentration of power demand, reduces the pressure on the power grid, improves the utilization efficiency of energy, and makes the power supply more green and sustainable. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, purposes and advantages of the present application will become more obvious;

[0033] Figure 1 It is a schematic diagram of the overall process of the intelligent demand management method of the virtual power plant based on photovoltaic power output prediction of the present application.

[0034] Figure 2 It is a schematic diagram of the process of step S4 of the intelligent demand management method of the virtual power plant based on photovoltaic power output prediction of the present application.

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

[0036] Figure 4 This is the implementation scenario diagram of this application. Detailed implementation manners

[0037] To better understand this application, various aspects of this application will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of the exemplary embodiments of this application, and do not limit the scope of this 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 drawings, for ease of illustration, the size, dimensions, and shape of the elements have been slightly adjusted. The drawings are only examples and are not drawn to an exact scale. As used herein, terms such as "substantially", "about", and similar terms are used as terms indicating approximation, rather than terms indicating degree, and are intended to account for the inherent deviations in measured or calculated values that would be recognized by a person of ordinary skill in the art. Additionally, in this application, the order in which the steps are described does not necessarily represent the order in which these processes occur in actual operation, unless otherwise clearly specified or derivable from the context. It should also be understood that expressions such as "including", "comprising", "having", "containing", and / or "including" are open-ended rather than closed-ended expressions in this specification, which means that there are the stated features, elements, and / or components, but do not exclude the existence of one or more other features, elements, components, and / or their combinations. Furthermore, when an expression such as "at least one of..." appears after a list of listed features, it modifies the entire list of features, rather than just individual elements in the list. Additionally, when describing the embodiments of this application, the use of "may" means "one or more embodiments of this application". And the term "exemplary" is intended to refer to an example or illustration. Unless otherwise defined, all terms used herein (including engineering terms and technical terms) have the same meaning as commonly understood by a person of ordinary skill in the art to which this application belongs. It should also be understood that unless clearly stated in this application, words defined in a common dictionary should be interpreted as having a meaning consistent with their meaning in the context of the relevant art, and should not be interpreted in an idealized or overly formal sense.

[0039] To solve the technical problems raised in the background art, this application provides a preferred embodiment: Please refer to Figure 4As shown in the figure, the implementation scenario of this embodiment is demonstrated. The implementation scenario is as follows: Collect data from the data collection terminal, transmit the data to the data processing terminal, and the data processing terminal performs analysis and calculation to obtain the stability impact index corresponding to each interruptible load at the demand side. Then, adjust each interruptible load at the demand side according to the stability impact index corresponding to each interruptible load at the demand side.

[0040] The specific content of this embodiment is as follows:

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

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

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

[0044] S11. Preset the data unit time interval, and periodically collect data according to the data unit time interval;

[0045] S12. Obtain the operation demand data corresponding to each time period at the demand side from the database, where the operation demand data corresponding to each time period at the demand side includes power demand prediction data and power supply side meteorological impact value data;

[0046] S13. Obtain the historical operation data of each interruptible load at the demand side corresponding to each time period at the demand side from the database, where the historical operation data of each interruptible load at the demand side corresponding to each time period at the demand side includes historical average operation power data, interruptible duration data, and production reduction rate data varying with the interruptible duration;

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

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

[0049] In this embodiment, S2 includes the following specific steps: Import the power demand prediction data and power supply side meteorological impact value data corresponding to each time period at the demand side into the load pre-demand response index calculation formula to calculate the load pre-demand response index corresponding to each time period at the demand side. Among them, the load pre-demand response index calculation formula corresponding to the t-th time period at the demand side is: , where t is the number corresponding to each time period at the demand side, and t is any item from 1 to N, is the power demand prediction data corresponding to the t-th time period at the demand side, t is the number corresponding to each time period at the demand side, and t is any item from 1 to N, is the minimum electricity demand corresponding to the demand side, is the maximum electricity demand corresponding to the demand side, and a is the weight of the electricity demand data ratio, is the data of the meteorological influence value of the supply side corresponding to the t-th time period of the demand side, is the reference value of the meteorological influence value of the supply side. It should be noted that this formula comprehensively analyzes the electricity demand prediction data corresponding to each time period of the demand side and the meteorological influence value data of the supply side to analyze the load pre-demand response index, improving the accuracy of the load pre-demand response index.

