CN115456440A - Method, device and system for evaluating power generation characteristics of distributed photovoltaic power station - Google Patents
Method, device and system for evaluating power generation characteristics of distributed photovoltaic power station Download PDFInfo
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Abstract
The invention provides a method, a device and a system for evaluating power generation characteristics of distributed photovoltaic power stations, and relates to the technical field of photovoltaic power generation.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a method, a device and a system for evaluating power generation characteristics of a distributed photovoltaic power station.
Background
With the shortage of global energy, clean, renewable and large-deposit solar energy gradually enters the sight of people after wind energy. Photovoltaic power generation is one of the most sustainable renewable energy technologies, and is oriented to the development direction of a novel power system, the photovoltaic installed scale needs to be further enlarged, the power generation efficiency is improved, and the power generation cost is reduced; meanwhile, the quality of electric energy needs to be improved, and grid-connected operation of photovoltaic power generation and a novel electric power system is realized.
Meanwhile, distributed photovoltaic power generation has the characteristics of being suitable for local conditions, clean, efficient and the like, and thus has attracted extensive attention of researchers. The solar photovoltaic power generation is influenced by the solar radiation intensity, so that the output power of the photovoltaic power generation has the characteristic of strong fluctuation; the output power of the photovoltaic power generation is influenced by factors such as weather, temperature, seasons and the like, and the concentrated grid connection of a large-scale distributed photovoltaic power station is difficult. In the face of such a situation, measures such as switching-off and power limiting are often adopted by a power grid to guarantee stability and safety, but the economic benefit of the distributed photovoltaic power station is greatly damaged, and the development of clean energy is not facilitated. Therefore, the method has important significance for researching the output characteristics and the aggregation characteristics of the distributed photovoltaic power station.
At present, research on photovoltaic power generation mainly focuses on prediction of output power characteristics of distributed photovoltaic power stations, so that a theoretical basis is provided for centralized grid connection of large-scale distributed photovoltaic power stations. However, the existing prediction mode is generally only suitable for performing short-term or ultra-short-term photovoltaic power generation power prediction, and research and analysis on the output characteristics of the distributed photovoltaic power station are lacked.
Disclosure of Invention
In view of the above, the present invention provides a method, an apparatus and a system for evaluating power generation characteristics of a distributed photovoltaic power station, so as to alleviate the above technical problems.
In a first aspect, an embodiment of the present invention provides a method for evaluating power generation characteristics of a distributed photovoltaic power station, where the method includes: acquiring historical data of all distributed photovoltaic power stations in a preset area, wherein the historical data comprises weather indexes of each day in a preset historical time period and output power of the distributed photovoltaic power stations corresponding to the weather indexes, and the weather indexes comprise a plurality of meteorological factors; aggregating the acquired historical data of the distributed photovoltaic power stations to generate aggregated historical data of all the distributed photovoltaic power stations in the preset area; extracting weather characteristic factors contained in a plurality of weather factors, and dividing weather types of each day in the aggregated historical data in the historical time period based on the weather characteristic factors; dividing the daily aggregated historical data in the historical time period into a data group corresponding to each weather type; performing statistical analysis on the aggregation historical data in the data group through a nonparametric kernel density estimation algorithm to generate power probability density distribution of the distributed photovoltaic power station in a preset area in a time domain; analyzing at least one of the following characteristics in the time domain based on the power probability density distribution and by means of a joint probability distribution: analyzing the relation between the meteorological factors and the output of the distributed photovoltaic power stations in a preset area, analyzing the output relation between different distributed photovoltaic power stations in the preset area, and analyzing the relation between the single distributed photovoltaic power station and the total output of all the distributed photovoltaic power stations in the preset area; generating a power spectral density map of the distributed photovoltaic power station in a preset area under a frequency domain through a power spectral density algorithm based on the data set, extracting preset frequency components from the power spectral density map by adopting a preset empirical mode decomposition algorithm, and evaluating the output characteristics of the distributed photovoltaic power station in the preset area under the frequency domain through the frequency components.
With reference to the first aspect, an embodiment of the present invention provides a first possible implementation manner of the first aspect, where the method further includes: acquiring original data of all the distributed photovoltaic power stations in a preset region in a preset historical time period; the original data comprise weather indexes of each day in a preset historical time period and output power of the distributed photovoltaic power station corresponding to the weather indexes; the weather indicator includes a meteorological factor in at least one of the following dimensions: solar radiation intensity, wind direction, temperature, humidity, and air pressure; preprocessing the original data to generate preprocessed data; normalizing the weather indexes of the preprocessed data, and normalizing a plurality of weather indexes to a preset dimensionality; and determining the data after the normalization processing as historical data of all the distributed photovoltaic power stations in the preset area in the preset historical time period.
With reference to the first aspect, an embodiment of the present invention provides a second possible implementation manner of the first aspect, where the step of extracting a weather characteristic factor included in the plurality of weather factors includes: calculating the influence coefficient of each meteorological factor on the photovoltaic output of the distributed photovoltaic power station by using a preset path algorithm by taking each meteorological factor as an independent variable and the output power as a dependent variable; extracting the corresponding meteorological factor as a meteorological characteristic factor when the influence coefficient is greater than a preset threshold value; or sorting according to the influence coefficients from large to small, and selecting a preset number of meteorological factors with the maximum influence coefficients to be determined as the meteorological characteristic factors.
With reference to the first aspect, an embodiment of the present invention provides a third possible implementation manner of the first aspect, where after acquiring historical data of all distributed photovoltaic power stations in a preset area, the method further includes: dividing historical data of the single distributed photovoltaic power station into a data group of the single distributed photovoltaic power station corresponding to each weather type; performing statistical analysis on the data group of the single distributed photovoltaic power station through the nonparametric kernel density estimation algorithm to generate power probability density distribution of the single distributed photovoltaic power station in a time domain; and generating a power spectral density map of the single distributed photovoltaic power station in the frequency domain through a power spectral density algorithm.
With reference to the first aspect, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect, where the step of performing statistical analysis on the aggregation historical data in the data group by using a nonparametric kernel density estimation algorithm to generate a power probability density distribution of the distributed photovoltaic power station in a preset area in a time domain includes: selecting multiple groups of data of preset days from the data group corresponding to each weather type; sampling each group of data according to a preset sampling interval to obtain sampling data corresponding to each group of data; performing nonparametric kernel density estimation analysis on the sampled data to generate nonparametric kernel function fitting curves corresponding to different weather types, wherein the nonparametric kernel function fitting curves are used for representing the probability distribution of the output power of the distributed photovoltaic power station in the preset area under different weather types; wherein the evaluation index corresponding to the probability distribution comprises at least one of the following indexes: residual square, correlation coefficient.
