Simulation method and system for fine-grained photovoltaic output under influence of multiple factors

Abstract

The application provides a fine-grained photovoltaic output simulation method and system considering the influence of multiple factors, considers the influence of geographical position, weather and time sequence on photovoltaic output, and can simulate a minute-level photovoltaic output curve, the application scheme provides a variety of granularity photovoltaic output curve simulation methods, has high accuracy, can freely configure photovoltaic power generation capacity in the subsequent, thereby providing a flexible and changeable experimental environment basis for virtual power plants, new power systems, micro-grid new energy consumption and the like; meanwhile, the subsequent can also be used to improve photovoltaic output prediction of a park and the like which needs to establish a photovoltaic station, so as to assist in decision-making of the establishment position, the expected power generation condition and benefit calculation.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of photovoltaic output simulation, and particularly relates to a fine-grained photovoltaic output simulation method and system considering the influence of multiple factors. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] With a large number of new energy connected to the power grid, the consumption of new energy has become a big challenge for smart grid, and a large number of scholars have developed corresponding dispatching strategies to consume photovoltaic, and new type of aggregation means such as virtual power plant are also taken into account and practice more and more. However, it is sometimes difficult to carry out flexible verification of whether the algorithm is effective due to limited actual resources, at this time, the simulation system is particularly important. In addition, for the park that has not yet established a photovoltaic station but plans to establish a photovoltaic system, according to its weather conditions, the output of the photovoltaic station after establishment is predicted and the installed capacity is planned, a reliable photovoltaic output simulation system that can adjust according to the actual geographical location and weather conditions is also needed. However, the current photovoltaic output simulation is either a random curve generation based on Monte Carlo algorithm simulation and using clustering algorithm to reduce, which cannot meet the real simulation scene of adapting to geographical location, weather and date, or an actual physical model is established, but the system is too complex and has poor flexibility, and for the prediction and estimation of the station that has not yet been established, many technical details are difficult to adapt to, thereby limiting the use.

[0004] At the same time, the simulation granularity of the photovoltaic output curve is also particularly important. For DER, the simulation granularity needs to reach 1 minute level, but weather information only exists at hour level many times, so how to simulate fine-grained output fluctuation through coarse-grained weather information is also worth thinking about. If it is simply interpolated or simply added with random disturbance, it is easy to lose fluctuation or fluctuation does not conform to the real physical law.

[0005] Therefore, how to quickly and flexibly simulate fine-grained photovoltaic output curve under the influence of geographical location, date, weather condition and other factors is a problem to be solved at present. SUMMARY

[0006] In order to overcome the shortcomings of the prior art, the present application provides a fine-grained photovoltaic output simulation method and system considering the influence of multiple factors, which considers the influence of geographical location, weather and time sequence on photovoltaic output, and gives a plurality of granularity photovoltaic output curve simulation methods, which has high accuracy and can freely configure photovoltaic power generation capacity subsequently.

[0007] In order to achieve the above object, the present application adopts the following technical solutions:

[0008] In a first aspect, the present application provides a fine-grained photovoltaic output simulation method considering the influence of multiple factors, comprising:

[0009] Based on the geographical location of the target area and the corresponding weather, a photovoltaic output curve of each time interval is simulated, and the curve fluctuation is determined according to the photovoltaic output curve of each time interval.

[0010] Based on the corresponding relationship between the photovoltaic output curve of the minute interval to be simulated and the photovoltaic output curve of each time interval, the sunrise time and sunset time of the target area are combined to determine the fluctuation of the photovoltaic output curve of the continuous minute interval and the corresponding maximum and minimum values of the photovoltaic output, and the time sequence change and the curve fluctuation of the corresponding photovoltaic output curve of each time interval are combined to simulate the photovoltaic output curve of the minute interval.

[0011] In a second aspect, the present application provides a fine-grained photovoltaic output simulation system considering the influence of multiple factors, comprising:

[0012] The first module is configured to simulate a photovoltaic output curve of each time interval based on the geographical location of the target area and the corresponding weather, and determine the curve fluctuation according to the photovoltaic output curve of each time interval.

[0013] The second module is configured to determine the fluctuation of the photovoltaic output curve of the continuous minute interval and the corresponding maximum and minimum values of the photovoltaic output based on the corresponding relationship between the photovoltaic output curve of the minute interval to be simulated and the photovoltaic output curve of each time interval, combine the sunrise time and sunset time of the target area, and combine the time sequence change and the curve fluctuation of the corresponding photovoltaic output curve of each time interval to simulate the photovoltaic output curve of the minute interval.