[0050] S3. Determine whether the load demand within 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, end; if the load demand is not met, proceed to step four;

[0051] In this embodiment, the specific steps of S3 are as follows: Compare the load demand response index corresponding to each time period of the demand side with the set load demand response index threshold. If the load demand response index corresponding to a certain time period of 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 proceed to step four; if the load demand response index corresponding to a certain time period of 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 model for the impact of interruptible loads on the demand side. Input the historical operating data of each interruptible load on the demand side into the model for the impact of interruptible loads on the demand side, and output the stability impact index corresponding to each interruptible load on the demand side. Adjust each interruptible load on the demand side according to the stability impact index corresponding to each interruptible load on the demand side.

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

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

[0055] S42. Obtain the 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 interruptible duration;

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

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

[0058] In this embodiment, the specific steps of S41 are as follows: Import the average historical operating power data of each time period corresponding to each interruptible load at the demand side into the inflection point quantity calculation formula to calculate the inflection point quantity corresponding to each interruptible load at the demand side. Among them, the inflection point quantity calculation formula corresponding to the j-th interruptible load at the demand side is: , where is the inflection point quantity corresponding to the j-th interruptible load at the demand side, is the average power of the (t + 1)-th time period corresponding to the j-th interruptible load at the demand side, is the average power of the t-th time period corresponding to the j-th interruptible load at the demand side. It should be noted that the meaning of this formula is: Whenever , n = n + 1, that is, when the increase or decrease of the average power of the (t + 1)-th time period corresponding to the j-th interruptible load at the demand side compared to the average power of the t-th time period is greater than or equal to 50%, mark this power average change point as an inflection point; Exemplarily, illustrate the calculation process of the inflection point quantity. If the set value of t is 15 minutes when calculating the inflection point quantity corresponding to a certain interruptible load, the average power of the (t + 1)-th time period corresponding to a certain interruptible load at the demand side is 1 kW, and the average power of the t-th time period is 2 kW, then mark the transition time point between the t-th time period and the (t + 1)-th time period corresponding to this interruptible load at the demand side as an inflection point, and at this time, the inflection point quantity is incremented by 1 on the original basis.

[0059] In this embodiment, the specific steps of S42 are as follows: Import the interruptible duration data and the production reduction rate data varying with the interruptible duration corresponding to each interruptible load at the demand side into the production impact index calculation formula to calculate the production impact index corresponding to each interruptible load at the demand side. Among them, the production impact index calculation formula corresponding to the j-th interruptible load at the demand side is: , where is the interruptible duration corresponding to the j-th interruptible load at the demand side, is the production reduction rate varying with the interruptible duration corresponding to the j-th interruptible load at the demand side, is the set allowable value of the production reduction rate. It should be noted that in this formula, the interruptible duration data and the production reduction rate data varying with the interruptible duration corresponding to each interruptible load at the demand side are comprehensively analyzed to analyze the influence of the interruptible duration and the production reduction rate on the production situation at the demand side, improving the accuracy of the production impact index. Exemplarily, illustrate the influence of the interruptible load interruption on the production situation at the demand side. In a certain painting workshop, if the stagnation time of the phosphating solution exceeds 30 minutes, it will cause 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, paint sedimentation and stratification will occur.