With reference to the first aspect, an embodiment of the present invention provides a fifth possible implementation manner of the first aspect, where the step of generating a power spectral density map of the distributed photovoltaic power plant in a preset area in a frequency domain through a power spectral density algorithm based on the data group includes:
selecting multiple groups of data of preset days from the data group corresponding to each weather type; extracting the output power corresponding to each weather type from the multiple groups of data; performing power spectral density analysis on the multiple groups of data according to the output power, and generating a power spectral density map corresponding to each weather type, wherein evaluation indexes corresponding to the power spectral density maps further comprise at least one of the following indexes: standard deviation of power, smoothness.
In a second aspect, an embodiment of the present invention further provides an apparatus for evaluating power generation characteristics of a distributed photovoltaic power station, where the apparatus includes: the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring historical data of all distributed photovoltaic power stations in a preset area, the historical data comprises weather indexes of each day in a preset historical time period and output power of the distributed photovoltaic power stations corresponding to the weather indexes, and the weather indexes comprise a plurality of meteorological factors; the data processing module is used for carrying out aggregation processing on the acquired historical data of the distributed photovoltaic power stations to generate aggregated historical data of all the distributed photovoltaic power stations in the preset area; the extracting module is used for extracting weather characteristic factors contained in the plurality of weather factors and dividing the weather types of each day in the historical time period in the aggregated historical data based on the weather characteristic factors; the dividing module is used for dividing the daily aggregated historical data in the historical time period into data groups corresponding to the weather types; the statistical module is used for carrying out statistical analysis on the aggregation historical data in the data group through a nonparametric kernel density estimation algorithm to generate power probability density distribution of the distributed photovoltaic power station in a preset area in a time domain; a first evaluation module for analyzing at least one of the following characteristics in the time domain based on the power probability density distribution and by means of a joint probability distribution: analyzing the relation between the meteorological factors and the output of the distributed photovoltaic power stations in a preset area, analyzing the output relation between different distributed photovoltaic power stations in the preset area, and analyzing the relation between the single distributed photovoltaic power station and the total output of all the distributed photovoltaic power stations in the preset area; the second evaluation module is used for generating a power spectral density map of the distributed photovoltaic power station in a preset area under a frequency domain through a power spectral density algorithm based on the data set, extracting preset frequency components from the power spectral density map by adopting a preset empirical mode decomposition algorithm, and evaluating the output characteristics of the distributed photovoltaic power station in the preset area under the frequency domain through the frequency components.
In a third aspect, an embodiment of the present invention further provides an evaluation system for power generation characteristics of a distributed photovoltaic power station, where the evaluation system includes a detection terminal, a data transmission terminal, a cloud server and a web page terminal, where the cloud server is configured with an evaluation apparatus for power generation characteristics of the distributed photovoltaic power station according to the second aspect; the detection terminal is used for acquiring data information of the distributed photovoltaic power station to provide historical data of the acquired distributed photovoltaic power station, and the data information is sent to the cloud server end through the data transmission terminal; the cloud server side is used for executing the evaluation method of the power generation characteristics of the distributed photovoltaic power station in the first aspect, and displaying the execution result through the webpage side.
In a fourth aspect, an embodiment of the present invention further provides a server, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the method according to the first aspect when executing the computer program.
In a fifth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the method described in the first aspect.
The embodiment of the invention brings the following beneficial effects:
the method, the device and the system for evaluating the power generation characteristics of the distributed photovoltaic power stations can acquire historical data of all the distributed photovoltaic power stations in a preset area, aggregate the historical data to generate aggregated historical data of all the distributed photovoltaic power stations in the preset area, extract meteorological feature factors contained in a plurality of meteorological factors from the historical data, divide weather types in a historical time period every day based on the meteorological feature factors, divide the historical data into data groups corresponding to each weather type according to the weather types, so that statistical analysis is performed on the historical data in the data groups through a nonparametric kernel density estimation algorithm to generate power probability density distribution of the distributed photovoltaic power stations in the preset area under a time domain, generate a power spectral density map of the distributed photovoltaic power stations in the preset area under a frequency domain through the power spectral density algorithm, further evaluate the output characteristics of the distributed photovoltaic power stations in the preset area under the time domain and the frequency domain respectively based on the power probability density distribution and the power spectral density map, fully consider weather indexes, divide the power spectral density distribution type distribution and combine the power probability density distribution and the power spectral density distribution to realize effective development of the distributed photovoltaic power stations in the time domain and the distribution, and the effective research on the output characteristics of the distributed photovoltaic power stations in the time domain, and the distribution, and the effective management of the distributed photovoltaic power grid, and the effective research of the development of the distributed photovoltaic power distribution.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a method for evaluating power generation characteristics of a distributed photovoltaic power station according to an embodiment of the present invention;
fig. 2 is a schematic diagram of output power curves of distributed photovoltaic power plants of different weather types according to an embodiment of the present invention;
fig. 3 is a probability distribution diagram of a nonparametric kernel function fitting curve of a distributed photovoltaic power station with different weather types according to an embodiment of the present invention;
fig. 4 is a comparison graph of non-parametric kernel function fitting curves and measured data for different weather types according to an embodiment of the present invention;
FIG. 5 is a probability distribution diagram of a nonparametric kernel function fitting curve of another distributed photovoltaic power plant of different weather types according to an embodiment of the invention;
FIG. 6 is a comparison graph of non-parametric kernel function fitted curves and measured data for another weather type according to an embodiment of the present invention;
FIG. 7 is a probability distribution diagram of a nonparametric kernel function fitting curve of another distributed photovoltaic power plant of different weather types according to an embodiment of the invention;
FIG. 8 is a comparison graph of non-parametric kernel function fit curves and measured data for another weather type according to an embodiment of the present invention;
fig. 9 is a power spectral density diagram of a distributed photovoltaic power plant on a sunny day according to an embodiment of the present invention;
FIG. 10 is a power spectral density plot for a cloudy distributed photovoltaic power plant according to an embodiment of the present invention;
fig. 11 is a power spectral density diagram of a distributed photovoltaic power plant in rainy days according to an embodiment of the present invention;
fig. 12 is a schematic structural diagram of an evaluation apparatus for power generation characteristics of a distributed photovoltaic power station according to an embodiment of the present invention;
fig. 13 is a schematic structural diagram of a server according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
At present, a great deal of research on distributed photovoltaic power stations mostly focuses on modeling of output characteristics of the distributed photovoltaic power stations, and research and analysis on weather input characteristics and output characteristics of the distributed photovoltaic power stations are lacked.
Based on this, the method, the device and the system for evaluating the power generation characteristics of the distributed photovoltaic power station provided by the embodiment of the invention can effectively research the output characteristics of the distributed photovoltaic power station so as to alleviate the technical problems.