[0014] In a third aspect, the present application provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein when the computer instructions are run by the processor, the method of the first aspect is completed.

[0015] In a fourth aspect, the present application provides a computer readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method of the first aspect is completed.

[0016] In a fifth aspect, the present application provides a computer program product comprising a computer program, wherein when the computer program is executed by a processor, the method of the first aspect is completed.

[0017] The one or more technical solutions have the following beneficial effects:

[0018] In the present application, the influence of geographical position, weather and time sequence on photovoltaic output is considered, the minute-level photovoltaic output curve can be simulated, the present application scheme gives a plurality of granularity photovoltaic output curve simulation methods, the accuracy is high, the subsequent photovoltaic power generation capacity can be freely configured, thereby providing flexible and changeable experimental environment basis for virtual power plant, new type power system, microgrid new energy consumption and the like scene;Meanwhile, the subsequent can also be indirectly for the park and the like needing to establish photovoltaic station to improve certain photovoltaic output prediction, to assist decision-making to establish position and expected power generation condition and benefit calculation.

[0019] Advantages of the additional aspects of the present application will be partially given in the following description, partially will become obvious from the following description, or be known by the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0020] The drawings accompanying the specification of this application form a part thereof, serve to provide further understanding of the application, and together with the description of the exemplary embodiments of the application and explanations thereof serve to explain the application, and do not constitute improper limitations on the application.

[0021] Figure 1 It is a schematic diagram for classification of the topological shape of the inflection point in the embodiment one of the present application;

[0022] Figure 2 It is a schematic diagram for 96-point photovoltaic output curve in the embodiment one of the present application Figure One ;

[0023] Figure 3 It is a schematic diagram for 96-point photovoltaic output curve in the embodiment one of the present application Figure Two ;

[0024] Figure 4 It is a schematic diagram for 96-point photovoltaic output curve in the embodiment one of the present application Figure Three ;

[0025] Figure 5 It is a schematic diagram for 96-point photovoltaic output curve in the embodiment one of the present application Figure Four ;

[0026] Figure 6 It is a schematic diagram for 288-point photovoltaic output curve in the embodiment one of the present application Figure One ;

[0027] Figure 7 It is a schematic diagram for 288-point photovoltaic output curve in the embodiment one of the present application Figure Two ;

[0028] Figure 8 It is a schematic diagram for 1440-point photovoltaic output curve in the embodiment one of the present application Figure One ;

[0029] Figure 9 Fig. 1 is a schematic diagram of a 1440-point photovoltaic output curve in an embodiment of the present application Figure Two . DETAILED DESCRIPTION

[0030] It should be noted that the following detailed description is merely exemplary in nature and is intended to provide further description of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0031] It is to be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application.

[0032] In the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0033] Embodiment One

[0034] The main idea of the present embodiment is to provide a fine-grained photovoltaic output simulation method considering the influence of multiple factors, so as to simulate various fine-grained photovoltaic output curves considering weather, geographical location, date and time sequence, and to provide a flexible experimental environment for virtual power plants, new power systems, micro-grid new energy consumption, etc., and to help verify the reliability of various prediction and control algorithms. At the same time, it can also be used to improve the photovoltaic output prediction of the park and other places that need to establish photovoltaic stations, to assist decision-making in establishing location and expected power generation and benefit calculation.

[0035] The present embodiment discloses a fine-grained photovoltaic output simulation method considering the influence of multiple factors, comprising:

[0036] Based on the geographical location of the target area and the corresponding weather, the photovoltaic output curve of each time interval is simulated, and the curve fluctuation is determined according to the photovoltaic output curve of each time interval.

[0037] Based on the corresponding relationship between the photovoltaic output curve of the minute interval to be simulated and the photovoltaic output of the photovoltaic output curve of each time interval, the sunrise time and the sunset time of the target area are determined, the fluctuation of the continuous minute interval photovoltaic output curve and the corresponding maximum and minimum values of the photovoltaic output are determined, and the minute interval photovoltaic output curve is simulated in combination with the time sequence change and the curve fluctuation of the corresponding photovoltaic output curve of each time interval.