[0060] In this embodiment, the specific steps of S43 are as follows: Import the number of inflection points and production impact index corresponding to each interruptible load at the demand side into the stability impact index calculation formula to calculate the stability impact index corresponding to each interruptible load at the demand side. Among them, the calculation formula for the stability impact index corresponding to the j-th interruptible load at the demand side is: , where is the reference value of the number of inflection points set, and 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 by considering the number of inflection points and production impact index corresponding to each interruptible load at the demand side. To improve the accuracy of the stability impact index, both the reference value of the number of inflection points and the weight of the proportion of the number of inflection points are set to jointly analyze the impact of the number of inflection points on the stability index.

[0061] Here, it should be noted that the set parameters (such as weights and thresholds, etc.) in this embodiment need to be set by those skilled in the art according to relevant experiments. The specific experimental method is as follows: Obtain the operation demand data corresponding to each time period at the demand side and the operation data of each historical interruptible load at the demand side, substitute them into each step in this embodiment to calculate the load pre-demand response index and stability impact index, and import the calculation results of the load pre-demand response index and stability impact index into the fitting software for continuous fitting, and output the values of the set parameters (such as weights and thresholds, etc.) with the highest degree of conformity of the load pre-demand response index and stability impact index.

[0062] According to the above implementation content, this embodiment has the following advantages compared with the prior art: This embodiment presets the data unit time interval, and periodically obtains the operation demand data corresponding to each time period at the demand side and the operation data of each historical interruptible load at the demand side according to the data unit time interval; Import the operation demand data corresponding to each time period at the demand side into the operation demand analysis strategy to calculate and obtain the load pre-demand response index corresponding to each time period at the demand side; Judge whether the load demand in each time period at the demand side is met according to the load pre-demand response index corresponding to each time period at the demand side. If the load demand is met, the process ends. If the load demand is not met, then proceed to step four; Establish an impact model of interruptible loads at the demand side, input the operation data of each historical interruptible load at the demand side into the impact model of interruptible loads at the demand side, output the stability impact index corresponding to each interruptible load at the demand side, and adjust each interruptible load at the demand side according to the stability impact index corresponding to each interruptible load at the demand side. Without human intervention, the system realizes dynamic regulation, preferentially adjusts interruptible loads, completes intelligent demand management, avoids excessive concentration of power demand, reduces the pressure on the power grid, improves the utilization efficiency of energy, and makes the power supply more green and sustainable.

[0063] Such as Figure 3As shown, this embodiment also provides an intelligent demand management system and method for a virtual power plant based on photovoltaic power output prediction, which is implemented based on the above-mentioned intelligent demand management method for a virtual power plant based on photovoltaic power output prediction. Specifically, it includes an operation data acquisition module, a load pre-demand analysis module, a load pre-demand judgment module, and an interrupted load adjustment module. The operation data acquisition module is used to preset the data unit time interval and periodically acquire the operation demand data corresponding to each time period at the demand side and the operation data of each interruptible load at the historical 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 at the demand side into the operation demand analysis strategy and calculate and obtain the load pre-demand response index corresponding to each time period at the demand side. The load pre-demand judgment module is used to judge whether the load demand within each time period at the demand side is satisfied according to the load pre-demand response index corresponding to each time period at the demand side. If the load demand is satisfied, the process ends; if the load demand is not satisfied, step four is performed. The interrupted load adjustment module is used to establish an impact model of interruptible loads at the demand side, input the operation data of each interruptible load at the historical demand side into the impact model of interruptible loads at the demand side, output the stability impact index corresponding to each interruptible load at the demand side, and adjust each interruptible load at the demand side according to the stability impact index corresponding to each interruptible load at the demand side.

[0064] For the specific steps of each unit module in the above-mentioned intelligent demand management system for a virtual power plant based on photovoltaic power output prediction of this application to implement the corresponding functions, reference can be made to the steps in the embodiments of the intelligent demand management method for a virtual power plant based on photovoltaic power output prediction in the foregoing text, which will not be elaborated here.