In order to facilitate understanding of the embodiment, a detailed description is first given of the method for evaluating the power generation characteristics of the distributed photovoltaic power station disclosed in the embodiment of the present invention.
In a possible implementation manner, an embodiment of the present invention provides a method for evaluating power generation characteristics of a distributed photovoltaic power station, and in particular, a flowchart of a method for evaluating power generation characteristics of a distributed photovoltaic power station as shown in fig. 1, where the method includes the following steps:
step S102, acquiring historical data of all distributed photovoltaic power stations in a preset area;
the preset area refers to any area where distributed photovoltaic can be distributed, such as a plain, a mountain area, a rural area, a city, and the like, and there are usually a plurality of distributed photovoltaic power stations in the preset area, and the number of the specific distributed photovoltaic power stations and the area size of the preset area may be set according to an actual use condition, which is not limited in the embodiment of the present invention.
Further, the historical data in the embodiment of the present invention includes weather indexes of each day in a preset historical time period and output power of the distributed photovoltaic power station corresponding to the weather indexes, and the weather indexes include a plurality of meteorological factors, such as solar radiation intensity, wind direction, wind power, temperature, humidity, air pressure, and the like.
In specific implementation, the historical data is obtained based on measured data of the distributed photovoltaic power station, for example, the historical time period may be a preset number of days, a month, or a quarter of the day in the past, and the historical data includes weather indicators and actual output power of the distributed photovoltaic power station every day in the historical time period. The historical data in step S102 in the embodiment of the present invention is obtained based on a large amount of measured data every day in the historical time period.
Step S104, aggregating the acquired historical data of the distributed photovoltaic power stations to generate aggregated historical data of all the distributed photovoltaic power stations in the preset area;
generally, the distributed photovoltaic power stations are distributed relatively randomly in a preset area, and in order to evaluate the output characteristics of the entire distributed photovoltaic power stations in the preset area and the output characteristics of the individual distributed photovoltaic power stations, historical data of all the distributed photovoltaic power stations in the preset area needs to be aggregated. For example, aggregation may be performed at the time of collection, integration of historical data collected for all distributed photovoltaic plants within the same day, and so on.
Step S106, extracting meteorological characteristic factors contained in the meteorological factors, and dividing the weather types of the aggregated historical data in the historical time period every day based on the meteorological characteristic factors;
step S108, dividing the daily aggregated historical data in the historical time period into data groups corresponding to each weather type;
in practical use, the meteorological characteristic factor generally refers to a dominant meteorological element affecting photovoltaic power generation among a plurality of meteorological factors. Moreover, the output characteristics of the distributed photovoltaic power stations have daily volatility, and under different weather characteristics, the output characteristics of the distributed photovoltaic power stations have obvious differences, which is mainly that the photovoltaic power generation powers of different distributed photovoltaic power stations are different due to different cloud layer shielding degrees and different ground light radiation intensities under different weather characteristics, and the photovoltaic power generation powers under the same weather are basically similar, so that different weather types can be divided accordingly. And, when dividing the weather type, the weather type of each day in the historical time period can be divided by means of a hierarchical clustering algorithm, for example, a minimum distance agglomerative hierarchical clustering algorithm, based on the weather characteristic factor.
After the weather types are obtained, the aggregated historical data can be continuously divided into data groups corresponding to each weather type, so that the following steps can be continuously performed.
In actual use, the weather types may include a sunny day, a cloudy day, and a precipitation day, and the precipitation day further includes a rainy day or a snow day, and the specific weather types may be further divided according to the geographical location of the distributed photovoltaic power station and the local actual lighting condition, which is not limited in this embodiment of the present invention.
Step S110, carrying out statistical analysis on the aggregation historical data in the data set through a nonparametric kernel density estimation algorithm to generate power probability density distribution of the distributed photovoltaic power station in a preset area in a time domain;
step S112, analyzing at least one of the following characteristics in the time domain through the joint probability distribution based on the power probability density distribution: analyzing the relation between meteorological factors and the output of distributed photovoltaic power stations in a preset area, analyzing the output relation between different distributed photovoltaic power stations in the preset area, and analyzing the relation between a single distributed photovoltaic power station and the total output of all the distributed photovoltaic power stations in the preset area;
step S114, generating a power spectral density map of the distributed photovoltaic power station in the preset area under the frequency domain through a power spectral density algorithm based on the data set, extracting preset frequency components from the power spectral density map by adopting a preset empirical mode decomposition algorithm, and evaluating the output characteristics of the distributed photovoltaic power station in the preset area under the frequency domain through the frequency components.
In step S114, a preset empirical mode decomposition method is adopted, so that different frequency components can be extracted from the output characteristics of a single distributed photovoltaic power station and the photovoltaic power stations in the preset area, and guidance can be provided for power grid scheduling and frequency modulation control of the distributed photovoltaic power stations and the photovoltaic power stations in the preset area.
In specific implementation, because the data group is a data group corresponding to each weather type, when aggregation historical data in the data group are counted, output characteristics of the distributed photovoltaic power station under different weather types, such as volatility, stability, probability distribution of output power and the like, and a long-term variation trend of the output power of photovoltaic power generation under each weather type can be analyzed, so that support is provided for aspects of multi-energy collaborative scheduling, optical storage collaborative grid connection and the like.
The non-parameter nuclear density estimation algorithm is adopted, so that the analysis of the photovoltaic power generation output characteristics with randomness and volatility can reach higher accuracy from the sample data (the aggregation historical data in the data set). In addition, considering that the output characteristics of the distributed photovoltaic power station have strong randomness and strong fluctuation, the conclusion obtained by analyzing only in the time domain is one-sidedly, and further research in the frequency domain needs to be carried out, that is, the power spectral density algorithm adopted in the step S114 can analyze the output data of the distributed photovoltaic power station in the preset area and explore the output characteristics of the distributed photovoltaic power station.
Therefore, the evaluation method for the power generation characteristics of the distributed photovoltaic power station, provided by the embodiment of the invention, can obtain historical data of all distributed photovoltaic power stations in a preset area, aggregate the historical data to generate aggregated historical data of all the distributed photovoltaic power stations in the preset area, extract meteorological feature factors contained in a plurality of meteorological factors from the historical data, divide weather types in a historical time period based on the meteorological feature factors every day, divide the historical data into data groups corresponding to each weather type according to the weather types, so that statistical analysis is performed on the historical data in the data groups through a nonparametric kernel density estimation algorithm to generate power probability density distribution of the distributed photovoltaic power stations in the preset area under a time domain, and a power spectral density map of the distributed photovoltaic power stations in the preset area is generated through the power spectral density algorithm.