[0038] The fine-grained photovoltaic output simulation method considering the influence of multiple factors proposed in the present embodiment will be described in detail as follows:

[0039] The 24-point reference output curve and the 96-point and 288-point maximum possible output curves are simulated based on the geophysical model and 24-point weather information by using PVLIB.

[0040] According to the PVLIB library usage rules, the corresponding model is established, and the 24-point weather data obtained from the weather source is input into PVLIB for simulation. The information input includes longitude and latitude, irradiance, humidity, temperature, etc.

[0041] The 24-point photovoltaic output curve is divided into volatility probability, specifically: in order to more reasonably simulate the photovoltaic output, the photovoltaic output curve of one day is divided into three possibilities: high volatility, low volatility, and no volatility. The three possible determination divisions are as follows:

[0042] After obtaining the 24-point photovoltaic output curve of the reference, the kurtosis, skewness and peak factor of the photovoltaic output curve are calculated. If two or more of the following conditions are met: kurtosis > kurtosis setting value (such as 0.65), skewness > skewness setting value (such as 1.2), and peak factor > peak factor setting value (such as 2.85), it is determined that the day is high volatility output; if only one condition is met, it is determined that the day is low volatility output; if none of the conditions are met, it is determined that the day is no volatility output.

[0043] The minute interval photovoltaic output curve obtained based on the 24-point photovoltaic output curve and the multi-split inflection point and amplitude combination under the consideration of multi-layer random probability, the minute interval photovoltaic output curve of the embodiment includes 96-point photovoltaic output curve, 288-point photovoltaic output curve and 1440-point photovoltaic output curve, according to the need, more granular minute interval photovoltaic output curve can be obtained according to the scheme of the embodiment, the embodiment takes the simulated 96-point photovoltaic output curve, 288-point photovoltaic output curve and 1440-point photovoltaic output curve as an example for detailed description.

[0044] The 96-point photovoltaic output curve is specifically: after the 24-point photovoltaic output curve is determined to be high volatility, medium volatility and no volatility, in order to generate a more realistic 96-point photovoltaic output curve, a point generation technology based on mean value is used in the embodiment.

[0045] Specifically, for a specific point on the 24-point photovoltaic output curve, it is considered that the point actually reflects the average power of the photovoltaic output in the hour, and in the 96-point photovoltaic output curve, each hour actually corresponds to 4 points. Therefore, when simulating the 96-point photovoltaic output curve, the point located at the mean is actually converted into a mean index by splitting around the point, and provided to the amplitude of the four points. The average of the amplitudes of the four points is the amplitude of the original point. By this method, the total power consumption of the generated 96-point photovoltaic output curve can be ensured to remain consistent with the 24-point photovoltaic output curve, thereby conforming to the actual physical world situation.

[0046] Since each point of the original 24-point photovoltaic output curve will now be split into 4 corresponding points, the original 24-point photovoltaic output curve is expanded to a 96-point photovoltaic output curve. The fluctuation characteristics of the four points in each period are particularly noteworthy. From the topological structure analysis, the mutual relationship of the fluctuation of the four points can be summarized as the existence of inflection points, which actually has only three possibilities: two inflection points, one inflection point, and zero inflection points. This is actually a certain description of high, medium, and low fluctuations. Generally, it is believed that the number of inflection points in a single period of high fluctuation weather is greater, while the number of inflection points in a single period of low fluctuation weather should be less. The specific determination of the number of inflection points is closely related to time in addition to probability.

[0047] Through analysis of a large amount of existing real photovoltaic station output data, it can be found that when the time is on the rising edge of sunrise, the number of inflection points in the photovoltaic output of the previous period is small, and often in an upward trend, while at the sunset stage, the number of inflection points is also not much, and often in a downward trend. The fluctuation change in the noon and afternoon period is more diverse, and the appearance of the number of inflection points is also more random. Therefore, under the premise of considering the time sequence logic and the probability of natural change, the distribution probability of the appearance of the inflection points in each period is designed as shown in Table 1, while several basic variables are set: sunrise time t r ; sunset time t d .

[0048] Table 1:

[0049]

[0050]

[0051] Table 1 is the average probability summarized according to the characteristics analysis of a large amount of real photovoltaic data corresponding to the period. According to the above probability and the corresponding number of inflection points, a corresponding length probability distribution sequence of time sequence length is established, and then a random number is used to actually sample and determine the number of inflection points of the corresponding period.