[0065] This embodiment also provides an electronic device, including: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory;

[0066] The processor executes the above-mentioned intelligent demand management method for a virtual power plant based on photovoltaic power 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 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function, and instructions for implementing the intelligent demand management method for a virtual power plant based on photovoltaic power output prediction provided in the above embodiment, etc.; the data storage area may store the data involved in the intelligent demand management method for a virtual power plant based on photovoltaic power output prediction provided in the above embodiment, etc.

[0068] The processor may include one or more processing cores. By running or executing instructions, programs, code sets, or instruction sets stored in the memory, the processor invokes the data stored in the memory to perform various functions of this application and process the data. 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 can be understood that for different devices, the electronic devices for implementing the above processor functions may also be others, and the embodiments of this application do not make specific limitations.

[0069] It may further include a communication bus, and the communication bus may include a path for transmitting information between the above components. The communication bus may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus may be divided into an address bus, a data bus, a control bus, etc.

[0070] This embodiment also proposes a computer-readable storage medium storing instructions, which when run on a computer, cause the computer to execute 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 optical disc, magnetic tape, a floppy disk, and an optical data storage device, etc.

[0072] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted 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. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0073] The term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0074] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solution formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing application concept. For example, a technical solution formed by mutually replacing the above features with technical features having similar functions (but not limited to) applied in the present application.

Claims

1. A virtual power plant intelligent demand management method based on photovoltaic power output prediction, characterized in that, Including: S1. A preset data unit time interval, and periodically obtain the operation demand data corresponding to each time period at the demand side and the operation data of each interruptible load at the historical demand side according to the data unit time interval; S2. Import the operation demand data corresponding to each time period at the demand side into the operation demand analysis strategy, and calculate and obtain the load pre-demand response index corresponding to each time period at the demand side; S3. Judge whether the load demand in each time period at the demand side is satisfied according to the load pre-demand response index corresponding to each time period at the demand side. If the load demand is satisfied, end. If the load demand is not satisfied, go to step four; S4. Establish an interruptible load impact model at the demand side, input the operation data of each interruptible load at the historical demand side into the interruptible load impact model at the demand side, output the stability impact index corresponding to each interruptible load at the demand side, and adjust each interruptible load at the demand side according to the stability impact index corresponding to each interruptible load at the demand side.

2. The intelligent demand management method for a virtual power plant based on photovoltaic output prediction according to claim 1, characterized in that, The S2 includes the following specific steps: Import the power demand prediction data and the power supply end meteorological influence value data corresponding to each time period into the load pre-demand response index calculation formula to calculate the load pre-demand response index corresponding to each time period at the power demand end. Among them, the calculation formula for the load pre-demand response index corresponding to the t-th time period at the power demand end is: , where t is the number corresponding to each time period at the power demand end, and t is any one of 1 to N. is the power demand prediction data corresponding to the t-th time period at the power demand end, t is the number corresponding to each time period at the power demand end, and t is any one of 1 to N. is the minimum power demand corresponding to the power demand end. is the maximum power demand corresponding to the power demand end, and a is the weight of the power demand data ratio. is the power supply end meteorological influence value data corresponding to the t-th time period at the power demand end. is the reference value of the power supply end meteorological influence value.

3. The intelligent demand management method for a virtual power plant based on photovoltaic output prediction according to claim 2, characterized in that, The S3 includes the following steps: Compare the load demand response index corresponding to each time period at the demand side with the set load demand response index threshold. If the load demand response index corresponding to a certain time period at 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 satisfied and go to step four; if the load demand response index corresponding to a certain time period at the demand side is less than the set load demand response index threshold, it is determined that the load demand is satisfied.

