CN111478374A - Dynamic evaluation method for probability distribution and prediction of wind-solar output characteristics - Google Patents
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Abstract
The invention belongs to the technical field of analysis of new energy output characteristics, and particularly relates to a dynamic evaluation method for probability distribution and prediction of wind and light output characteristics.
Description
Technical Field
The invention belongs to the technical field of analysis of new energy output characteristics, and particularly relates to a dynamic evaluation method for probability distribution analysis and prediction of wind and light output characteristics.
Background
The new energy is developed rapidly, and in a high-proportion new energy power grid, the influence of the output characteristic of the new energy on the power grid must be fully and comprehensively considered, so that the safe and stable operation of the power grid can be ensured. The core problem of the new energy output characteristic is to analyze the output characteristics of different types of new energy and the whole new energy under different time scales and different space scales according to the output characteristics of the new energy, determine the time sequence characteristic and the correlation of the new energy output, and predict the characteristics of the new energy in the future based on new energy planning. By dynamic evaluation analysis and prediction of the new energy output characteristics, support can be provided for safe operation of a power grid containing high-proportion new energy, the safe and stable operation level of the power grid is improved, and the method has important significance for power grid operation.
At present, the analysis on the output characteristics of new energy mainly focuses on the following two aspects:
(1) and establishing a new energy output characteristic evaluation index. Zheng trade et al published 'solar energy science and report' in 2018 on 'large-scale new energy power generation base output characteristic research', established three evaluation indexes of wind-light correlation, complementarity and randomness, and carried out base output characteristic analysis by adopting a multi-space-time scene division mode; in 2017, power construction of Wanghoukun et al published a characteristic analysis and prediction method overview of distributed photovoltaic power generation, a mathematical model of photovoltaic output characteristics is established, the current domestic and foreign research situations of distributed photovoltaic output characteristics, a prediction method and prediction software are discussed, and suggestions are provided for popularization and application of distributed photovoltaic; korean willow and the like in 2016, published research on power output characteristics and correlation of a wind, light, water and fire combined operation power grid, and put forward seasonal characteristics, daily characteristics and probability distribution curves of photovoltaic and wind power so as to provide a basis for operation of the wind, light, water and fire combined operation power grid; wang Jian, etc. published photovoltaic output characteristic index system and classification typical curve research in 'Power demand side management' in 2017, and proposed a photovoltaic output evaluation index system for power grid operation.
(2) And analyzing the new energy output characteristics of the regional power grid. In the Anhui Power of 2017, Li is bright, and the characteristics of the sun power characteristics, the output correlation, the probability distribution, the monthly power generation amount and the like of the wind power and the photovoltaic power generation of Anhui are analyzed by the analysis and suggestion of the New energy generation characteristics of the Anhui power grid; houting et al in 2016 published analysis on output characteristics of wind power and photovoltaic power stations in typical areas of Hubei province, and randomness, volatility, time sequence correlation, complementary characteristics and the like of wind power and photovoltaic output in Hubei are analyzed based on historical output data; zhao Lu et al in 2016 electric power science and engineering published Hubei new energy output characteristic analysis and research on influence on a power grid, and analyzed output distribution characteristics, output time characteristics and influence on load characteristics of wind power and photovoltaic; in 2018, the ' electrician ' of Von Shiui and the like, statistical characteristic analysis ' of wind power and photovoltaic output in typical areas of south China power grids is published, and fluctuation, seasonal characteristics, simultaneity, relevance and the like of the output of wind power and photovoltaic power stations are analyzed.
In summary, the evaluation of the output characteristics of the new energy is mainly focused on index analysis of randomness, volatility and the like of the output, the output characteristics of the new energy are not only calculated on indexes of randomness and volatility, but also analyzed on output probability characteristics and distribution in different seasons and predicted according to future planned capacity of a new energy station, and important practical values are provided for operation and consumption analysis of a high-proportion new energy power grid.