[0052] After the probability system is determined, the appearance form classification and its probability of the inflection point are also the focus. The topology shape classification of the inflection point is shown in Table 2, wherein 2-1 describes a trend of first rising, then falling and then rising, 2-2 describes a trend of first falling, then rising and then falling; 1-1 describes a trend of first rising and then falling, and the second point is an inflection point, 1-2 describes a trend of first rising and then falling, and the third point is an inflection point, 1-3 describes a trend of first falling and then rising, and the second point is an inflection point, 1-4 describes a trend of first falling and then rising, and the third point is an inflection point; 0-1 describes a rising trend, and 0-2 describes a falling trend. Figure 1

[0053] Table 2:

[0054]

[0055]

[0056] The probability in Table 2 is also an average possible probability summarized according to the analysis of the characteristics of a large number of real photovoltaic data in the corresponding period. By using a random number sampler to sample and determine, the inflection point type corresponding to the inflection point is determined.

[0057] After the appearance of the inflection point is determined, the problem of the volatility of the period is solved, and then the amplitude of each point is determined. In fact, the embodiment of high volatility, medium volatility and low volatility is also inseparable from the amplitude variation. Similarly, for the 2-inflection-point volatility, the amplitude fluctuation is greater, and the volatility is naturally stronger. On the basis of considering randomness, the actual physical world output upper limit and the unity of the single period average and the 24-point photovoltaic output curve also need to be considered. First, it is necessary to determine that the average value of the 4-point output of each period must still be the output value corresponding to the 24-point photovoltaic output curve in the corresponding period, so as to ensure that the total power of a day does not change, and the power does not appear misaligned in the same day due to increased volatility.

[0058] After considering the volatility of each inflection point, the design system of the amplitude is as follows. First, the upper and lower limits of the output of each point are determined: P max and P min . Assuming that the amplitude of the 24-point photovoltaic output is P0, the maximum 96-point output amplitude obtained by the PVLIB simulation is P Lmax , P min , and the embodiment P min is uniformly taken as 0.6*P0, P max is as follows:

[0059] Assuming that the sunrise time is t sunrise , and the sunset time is t sunset , there is Table 3: ​

[0060] Table 3:

[0061]

[0062] The value range of the four points is shown in Table 4:

[0063] Table 4:

[0064] Inflection point type [P2] [P2] [P3] [P4] 2-1 (P min , P max )]]> (P1, P max )]]> (P min , P2)]]> (P3, P max )]]> 2-2 (P min , P max )]]> (P min , P1) (P2, P max )]]> (P min , P3)]]> 1-1 (P min , P max )]]> (P1, P max )]]> (P min , P2)]]> (P min , P3)]]> 1-2 (P min , P max )]]> (P1, P max )]]> (P2, P max )]]> (P min , P3)]]> 1-3 (P min , P max )]]> (P min , P1)]]> (P2, P max )]]> (P3, P max )]]> 1-4 (P min , P max )]]> (P min , P1)]]> (P min , P2)]]> (P3, P max )]]> 0-1 (P min , P max )]]> (P1, P max )]]> (P2, P max )]]> (P2, P max )]]> 0-2 (P min , P max )]]> (P min , P1)]]> (P min , P2)]]> (P min , P3)]]>

[0065] And P1+P2+P3+P4=4*P0

[0066] After determining the upper and lower boundaries of each point, in order to ensure that the constraint conditions are met and the maximum fluctuation is possible, linear programming technology is used, and the 96 points of a day are regarded as decision variables, and the value range of each point is regarded as the size constraint of the decision variable. At the same time, the average value of the four points corresponding to each hour is equal to the value of the corresponding point in the 24-point curve. The objective function of linear programming is selected as the sum of the absolute values of the differences between the 96-point curve and the most possible smooth curve. After establishing the linear programming mathematical model in the above manner, unified solution is carried out, and the curve solved is the maximum fluctuation curve possible under the current inflection point configuration.

[0067] The generation method of the most smooth curve is as follows: the original 24-point curve is interpolated to obtain a 96-point curve, and then the power generation of each period is obtained by area integration, and then it is inversely transformed into an average value curve, thereby obtaining a most smooth 96-point average value power curve.

[0068] At this point, the maximum fluctuation curve and the most smooth curve have been obtained, and next, according to the high fluctuation, low fluctuation, and no fluctuation divided before, the two curves are time-periodically multi-probability weighted. For the no fluctuation curve, the most smooth curve is the default power curve of the day. For the low fluctuation curve and the high fluctuation curve, the proportion of the weighting of the two curves changes with time sequence, and the specific design is shown in Table 5.