In actual use, the analysis process performed in the time domain in step S112 is that the used joint probability distribution, which is also commonly referred to as joint Copula probability distribution, is mainly used to analyze a relationship between the meteorological factor and the output of the distributed photovoltaic power stations in the preset area, analyze an output relationship between different distributed photovoltaic power stations in the preset area, and analyze a relationship between a single distributed photovoltaic power station and the total output of all distributed photovoltaic power stations in the preset area.
In addition, when performing joint Copula probability distribution, a Copula function is usually required to be introduced, and currently, common Copula functions include a normal Copula function, a t-Copula function, an archimedes Copula function, and the like. In the commonly used single-parameter binary Archimedes Copula function, the Frank Copula function is better for describing a symmetrical related variable, the Gumbel Copula function is advantageous when describing a variable which is related at the upper tail and gradually independent at the lower tail, and the Clayton Copula function is more suitable for describing a variable which is related at the upper tail and gradually independent at the lower tail. The joint probability distribution characteristics of the solar radiation intensity and the output power of the distributed photovoltaic power station can be judged by utilizing the describing capability of the three Copula functions on different variables, and then the construction of an optimal Copula function model between the input and the output of the distributed photovoltaic power station can be discussed, so that the method has important significance for analyzing and predicting the output characteristics of the distributed photovoltaic power station.
Further, when analyzing the relation between the meteorological factor and the output of the distributed photovoltaic power station in the preset area, a preset meteorological factor may be selected, for example, one of several meteorological characteristic factors, such as solar radiation intensity, wind direction, temperature and air pressure, may also be selected, and then a joint probability distribution analysis is performed based on the extracted preset meteorological factor and the output power of the distributed photovoltaic power station to generate the relation between the preset meteorological factor and the output power of the distributed photovoltaic power station, and generally, for the same meteorological factor, the output characteristics of the distributed photovoltaic power station have the same trend, although their own rated powers are different, the trends on the photovoltaic output are substantially the same, or the output trend has a certain ductility; the output characteristics between any two distributed photovoltaic power stations tend to change in a consistent manner, but it has also been found that the magnitude of the fluctuation varies greatly due to the different power ratings of the different power stations.
Further, when the output relationship between different distributed photovoltaic power stations in a preset area is analyzed, it is also found that the output curves of non-adjacent distributed photovoltaic power stations are different under the same weather type, and the output correlation is low mainly due to different positions. Moreover, when the relationship between the total output of the single distributed photovoltaic power station and the total output of all the distributed photovoltaic power stations in the preset area is analyzed, it is found that the total output of all the distributed photovoltaic power stations in the preset area, that is, the fluctuation of the total output power, is reduced along with the gradual expansion of the range of the preset area and the increase of the number of the distributed photovoltaic power stations due to the difference of the spatial positions of the single distributed photovoltaic power stations in the preset area.
In addition, after the weather types are obtained, an output power curve corresponding to each weather type can be further generated, so that the output power of the distributed photovoltaic power station under different weather types can be evaluated conveniently. Specifically, based on the weather types, a plurality of sampling points corresponding to each weather type and the output power of each sampling point can be sequentially selected from historical data according to a time sequence in a preset time period by taking a day as a unit; generating an output power curve of the distributed photovoltaic power station corresponding to each weather type by taking the sampling point as an abscissa and the output power as an ordinate; and evaluating the output power of the distributed photovoltaic power station under different weather types according to the output power curve.
For example, in a range of one day, 6 am to 6 pm are selected as a preset time period, sampling points are sequentially selected at certain time intervals, for example, 5min is used as a sampling period, and then according to the time sequence of the sampling points, the sampling points are used as abscissa and the output power is used as ordinate, an output power curve of the distributed photovoltaic power station is generated.
For convenience of understanding, fig. 2 shows a schematic diagram of output power curves of distributed photovoltaic power stations of different weather types, which respectively show output power curves of three known weather types of a sunny day, a cloudy day and a rainy day, (1) shows the sunny day, (2) shows the cloudy day, and (3) shows the rainy day, wherein an abscissa is a sampling point and an ordinate is output power of the distributed photovoltaic power station. As can be seen from fig. 2, the photovoltaic power generation curves of different distributed photovoltaic power stations are different, and the power generation power in a sunny day is relatively stable, increases with the increase of the illumination intensity, and decreases with the decrease of the illumination intensity; the generated power in cloudy days fluctuates greatly; the generated power in rainy days is generally low.
In practical use, the distributed photovoltaic power station in the embodiment of the present invention refers to a distributed photovoltaic power station, such as a distributed solar panel, and the like, and when data is collected by the distributed photovoltaic power station, data collection is performed by using a data collector and data collection is performed in a manual entry manner, so that it is impossible to avoid occurrence of collector failure, manual entry error, or abnormal event, which may cause data loss or generation of partial abnormal data. If these abnormal values are ignored, the result may be incorrect in some scenarios, so before the historical data is obtained in step S102, the abnormal values generally need to be identified and cleaned, so as to ensure the accuracy of the subsequent result.
Therefore, the method for evaluating the power generation characteristics of the distributed photovoltaic power station provided by the embodiment of the invention further comprises a data preprocessing process, and specifically, the data preprocessing process comprises the following steps:
(1) Acquiring original data of all distributed photovoltaic power stations in a preset area within a preset historical time period;
the original data comprises weather indexes of each day in a preset historical time period and output power of the distributed photovoltaic power station corresponding to the weather indexes; the weather indexes further comprise at least one meteorological factor with the following dimensions, such as meteorological factors of solar radiation intensity, wind direction, wind power, temperature, humidity, air pressure and the like;
(2) Preprocessing the original data to generate preprocessed data;
the preprocessing process generally refers to a process of processing abnormal data in original data, for example, performing data cleaning, and the like, and the common abnormal data cleaning includes 4 methods of deleting, regarding as missing values, correcting average values, and capping. When weather index data and output characteristic data of the distributed photovoltaic power station are processed, a simple statistical analysis method is often adopted to detect abnormal data, abnormal data are cleaned through two methods, namely a deleting method and an average value correcting method, the method adopted in the specific preprocessing process can be set according to actual use conditions, and the method is not limited in the embodiment of the invention.
(3) Normalizing the weather indexes of the preprocessed data, and normalizing the weather indexes to a preset dimension;
(4) And determining the data after the normalization processing as historical data of all distributed photovoltaic power stations in a preset region in a preset historical time period.
During specific implementation, weather indexes collected from the distributed photovoltaic power station include a plurality of meteorological factors with different dimensions, so that in order to eliminate the influence of differences on different variable values and dimensions on analysis and statistics, the data of photovoltaic power generation needs to be normalized, so that the various weather indexes are in the same dimension, a foundation is laid for input feature extraction work, and at present, two common normalization algorithms are mainly adopted: the min-max normalization method and the Z-score normalization method, and the specific normalization processing method may also be set according to the actual use situation, which is not limited in the embodiment of the present invention.