The publication number is: the invention patent document of CN110443511A discloses a wind power output characteristic analysis method based on time-interval accumulated electric quantity distribution, which includes: acquiring historical data of a wind power plant in a research period; according to the original data, per unit processing of the wind power plant is carried out; dividing the output of the wind power plant into N intervals; counting the accumulated electric quantity of the output level of the wind power in a certain interval at a fixed time period every day in a research period; the method comprises the steps of calculating the time-interval accumulated electric quantity of the wind power output, namely the time-interval accumulated electric quantity, and analyzing the wind power time-interval power generation output characteristic according to the time-interval accumulated electric quantity, namely the time-interval accumulated.
Disclosure of Invention
The invention aims to provide a dynamic evaluation method for wind and light output characteristic probability distribution and prediction aiming at the problems in the prior art, which mainly analyzes the output characteristics of new energy under different time scales, takes years, seasons and days as the time scales, finally predicts the output characteristics in the future according to the installation plan of the new energy and provides technical support for the operation of a high-proportion new energy power grid.
The technical scheme of the invention is as follows:
a dynamic evaluation method for wind and light output characteristic probability distribution and prediction is suitable for a new energy power grid, comprises wind power generation and photovoltaic power generation, and comprises the following steps:
s1, dividing the analysis time scale into years, seasons, months and days according to the historical output data of the new energy station;
s2, under the annual time scale, selecting the maximum photovoltaic daily generation efficiency, the maximum wind power daily generation efficiency and the minimum daily generation efficiency, and analyzing the annual variation trend of the photovoltaic efficiency, the wind power efficiency and the total generation efficiency of the photovoltaic efficiency and the wind power efficiency to obtain the photovoltaic efficiency, the wind power efficiency and the total output characteristics of the photovoltaic efficiency and the wind power efficiency in one year;
s3, selecting the maximum photovoltaic daily generation efficiency, the generation efficiency of all sampling points of wind power and the generation efficiency of all sampling points of total output of wind power and photovoltaic under the time scale of seasons, arranging according to a descending order to obtain the photovoltaic, wind power and total generation efficiency continuous curves of the photovoltaic and wind power in each season, and counting the probability distribution of each generation efficiency interval;
s4, analyzing the characteristics of the curves of wind power, photovoltaic power and the total generating efficiency of the wind power and the photovoltaic power in all time scales of the day and the time scales of each season, analyzing the installed proportion of the photovoltaic power and the wind power and the contribution of the installed proportion to the total output of the photovoltaic power and the wind power, and analyzing the change characteristics of the new energy daily generating efficiency in different seasons;
s5, calculating the correlation and the overall volatility of the output of the new energy station under the original data sampling frequency;
s6, based on the analysis results of the steps S2-S5, predicting the probability distribution characteristics of the new energy season and the daily output of the second year time scale according to the increase conditions of the photovoltaic and wind power installations of the second year time scale;
and S7, repeating the steps S2-S6, and completing the rolling analysis of the output probability distribution of the new energy of the regional power grid and the prediction of the new energy consumption space in different years.
Specifically, the dynamic evaluation method for analyzing and predicting the probability distribution of the wind and light power generation output characteristics of the regional power grid is suitable for all local power grids and provincial power grids.
Specifically, in the step S6, according to the increase condition of the photovoltaic and wind power installation at the time scale of the second year, the probability distribution characteristics of the new energy season and the solar output at the time of the second year are predicted; the characteristic analysis comprises new energy station output correlation analysis, output probability distribution analysis and output characteristic prediction analysis.
Specifically, in step S7, performing rolling analysis and consumption space prediction on the probability distribution of the new energy output of the regional power grid in different years; the rolling analysis includes the output characteristics at different time scales, and the consumption space prediction includes the probability consumption space at different time periods.
Most of the existing methods for analyzing the characteristics of the wind-solar power grid analyze the characteristics of the wind-solar power grid from the whole full time domain, do not relate to the research on the wind power output characteristics of fixed time intervals every day in a research period, and cannot fully reflect the technical problems of the wind power output characteristics of different time intervals and the like.