[0069] Table 5:

[0070]

[0071]

[0072] In Table 5, a refers to the weighting proportion of the most fluctuation curve. The range in Table 5 refers to a number randomly taken as the weighting proportion in the range.

[0073] At this point, the 24 points are expanded to 96 points.

[0074] The 288-point photovoltaic output curve is obtained based on the multi-split inflection point and amplitude combination under the multi-layer random probability consideration of the 96-point photovoltaic output curve: after obtaining the 96-point photovoltaic output curve through the above-mentioned technology, in order to generate a more realistic 288-point photovoltaic output curve, the embodiment again uses the point generation technology based on mean value multi-splitting described above. The difference is that in the 288-point photovoltaic output curve, every 15 minutes actually corresponds to 3 points. Therefore, when simulating the 288-point photovoltaic output curve, the point located at the mean value is actually converted into a mean value index by splitting around a point, which is provided for the amplitude of the three points. The average of the amplitudes of the three points is the amplitude of the original point. Through this method, the total power consumption of the generated 288-point photovoltaic output curve can be ensured to remain consistent with the 96-point photovoltaic output curve, thereby meeting the realistic physical world conditions.

[0075] Similar to the above but different, here the number and type of inflection points are no longer artificially randomly given, but are directly calculated according to mathematical programming to obtain the maximum possible fluctuation curve under the condition of 288 points. Let the upper limit of the amplitude corresponding to each time point be the corresponding highest possible output P max1 again obtained by the above method, and let the output code of the 3 points in each period be P 11 , P 22 , P 33 , let the photovoltaic output amplitude of the simulated 96-point photovoltaic output curve be P 00 :

[0076] P 11 +P 22 +P 33 = 3*P 00

[0077] The decision variable is set to 288 points to be simulated, and the constraints of each point are as above. The objective function is the sum of the absolute values of the differences between the 288 points to be simulated and the smoothest curve. The smoothest curve is obtained in the same way as in the above scheme. Solving again can obtain the maximum fluctuation possible curve.

[0078] Next, the two curves are also divided into high fluctuation, low fluctuation, and no fluctuation days according to the previous division, and are subjected to multi-probability weighting by period. For the no fluctuation curve, the smoothest curve is the output curve of the day by default. For the low fluctuation curve and the high fluctuation curve, the proportion of the weighting of the two changes with time sequence, and the specific design is shown in the following Table 6.

[0079] Table 6:

[0080]

[0081] The 1440-point photovoltaic output curve simulation based on the smooth splitting of the 288-point photovoltaic output curve: Since the interval is very short from 5 minutes to 1 minute, the weather generally does not have a large fluctuation, but changes along the trend represented by the 5-minute interval. Therefore, when expanding from 288 points to 1440 points, although the 5-point average is still equal to the value of the corresponding point of the original 288 points, no artificial fluctuation is introduced, but the 288 points are directly linearly divided to obtain the 1440-point curve in an interpolation-like manner.

[0082] The simulation effect of the 96-point photovoltaic output curve under different weather conditions is shown in Figures 2-5 The simulation effect of the 288-point photovoltaic output curve is shown in Figures 6-7 The simulation effect of the 1440-point photovoltaic output curve is shown in Figures 8-9 The simulation effect of the 1440-point photovoltaic output curve is shown in

[0083] The algorithm of the present application exists in the form of a python library service and is integrated into a smart computing all-in-one machine. When simulation is needed, the relevant weather data, date, and geographic location are input, and the smart computing all-in-one machine will automatically start the simulation service and output the required multiple-granularity photovoltaic output curve, providing 1-minute-level simulation service, and also providing a test environment basis for multiple control and regulation services.

[0084] Embodiment Two

[0085] The purpose of this embodiment is to provide a fine-granularity photovoltaic output simulation system considering multiple factors, which comprises:

[0086] The first module is configured to simulate the photovoltaic output curve of each time interval based on the geographic location of the target area and its corresponding weather, and determine the curve fluctuation according to the photovoltaic output curve of each time interval.

[0087] The second module is configured to determine the fluctuation of the photovoltaic output curve of the continuous minute interval and its corresponding maximum and minimum photovoltaic output based on the corresponding relationship between the minute interval photovoltaic output curve and the photovoltaic output of each time interval, in combination with the sunrise time and sunset time of the target area, and simulate the minute interval photovoltaic output curve in combination with the time sequence change and the curve fluctuation of the corresponding each time interval photovoltaic output curve.