In addition, in order to accurately describe the output characteristics of the distributed photovoltaic power station, help operation scheduling personnel to accurately master the output influence indexes of the distributed photovoltaic power station, improve the output prediction precision of photovoltaic power generation, and further enhance the grid-connected stability of the distributed photovoltaic power station, the weather indexes subjected to normalization processing need to be further processed, so that weather characteristic factors can be extracted from a plurality of weather factors. Specifically, in the embodiment of the present invention, when extracting the weather feature factor, a path algorithm is used, so that the extracting the weather feature factor in step S104 includes the following processes:
(1) Calculating the influence coefficient of each meteorological factor on the photovoltaic output of the distributed photovoltaic power station by using a preset path algorithm by taking each meteorological factor as an independent variable and the output power as a dependent variable;
(2) Extracting corresponding meteorological factors as meteorological characteristic factors when the influence coefficient is larger than a preset threshold value; or sorting according to the influence coefficients from large to small, and selecting a preset number of meteorological factors with the largest influence coefficients to be determined as meteorological characteristic factors.
For the sake of easy understanding, x represents an independent variable and y represents a dependent variable, and for the plurality of weather factors, there are the independent variables x1, x2, x3, …, xl and the dependent variables y, l representing the number of weather factors. The independent variable x can be obtained by the analysis theory of the drift diameter algorithm m The direct path coefficient for (m =1,2, …, l) for the dependent variable y is:
wherein n is the number of samples, i =1,2, …, n; xm represents the m-th meteorological factor,
represents the average value of the meteorological factor,
the average value of the output power is indicated.
a m For the partial regression coefficients, the formula is calculated as follows:
independent variable x m By a variable x k The indirect path coefficient for the dependent variable y (k =1,2, …, l, k ≠ m) is:
wherein r is a cross correlation coefficient, and the calculation formula is as follows:
because different variables are normalized, the minimum value of the different variables is 0, and the maximum value of the different variables is 1. Furthermore, the maximum influence degree of different weather factors, namely the meteorological factors, on the photovoltaic power can be represented by the sum of direct and indirect path coefficients, and the calculation formula is as follows:
through the analysis of the photovoltaic output and the meteorological factors, the influence of each meteorological factor on the photovoltaic output can be determined, and then the characteristic variable with larger influence and stronger correlation is selected as the meteorological characteristic factor.
In actual use, according to the influence of each meteorological factor obtained by analysis on photovoltaic output, the solar radiation intensity, wind direction, temperature and air pressure are generally selected as meteorological characteristic factors.
Further, the above step S112 and step S114 are processes of statistical analysis, including probability statistics under time domain characteristic analysis and probability statistics under frequency domain characteristic analysis. Moreover, since the step S110 is a statistical analysis of the aggregation historical data, the analysis of the output characteristics of the photovoltaic power station in the time domain and the frequency domain includes an analysis of the aggregation characteristics of the distributed photovoltaic power stations in the time domain and the frequency domain, and an analysis of the output characteristics of a single distributed photovoltaic power station.
Therefore, after the above-mentioned historical data of all distributed photovoltaic power plants in the preset area is obtained, the following process is also included: dividing historical data of a single distributed photovoltaic power station into a data group of the single distributed photovoltaic power station corresponding to each weather type; performing statistical analysis on a data group based on a single distributed photovoltaic power station through a nonparametric kernel density estimation algorithm to generate power probability density distribution of the single distributed photovoltaic power station in a time domain; and generating a power spectral density map of the single distributed photovoltaic power station in the frequency domain through a power spectral density algorithm.
Specifically, the analysis process of the time domain features is discussed first, and at this time, in step S110, the statistical analysis is performed on the aggregation historical data in the data group through the non-parametric kernel density estimation algorithm to generate the power probability density distribution of the distributed photovoltaic power station in the preset area in the time domain, including the following steps:
(1) Selecting multiple groups of data of preset days from the data group corresponding to each weather type;
(2) Sampling each group of data according to a preset sampling interval to obtain sampling data corresponding to each group of data;
for example, representative sunny days, cloudy days and 8 days of rainy days in 3 months of a certain distributed photovoltaic power station are selected, 5 minutes, 15 minutes and 1 hour are respectively taken as sampling intervals, and each group of data is adopted, so that the sampling data of each group of data can be obtained.
(3) And carrying out nonparametric kernel density estimation analysis on the sampled data to generate nonparametric kernel function fitting curves corresponding to different weather types, wherein the nonparametric kernel function fitting curves are used for representing the probability distribution of the output power of the distributed photovoltaic power station in the preset area under different weather types.
In practical use, the MATLAB software is usually used to assist non-parametric kernel density estimation analysis, and probability distribution maps of non-parametric kernel function fitting curves corresponding to different weather types are generated and compared with the frequency histogram of the measured data.
For ease of understanding, fig. 3 shows a probability distribution graph of nonparametric kernel function fitting curves of distributed photovoltaic power plants of different weather types, wherein the abscissa represents photovoltaic contribution in different weather, and the ordinate represents nonparametric kernel density estimation probability distribution of the contribution distribution, and fig. 4 shows a comparison graph of the nonparametric kernel function fitting curves of different weather types corresponding to fig. 3 and measured data. Also, fig. 3 and 4 are schematic diagrams showing a sampling interval of 5 minutes.
Further, fig. 5 and fig. 6 respectively show a probability distribution graph of nonparametric kernel function fitting curves of the distributed photovoltaic power station of different weather types at a sampling interval of 15 minutes, and a comparison graph of the nonparametric kernel function fitting curves of the different weather types and the measured data; and fig. 7 and 8 respectively show a probability distribution diagram of nonparametric kernel function fitting curves of the distributed photovoltaic power station of different weather types when the sampling interval is 1 hour, and a comparison diagram of the nonparametric kernel function fitting curves of the different weather types and the measured data.
Based on fig. 3 to 8, in each comparison graph, actually shown is measured data in the form of a histogram, and in the step S110, when the output characteristics of the distributed photovoltaic power station in the time domain are evaluated based on the power probability density distribution, the following evaluation conclusion can be drawn:
(1) Under the measured data, the measured data probability distribution histograms on sunny days and cloudy days are similar, and under different output powers, the probability distribution is uniform; the output power probability distribution in rainy days is concentrated in a low output power interval, and the probability distribution density is gradually reduced along with the increase of the output power. Therefore, the output power of the distributed photovoltaic power station is similar in sunny days and cloudy days, and the output power of the distributed photovoltaic power station is greatly reduced in rainy days.