The invention has the beneficial effects that: the method mainly aims at the uncertainty of wind power and photovoltaic output in a new energy power grid, provides and analyzes the output characteristics of new energy under different time scales from the aspect of probability distribution, and provides probability distribution calculation methods and indexes under different time scales; the method is characterized in that the method is innovatively combined with future development planning, the correlation among new energy stations is considered, the new energy output characteristics under different time scales are predicted, rolling comparison analysis is carried out, decision and guidance are provided for the operation of the power grid, specifically, the new energy output probability distribution characteristics which finally fall under the time scale of the day are provided with the time scales of the year, the season and the day, the future output characteristics are predicted according to the new energy installation planning, and technical support is provided for the operation of the high-proportion new energy power grid.
Description of the drawings:
FIG. 1 is a flowchart of a dynamic evaluation method for wind/solar output characteristic probability distribution and prediction according to the present invention.
Detailed Description
The following examples are provided to illustrate the embodiments of the present invention in detail.
Fig. 1 is a calculation flowchart of the dynamic evaluation method for wind/solar output characteristic probability distribution and prediction, which is provided by the present invention, and the method is suitable for new energy power grids including wind power generation and photovoltaic power generation, and is suitable for regional power grids of all local power grids and provincial power grids. The implementation steps are as follows:
(1) dividing the analysis time scale into years, seasons, months and days according to the historical output data of the new energy station;
(2) under the time scale of the year, selecting the maximum generation efficiency of the photovoltaic day, the maximum generation efficiency of the wind power day and the minimum generation efficiency of the day, analyzing the annual variation trend of the total generation efficiency of the wind power, the photovoltaic and the wind power and the photovoltaic to obtain the total output characteristics of different types of new energy and new energy in the year, wherein the output characteristics comprise the volatility, the daily maximum value, the average value and the output interval probability distribution of the total output of the wind power, the photovoltaic and the wind power and the photovoltaic;
(3) under the time scale of season, select photovoltaic day maximum power generation efficiency, the generating efficiency of all sampling points of wind-powered electricity generation and photovoltaic total output, arrange according to the descending order, obtain the generating efficiency continuation curve in every season, the physical meaning of every point on the curve is: the number of days of the power generation efficiency corresponding to the ordinate or more is the abscissa; counting the probability distribution of each power generation efficiency interval;
(4) under the time scale of day, analyzing the characteristics of wind power, photovoltaic power and total power generation efficiency curves of the wind power and the photovoltaic power in all time scales of day in each season, and analyzing the installed occupation ratios of new energy of different types and the contribution of the new energy to the total output; analyzing the change characteristics of the daily power generation efficiency of the new energy in different seasons, including but not limited to the analysis of the change trend, the maximum value, the minimum value, the maximum probability interval and the like of the daily power generation efficiency;
(5) under the original data sampling frequency, calculating the correlation and the overall volatility of the output of the new energy station;
(6) based on the analysis results of S2-S5, according to the installation growth condition of various types of new energy on the time scale of the second year, predicting the probability distribution characteristics of the new energy season and the daily output on the time scale of the second year, wherein the characteristic analysis comprises the analysis of the correlation of the output of the new energy field station, the analysis of the probability distribution of the output and the prediction and analysis of the output characteristic, and rolling calculation and comparison correction analysis are carried out in the way;
(7) and repeating the steps S2-S6, and completing rolling analysis of the output probability distribution of the regional power grid new energy in different years and new energy consumption space prediction, wherein the rolling analysis comprises output characteristics in different time scales, and the consumption space prediction comprises probability consumption spaces in different time periods.