[0088] In more embodiments, there are also provided:

[0089] An electronic device comprising a memory and a processor, and computer instructions stored on the memory and running on the processor, when the computer instructions are run by the processor, the method described in embodiment one is completed. For brevity, this will not be repeated here.

[0090] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), and the processor can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0091] The memory can include read-only memory and random access memory, and provide instructions and data to the processor, and a portion of the memory can also include non-volatile random access memory. For example, the memory can also store device type information.

[0092] A computer readable storage medium for storing computer instructions, which are executed by a processor to complete the method described in embodiment one.

[0093] The method in embodiment one can be directly embodied as a hardware processor to complete, or be completed by a combination of hardware and software modules in the processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers, etc. mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory to complete the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0094] A computer program product includes a computer program that, when executed by a processor, implements the method described in embodiment one.

[0095] The present application also provides at least one computer program product tangibly stored on a non-transitory computer readable storage medium. The computer program product includes computer executable instructions, such as instructions included in program modules, which are executed in devices on target real or virtual processors to perform processes / methods as described above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In various embodiments, the functions of the program modules can be combined or divided as needed between program modules. Machine executable instructions for program modules can be executed within a local or distributed device. In a distributed device, program modules can be located in local and remote storage media.

[0096] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages. The computer program code can execute entirely on a computer, a special purpose computer, or other programmable apparatus to produce the functions / acts specified in the flow diagrams and / or block diagrams. The program code can execute entirely on a computer, a special purpose computer, or other programmable apparatus, as a stand-alone software package, partly on the computer and partly on a remote computer, or entirely on the remote computer or server.

[0097] In the context of the present application, the computer program code or related data can be carried by any suitable carrier to enable the device, apparatus or processor to perform the various processes and operations described above. Examples of carriers include signals, computer readable media, and the like. Examples of signals can include electrical, optical, radio, sound or other forms of propagated signals, such as carrier waves, infrared signals, and the like.

[0098] Those skilled in the art can realize that the units and algorithm steps of the examples described in conjunction with the present embodiments can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0099] Although the specific embodiments of the present application have been described in conjunction with the accompanying drawings, the above description is not intended to limit the scope of the present application. Those skilled in the art should understand that various modifications or variations can be made to the technical solutions of the present application without departing from the scope of the present application, and such modifications or variations are still within the scope of the present application.

Claims

1. A fine-grained photovoltaic power output simulation method considering the influence of multiple factors, characterized in that, include: Based on the geographical location of the target area and its corresponding weather, the photovoltaic power output curve at hourly intervals is obtained through simulation, and the fluctuation of the curve is determined based on the photovoltaic power output curve at hourly intervals. Based on the correspondence between the photovoltaic output curve at minute intervals and the photovoltaic output curve at hourly intervals, and combined with the sunrise and sunset times of the target area, the fluctuation of the photovoltaic output curve at continuous minute intervals and its corresponding maximum and minimum values ​​are determined. Combined with the temporal changes and the curve fluctuation of the corresponding photovoltaic output curve at hourly intervals, the photovoltaic output curve at minute intervals is simulated. The correspondence between the photovoltaic output curve based on minute intervals and the photovoltaic output curve based on hourly intervals is established. Combined with the sunrise and sunset times of the target area, the fluctuation of the continuous minute-interval photovoltaic output curve and its corresponding maximum and minimum photovoltaic output values ​​are determined. Furthermore, by combining the temporal changes and the corresponding fluctuation of the hourly-interval photovoltaic output curve, the minute-interval photovoltaic output curve is simulated. Specifically: Determine the number of reference points on the photovoltaic output curve at the minute interval on the corresponding hour interval photovoltaic output curve; Based on the number of reference points determined, the maximum and minimum output values ​​of each reference point are determined. The solution is performed with the objective of minimizing the sum of the absolute values ​​of the differences between the photovoltaic output curves at minute intervals, and the maximum possible fluctuation curve is obtained. Interpolation is performed on the photovoltaic output curves at hourly intervals to obtain the smoothest photovoltaic output curves at minute intervals; The photovoltaic output curves at hourly intervals are weighted by probability across different time periods to obtain photovoltaic output curves at minute intervals.