(2) As can be seen from the probability distribution diagram of the nonparametric kernel function fitting curve, the skewness of the nonparametric kernel function fitting curve in rainy days is near 0, while the skewness of the nonparametric kernel function fitting curve in sunny days and cloudy days is greater than 0, and the whole body presents a left-skewed trend; the peak value of the nonparametric kernel function fitting curve in rainy days is far larger than that in sunny days and cloudy days; the symmetry of the nonparametric kernel function fitting curve in rainy days is better than that in sunny days and cloudy days, which shows that the output power of the distributed photovoltaic power station is higher and the power probability distribution is more uniform in the weather states of the sunny days and the cloudy days.
(3) As can be seen from the comparison graph of the nonparametric kernel function fitting curve and the actually measured data of different weather types, the fitting degree of the nonparametric kernel density fitting curve and the actually measured data frequency histogram in rainy days is the best; the second in cloudy days; while the fit on a sunny day is the worst. In the face of sample data with strong probability fluctuation, the non-parametric kernel density estimation algorithm has a more obvious advantage because no distribution assumption is needed. However, when the probability fluctuation characteristic is weak and the probability distribution regularity of the variable is obvious, the advantages of the non-parameter kernel density estimation algorithm are no longer outstanding.
In addition, in the above figures, it can be seen that, under the same weather index, the smaller the sampling interval is, the greater the degree of coincidence between the non-parametric kernel density fitting curve and the actually measured data frequency histogram is. Therefore, when sampling, the sampling time should be shortened as much as possible to obtain a more accurate conclusion.
Further, in order to analyze the time domain characteristics of the photovoltaic output more comprehensively, the probability distribution of the output power of the distributed photovoltaic power station may be analyzed in detail, and specifically, corresponding evaluation indexes may be calculated, for example, the evaluation indexes include a residual sum of squares, correlation coefficients, and the like, where the residual sum of squares is used to represent a fitting error value of the distribution function to the probability distribution of the output power of the distributed photovoltaic power station, a smaller sum of squares of the residuals indicates a better fitting effect of the distribution function, and the correlation coefficients are used to represent a linear correlation between two sets of data, and a closer coefficient of the correlation is to 1 indicates a higher similarity between a corresponding value of the distribution function and an output power distribution value of the actual distributed photovoltaic power station.
Wherein, the sum of the squares of the residuals and the correlation coefficient can be calculated by the following formulas:
wherein E is SSE Representing the sum of the squares of the residuals, ξ representing the correlation coefficient; y' i Represents the value of the distribution function, y i Table output power of the distributed photovoltaic plant, n is the sample set size, cov () is the covariance, D () represents the variance.
Further, the analysis of the frequency domain characteristics is mainly performed in S114, because the input and output signals of the distributed photovoltaic power plant not only change with time, but also relate to frequency and phase information, and therefore, the frequency structure of the signals can be analyzed, and the signals are further described in the frequency domain.
Specifically, the frequency domain characteristic analysis generally includes the following processes:
(1) After a plurality of groups of data of preset days are selected from the data group corresponding to each weather type, extracting the output power corresponding to each weather type from the plurality of groups of data;
(2) Performing power spectral density analysis on the multiple groups of data according to the output power to generate a power spectral density map corresponding to each weather type;
for example, output power data of representative 8 days of sunny days, cloudy days and rainy days in 3 months of a certain distributed photovoltaic power station are selected, simulation can be performed by means of MATLAB, a power spectral density map is drawn, and then the output rule of the distributed photovoltaic power station under each weather type is analyzed based on the power spectral density map, for example, the output rule of the distributed photovoltaic power station is researched by observing indexes such as kurtosis and skewness.
For ease of understanding, fig. 9 to 11 show a power spectral density map of a sunny distributed photovoltaic power plant, a cloudy distributed photovoltaic power plant, and a rainy distributed photovoltaic power plant, respectively.
Where the power spectral density map is generated from the output power, the logarithm is taken of the relative frequency, in order to pull up those components with lower amplitudes relative to the high-amplitude components in order to observe periodic signals that are masked in low-amplitude noise. The power spectral density of a signal can be used to characterize the energy distribution of this signal in the frequency domain, and thus, in fig. 9-11, the abscissa represents logarithmic frequency and the ordinate represents amplitude.
As can be seen from fig. 9 to 11, the power spectrum in sunny, cloudy and rainy days includes 3 different characteristic regions: at a frequency of 5 x 10 -3 And 2X 10 -1 In between, the power spectral density map is more fluctuating and has larger amplitude. Frequency less than 5 x 10 -3 In time, the power spectral density on a sunny day is more stable; the power spectral density of the cloudy day is in an ascending trend; the power spectral density in rainy days is in a downward trend. Frequency greater than 2 x 10 -1 In the time, the power spectral density graph presents an obvious linear characteristic, so that a new idea is provided for the sectional type output characteristic prediction modeling of the distributed photovoltaic power station based on different power spectral densities of the distributed photovoltaic power station in different intervals shown in fig. 9 to 11.
In addition, in order to analyze the frequency domain characteristics of the photovoltaic output more comprehensively, the corresponding evaluation indexes of the power spectral density map further comprise at least one of the following indexes: the power standard deviation and the smoothness are obtained, so the output fluctuation characteristics of the distributed photovoltaic power station under the frequency domain characteristics can be described by adopting the following evaluation indexes:
(1) Standard deviation of power:
where N represents the number of sample points, P s_av Represents the mean value of the output power of the distributed photovoltaic plant. P s (k) -output power of the distributed photovoltaic plant at sampling point k;
the power standard deviation is used to measure how much a random variable deviates from its mean. If the output power curve of the distributed photovoltaic power station is smoother, the standard deviation of the output power curve is smaller, namely the power fluctuation deviation mean value is smaller, and the index can be used for evaluating the fluctuation condition of the output power of the distributed photovoltaic power station under the time scale.
(2) Smoothness:
wherein, P s_rate The rated capacity of the distributed photovoltaic power station is represented, and within the same duration, the smaller the smoothness is, the smoother the output power output of the distributed photovoltaic power station is.
In specific implementation, the selection condition of the index may be selected and calculated according to an actual condition, which is not limited in the embodiment of the present invention.
Further, the analysis of the output characteristics of the time domain and the frequency domain is obtained by analyzing a single distributed photovoltaic power station in a preset area, and the analysis of the cluster aggregation characteristics of all distributed photovoltaic power stations in the preset area can also be performed according to the above method. For example, in the time domain, a non-parametric kernel density estimation algorithm is adopted to calculate the data, and a non-parametric kernel function fitting curve probability distribution map of the output power under different weather types is drawn. After analysis, it is found that when the weather is sunny, the aggregate output characteristics of all the distributed photovoltaic power stations in the preset area are strong in fluctuation, good in stability and high in output power; the second in cloudy days; while the rain is the weakest in fluctuation. In the aspect of frequency domain, empirical mode decomposition can be used for analysis, and it is found that the output characteristics of the distributed photovoltaic power stations of the cluster distributed photovoltaic power stations on sunny days are gradually reduced, wherein the long-term change trend of the output power of the distributed photovoltaic power stations is different from that of a single distributed photovoltaic power station; the output characteristics in cloudy and rainy days tend to increase.