4. The intelligent demand management method for a virtual power plant based on photovoltaic output prediction according to claim 3, wherein, The specific steps of the S4 are as follows: S41. Obtain the number of inflection points corresponding to each interruptible load at the demand side from the average value data of the historical operation power of each time period corresponding to each interruptible load at the demand side; S42. Obtain the production impact index corresponding to each interruptible load at the demand side from the interruptible duration data corresponding to each interruptible load at the demand side and the production reduction rate data varying with the interruptible duration; S43. Obtain the stability impact index corresponding to each interruptible load at the demand side from the number of inflection points and the production impact index corresponding to each interruptible load at the demand side; S44. Arrange the stability impact indexes corresponding to each interruptible load at the demand side in ascending order, and adjust each interruptible load at the demand side according to the arrangement result of the stability impact indexes corresponding to each interruptible load at the demand side.

5. The intelligent demand management method for a virtual power plant based on photovoltaic power output prediction according to claim 4, characterized in that, The specific steps of S41 are as follows: Import the average historical operating power data of each time period corresponding to each interruptible load at the demand side into the inflection point number calculation formula to calculate the number of inflection points corresponding to each interruptible load at the demand side. Among them, the calculation formula for the number of inflection points corresponding to the j-th interruptible load at the demand side is: , where is the number of inflection points corresponding to the j-th interruptible load at the demand side, is the average power value of the (t + 1)-th time period corresponding to the j-th interruptible load at the demand side, is the average power value of the t-th time period corresponding to the j-th interruptible load at the demand side.

6. The intelligent demand management method for a virtual power plant based on photovoltaic power output prediction according to claim 5, wherein The specific steps of S42 are as follows: Import the interruptible duration data corresponding to each interruptible load at the demand side and the production reduction rate data varying with the interruptible duration into the production impact index calculation formula to calculate the production impact index corresponding to each interruptible load at the demand side. Among them, the production impact index calculation formula corresponding to the j-th interruptible load at the demand side is: , where is the interruptible duration corresponding to the j-th interruptible load at the demand side, is the production reduction rate varying with the interruptible duration corresponding to the j-th interruptible load at the demand side, is the set allowable value of the production reduction rate.

7. The intelligent demand management method for a virtual power plant based on photovoltaic power output prediction according to claim 6, wherein The specific steps of S43 are as follows: Import the number of inflection points and production impact index corresponding to each interruptible load at the demand side into the stability impact index calculation formula to calculate the stability impact index corresponding to each interruptible load at the demand side. Among them, the calculation formula for the stability impact index corresponding to the j-th interruptible load at the demand side is: , where is the set reference value of the number of inflection points, and 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 by considering the number of inflection points and production impact index corresponding to each interruptible load at the demand side. To improve the accuracy of the stability impact index, both the reference value of the number of inflection points and the weight of the proportion of the number of inflection points are set to jointly analyze the impact of the number of inflection points on the stability index.

8. The intelligent demand management system of the virtual power plant based on photovoltaic output prediction is implemented based on the intelligent demand management method of the virtual power plant based on photovoltaic output prediction according to any one of claims 1-7, and is characterized in that Specifically, it includes an operation data acquisition module, a load pre-demand analysis module, a load pre-demand judgment module, and an interruptible load adjustment module. The operation data acquisition module is used to acquire the operation demand data corresponding to each time period at the demand side and the operation data of each interruptible load at the historical demand side; The load pre-demand analysis module is used to import the operation demand data corresponding to each time period at the demand side into the operation demand analysis strategy, and calculate and obtain the load pre-demand response index corresponding to each time period at the demand side; The load pre-demand judgment module is used to judge whether the load demand in each time period at the demand side is satisfied according to the load pre-demand response index corresponding to each time period at the demand side. If the load demand is satisfied, end. If the load demand is not satisfied, go to step four; The interruptible load regulation module is used to establish an impact model of the demand-side interruptible load. The historical operation data of each interruptible load on the demand side is input into the impact model of the demand-side interruptible load, and the stability impact 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 impact index corresponding to each interruptible load on the demand side.

9. 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 the virtual power plant based on photovoltaic output prediction according to any one of claims 1-7 by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that, Stores instructions that, when run on a computer, cause the computer to execute the intelligent demand management method of the virtual power plant based on photovoltaic output prediction according to any one of claims 1-7.

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