2. The fine-grained photovoltaic power output simulation method considering the influence of multiple factors as described in claim 1, characterized in that, The fluctuation of the photovoltaic output curve at the hourly interval is determined as follows: Determine the kurtosis, skewness, and peak factor corresponding to the photovoltaic power output curve at hourly intervals; Determine whether the kurtosis corresponding to the photovoltaic output curve at hourly intervals is greater than the kurtosis set value, whether the skewness is greater than the skewness set value, and whether the peak factor is greater than the peak factor set value. If two or three of the above conditions are met, the photovoltaic output curve for the corresponding hourly interval will be highly volatile. If any of the above conditions are met, the photovoltaic output curve for the corresponding hourly interval will have low fluctuations. If none of the above conditions are met, the photovoltaic output curve for the corresponding hourly interval will be without fluctuation.

3. The fine-grained photovoltaic power output simulation method considering the influence of multiple factors as described in claim 1, characterized in that, When the photovoltaic output curve at the determined minute interval is a photovoltaic output curve at a 15-minute interval, determine the maximum and minimum values ​​of the photovoltaic output curve at each 15-minute interval relative to the corresponding reference point, specifically as follows: Considering the sunrise time, sunset time, and probability of natural changes in the target area, determine the number of different inflection points of the photovoltaic output curve at 15-minute intervals and the corresponding probability of fluctuation under different curve fluctuations of the hourly photovoltaic output curve; Determine the upper and lower limits of output at each point, and combine the fluctuation of each inflection point with the sunrise and sunset times of the target area to determine the maximum and minimum output values ​​at each point.

4. The fine-grained photovoltaic power output simulation method considering the influence of multiple factors as described in claim 1, characterized in that, Based on the fluctuations of the photovoltaic output curves at hourly intervals, the curves are subjected to multi-probability weighting processing at different time periods to obtain the photovoltaic output curves at minute intervals, as follows: For photovoltaic power output curves with no fluctuations in hourly intervals, the smoothest curve determined is the final power output curve. For photovoltaic power output curves with low and high fluctuations at hourly intervals, the weighting ratio of low and high fluctuations is determined based on the time series changes to obtain the final power output curve.

5. A fine-grained photovoltaic power output simulation method considering the influence of multiple factors as described in any one of claims 1-4, characterized in that, The sum of the output values ​​corresponding to the photovoltaic output curves at multiple minute intervals is equal to the output value corresponding to the photovoltaic output curve at the corresponding hour interval.

6. A fine-grained photovoltaic power output simulation system considering the influence of multiple factors, characterized in that, include: The first module is configured to: simulate and obtain the photovoltaic power output curve at hourly intervals based on the geographical location of the target area and its corresponding weather, and determine the curve fluctuation based on the photovoltaic power output curve at hourly intervals; The second module is configured to: based on the correspondence between the photovoltaic output curve at minute intervals and the photovoltaic output curve at hourly intervals, and combined with the sunrise and sunset times of the target area, determine the fluctuation of the photovoltaic output curve at continuous minute intervals and its corresponding maximum and minimum values ​​of photovoltaic output, and combine the time sequence changes and the curve fluctuation of the corresponding photovoltaic output curve at hourly intervals to simulate and obtain the photovoltaic output curve at minute intervals. The correspondence between the photovoltaic output curve based on minute intervals and the photovoltaic output curve based on hourly intervals is established. Combined with the sunrise and sunset times of the target area, the fluctuation of the continuous minute-interval photovoltaic output curve and its corresponding maximum and minimum photovoltaic output values ​​are determined. Furthermore, by combining the temporal changes and the corresponding fluctuation of the hourly-interval photovoltaic output curve, the minute-interval photovoltaic output curve is simulated. Specifically: Determine the number of reference points on the photovoltaic output curve at the minute interval on the corresponding hour interval photovoltaic output curve; Based on the number of reference points determined, the maximum and minimum output values ​​of each reference point are determined. The solution is performed with the objective of minimizing the sum of the absolute values ​​of the differences between the photovoltaic output curves at minute intervals, and the maximum possible fluctuation curve is obtained. Interpolation is performed on the photovoltaic output curves at hourly intervals to obtain the smoothest photovoltaic output curves at minute intervals; The photovoltaic output curves at hourly intervals are weighted by probability across different time periods to obtain photovoltaic output curves at minute intervals.

7. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-5.

9. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the method described in any one of claims 1-5.

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