In conclusion, the evaluation method for the power generation characteristics of the distributed photovoltaic power station, provided by the embodiment of the invention, can evaluate the output characteristics of the distributed photovoltaic power station, is beneficial to disclosing the output of distributed photovoltaic power generation and the aggregation characteristic mechanism thereof, and has important significance for supporting distributed photovoltaic resource development and power grid optimization scheduling.
Further, on the basis of the above embodiment, an embodiment of the present invention further provides an evaluation apparatus for power generation characteristics of a distributed photovoltaic power station, as shown in fig. 12, which includes:
the acquiring module 10 is configured to acquire historical data of all distributed photovoltaic power stations in a preset area, where the historical data includes weather indexes of each day in a preset historical time period and output power of the distributed photovoltaic power stations corresponding to the weather indexes, and the weather indexes include a plurality of meteorological factors;
the data processing module 12 is configured to aggregate the acquired historical data of the distributed photovoltaic power stations to generate aggregated historical data of all the distributed photovoltaic power stations in the preset area;
an extracting module 14, configured to extract weather characteristic factors included in a plurality of weather factors, and divide the weather types of each day in the historical time period in the aggregated historical data based on the weather characteristic factors;
a dividing module 16, configured to divide the aggregated historical data of each day in the historical time period into data groups corresponding to each weather type;
the statistical module 18 is configured to perform statistical analysis on the aggregation historical data in the data group through a non-parameter kernel density estimation algorithm to generate power probability density distribution of the distributed photovoltaic power station in a preset region in a time domain;
a first evaluation module 20 for analyzing at least one of the following characteristics in the time domain based on the power probability density distribution and by means of a joint probability distribution: analyzing the relation between the meteorological factors and the output of the distributed photovoltaic power stations in a preset area, analyzing the output relation between different distributed photovoltaic power stations in the preset area, and analyzing the relation between the single distributed photovoltaic power station and the total output of all the distributed photovoltaic power stations in the preset area;
the second evaluation module 22 is configured to generate a power spectral density map of the distributed photovoltaic power station in a preset region in a frequency domain through a power spectral density algorithm based on the data set, extract preset frequency components from the power spectral density map by using a preset empirical mode decomposition algorithm, and evaluate the output characteristics of the distributed photovoltaic power station in the preset region in the frequency domain through the frequency components.
The evaluation device for the power generation characteristics of the distributed photovoltaic power station provided by the embodiment of the invention has the same technical characteristics as the evaluation method for the power generation characteristics of the distributed photovoltaic power station provided by the embodiment, so that the same technical problems can be solved, and the same technical effects can be achieved.
Further, the embodiment of the invention also provides an evaluation system of the power generation characteristics of the distributed photovoltaic power station, which comprises a detection terminal, a data transmission terminal, a cloud server end and a webpage end, wherein the cloud server end is provided with the evaluation device of the power generation characteristics of the distributed photovoltaic power station;
the detection terminal is used for acquiring data information of the distributed photovoltaic power station to provide historical data of the acquired distributed photovoltaic power station, and the data information is sent to the cloud server end through the data transmission terminal;
the cloud server side is used for executing the method for evaluating the power generation characteristics of the distributed photovoltaic power station shown in fig. 1, and displaying the execution result through the webpage side.
Further, an embodiment of the present invention further provides a server, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the above method.
In practical use, the server provided in the embodiment of the present invention may be used as a server in a cloud-edge collaborative management and control system, for example, the server may be used as a terminal server to establish a communication connection between a terminal device and a cloud service platform, may also be used as an edge server to establish a communication connection between an edge device and a cloud service platform, or may be directly used as a management and control server, and may monitor computing resources and the like of the cloud service platform and the edge device processing system in real time, receive a task processing request submitted by the terminal device, and allocate a processing task to the cloud processing platform and the edge device processing system according to the computing resources and the task processing request, so as to implement a method for cloud-edge collaborative intelligent analysis, thereby implementing characteristics and application requirements around a power grid algorithm, deeply researching a power grid algorithm cloud-edge collaborative technology, and providing a technical support for improving real-time performance and an intelligent level of the power grid algorithm system.
Further, an embodiment of the present invention further provides a schematic structural diagram of a server, as shown in fig. 13, which is the schematic structural diagram of the server, where the server includes a processor 131 and a memory 130, the memory 130 stores computer-executable instructions that can be executed by the processor 131, and the processor 131 executes the computer-executable instructions to implement the method.
In the embodiment shown in fig. 13, the server further comprises a bus 132 and a communication interface 133, wherein the processor 131, the communication interface 133 and the memory 130 are connected by the bus 132.
The Memory 130 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 133 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used. The bus 132 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus 132 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one double-headed arrow is shown in FIG. 13, but that does not indicate only one bus or one type of bus.
The processor 131 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 131. The Processor 131 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and the processor 131 reads information in the memory and performs the method in combination with hardware thereof.
The computer program product of the method, the apparatus, and the system for evaluating power generation characteristics of a distributed photovoltaic power plant provided by the embodiments of the present invention includes a computer-readable storage medium storing program codes, where instructions included in the program codes may be used to execute the method described in the foregoing method embodiments, and specific implementation may refer to the method embodiments, which are not described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in a specific case to those skilled in the art.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that the following embodiments are merely illustrative of the present invention, and not restrictive, and the scope of the present invention is not limited thereto: those skilled in the art can still make modifications or changes to the embodiments described in the foregoing embodiments, or make equivalent substitutions for some features, within the scope of the disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for evaluating power generation characteristics of a distributed photovoltaic power station, the method comprising:
acquiring historical data of all distributed photovoltaic power stations in a preset area, wherein the historical data comprises weather indexes of each day in a preset historical time period and output power of the distributed photovoltaic power stations corresponding to the weather indexes, and the weather indexes comprise a plurality of meteorological factors;
aggregating the acquired historical data of the distributed photovoltaic power stations to generate aggregated historical data of all the distributed photovoltaic power stations in the preset area;
extracting weather characteristic factors contained in a plurality of weather factors, and dividing weather types of each day in the aggregated historical data in the historical time period based on the weather characteristic factors;
dividing the daily aggregated historical data in the historical time period into data groups corresponding to the weather types;
performing statistical analysis on the aggregated historical data in the data group through a nonparametric kernel density estimation algorithm to generate power probability density distribution of the distributed photovoltaic power station in a preset area in a time domain;
analyzing at least one of the following characteristics in the time domain based on the power probability density distribution and by a joint probability distribution: analyzing the relation between the meteorological factors and the output of the distributed photovoltaic power stations in a preset area, analyzing the output relation between different distributed photovoltaic power stations in the preset area, and analyzing the relation between the single distributed photovoltaic power station and the total output of all the distributed photovoltaic power stations in the preset area;
generating a power spectral density map of the distributed photovoltaic power station in a preset area under a frequency domain through a power spectral density algorithm based on the data set, extracting preset frequency components from the power spectral density map through a preset empirical mode decomposition algorithm, and evaluating the output characteristics of the distributed photovoltaic power station in the preset area under the frequency domain through the frequency components.