According to the method, from the angle of uncertainty of new energy output, the output characteristics of different types of new energy and the total output characteristics of the new energy under different time scales are analyzed, and the probability distribution characteristics of the new energy output are obtained; and (4) according to the new energy planning, predicting and evaluating the future new energy power generation efficiency. The dynamic evaluation method for the probability distribution and prediction of the wind-solar output characteristics is realized by the following technical scheme:
1) collecting new energy output data and installed capacity, and calculating the output characteristics of different types of new energy and the contribution of the output characteristics to the total output of the new energy from three different time scales of year, season and day to obtain the probability distribution characteristic of the power generation efficiency of the new energy;
2) according to the existing calculation result and new energy planning, probability distribution and output characteristics of the future new energy power generation efficiency are predicted, and the change trend, the extreme value and the distribution characteristics of the power generation efficiency value intervals of the new energy in different seasons are calculated.
Finally, it should be noted that the above examples are only used to illustrate the technical solutions of the present invention and not to limit the same; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art will understand that: modifications to the specific embodiments of the invention or equivalent substitutions for parts of the technical features may be made; without departing from the spirit of the present invention, it is intended to cover all aspects of the invention as defined by the appended claims.
Claims (4)
1. A dynamic evaluation method for wind and light output characteristic probability distribution and prediction is suitable for new energy-containing power grids, including wind power generation and photovoltaic power generation, and is characterized by comprising the following steps:
s1, dividing the analysis time scale into years, seasons, months and days according to the historical output data of the new energy station;
s2, under the annual time scale, selecting the maximum photovoltaic daily generation efficiency, the maximum wind power daily generation efficiency and the minimum daily generation efficiency, and analyzing the annual variation trend of the photovoltaic efficiency, the wind power efficiency and the total generation efficiency of the photovoltaic efficiency and the wind power efficiency to obtain the photovoltaic efficiency, the wind power efficiency and the total output characteristics of the photovoltaic efficiency and the wind power efficiency in one year;
s3, selecting the maximum photovoltaic daily generation efficiency, the generation efficiency of all sampling points of wind power and the generation efficiency of all sampling points of total output of wind power and photovoltaic under the time scale of seasons, arranging according to a descending order to obtain the photovoltaic, wind power and total generation efficiency continuous curves of the photovoltaic and wind power in each season, and counting the probability distribution of each generation efficiency interval;
s4, analyzing the characteristics of the curves of wind power, photovoltaic power and the total generating efficiency of the wind power and the photovoltaic power in all time scales of the day and the time scales of each season, analyzing the installed proportion of the photovoltaic power and the wind power and the contribution of the installed proportion to the total output of the photovoltaic power and the wind power, and analyzing the change characteristics of the new energy daily generating efficiency in different seasons;
s5, calculating the correlation and the overall volatility of the output of the new energy station under the original data sampling frequency;
s6, based on the analysis results of the steps S2-S5, predicting the probability distribution characteristics of the new energy season and the daily output of the second year time scale according to the increase conditions of the photovoltaic and wind power installations of the second year time scale;
and S7, repeating the steps S2-S6, and completing the rolling analysis of the output probability distribution of the new energy of the regional power grid and the prediction of the new energy consumption space in different years.
2. The method for dynamically evaluating the probability distribution and prediction of the wind and solar power generation output characteristics according to claim 1, wherein the method for dynamically evaluating the probability distribution analysis and prediction of the wind and solar power generation output characteristics of the regional power grid is applicable to all local power grids and provincial power grids.
3. The method for dynamically evaluating the probability distribution and prediction of wind and solar power generation characteristics according to claim 1, wherein in step S6, the probability distribution characteristics of new energy season and solar power generation at the second year are predicted according to the growth conditions of photovoltaic and wind power generation installed devices at the second year time scale; the characteristic analysis comprises new energy station output correlation analysis, output probability distribution analysis and output characteristic prediction analysis.
4. The method for dynamically evaluating the probability distribution and prediction of wind/solar power output characteristics according to claim 1, wherein in step S7, the rolling analysis and the spatial prediction of the consumption of the regional power grid new energy output probability distribution in different years are performed; the rolling analysis includes the output characteristics at different time scales, and the consumption space prediction includes the probability consumption space at different time periods.
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