2. The method of claim 1, further comprising:
acquiring original data of all the distributed photovoltaic power stations in a preset region in a preset historical time period; the original data comprise weather indexes of each day in a preset historical time period and output power of the distributed photovoltaic power station corresponding to the weather indexes; the weather indicator includes at least one of the following dimensional meteorological factors: solar radiation intensity, wind direction, temperature, humidity and air pressure;
preprocessing the original data to generate preprocessed data;
normalizing the weather indexes of the preprocessed data, and normalizing a plurality of weather indexes to a preset dimensionality;
and determining the data after the normalization processing as historical data of all the distributed photovoltaic power stations in the preset area in the preset historical time period.
3. The method according to claim 1, wherein the step of extracting the weather characteristic factors included in the plurality of weather factors comprises:
calculating the influence coefficient of each meteorological factor on the photovoltaic output of the distributed photovoltaic power station by using a preset path algorithm by taking each meteorological factor as an independent variable and the output power as a dependent variable;
extracting the corresponding meteorological factor as a meteorological characteristic factor when the influence coefficient is greater than a preset threshold value; alternatively, the first and second electrodes may be,
and sorting according to the influence coefficients from large to small, and selecting a preset number of meteorological factors with the maximum influence coefficients as the meteorological characteristic factors.
4. The method of claim 1, wherein after obtaining historical data of all distributed photovoltaic power plants in a predetermined area, the method further comprises:
dividing historical data of the single distributed photovoltaic power station into a data group of the single distributed photovoltaic power station corresponding to each weather type;
performing statistical analysis on the data group of the single distributed photovoltaic power station through the nonparametric kernel density estimation algorithm to generate power probability density distribution of the single distributed photovoltaic power station in a time domain; and generating a power spectral density map of the single distributed photovoltaic power station in the frequency domain through a power spectral density algorithm.
5. The method of claim 1, wherein the step of generating the power probability density distribution of the distributed photovoltaic power station in the preset area in the time domain by performing statistical analysis on the aggregated historical data in the data group through a nonparametric kernel density estimation algorithm comprises:
selecting multiple groups of data of preset days from the data group corresponding to each weather type;
sampling each group of data according to a preset sampling interval to obtain sampling data corresponding to each group of data;
carrying out nonparametric kernel density estimation analysis on the sampled data to generate nonparametric kernel function fitting curves corresponding to different weather types, wherein the nonparametric kernel function fitting curves are used for representing the probability distribution of the output power of the distributed photovoltaic power station in the preset area under different weather types; wherein the evaluation index corresponding to the probability distribution comprises at least one of the following indexes: residual square, correlation coefficient.
6. The method according to claim 1, wherein the step of generating a power spectral density map of the distributed photovoltaic power plant in the frequency domain in the predetermined area by means of a power spectral density algorithm based on the data set comprises:
selecting multiple groups of data of preset days from the data group corresponding to each weather type;
extracting the output power corresponding to each weather type from the multiple groups of data;
performing power spectral density analysis on the multiple groups of data according to the output power, and generating a power spectral density map corresponding to each weather type, wherein evaluation indexes corresponding to the power spectral density maps further comprise at least one of the following indexes: standard deviation of power, smoothness.
7. An apparatus for evaluating power generation characteristics of a distributed photovoltaic power plant, the apparatus comprising:
the system comprises an acquisition module, a power management module and a power management module, wherein the acquisition module is used for acquiring historical data of all distributed photovoltaic power stations in a preset area, the historical data comprises weather indexes of each day in a preset historical time period and output power of the distributed photovoltaic power stations corresponding to the weather indexes, and the weather indexes comprise a plurality of meteorological factors;
the data processing module is used for carrying out aggregation processing on the acquired historical data of the distributed photovoltaic power stations to generate aggregated historical data of all the distributed photovoltaic power stations in the preset area;
the extracting module is used for extracting weather characteristic factors contained in the plurality of weather factors and dividing the weather types of each day in the aggregated historical data in the historical time period based on the weather characteristic factors;
the dividing module is used for dividing the daily aggregated historical data in the historical time period into data groups corresponding to the weather types;
the statistical module is used for carrying out statistical analysis on the aggregation historical data in the data group through a nonparametric kernel density estimation algorithm to generate power probability density distribution of the distributed photovoltaic power station in a preset area in a time domain;
a first evaluation module for analyzing at least one of the following characteristics in the time domain by means of a joint probability distribution based on the power probability density distribution: analyzing the relation between the meteorological factors and the output of the distributed photovoltaic power stations in a preset area, analyzing the output relation between different distributed photovoltaic power stations in the preset area, and analyzing the relation between the single distributed photovoltaic power station and the total output of all the distributed photovoltaic power stations in the preset area;
the second evaluation module is used for generating a power spectral density map of the distributed photovoltaic power station in a preset area under a frequency domain through a power spectral density algorithm based on the data set, extracting preset frequency components from the power spectral density map by adopting a preset empirical mode decomposition algorithm, and evaluating the output characteristics of the distributed photovoltaic power station in the preset area under the frequency domain through the frequency components.
8. An evaluation system for power generation characteristics of a distributed photovoltaic power station, which is characterized by comprising a detection terminal, a data transmission terminal, a cloud server terminal and a webpage terminal, wherein the cloud server terminal is provided with an evaluation device for power generation characteristics of the distributed photovoltaic power station according to claim 7;
the detection terminal is used for acquiring data information of the distributed photovoltaic power station to provide historical data of the acquired distributed photovoltaic power station, and the data information is sent to the cloud server end through the data transmission terminal;
the cloud server side is used for executing the method for evaluating the power generation characteristics of the distributed photovoltaic power station as claimed in any one of claims 1 to 7, and displaying the execution result through the webpage side.
9. A server, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any of claims 1-6 when executing the computer program.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, performs the steps of the method of any of the preceding claims 1-6.
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| CN116070822B (en) * | 2023-01-03 | 2024-05-03 | 国网湖南省电力有限公司 | Method and system for calculating output simultaneous coefficients of regional photovoltaic power station |
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